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bioMate-AI/biomate-bioconductor-kb
BioMate-KB Bioconductor Skills — 200 packages (top 100 by downloads + 100 rising stars) as vignette-grounded Claude/agent skills, with per-package workflow recipes
History
Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.
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skills/annotation/biomart/SKILL.md
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skills/annotation/kegggraph/SKILL.md
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skills/enrichment/enrichplot/SKILL.md
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skills/enrichment/reactomepa/SKILL.md
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skills/epigenomics/bumphunter/SKILL.md
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skills/epigenomics/chipseeker/SKILL.md
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skills/epigenomics/epigrahmm/SKILL.md
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skills/epigenomics/extrachips/SKILL.md
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skills/epigenomics/hicdcplus/SKILL.md
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skills/epigenomics/hicexperiment/SKILL.md
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skills/epigenomics/hicontacts/SKILL.md
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skills/epigenomics/macsr/SKILL.md
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skills/epigenomics/miasim/SKILL.md
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skills/epigenomics/minfi/SKILL.md
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skills/general/basilisk/SKILL.md
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skills/general/biobase/SKILL.md
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skills/general/biovizbase/SKILL.md
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skills/general/dose/SKILL.md
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skills/general/experimenthub/SKILL.md
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skills/general/faers/SKILL.md
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skills/general/gdsfmt/SKILL.md
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skills/general/genefilter/SKILL.md
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skills/general/geneplotter/SKILL.md
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skills/general/graphite/SKILL.md
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skills/general/gypsum/SKILL.md
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skills/general/immunotation/SKILL.md
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skills/general/mosbi/SKILL.md
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skills/general/multiassayexperiment/SKILL.md
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skills/general/pcamethods/SKILL.md
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skills/general/qvalue/SKILL.md
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skills/general/residualmatrix/SKILL.md
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skills/general/seqlogo/SKILL.md
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skills/general/topgo/SKILL.md
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skills/genomics/annotate/SKILL.md
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skills/genomics/beer/SKILL.md
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skills/genomics/biocviews/SKILL.md
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skills/genomics/biostrings/SKILL.md
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skills/genomics/bsgenome/SKILL.md
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skills/genomics/clusterprofiler/SKILL.md
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skills/genomics/cogeqc/SKILL.md
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skills/genomics/complexheatmap/SKILL.md
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skills/genomics/consensusclusterplus/SKILL.md
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skills/genomics/decipher/SKILL.md
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skills/genomics/dnacopy/SKILL.md
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skills/genomics/doubletrouble/SKILL.md
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skills/genomics/epialleler/SKILL.md
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skills/genomics/genomicalignments/SKILL.md
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skills/genomics/genomicfeatures/SKILL.md
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skills/genomics/genomicranges/SKILL.md
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skills/genomics/ggbio/SKILL.md
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skills/genomics/ggmanh/SKILL.md
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skills/genomics/ggtree/SKILL.md
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skills/genomics/gosemsim/SKILL.md
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skills/genomics/gviz/SKILL.md
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skills/genomics/keggrest/SKILL.md
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skills/genomics/lintind/SKILL.md
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skills/genomics/msa2dist/SKILL.md
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skills/genomics/organismdbi/SKILL.md
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skills/genomics/rblast/SKILL.md
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skills/genomics/rsamtools/SKILL.md
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skills/genomics/rtracklayer/SKILL.md
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skills/genomics/shortread/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/genomics/supersigs/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/genomics/syntenet/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/genomics/tfbstools/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/genomics/txdbmaker/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/imaging/cytoviewer/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/imaging/ebimage/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/imaging/flowcore/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/imaging/lisaclust/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/metabolomics/rgoslin/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/metagenomics/biomformat/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/metagenomics/dirichletmultinomial/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/metagenomics/microbiome/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/metagenomics/phyloseq/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/bandle/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/bionar/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/drugtargetinteractions/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/matrixqcvis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/mixomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/msbackendmassbank/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/mscoreutils/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/msdatahub/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/msnbase/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/mzid/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/mzr/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/phipdata/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/psmatch/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/qfeatures/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/proteomics/stringdb/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/proteomics/targetdecoy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/single-cell/cytomem/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/single-cell/demuxmix/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/single-cell/hoodscanr/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/single-cell/mudata/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/single-cell/screclassify/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/single-cell/singlecellexperiment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/single-cell/treesummarizedexperiment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/single-cell/zellkonverter/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/affy/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/apeglm/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/asurat/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/aucell/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/awst/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/batchelor/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/bindingsitefinder/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/bloodgen3module/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/busseq/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/cageminer/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/cardelino/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/cbnplot/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/cellbarcode/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/cellxgenedp/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/condiments/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/cotan/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Instructions only
First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/crisprbase/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/crisprdesign/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/crisprscore/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/crisprviz/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/dada2/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/deconvr/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/deseq2/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/despace/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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skills/transcriptomics/dexma/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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skills/transcriptomics/dino/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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skills/transcriptomics/dropletutils/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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skills/transcriptomics/easier/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/edger/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/enhancedvolcano/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/epistack/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/escher/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/fgsea/SKILL.md
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/findit2/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/gatom/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/genomicinstability/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/genomicsupersignature/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/geoquery/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/geotcgadata/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/glmgampoi/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/granulator/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/gseabase/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/gsva/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/harmonizr/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/illuminaio/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/intercellar/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/limma/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/mast/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/mastr/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/metabocoreutils/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/metapod/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/mirtarrnaseq/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/monocle/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/msa/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/msstatslip/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/nanotube/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/nnsvg/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/omicsviewer/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/ompbam/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/orfhunter/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/pathview/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/proteodisco/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/ptairms/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/ribodipa/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/scanmir/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/scannotatr/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/scarray/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/scater/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/sccomp/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/scdblfinder/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/scdesign3/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/scran/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/scuttle/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
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skills/transcriptomics/sechm/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/sgcp/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/siggenes/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/simbu/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/simpleseg/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/singler/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/spatialde/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/spatialexperiment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/splicewiz/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/spotclean/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/spotsweeper/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/standr/SKILL.md
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c9bd4d8eb6ed → c9bd4d8eb6ed
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → c9bd4d8eb6ed
No earlier version was observed for comparison.
skills/transcriptomics/statial/SKILL.md
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skills/transcriptomics/tximport/SKILL.md
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skills/transcriptomics/visse/SKILL.md
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skills/transcriptomics/voyager/SKILL.md
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Heat
0.2- Star growth, last 7 daysNot available
- Star growth, last 24 hoursNot available
- Rank movement, last 7 daysNot available
- Commits, last 30 daysNot available
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekNot available
Heat never changes the order by stars, and sponsorship never touches it. How heat works
Stars over time
Individual skills in this repository
- bioconductor-biomart
skills/annotation/biomart - bioconductor-kegggraph
skills/annotation/kegggraph - bioconductor-enrichplot
skills/enrichment/enrichplot - bioconductor-reactomepa
skills/enrichment/reactomepa - bioconductor-bumphunter
skills/epigenomics/bumphunter - bioconductor-chipseeker
skills/epigenomics/chipseeker - bioconductor-epigrahmm
skills/epigenomics/epigrahmm - bioconductor-extrachips
skills/epigenomics/extrachips - bioconductor-hicdcplus
skills/epigenomics/hicdcplus - bioconductor-hicexperiment
skills/epigenomics/hicexperiment - bioconductor-hicontacts
skills/epigenomics/hicontacts - bioconductor-macsr
skills/epigenomics/macsr - bioconductor-miasim
skills/epigenomics/miasim - bioconductor-minfi
skills/epigenomics/minfi - bioconductor-basilisk
skills/general/basilisk - bioconductor-biobase
skills/general/biobase - bioconductor-biovizbase
skills/general/biovizbase - bioconductor-dose
skills/general/dose - bioconductor-experimenthub
skills/general/experimenthub - bioconductor-faers
skills/general/faers - bioconductor-gdsfmt
skills/general/gdsfmt - bioconductor-genefilter
skills/general/genefilter - bioconductor-geneplotter
skills/general/geneplotter - bioconductor-graphite
skills/general/graphite - bioconductor-gypsum
skills/general/gypsum - bioconductor-immunotation
skills/general/immunotation - bioconductor-mosbi
skills/general/mosbi - bioconductor-multiassayexperiment
skills/general/multiassayexperiment - bioconductor-pcamethods
skills/general/pcamethods - bioconductor-qvalue
skills/general/qvalue - bioconductor-residualmatrix
skills/general/residualmatrix - bioconductor-seqlogo
skills/general/seqlogo - bioconductor-topgo
skills/general/topgo - bioconductor-annotate
skills/genomics/annotate - bioconductor-beer
skills/genomics/beer - bioconductor-biocviews
skills/genomics/biocviews - bioconductor-biostrings
skills/genomics/biostrings - bioconductor-bsgenome
skills/genomics/bsgenome - bioconductor-clusterprofiler
skills/genomics/clusterprofiler - bioconductor-cogeqc
skills/genomics/cogeqc - bioconductor-complexheatmap
skills/genomics/complexheatmap - bioconductor-consensusclusterplus
skills/genomics/consensusclusterplus - bioconductor-decipher
skills/genomics/decipher - bioconductor-dnacopy
skills/genomics/dnacopy - bioconductor-doubletrouble
skills/genomics/doubletrouble - bioconductor-epialleler
skills/genomics/epialleler - bioconductor-genomicalignments
skills/genomics/genomicalignments - bioconductor-genomicfeatures
skills/genomics/genomicfeatures - bioconductor-genomicranges
skills/genomics/genomicranges - bioconductor-ggbio
skills/genomics/ggbio - bioconductor-ggmanh
skills/genomics/ggmanh - bioconductor-ggtree
skills/genomics/ggtree - bioconductor-gosemsim
skills/genomics/gosemsim - bioconductor-gviz
skills/genomics/gviz - bioconductor-keggrest
skills/genomics/keggrest - bioconductor-lintind
skills/genomics/lintind - bioconductor-msa2dist
skills/genomics/msa2dist - bioconductor-organismdbi
skills/genomics/organismdbi - bioconductor-rblast
skills/genomics/rblast - bioconductor-rsamtools
skills/genomics/rsamtools - bioconductor-rtracklayer
skills/genomics/rtracklayer - bioconductor-shortread
skills/genomics/shortread - bioconductor-supersigs
skills/genomics/supersigs - bioconductor-syntenet
skills/genomics/syntenet - bioconductor-tfbstools
skills/genomics/tfbstools - bioconductor-txdbmaker
skills/genomics/txdbmaker - bioconductor-cytoviewer
skills/imaging/cytoviewer - bioconductor-ebimage
skills/imaging/ebimage - bioconductor-flowcore
skills/imaging/flowcore - bioconductor-lisaclust
skills/imaging/lisaclust - bioconductor-rgoslin
skills/metabolomics/rgoslin - bioconductor-biomformat
skills/metagenomics/biomformat - bioconductor-dirichletmultinomial
skills/metagenomics/dirichletmultinomial - bioconductor-microbiome
skills/metagenomics/microbiome - bioconductor-phyloseq
skills/metagenomics/phyloseq - bioconductor-bandle
skills/proteomics/bandle - bioconductor-bionar
skills/proteomics/bionar - bioconductor-drugtargetinteractions
skills/proteomics/drugtargetinteractions - bioconductor-matrixqcvis
skills/proteomics/matrixqcvis - bioconductor-mixomics
skills/proteomics/mixomics - bioconductor-msbackendmassbank
skills/proteomics/msbackendmassbank - bioconductor-mscoreutils
skills/proteomics/mscoreutils - bioconductor-msdatahub
skills/proteomics/msdatahub - bioconductor-msnbase
skills/proteomics/msnbase - bioconductor-mzid
skills/proteomics/mzid - bioconductor-mzr
skills/proteomics/mzr - bioconductor-phipdata
skills/proteomics/phipdata - bioconductor-psmatch
skills/proteomics/psmatch - bioconductor-qfeatures
skills/proteomics/qfeatures - bioconductor-stringdb
skills/proteomics/stringdb - bioconductor-targetdecoy
skills/proteomics/targetdecoy - bioconductor-cytomem
skills/single-cell/cytomem - bioconductor-demuxmix
skills/single-cell/demuxmix - bioconductor-hoodscanr
skills/single-cell/hoodscanr - bioconductor-mudata
skills/single-cell/mudata - bioconductor-screclassify
skills/single-cell/screclassify - bioconductor-singlecellexperiment
skills/single-cell/singlecellexperiment - bioconductor-treesummarizedexperiment
skills/single-cell/treesummarizedexperiment - bioconductor-zellkonverter
skills/single-cell/zellkonverter - bioconductor-affy
skills/transcriptomics/affy - bioconductor-apeglm
skills/transcriptomics/apeglm - bioconductor-asurat
skills/transcriptomics/asurat - bioconductor-aucell
skills/transcriptomics/aucell - bioconductor-awst
skills/transcriptomics/awst - bioconductor-batchelor
skills/transcriptomics/batchelor - bioconductor-bindingsitefinder
skills/transcriptomics/bindingsitefinder - bioconductor-bloodgen3module
skills/transcriptomics/bloodgen3module - bioconductor-busseq
skills/transcriptomics/busseq - bioconductor-cageminer
skills/transcriptomics/cageminer - bioconductor-cardelino
skills/transcriptomics/cardelino - bioconductor-cbnplot
skills/transcriptomics/cbnplot - bioconductor-cellbarcode
skills/transcriptomics/cellbarcode - bioconductor-cellxgenedp
skills/transcriptomics/cellxgenedp - bioconductor-condiments
skills/transcriptomics/condiments - bioconductor-cotan
skills/transcriptomics/cotan - bioconductor-crisprbase
skills/transcriptomics/crisprbase - bioconductor-crisprdesign
skills/transcriptomics/crisprdesign - bioconductor-crisprscore
skills/transcriptomics/crisprscore - bioconductor-crisprviz
skills/transcriptomics/crisprviz - bioconductor-dada2
skills/transcriptomics/dada2 - bioconductor-deconvr
skills/transcriptomics/deconvr - bioconductor-deseq2
skills/transcriptomics/deseq2 - bioconductor-despace
skills/transcriptomics/despace - bioconductor-dexma
skills/transcriptomics/dexma - bioconductor-dino
skills/transcriptomics/dino - bioconductor-dropletutils
skills/transcriptomics/dropletutils - bioconductor-easier
skills/transcriptomics/easier - bioconductor-edger
skills/transcriptomics/edger - bioconductor-enhancedvolcano
skills/transcriptomics/enhancedvolcano - bioconductor-epistack
skills/transcriptomics/epistack - bioconductor-escher
skills/transcriptomics/escher - bioconductor-fgsea
skills/transcriptomics/fgsea - bioconductor-findit2
skills/transcriptomics/findit2 - bioconductor-gatom
skills/transcriptomics/gatom - bioconductor-genomicinstability
skills/transcriptomics/genomicinstability - bioconductor-genomicsupersignature
skills/transcriptomics/genomicsupersignature - bioconductor-geoquery
skills/transcriptomics/geoquery - bioconductor-geotcgadata
skills/transcriptomics/geotcgadata - bioconductor-glmgampoi
skills/transcriptomics/glmgampoi - bioconductor-granulator
skills/transcriptomics/granulator - bioconductor-gseabase
skills/transcriptomics/gseabase - bioconductor-gsva
skills/transcriptomics/gsva - bioconductor-harmonizr
skills/transcriptomics/harmonizr - bioconductor-illuminaio
skills/transcriptomics/illuminaio - bioconductor-intercellar
skills/transcriptomics/intercellar - bioconductor-limma
skills/transcriptomics/limma - bioconductor-mast
skills/transcriptomics/mast - bioconductor-mastr
skills/transcriptomics/mastr - bioconductor-metabocoreutils
skills/transcriptomics/metabocoreutils - bioconductor-metapod
skills/transcriptomics/metapod - bioconductor-mirtarrnaseq
skills/transcriptomics/mirtarrnaseq - bioconductor-monocle
skills/transcriptomics/monocle - bioconductor-msa
skills/transcriptomics/msa - bioconductor-msstatslip
skills/transcriptomics/msstatslip - bioconductor-nanotube
skills/transcriptomics/nanotube - bioconductor-nnsvg
skills/transcriptomics/nnsvg - bioconductor-omicsviewer
skills/transcriptomics/omicsviewer - bioconductor-ompbam
skills/transcriptomics/ompbam - bioconductor-orfhunter
skills/transcriptomics/orfhunter - bioconductor-pathview
skills/transcriptomics/pathview - bioconductor-proteodisco
skills/transcriptomics/proteodisco - bioconductor-ptairms
skills/transcriptomics/ptairms - bioconductor-ribodipa
skills/transcriptomics/ribodipa - bioconductor-scanmir
skills/transcriptomics/scanmir - bioconductor-scannotatr
skills/transcriptomics/scannotatr - bioconductor-scarray
skills/transcriptomics/scarray - bioconductor-scater
skills/transcriptomics/scater - bioconductor-sccomp
skills/transcriptomics/sccomp - bioconductor-scdblfinder
skills/transcriptomics/scdblfinder - bioconductor-scdesign3
skills/transcriptomics/scdesign3 - bioconductor-scran
skills/transcriptomics/scran - bioconductor-scuttle
skills/transcriptomics/scuttle - bioconductor-sechm
skills/transcriptomics/sechm - bioconductor-sgcp
skills/transcriptomics/sgcp - bioconductor-siggenes
skills/transcriptomics/siggenes - bioconductor-simbu
skills/transcriptomics/simbu - bioconductor-simpleseg
skills/transcriptomics/simpleseg - bioconductor-singler
skills/transcriptomics/singler - bioconductor-spatialde
skills/transcriptomics/spatialde - bioconductor-spatialexperiment
skills/transcriptomics/spatialexperiment - bioconductor-splicewiz
skills/transcriptomics/splicewiz - bioconductor-spotclean
skills/transcriptomics/spotclean - bioconductor-spotsweeper
skills/transcriptomics/spotsweeper - bioconductor-standr
skills/transcriptomics/standr - bioconductor-statial
skills/transcriptomics/statial - bioconductor-sva
skills/transcriptomics/sva - bioconductor-tanggle
skills/transcriptomics/tanggle - bioconductor-tdbasedufeadv
skills/transcriptomics/tdbasedufeadv - bioconductor-trajectoryutils
skills/transcriptomics/trajectoryutils - bioconductor-transformgampoi
skills/transcriptomics/transformgampoi - bioconductor-treeio
skills/transcriptomics/treeio - bioconductor-treekor
skills/transcriptomics/treekor - bioconductor-tximport
skills/transcriptomics/tximport - bioconductor-visse
skills/transcriptomics/visse - bioconductor-voyager
skills/transcriptomics/voyager - bioconductor-zenith
skills/transcriptomics/zenith - bioconductor-annotationhub
skills/variant-calling/annotationhub - bioconductor-snpstats
skills/variant-calling/snpstats - bioconductor-variantannotation
skills/variant-calling/variantannotation - bioconductor-vsn
skills/variant-calling/vsn
How it works
bioconductor-biomart
When your agent uses it
In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. bio
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
4,723 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore18,153 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-kegggraph
When your agent uses it
KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essenti
What it loads into context
Frontmatter
214 tokensAlways available to the agent
SKILL.md
1,303 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,358 bytesSKILL.mdcore5,358 bytesSKILL.mdcore5,358 bytesSKILL.mdcore5,358 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-enrichplot
When your agent uses it
The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualizati
What it loads into context
Frontmatter
206 tokensAlways available to the agent
SKILL.md
1,308 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,397 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-reactomepa
When your agent uses it
Reactome is a free, open-source, curated and peer-reviewed pathway database. Their goal is to provide intuitive bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge.
What it loads into context
Frontmatter
146 tokensAlways available to the agent
SKILL.md
2,147 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Scientific Assumptions
- Common Pitfalls
- Key Parameters
- Result Interpretation
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore9,661 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-bumphunter
When your agent uses it
Tools for finding bumps in genomic data
What it loads into context
Frontmatter
156 tokensAlways available to the agent
SKILL.md
1,266 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,676 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-chipseeker
When your agent uses it
ChIPseeker is a Bioconductor package for annotating ChIP-seq data analysis. Peak Annotation is performed by the annotatePeak function. The position and strand information of nearest genes are reported, in addition to the distance from the p
What it loads into context
Frontmatter
124 tokensAlways available to the agent
SKILL.md
3,989 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore15,251 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-epigrahmm
When your agent uses it
epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.
What it loads into context
Frontmatter
180 tokensAlways available to the agent
SKILL.md
1,716 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore6,919 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-extrachips
When your agent uses it
This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented.
What it loads into context
Frontmatter
155 tokensAlways available to the agent
SKILL.md
1,153 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,811 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-hicdcplus
When your agent uses it
Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con
What it loads into context
Frontmatter
178 tokensAlways available to the agent
SKILL.md
1,352 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,353 bytes
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View the complete source on GitHubbioconductor-hicexperiment
When your agent uses it
R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package.
What it loads into context
Frontmatter
220 tokensAlways available to the agent
SKILL.md
982 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,112 bytes
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View the complete source on GitHubbioconductor-hicontacts
When your agent uses it
HiContacts provides a collection of tools to analyse and visualize Hi-C datasets imported in R by HiCExperiment.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,250 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,989 bytes
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View the complete source on GitHubbioconductor-macsr
When your agent uses it
The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
875 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,560 bytes
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View the complete source on GitHubbioconductor-miasim
When your agent uses it
Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
897 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,747 bytes
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View the complete source on GitHubbioconductor-minfi
When your agent uses it
Tools to analyze & visualize Illumina Infinium methylation arrays.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
4,264 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore16,568 bytes
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View the complete source on GitHubbioconductor-basilisk
When your agent uses it
Installs a self-contained conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to e
What it loads into context
Frontmatter
201 tokensAlways available to the agent
SKILL.md
1,289 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,682 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-biobase
When your agent uses it
Functions that are needed by many other packages or which replace R functions.
What it loads into context
Frontmatter
151 tokensAlways available to the agent
SKILL.md
1,146 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,205 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-biovizbase
When your agent uses it
The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and enc
What it loads into context
Frontmatter
208 tokensAlways available to the agent
SKILL.md
1,308 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,448 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-dose
When your agent uses it
This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enric
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
651 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore2,600 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-experimenthub
When your agent uses it
This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated me
What it loads into context
Frontmatter
163 tokensAlways available to the agent
SKILL.md
983 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,285 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-faers
When your agent uses it
The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis.
What it loads into context
Frontmatter
149 tokensAlways available to the agent
SKILL.md
803 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,290 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-gdsfmt
When your agent uses it
Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited
What it loads into context
Frontmatter
202 tokensAlways available to the agent
SKILL.md
1,205 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,916 bytesSKILL.mdcore4,916 bytesSKILL.mdcore4,916 bytesSKILL.mdcore4,916 bytes
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View the complete source on GitHubbioconductor-genefilter
When your agent uses it
Some basic functions for filtering genes.
What it loads into context
Frontmatter
157 tokensAlways available to the agent
SKILL.md
1,186 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,987 bytes
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View the complete source on GitHubbioconductor-geneplotter
When your agent uses it
Functions for plotting genomic data
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
1,033 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,388 bytesSKILL.mdcore4,388 bytes
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View the complete source on GitHubbioconductor-graphite
When your agent uses it
Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases.
What it loads into context
Frontmatter
190 tokensAlways available to the agent
SKILL.md
1,300 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,555 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-gypsum
When your agent uses it
Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrative and management tasks. Check out the docu
What it loads into context
Frontmatter
195 tokensAlways available to the agent
SKILL.md
1,231 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,597 bytesSKILL.mdcore5,597 bytesSKILL.mdcore5,597 bytes
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View the complete source on GitHubbioconductor-immunotation
When your agent uses it
MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide
What it loads into context
Frontmatter
174 tokensAlways available to the agent
SKILL.md
1,071 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,072 bytes
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View the complete source on GitHubbioconductor-mosbi
When your agent uses it
This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract biclus
What it loads into context
Frontmatter
176 tokensAlways available to the agent
SKILL.md
1,388 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,467 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-multiassayexperiment
When your agent uses it
Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of st
What it loads into context
Frontmatter
174 tokensAlways available to the agent
SKILL.md
1,208 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,216 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-pcamethods
When your agent uses it
Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA o
What it loads into context
Frontmatter
227 tokensAlways available to the agent
SKILL.md
1,497 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,927 bytes
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View the complete source on GitHubbioconductor-qvalue
When your agent uses it
This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
1,475 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,188 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-residualmatrix
When your agent uses it
Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
1,176 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,237 bytes
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View the complete source on GitHubbioconductor-seqlogo
When your agent uses it
seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990).
What it loads into context
Frontmatter
152 tokensAlways available to the agent
SKILL.md
1,041 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,277 bytes
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View the complete source on GitHubbioconductor-topgo
When your agent uses it
topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and
What it loads into context
Frontmatter
199 tokensAlways available to the agent
SKILL.md
1,390 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,908 bytes
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View the complete source on GitHubbioconductor-annotate
When your agent uses it
This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
What it loads into context
Frontmatter
184 tokensAlways available to the agent
SKILL.md
2,565 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Scientific Assumptions
- Common Pitfalls
- Key Parameters
- Result Interpretation
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytesSKILL.mdcore11,858 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-beer
When your agent uses it
BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
1,283 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,281 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-biocviews
When your agent uses it
Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and '
What it loads into context
Frontmatter
209 tokensAlways available to the agent
SKILL.md
1,209 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,025 bytesSKILL.mdcore5,025 bytesSKILL.mdcore5,025 bytesSKILL.mdcore5,025 bytesSKILL.mdcore5,025 bytesSKILL.mdcore5,025 bytes
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View the complete source on GitHubbioconductor-biostrings
When your agent uses it
Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences.
What it loads into context
Frontmatter
177 tokensAlways available to the agent
SKILL.md
1,232 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,229 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-bsgenome
When your agent uses it
Infrastructure shared by all the Biostrings-based genome data packages.
What it loads into context
Frontmatter
168 tokensAlways available to the agent
SKILL.md
1,111 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,813 bytes
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View the complete source on GitHubbioconductor-clusterprofiler
When your agent uses it
This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources
What it loads into context
Frontmatter
166 tokensAlways available to the agent
SKILL.md
933 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,101 bytes
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View the complete source on GitHubbioconductor-cogeqc
When your agent uses it
cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualizat
What it loads into context
Frontmatter
178 tokensAlways available to the agent
SKILL.md
1,275 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,194 bytes
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View the complete source on GitHubbioconductor-complexheatmap
When your agent uses it
Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various
What it loads into context
Frontmatter
201 tokensAlways available to the agent
SKILL.md
1,340 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,616 bytes
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View the complete source on GitHubbioconductor-consensusclusterplus
When your agent uses it
algorithm for determining cluster count and membership by stability evidence in unsupervised analysis
What it loads into context
Frontmatter
155 tokensAlways available to the agent
SKILL.md
1,225 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,966 bytes
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View the complete source on GitHubbioconductor-decipher
When your agent uses it
A toolset for deciphering and managing biological sequences.
What it loads into context
Frontmatter
180 tokensAlways available to the agent
SKILL.md
1,497 tokensWhen the skill is activated
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0 tokensLoaded on request
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The playbook
- Dependencies & Environment
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- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,331 bytes
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View the complete source on GitHubbioconductor-dnacopy
When your agent uses it
Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.
What it loads into context
Frontmatter
178 tokensAlways available to the agent
SKILL.md
1,396 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,582 bytes
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View the complete source on GitHubbioconductor-doubletrouble
When your agent uses it
doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a s
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
860 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
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- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,659 bytes
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View the complete source on GitHubbioconductor-epialleler
When your agent uses it
Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Among other things, this package can also extract and visualise methylation patterns and assess allele specificity of methylation.
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
1,765 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Workflows
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- Data Requirements
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- Best Practices
- Common Pitfalls
- Alternatives
- References
Files
SKILL.mdcore6,839 bytes
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View the complete source on GitHubbioconductor-genomicalignments
When your agent uses it
Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working wit
What it loads into context
Frontmatter
201 tokensAlways available to the agent
SKILL.md
1,348 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
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- When NOT to Use
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- Key Parameters
- Best Practices
- Common Pitfalls
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- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,564 bytes
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View the complete source on GitHubbioconductor-genomicfeatures
When your agent uses it
Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a
What it loads into context
Frontmatter
196 tokensAlways available to the agent
SKILL.md
1,202 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
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- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,899 bytes
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View the complete source on GitHubbioconductor-genomicranges
When your agent uses it
The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose
What it loads into context
Frontmatter
119 tokensAlways available to the agent
SKILL.md
5,119 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
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Files
SKILL.mdcore18,641 bytes
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View the complete source on GitHubbioconductor-ggbio
When your agent uses it
The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor
What it loads into context
Frontmatter
169 tokensAlways available to the agent
SKILL.md
1,182 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,603 bytes
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View the complete source on GitHubbioconductor-ggmanh
When your agent uses it
Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints.
What it loads into context
Frontmatter
173 tokensAlways available to the agent
SKILL.md
1,332 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Workflows
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- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
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- References
Files
SKILL.mdcore5,397 bytes
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View the complete source on GitHubbioconductor-ggtree
When your agent uses it
'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data.
What it loads into context
Frontmatter
222 tokensAlways available to the agent
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1,511 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Dependencies & Environment
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- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,148 bytes
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View the complete source on GitHubbioconductor-gosemsim
When your agent uses it
The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R packag
What it loads into context
Frontmatter
143 tokensAlways available to the agent
SKILL.md
884 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
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- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore3,929 bytes
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View the complete source on GitHubbioconductor-gviz
When your agent uses it
Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to
What it loads into context
Frontmatter
193 tokensAlways available to the agent
SKILL.md
1,261 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,379 bytes
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View the complete source on GitHubbioconductor-keggrest
When your agent uses it
A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see <https://www.kegg.jp/kegg/rest/>). Note that KEGGREST
What it loads into context
Frontmatter
219 tokensAlways available to the agent
SKILL.md
1,270 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,031 bytesSKILL.mdcore5,031 bytes
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View the complete source on GitHubbioconductor-lintind
When your agent uses it
When we combine gene-editing technology and sequencing technology, we need to reconstruct a lineage tree from alleles generated and calculate the similarity between each pair of groups. FindIndel() and IndelForm() function will help you align each read to reference sequence and generate scar form strings respectively. IndelIdents() function will help you to define a scar form for each cell or read. IndelPlot() function will help you to visualize the distribution of deletion and insertion. TagPro
What it loads into context
Frontmatter
210 tokensAlways available to the agent
SKILL.md
1,646 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
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- When NOT to Use
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- Best Practices
- Common Pitfalls
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- Citations
- References
Files
SKILL.mdcore6,742 bytes
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View the complete source on GitHubbioconductor-msa2dist
When your agent uses it
MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis. It uses scoring matrices to be used in these pairwise distance calcualtions which can be adapted to any scoring for DNA or AA characters. E.g. by using literal distances MSA2dist calculates pairwise IUPAC distances.
What it loads into context
Frontmatter
163 tokensAlways available to the agent
SKILL.md
1,361 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Workflows
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- Data Requirements
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- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,175 bytes
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View the complete source on GitHubbioconductor-organismdbi
When your agent uses it
The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods.
What it loads into context
Frontmatter
191 tokensAlways available to the agent
SKILL.md
1,349 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Dependencies & Environment
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- When NOT to Use
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- Key Parameters
- Best Practices
- Common Pitfalls
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- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,520 bytes
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View the complete source on GitHubbioconductor-rblast
When your agent uses it
Seamlessly interfaces the Basic Local Alignment Search Tool (BLAST) to search genetic sequence data bases. This work was partially supported by grant no. R21HG005912 from the National Human Genome Research Institute.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
1,288 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Workflows
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- Best Practices
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- Alternatives
- Citations
- References
Files
SKILL.mdcore4,890 bytes
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View the complete source on GitHubbioconductor-rsamtools
When your agent uses it
This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files.
What it loads into context
Frontmatter
210 tokensAlways available to the agent
SKILL.md
1,283 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Dependencies & Environment
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- Best Practices
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Files
SKILL.mdcore5,178 bytes
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View the complete source on GitHubbioconductor-rtracklayer
When your agent uses it
Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/imp
What it loads into context
Frontmatter
214 tokensAlways available to the agent
SKILL.md
1,238 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
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- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,973 bytes
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View the complete source on GitHubbioconductor-shortread
When your agent uses it
This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, an
What it loads into context
Frontmatter
190 tokensAlways available to the agent
SKILL.md
1,227 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
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- When NOT to Use
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- Best Practices
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- Run this on BioMate
Files
SKILL.mdcore5,037 bytes
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View the complete source on GitHubbioconductor-supersigs
When your agent uses it
Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife).
What it loads into context
Frontmatter
128 tokensAlways available to the agent
SKILL.md
1,438 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Workflows
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Files
SKILL.mdcore5,907 bytes
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View the complete source on GitHubbioconductor-syntenet
When your agent uses it
syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-bas
What it loads into context
Frontmatter
166 tokensAlways available to the agent
SKILL.md
1,412 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Workflows
- When to Use
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- Key Parameters
- Best Practices
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- Alternatives
- Citations
- References
Files
SKILL.mdcore6,158 bytes
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View the complete source on GitHubbioconductor-tfbstools
When your agent uses it
TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It c
What it loads into context
Frontmatter
215 tokensAlways available to the agent
SKILL.md
1,514 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
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- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,935 bytes
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View the complete source on GitHubbioconductor-txdbmaker
When your agent uses it
A set of tools for making TxDb objects from genomic annotations from various sources (e.g. UCSC, Ensembl, and GFF files). These tools allow the user to download the genomic locations of transcripts, exons, and CDS, for a given assembly, and
What it loads into context
Frontmatter
216 tokensAlways available to the agent
SKILL.md
1,261 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Dependencies & Environment
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- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
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- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,104 bytesSKILL.mdcore5,104 bytes
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View the complete source on GitHubbioconductor-cytoviewer
When your agent uses it
This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image do
What it loads into context
Frontmatter
151 tokensAlways available to the agent
SKILL.md
1,262 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Workflows
- When to Use
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- Data Requirements
- Key Parameters
- Best Practices
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- Citations
- References
Files
SKILL.mdcore5,759 bytes
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View the complete source on GitHubbioconductor-ebimage
When your agent uses it
EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. Thi
What it loads into context
Frontmatter
201 tokensAlways available to the agent
SKILL.md
1,444 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
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- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
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- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,135 bytes
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View the complete source on GitHubbioconductor-flowcore
When your agent uses it
Provides S4 data structures and basic functions to deal with flow cytometry data.
What it loads into context
Frontmatter
163 tokensAlways available to the agent
SKILL.md
1,207 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
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- Run this on BioMate
Files
SKILL.mdcore5,125 bytes
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View the complete source on GitHubbioconductor-lisaclust
When your agent uses it
lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.
What it loads into context
Frontmatter
122 tokensAlways available to the agent
SKILL.md
1,364 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Workflows
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- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
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- Citations
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Files
SKILL.mdcore5,581 bytes
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View the complete source on GitHubbioconductor-rgoslin
When your agent uses it
The R implementation for the Grammar of Succint Lipid Nomenclature parses different short hand notation dialects for lipid names. It normalizes them to a standard name. It further provides calculated monoisotopic masses and sum formulas for each successfully parsed lipid name and supplements it with LIPID MAPS Category and Class information. Also, the structural level and further structural details about the head group, fatty acyls and functional groups are returned, where applicable.
What it loads into context
Frontmatter
170 tokensAlways available to the agent
SKILL.md
1,032 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
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The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,104 bytes
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View the complete source on GitHubbioconductor-biomformat
When your agent uses it
This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limi
What it loads into context
Frontmatter
218 tokensAlways available to the agent
SKILL.md
1,131 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,518 bytes
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View the complete source on GitHubbioconductor-dirichletmultinomial
When your agent uses it
Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed fu
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
1,031 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
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- Best Practices
- Common Pitfalls
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- Citations
- References
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Files
SKILL.mdcore3,978 bytes
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View the complete source on GitHubbioconductor-microbiome
When your agent uses it
Utilities for microbiome analysis.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
863 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore3,658 bytes
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View the complete source on GitHubbioconductor-phyloseq
When your agent uses it
phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data.
What it loads into context
Frontmatter
199 tokensAlways available to the agent
SKILL.md
1,384 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,442 bytes
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View the complete source on GitHubbioconductor-bandle
When your agent uses it
The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data.
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
1,304 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,145 bytes
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View the complete source on GitHubbioconductor-bionar
When your agent uses it
the R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
1,309 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,169 bytes
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View the complete source on GitHubbioconductor-drugtargetinteractions
When your agent uses it
Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain.
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
1,185 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,936 bytes
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View the complete source on GitHubbioconductor-matrixqcvis
When your agent uses it
Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualiz
What it loads into context
Frontmatter
180 tokensAlways available to the agent
SKILL.md
1,072 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,593 bytes
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View the complete source on GitHubbioconductor-mixomics
When your agent uses it
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducin
What it loads into context
Frontmatter
183 tokensAlways available to the agent
SKILL.md
1,346 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,502 bytes
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View the complete source on GitHubbioconductor-msbackendmassbank
When your agent uses it
Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package.
What it loads into context
Frontmatter
148 tokensAlways available to the agent
SKILL.md
943 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,139 bytesSKILL.mdcore4,139 bytes
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View the complete source on GitHubbioconductor-mscoreutils
When your agent uses it
MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning, baseline estimation),
What it loads into context
Frontmatter
194 tokensAlways available to the agent
SKILL.md
1,114 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,855 bytes
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View the complete source on GitHubbioconductor-msdatahub
When your agent uses it
The MsDataHub package uses the ExperimentHub infrastructure to distribute raw mass spectrometry data files, peptide spectrum matches or quantitative data from proteomics and metabolomics experiments.
What it loads into context
Frontmatter
140 tokensAlways available to the agent
SKILL.md
1,244 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,040 bytes
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View the complete source on GitHubbioconductor-msnbase
When your agent uses it
MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
3,456 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore12,723 bytes
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View the complete source on GitHubbioconductor-mzid
When your agent uses it
A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact th
What it loads into context
Frontmatter
215 tokensAlways available to the agent
SKILL.md
1,090 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,502 bytes
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View the complete source on GitHubbioconductor-mzr
When your agent uses it
mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XC
What it loads into context
Frontmatter
215 tokensAlways available to the agent
SKILL.md
1,168 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,702 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-phipdata
When your agent uses it
PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.
What it loads into context
Frontmatter
144 tokensAlways available to the agent
SKILL.md
1,340 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,439 bytes
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View the complete source on GitHubbioconductor-psmatch
When your agent uses it
The PSMatch package helps proteomics practitioners to load, handle and manage Peptide Spectrum Matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and m
What it loads into context
Frontmatter
197 tokensAlways available to the agent
SKILL.md
1,320 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,724 bytesSKILL.mdcore5,724 bytes
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View the complete source on GitHubbioconductor-qfeatures
When your agent uses it
The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assa
What it loads into context
Frontmatter
187 tokensAlways available to the agent
SKILL.md
1,526 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,694 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-stringdb
When your agent uses it
tags: [bioconductor, r, proteomics, vignette-grounded]
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
1,236 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,480 bytesSKILL.mdcore5,480 bytesSKILL.mdcore5,480 bytes
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View the complete source on GitHubbioconductor-targetdecoy
When your agent uses it
A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum
What it loads into context
Frontmatter
156 tokensAlways available to the agent
SKILL.md
1,384 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,819 bytes
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View the complete source on GitHubbioconductor-cytomem
When your agent uses it
MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry,
What it loads into context
Frontmatter
181 tokensAlways available to the agent
SKILL.md
1,430 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore6,095 bytes
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View the complete source on GitHubbioconductor-demuxmix
When your agent uses it
A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided.
What it loads into context
Frontmatter
186 tokensAlways available to the agent
SKILL.md
1,498 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,998 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-hoodscanr
When your agent uses it
hoodscanR is an user-friendly R package providing functions to assist cellular neighborhood analysis of any spatial transcriptomics data with single-cell resolution. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. The package can result in cell-level neighborhood annotation output, along with funtions to perform neighborhood colocalization analysis and neighborhood-based ce
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
1,370 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,789 bytes
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View the complete source on GitHubbioconductor-mudata
When your agent uses it
Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
1,079 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,180 bytes
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View the complete source on GitHubbioconductor-screclassify
When your agent uses it
A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
777 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,274 bytes
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View the complete source on GitHubbioconductor-singlecellexperiment
When your agent uses it
Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metada
What it loads into context
Frontmatter
134 tokensAlways available to the agent
SKILL.md
4,028 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore16,409 bytes
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View the complete source on GitHubbioconductor-treesummarizedexperiment
When your agent uses it
TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data.
What it loads into context
Frontmatter
163 tokensAlways available to the agent
SKILL.md
1,298 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytesSKILL.mdcore5,559 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-zellkonverter
When your agent uses it
Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes f
What it loads into context
Frontmatter
194 tokensAlways available to the agent
SKILL.md
1,225 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,038 bytes
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View the complete source on GitHubbioconductor-affy
When your agent uses it
The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
1,431 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,635 bytes
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View the complete source on GitHubbioconductor-apeglm
When your agent uses it
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
What it loads into context
Frontmatter
195 tokensAlways available to the agent
SKILL.md
1,658 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,657 bytes
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View the complete source on GitHubbioconductor-asurat
When your agent uses it
ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
What it loads into context
Frontmatter
164 tokensAlways available to the agent
SKILL.md
1,344 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,616 bytes
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View the complete source on GitHubbioconductor-aucell
When your agent uses it
AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
What it loads into context
Frontmatter
207 tokensAlways available to the agent
SKILL.md
1,433 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,252 bytes
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View the complete source on GitHubbioconductor-awst
When your agent uses it
We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experi
What it loads into context
Frontmatter
202 tokensAlways available to the agent
SKILL.md
1,057 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,541 bytes
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View the complete source on GitHubbioconductor-batchelor
When your agent uses it
Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
4,097 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore17,056 bytes
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View the complete source on GitHubbioconductor-bindingsitefinder
When your agent uses it
Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette.
What it loads into context
Frontmatter
123 tokensAlways available to the agent
SKILL.md
1,581 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,958 bytes
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View the complete source on GitHubbioconductor-bloodgen3module
When your agent uses it
The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.11
What it loads into context
Frontmatter
140 tokensAlways available to the agent
SKILL.md
1,313 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
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Files
SKILL.mdcore5,662 bytes
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View the complete source on GitHubbioconductor-busseq
When your agent uses it
BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value ca
What it loads into context
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The playbook
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- Best Practices
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- Citations
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Files
SKILL.mdcore5,979 bytes
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View the complete source on GitHubbioconductor-cageminer
When your agent uses it
This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.
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The playbook
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Files
SKILL.mdcore5,065 bytes
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View the complete source on GitHubbioconductor-cardelino
When your agent uses it
Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stoc
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The playbook
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Files
SKILL.mdcore5,794 bytes
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View the complete source on GitHubbioconductor-cbnplot
When your agent uses it
This package provides the visualization of bayesian network inferred from gene expression data. The networks are based on enrichment analysis results inferred from packages including clusterProfiler and ReactomePA. The networks between pathways and genes inside the pathways can be inferred and visualized.
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The playbook
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Files
SKILL.mdcore5,253 bytes
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View the complete source on GitHubbioconductor-cellbarcode
When your agent uses it
The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. \code{CellBarcode} can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data.
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The playbook
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Files
SKILL.mdcore5,547 bytes
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View the complete source on GitHubbioconductor-cellxgenedp
When your agent uses it
The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading.
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The playbook
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Files
SKILL.mdcore4,209 bytes
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View the complete source on GitHubbioconductor-condiments
When your agent uses it
This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format.
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Files
SKILL.mdcore6,263 bytes
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View the complete source on GitHubbioconductor-cotan
When your agent uses it
Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate
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The playbook
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Files
SKILL.mdcore6,774 bytes
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View the complete source on GitHubbioconductor-crisprbase
When your agent uses it
Provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported.
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1,651 tokensWhen the skill is activated
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The playbook
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Files
SKILL.mdcore6,000 bytes
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View the complete source on GitHubbioconductor-crisprdesign
When your agent uses it
Provides a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including
What it loads into context
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The playbook
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Files
SKILL.mdcore4,912 bytes
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View the complete source on GitHubbioconductor-crisprscore
When your agent uses it
Provides R wrappers of several on-target and off-target scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target cutting efficiency scoring methods are RuleSet1, Azimuth, DeepHF, DeepCpf1, enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods are available for off-target specificity prediction. The package also provides a Lindel-derived score to predict the probability of a gRNA to
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2,193 tokensWhen the skill is activated
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The playbook
- Workflows
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Files
SKILL.mdcore7,433 bytes
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View the complete source on GitHubbioconductor-crisprviz
When your agent uses it
Provides functionalities to visualize and contextualize CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases and applications. Works in conjunction with the crisprBase and crisprDesign Bioconductor packages. Plots are produced using the Gviz framework.
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1,303 tokensWhen the skill is activated
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Files
SKILL.mdcore5,253 bytes
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View the complete source on GitHubbioconductor-dada2
When your agent uses it
The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, an
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1,347 tokensWhen the skill is activated
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The playbook
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- Run this on BioMate
Files
SKILL.mdcore5,413 bytes
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View the complete source on GitHubbioconductor-deconvr
When your agent uses it
This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BS
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1,420 tokensWhen the skill is activated
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The playbook
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Files
SKILL.mdcore6,035 bytes
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View the complete source on GitHubbioconductor-deseq2
When your agent uses it
Uses DESeq2 to estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.
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95 tokensAlways available to the agent
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4,941 tokensWhen the skill is activated
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The playbook
- Dependencies & Environment
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- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore19,408 bytes
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View the complete source on GitHubbioconductor-despace
When your agent uses it
Intuitive framework for identifying spatially variable genes (SVGs) via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the
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1,390 tokensWhen the skill is activated
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The playbook
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Files
SKILL.mdcore6,320 bytes
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View the complete source on GitHubbioconductor-dexma
When your agent uses it
performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results.
What it loads into context
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1,548 tokensWhen the skill is activated
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The playbook
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Files
SKILL.mdcore6,538 bytes
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View the complete source on GitHubbioconductor-dino
When your agent uses it
Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such
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1,598 tokensWhen the skill is activated
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The playbook
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Files
SKILL.mdcore6,560 bytes
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View the complete source on GitHubbioconductor-dropletutils
When your agent uses it
The filtering uses intelligent methods to generate output 10X matrices as would be otherwise generated by CellRanger. The ranking option serves to illustrate the number of detected barcodes in the sample.
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The playbook
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- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore16,778 bytes
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View the complete source on GitHubbioconductor-easier
When your agent uses it
This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in
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1,773 tokensWhen the skill is activated
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The playbook
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Files
SKILL.mdcore7,405 bytes
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View the complete source on GitHubbioconductor-edger
When your agent uses it
Estimates differential gene expression for short read sequence count using methods appropriate for count data. If you have paired data you may also want to consider Tophat/Cufflinks. Input must be raw count data for each sequence arranged i
What it loads into context
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110 tokensAlways available to the agent
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3,162 tokensWhen the skill is activated
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0 tokensLoaded on request
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The playbook
- Dependencies & Environment
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- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore12,178 bytes
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When your agent uses it
Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many po
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1,225 tokensWhen the skill is activated
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The playbook
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- Best Practices
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- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,216 bytes
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View the complete source on GitHubbioconductor-epistack
When your agent uses it
The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provi
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240 tokensAlways available to the agent
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2,619 tokensWhen the skill is activated
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The playbook
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Files
SKILL.mdcore9,557 bytes
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View the complete source on GitHubbioconductor-escher
When your agent uses it
The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to
What it loads into context
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175 tokensAlways available to the agent
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1,646 tokensWhen the skill is activated
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0 tokensLoaded on request
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The playbook
- Workflows
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- Citations
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Files
SKILL.mdcore6,728 bytes
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View the complete source on GitHubbioconductor-fgsea
When your agent uses it
A tabular file with gene symbols in the first column, and a ranked statistic (e.g. t-statistic or log fold-change) in the second column
What it loads into context
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158 tokensAlways available to the agent
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2,130 tokensWhen the skill is activated
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The playbook
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- Result Interpretation
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore9,614 bytes
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View the complete source on GitHubbioconductor-findit2
When your agent uses it
This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function lik
What it loads into context
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175 tokensAlways available to the agent
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1,251 tokensWhen the skill is activated
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The playbook
- Workflows
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Files
SKILL.mdcore4,909 bytes
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View the complete source on GitHubbioconductor-gatom
When your agent uses it
This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.
What it loads into context
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114 tokensAlways available to the agent
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1,657 tokensWhen the skill is activated
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0 tokensLoaded on request
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The playbook
- Workflows
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- Best Practices
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- Citations
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Files
SKILL.mdcore6,827 bytes
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View the complete source on GitHubbioconductor-genomicinstability
When your agent uses it
This package contain functions to run genomic instability analysis (GIA) from scRNA-Seq data. GIA estimates the association between gene expression and genomic location of the coding genes. It uses the aREA algorithm to quantify the enrichment of sets of contiguous genes (loci-blocks) on the gene expression profiles and estimates the Genomic Instability Score (GIS) for each analyzed cell.
What it loads into context
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142 tokensAlways available to the agent
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1,412 tokensWhen the skill is activated
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The playbook
- Workflows
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- Best Practices
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- Citations
- References
Files
SKILL.mdcore6,213 bytes
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View the complete source on GitHubbioconductor-genomicsupersignature
When your agent uses it
Connect new gene expression profile with the relevant information from the existing databases, such as previous publications, MeSH terms, and gene sets.
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1,916 tokensWhen the skill is activated
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The playbook
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Files
SKILL.mdcore8,822 bytes
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View the complete source on GitHubbioconductor-geoquery
When your agent uses it
This tool fetches microarray data directly from GEO database, based on the GEOQuery R package. Given a GSE accession ID, it returns an Rdata object containing the data and a text file (.cond file) summarizing the conditions of the experimen
What it loads into context
Frontmatter
223 tokensAlways available to the agent
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1,438 tokensWhen the skill is activated
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The playbook
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- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,654 bytes
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View the complete source on GitHubbioconductor-geotcgadata
When your agent uses it
Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.
What it loads into context
Frontmatter
156 tokensAlways available to the agent
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2,350 tokensWhen the skill is activated
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The playbook
- Workflows
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- Best Practices
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- Alternatives
- Citations
- References
Files
SKILL.mdcore8,646 bytes
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View the complete source on GitHubbioconductor-glmgampoi
When your agent uses it
Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments.
What it loads into context
Frontmatter
204 tokensAlways available to the agent
SKILL.md
1,306 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,382 bytes
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View the complete source on GitHubbioconductor-granulator
When your agent uses it
granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well.
What it loads into context
Frontmatter
156 tokensAlways available to the agent
SKILL.md
1,602 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore6,747 bytes
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View the complete source on GitHubbioconductor-gseabase
When your agent uses it
This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA).
What it loads into context
Frontmatter
178 tokensAlways available to the agent
SKILL.md
1,300 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,546 bytesSKILL.mdcore5,546 bytes
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View the complete source on GitHubbioconductor-gsva
When your agent uses it
Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data fr
What it loads into context
Frontmatter
199 tokensAlways available to the agent
SKILL.md
1,343 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,730 bytes
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View the complete source on GitHubbioconductor-harmonizr
When your agent uses it
An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.
What it loads into context
Frontmatter
157 tokensAlways available to the agent
SKILL.md
1,146 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,772 bytes
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View the complete source on GitHubbioconductor-illuminaio
When your agent uses it
Tools for parsing Illumina's microarray output files, including IDAT.
What it loads into context
Frontmatter
177 tokensAlways available to the agent
SKILL.md
1,039 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,325 bytesSKILL.mdcore4,325 bytes
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View the complete source on GitHubbioconductor-intercellar
When your agent uses it
InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell comm
What it loads into context
Frontmatter
152 tokensAlways available to the agent
SKILL.md
863 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,942 bytes
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View the complete source on GitHubbioconductor-limma
When your agent uses it
Given a matrix of counts (e.g. from featureCounts) and optional information about the genes, this tool performs differential expression (DE) using the limma Bioconductor package and produces plots and tables useful in DE analysis. Interacti
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
3,002 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore11,579 bytes
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View the complete source on GitHubbioconductor-mast
When your agent uses it
Methods and models for handling zero-inflated single cell assay data.
What it loads into context
Frontmatter
172 tokensAlways available to the agent
SKILL.md
1,507 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,075 bytes
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View the complete source on GitHubbioconductor-mastr
When your agent uses it
mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of g
What it loads into context
Frontmatter
137 tokensAlways available to the agent
SKILL.md
1,494 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore6,212 bytes
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View the complete source on GitHubbioconductor-metabocoreutils
When your agent uses it
MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound ma
What it loads into context
Frontmatter
202 tokensAlways available to the agent
SKILL.md
1,488 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,176 bytes
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View the complete source on GitHubbioconductor-metapod
When your agent uses it
Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or fo
What it loads into context
Frontmatter
207 tokensAlways available to the agent
SKILL.md
1,292 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,542 bytes
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View the complete source on GitHubbioconductor-mirtarrnaseq
When your agent uses it
mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments.
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
2,037 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore7,840 bytes
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View the complete source on GitHubbioconductor-monocle
When your agent uses it
Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progre
What it loads into context
Frontmatter
177 tokensAlways available to the agent
SKILL.md
1,135 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,753 bytes
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View the complete source on GitHubbioconductor-msa
When your agent uses it
The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any externa
What it loads into context
Frontmatter
182 tokensAlways available to the agent
SKILL.md
1,091 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,583 bytes
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View the complete source on GitHubbioconductor-msstatslip
When your agent uses it
Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages.
What it loads into context
Frontmatter
142 tokensAlways available to the agent
SKILL.md
1,109 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,470 bytes
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View the complete source on GitHubbioconductor-nanotube
When your agent uses it
NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
906 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,868 bytes
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View the complete source on GitHubbioconductor-nnsvg
When your agent uses it
Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more
What it loads into context
Frontmatter
167 tokensAlways available to the agent
SKILL.md
856 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,702 bytes
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View the complete source on GitHubbioconductor-omicsviewer
When your agent uses it
omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existin
What it loads into context
Frontmatter
153 tokensAlways available to the agent
SKILL.md
1,212 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,189 bytes
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View the complete source on GitHubbioconductor-ompbam
When your agent uses it
This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged in
What it loads into context
Frontmatter
171 tokensAlways available to the agent
SKILL.md
941 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,976 bytes
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View the complete source on GitHubbioconductor-orfhunter
When your agent uses it
The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules fr
What it loads into context
Frontmatter
184 tokensAlways available to the agent
SKILL.md
2,120 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore7,690 bytes
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View the complete source on GitHubbioconductor-pathview
When your agent uses it
Pathview is a stand-alone software package for pathway based data integration and visualization.
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
2,299 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Scientific Assumptions
- Common Pitfalls
- Key Parameters
- Result Interpretation
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore10,366 bytesSKILL.mdcore10,366 bytesSKILL.mdcore10,366 bytes
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View the complete source on GitHubbioconductor-proteodisco
When your agent uses it
ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings.
What it loads into context
Frontmatter
194 tokensAlways available to the agent
SKILL.md
2,472 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore9,153 bytes
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View the complete source on GitHubbioconductor-ptairms
When your agent uses it
This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and qua
What it loads into context
Frontmatter
162 tokensAlways available to the agent
SKILL.md
1,542 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore6,097 bytes
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View the complete source on GitHubbioconductor-ribodipa
When your agent uses it
This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization.
What it loads into context
Frontmatter
136 tokensAlways available to the agent
SKILL.md
1,853 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore7,275 bytesSKILL.mdcore7,275 bytes
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View the complete source on GitHubbioconductor-scanmir
When your agent uses it
A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment).
What it loads into context
Frontmatter
156 tokensAlways available to the agent
SKILL.md
1,105 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,714 bytes
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View the complete source on GitHubbioconductor-scannotatr
When your agent uses it
The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.
What it loads into context
Frontmatter
143 tokensAlways available to the agent
SKILL.md
1,285 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,302 bytes
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View the complete source on GitHubbioconductor-scarray
When your agent uses it
Provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language.
What it loads into context
Frontmatter
133 tokensAlways available to the agent
SKILL.md
959 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore3,874 bytes
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View the complete source on GitHubbioconductor-scater
When your agent uses it
A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
4,257 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore16,195 bytes
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View the complete source on GitHubbioconductor-sccomp
When your agent uses it
A robust and outlier-aware method for testing differential tissue composition from single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.
What it loads into context
Frontmatter
124 tokensAlways available to the agent
SKILL.md
1,734 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore7,465 bytes
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View the complete source on GitHubbioconductor-scdblfinder
When your agent uses it
The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly fou
What it loads into context
Frontmatter
200 tokensAlways available to the agent
SKILL.md
1,378 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,939 bytes
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View the complete source on GitHubbioconductor-scdesign3
When your agent uses it
We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positi
What it loads into context
Frontmatter
159 tokensAlways available to the agent
SKILL.md
1,253 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,011 bytes
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View the complete source on GitHubbioconductor-scran
When your agent uses it
Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
4,605 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore18,050 bytes
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View the complete source on GitHubbioconductor-scuttle
When your agent uses it
Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages.
What it loads into context
Frontmatter
178 tokensAlways available to the agent
SKILL.md
1,358 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,145 bytes
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View the complete source on GitHubbioconductor-sechm
When your agent uses it
sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package.
What it loads into context
Frontmatter
133 tokensAlways available to the agent
SKILL.md
1,148 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,639 bytes
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View the complete source on GitHubbioconductor-sgcp
When your agent uses it
SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.
What it loads into context
Frontmatter
143 tokensAlways available to the agent
SKILL.md
1,430 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore6,120 bytes
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View the complete source on GitHubbioconductor-siggenes
When your agent uses it
Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM).
What it loads into context
Frontmatter
189 tokensAlways available to the agent
SKILL.md
1,439 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,245 bytes
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View the complete source on GitHubbioconductor-simbu
When your agent uses it
SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.
What it loads into context
Frontmatter
154 tokensAlways available to the agent
SKILL.md
1,424 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,623 bytes
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View the complete source on GitHubbioconductor-simpleseg
When your agent uses it
Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained f
What it loads into context
Frontmatter
159 tokensAlways available to the agent
SKILL.md
1,169 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,956 bytes
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View the complete source on GitHubbioconductor-singler
When your agent uses it
Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.
What it loads into context
Frontmatter
159 tokensAlways available to the agent
SKILL.md
1,000 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,289 bytes
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View the complete source on GitHubbioconductor-spatialde
When your agent uses it
SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk.
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
1,510 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore6,145 bytes
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View the complete source on GitHubbioconductor-spatialexperiment
When your agent uses it
Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordina
What it loads into context
Frontmatter
194 tokensAlways available to the agent
SKILL.md
1,275 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,584 bytes
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View the complete source on GitHubbioconductor-splicewiz
When your agent uses it
The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across
What it loads into context
Frontmatter
159 tokensAlways available to the agent
SKILL.md
2,068 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore8,259 bytes
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View the complete source on GitHubbioconductor-spotclean
When your agent uses it
SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontamin
What it loads into context
Frontmatter
150 tokensAlways available to the agent
SKILL.md
1,422 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,905 bytes
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View the complete source on GitHubbioconductor-spotsweeper
When your agent uses it
Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts. Scales linearly with the number of spots and is designed to be used with 'SpatialExperiment' objects.
What it loads into context
Frontmatter
156 tokensAlways available to the agent
SKILL.md
1,461 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,939 bytes
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View the complete source on GitHubbioconductor-standr
When your agent uses it
standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations.
What it loads into context
Frontmatter
146 tokensAlways available to the agent
SKILL.md
1,302 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,305 bytes
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View the complete source on GitHubbioconductor-statial
When your agent uses it
Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.
What it loads into context
Frontmatter
181 tokensAlways available to the agent
SKILL.md
1,371 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,858 bytes
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View the complete source on GitHubbioconductor-sva
When your agent uses it
The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensi
What it loads into context
Frontmatter
201 tokensAlways available to the agent
SKILL.md
1,430 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore6,046 bytes
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View the complete source on GitHubbioconductor-tanggle
When your agent uses it
Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) <doi:10.1093/bioinformatics/bty633> and 'phangorn' Schliep (2011) <d
What it loads into context
Frontmatter
220 tokensAlways available to the agent
SKILL.md
1,213 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,463 bytes
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View the complete source on GitHubbioconductor-tdbasedufeadv
When your agent uses it
This is an advanced version of TDbasedUFE, which is a comprehensive package to perform Tensor decomposition based unsupervised feature extraction. In contrast to TDbasedUFE which can perform simple the feature selection and the multiomics analyses, this package can perform more complicated and advanced features, but they are not so popularly required. Only users who require more specific features can make use of its functionality.
What it loads into context
Frontmatter
166 tokensAlways available to the agent
SKILL.md
1,840 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore7,134 bytes
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View the complete source on GitHubbioconductor-trajectoryutils
When your agent uses it
Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime infere
What it loads into context
Frontmatter
184 tokensAlways available to the agent
SKILL.md
1,177 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,279 bytes
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View the complete source on GitHubbioconductor-transformgampoi
When your agent uses it
Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals).
What it loads into context
Frontmatter
164 tokensAlways available to the agent
SKILL.md
1,256 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,222 bytes
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View the complete source on GitHubbioconductor-treeio
When your agent uses it
'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated
What it loads into context
Frontmatter
176 tokensAlways available to the agent
SKILL.md
971 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore4,048 bytes
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View the complete source on GitHubbioconductor-treekor
When your agent uses it
treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; qua
What it loads into context
Frontmatter
164 tokensAlways available to the agent
SKILL.md
1,416 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,971 bytes
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View the complete source on GitHubbioconductor-tximport
When your agent uses it
Current version only works in 'merge' mode: A single table of gene summarizations is generated with one column for each sample file.
What it loads into context
Frontmatter
133 tokensAlways available to the agent
SKILL.md
1,717 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Scientific Assumptions
- Common Pitfalls
- Key Parameters
- Result Interpretation
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore8,211 bytes
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View the complete source on GitHubbioconductor-visse
When your agent uses it
This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches.
What it loads into context
Frontmatter
143 tokensAlways available to the agent
SKILL.md
1,445 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore5,457 bytes
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View the complete source on GitHubbioconductor-voyager
When your agent uses it
SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The
What it loads into context
Frontmatter
181 tokensAlways available to the agent
SKILL.md
1,603 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore6,209 bytes
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View the complete source on GitHubbioconductor-zenith
When your agent uses it
Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream().
What it loads into context
Frontmatter
166 tokensAlways available to the agent
SKILL.md
1,122 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflows
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
Files
SKILL.mdcore4,578 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-annotationhub
When your agent uses it
This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
2,932 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- Do NOT Use When
- Data Requirements
- Installation
- Workflows
- Key Functions & Parameters
- Scientific Assumptions
- Result Interpretation
- Best Practices
- When to Choose This vs Alternatives
- Benchmark Evidence
- Common Errors & Troubleshooting
- Additional Notes from Official Documentation
- Resources
- Run this on BioMate
Files
SKILL.mdcore11,843 bytes
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View the complete source on GitHubbioconductor-snpstats
When your agent uses it
Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes.
What it loads into context
Frontmatter
190 tokensAlways available to the agent
SKILL.md
1,361 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,693 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioconductor-variantannotation
When your agent uses it
Annotate variants, compute amino acid coding changes, predict coding outcomes.
What it loads into context
Frontmatter
193 tokensAlways available to the agent
SKILL.md
1,516 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore5,981 bytes
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View the complete source on GitHubbioconductor-vsn
When your agent uses it
The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of th
What it loads into context
Frontmatter
209 tokensAlways available to the agent
SKILL.md
1,700 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Dependencies & Environment
- When to Use
- When NOT to Use
- Data Requirements
- Key Parameters
- Best Practices
- Common Pitfalls
- Alternatives
- Citations
- References
- Run this on BioMate
Files
SKILL.mdcore7,356 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubSkills included
- bioconductor-biomart
- bioconductor-kegggraph
- bioconductor-enrichplot
- bioconductor-reactomepa
- bioconductor-bumphunter
- bioconductor-chipseeker
- bioconductor-epigrahmm
- bioconductor-extrachips
- bioconductor-hicdcplus
- bioconductor-hicexperiment
- bioconductor-hicontacts
- bioconductor-macsr
- bioconductor-miasim
- bioconductor-minfi
- bioconductor-basilisk
- bioconductor-biobase
- bioconductor-biovizbase
- bioconductor-dose
- bioconductor-experimenthub
- bioconductor-faers
- bioconductor-gdsfmt
- bioconductor-genefilter
- bioconductor-geneplotter
- bioconductor-graphite
- bioconductor-gypsum
- bioconductor-immunotation
- bioconductor-mosbi
- bioconductor-multiassayexperiment
- bioconductor-pcamethods
- bioconductor-qvalue
- bioconductor-residualmatrix
- bioconductor-seqlogo
- bioconductor-topgo
- bioconductor-annotate
- bioconductor-beer
- bioconductor-biocviews
- bioconductor-biostrings
- bioconductor-bsgenome
- bioconductor-clusterprofiler
- bioconductor-cogeqc
- bioconductor-complexheatmap
- bioconductor-consensusclusterplus
- bioconductor-decipher
- bioconductor-dnacopy
- bioconductor-doubletrouble
- bioconductor-epialleler
- bioconductor-genomicalignments
- bioconductor-genomicfeatures
- bioconductor-genomicranges
- bioconductor-ggbio
- bioconductor-ggmanh
- bioconductor-ggtree
- bioconductor-gosemsim
- bioconductor-gviz
- bioconductor-keggrest
- bioconductor-lintind
- bioconductor-msa2dist
- bioconductor-organismdbi
- bioconductor-rblast
- bioconductor-rsamtools
- bioconductor-rtracklayer
- bioconductor-shortread
- bioconductor-supersigs
- bioconductor-syntenet
- bioconductor-tfbstools
- bioconductor-txdbmaker
- bioconductor-cytoviewer
- bioconductor-ebimage
- bioconductor-flowcore
- bioconductor-lisaclust
- bioconductor-rgoslin
- bioconductor-biomformat
- bioconductor-dirichletmultinomial
- bioconductor-microbiome
- bioconductor-phyloseq
- bioconductor-bandle
- bioconductor-bionar
- bioconductor-drugtargetinteractions
- bioconductor-matrixqcvis
- bioconductor-mixomics
- bioconductor-msbackendmassbank
- bioconductor-mscoreutils
- bioconductor-msdatahub
- bioconductor-msnbase
- bioconductor-mzid
- bioconductor-mzr
- bioconductor-phipdata
- bioconductor-psmatch
- bioconductor-qfeatures
- bioconductor-stringdb
- bioconductor-targetdecoy
- bioconductor-cytomem
- bioconductor-demuxmix
- bioconductor-hoodscanr
- bioconductor-mudata
- bioconductor-screclassify
- bioconductor-singlecellexperiment
- bioconductor-treesummarizedexperiment
- bioconductor-zellkonverter
- bioconductor-affy
- bioconductor-apeglm
- bioconductor-asurat
- bioconductor-aucell
- bioconductor-awst
- bioconductor-batchelor
- bioconductor-bindingsitefinder
- bioconductor-bloodgen3module
- bioconductor-busseq
- bioconductor-cageminer
- bioconductor-cardelino
- bioconductor-cbnplot
- bioconductor-cellbarcode
- bioconductor-cellxgenedp
- bioconductor-condiments
- bioconductor-cotan
- bioconductor-crisprbase
- bioconductor-crisprdesign
- bioconductor-crisprscore
- bioconductor-crisprviz
- bioconductor-dada2
- bioconductor-deconvr
- bioconductor-deseq2
- bioconductor-despace
- bioconductor-dexma
- bioconductor-dino
- bioconductor-dropletutils
- bioconductor-easier
- bioconductor-edger
- bioconductor-enhancedvolcano
- bioconductor-epistack
- bioconductor-escher
- bioconductor-fgsea
- bioconductor-findit2
- bioconductor-gatom
- bioconductor-genomicinstability
- bioconductor-genomicsupersignature
- bioconductor-geoquery
- bioconductor-geotcgadata
- bioconductor-glmgampoi
- bioconductor-granulator
- bioconductor-gseabase
- bioconductor-gsva
- bioconductor-harmonizr
- bioconductor-illuminaio
- bioconductor-intercellar
- bioconductor-limma
- bioconductor-mast
- bioconductor-mastr
- bioconductor-metabocoreutils
- bioconductor-metapod
- bioconductor-mirtarrnaseq
- bioconductor-monocle
- bioconductor-msa
- bioconductor-msstatslip
- bioconductor-nanotube
- bioconductor-nnsvg
- bioconductor-omicsviewer
- bioconductor-ompbam
- bioconductor-orfhunter
- bioconductor-pathview
- bioconductor-proteodisco
- bioconductor-ptairms
- bioconductor-ribodipa
- bioconductor-scanmir
- bioconductor-scannotatr
- bioconductor-scarray
- bioconductor-scater
- bioconductor-sccomp
- bioconductor-scdblfinder
- bioconductor-scdesign3
- bioconductor-scran
- bioconductor-scuttle
- bioconductor-sechm
- bioconductor-sgcp
- bioconductor-siggenes
- bioconductor-simbu
- bioconductor-simpleseg
- bioconductor-singler
- bioconductor-spatialde
- bioconductor-spatialexperiment
- bioconductor-splicewiz
- bioconductor-spotclean
- bioconductor-spotsweeper
- bioconductor-standr
- bioconductor-statial
- bioconductor-sva
- bioconductor-tanggle
- bioconductor-tdbasedufeadv
- bioconductor-trajectoryutils
- bioconductor-transformgampoi
- bioconductor-treeio
- bioconductor-treekor
- bioconductor-tximport
- bioconductor-visse
- bioconductor-voyager
- bioconductor-zenith
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- bioconductor-variantannotation
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- May 27, 2026
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Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 4, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 43 notes
skills/annotation/kegggraph/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/annotation/kegggraph/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/annotation/kegggraph/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/general/gdsfmt/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/general/gdsfmt/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/general/gdsfmt/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/general/geneplotter/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/general/gypsum/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/general/gypsum/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/genomics/annotate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- 844 network referencesin 191 files: biomart/SKILL.md:10 deseq2/SKILL.md:10 limma/SKILL.md:10 and 188 more
- 1 obfuscation signalin 1 file: proteodisco/SKILL.md:100
- No instruction overrides
- No executable scripts
- No access to secrets
- No requests to vote in Hot
Show all 845 signals by file
skills/annotation/biomart/SKILL.md
- Network accessline 10
> **Domain:** Annotation / ID mapping > **Bioconductor:** [biomaRt](https://bioconductor.org/packages/release/bioc/html/biomaRt.html) - Network accessline 11
> **Bioconductor:** [biomaRt](https://bioconductor.org/packages/release/bioc/html/biomaRt.html) - Network accessline 213
- **Durinck S et al. (2009). Nat Protocols 4:1184.** [PMID:19247284](https://pubmed.ncbi.nlm.nih.gov/19247284) biomaRt enables programmatic access to Ensembl, replacing manual download of annotation files. - Network accessline 218
### `Timeout / curl error connecting to Ensembl` - Network accessline 326
It is possible to query archived versions of Ensembl through biomaRt , so you can maintain consistent annotation throughout the duration of a project. biomaRt provides the function listEnsemblArchives() to view the available Ensembl archive - Network accessline 327
biomaRt provides the function listEnsemblArchives() to view the available Ensembl archives. This function takes no arguments, and produces a table containing the name and version number of the available archives, the date they were first re - Network accessline 328
Alternatively, one can use the https://www.ensembl.org website to find an archived version. From the main page scroll down the bottom of the page, click on â<U+0080><U+0098>view in Archiveâ<U+0080><U+0099> and select the archive you need. - Network accessline 336
## name date url version current_release ## 1 Ensembl GRCh37 Feb 2014 https://grch37.ensembl.org GRCh37 - Network accessline 337
## 1 Ensembl GRCh37 Feb 2014 https://grch37.ensembl.org GRCh37 - Network accessline 338
## 2 Ensembl 115 Sep 2025 https://sep2025.archive.ensembl.org 115 * - Network accessline 339
## 3 Ensembl 114 May 2025 https://may2025.archive.ensembl.org 114 - Network accessline 340
## 4 Ensembl 113 Oct 2024 https://oct2024.archive.ensembl.org 113 - Network accessline 341
## 5 Ensembl 112 May 2024 https://may2024.archive.ensembl.org 112 - Network accessline 342
## 6 Ensembl 111 Jan 2024 https://jan2024.archive.ensembl.org 111 - Network accessline 343
## 7 Ensembl 110 Jul 2023 https://jul2023.archive.ensembl.org 110 - Network accessline 344
## 8 Ensembl 109 Feb 2023 https://feb2023.archive.ensembl.org 109 - Network accessline 345
## 9 Ensembl 108 Oct 2022 https://oct2022.archive.ensembl.org 108 - Network accessline 346
## 10 Ensembl 107 Jul 2022 https://jul2022.archive.ensembl.org 107 - Network accessline 347
## 11 Ensembl 106 Apr 2022 https://apr2022.archive.ensembl.org 106 - Network accessline 348
## 12 Ensembl 105 Dec 2021 https://dec2021.archive.ensembl.org 105 - Network accessline 349
## 13 Ensembl 104 May 2021 https://may2021.archive.ensembl.org 104 - Network accessline 350
## 14 Ensembl 103 Feb 2021 https://feb2021.archive.ensembl.org 103 - Network accessline 351
## 15 Ensembl 102 Nov 2020 https://nov2020.archive.ensembl.org 102 - Network accessline 352
## 16 Ensembl 101 Aug 2020 https://aug2020.archive.ensembl.org 101 - Network accessline 353
## 17 Ensembl 100 Apr 2020 https://apr2020.archive.ensembl.org 100 - Network accessline 354
## 18 Ensembl 80 May 2015 https://may2015.archive.ensembl.org 80 - Network accessline 355
## 19 Ensembl 77 Oct 2014 https://oct2014.archive.ensembl.org 77 - Network accessline 356
## 20 Ensembl 75 Feb 2014 https://feb2014.archive.ensembl.org 75 - Network accessline 357
## 21 Ensembl 54 May 2009 https://may2009.archive.ensembl.org 54 - Network accessline 386
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/biomaRt.html - Network accessline 387
- **Vignette:** https://bioconductor.org/packages/release/bioc/html/biomaRt.html - Network accessline 388
- **GitHub:** - **Bioconductor support forum:** https://support.bioconductor.org/ - **Paper:** Durinck S et al. (2009). Nature Protocols, 4:1184-1191. - Network accessline 389
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 399
This skill is the **knowledge layer** — when, why, and how to use `biomart`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=biomart)* - Network accessline 401
▶ **[Open `biomart` on BioMate →](https://www.biomate.ai?ref=kb&pkg=biomart)**
skills/annotation/kegggraph/SKILL.md
- Network accessline 57
## References - Homepage: https://bioconductor.org/packages/KEGGgraph - Network accessline 58
- Homepage: https://bioconductor.org/packages/KEGGgraph - Network accessline 59
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf - Network accessline 66
This skill is the **knowledge layer** — when, why, and how to use `kegggraph`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=kegggra - Network accessline 68
▶ **[Open `kegggraph` on BioMate →](https://www.biomate.ai?ref=kb&pkg=kegggraph)**
skills/enrichment/enrichplot/SKILL.md
- Network accessline 58
## References - Homepage: https://bioconductor.org/packages/enrichplot - Network accessline 59
- Homepage: https://bioconductor.org/packages/enrichplot - Network accessline 60
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/enrichplot/inst/doc/enrichplot.html - Network accessline 67
This skill is the **knowledge layer** — when, why, and how to use `enrichplot`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=enrich - Network accessline 69
▶ **[Open `enrichplot` on BioMate →](https://www.biomate.ai?ref=kb&pkg=enrichplot)**
skills/enrichment/reactomepa/SKILL.md
- Network accessline 72
- Yu G, He QY. (2016). ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems, 12(2):477-479. [PMID:26661513](https://pubmed.ncbi.nlm.nih.gov/26661513) doi:10.1039/C5MB00663E - Network accessline 81
This skill is the **knowledge layer** — when, why, and how to use `reactomepa`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=reacto - Network accessline 83
▶ **[Open `reactomepa` on BioMate →](https://www.biomate.ai?ref=kb&pkg=reactomepa)**
skills/epigenomics/bumphunter/SKILL.md
- Network accessline 63
## References - Homepage: https://bioconductor.org/packages/bumphunter - Network accessline 64
- Homepage: https://bioconductor.org/packages/bumphunter - Network accessline 65
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/bumphunter/inst/doc/bumphunter.pdf - Network accessline 72
This skill is the **knowledge layer** — when, why, and how to use `bumphunter`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=bumphu - Network accessline 74
▶ **[Open `bumphunter` on BioMate →](https://www.biomate.ai?ref=kb&pkg=bumphunter)**
skills/epigenomics/chipseeker/SKILL.md
- Network accessline 10
> **Domain:** ChIP-seq / ATAC-seq > **Bioconductor:** [ChIPseeker](https://bioconductor.org/packages/release/bioc/html/ChIPseeker.html) - Network accessline 11
> **Bioconductor:** [ChIPseeker](https://bioconductor.org/packages/release/bioc/html/ChIPseeker.html) - Network accessline 211
- **Yu G et al. (2015). Bioinformatics 31:2382.** [PMID:25759349](https://pubmed.ncbi.nlm.nih.gov/25759349) - Network accessline 349
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/ChIPseeker.html - Network accessline 350
- **Vignette:** https://bioconductor.org/packages/release/bioc/html/ChIPseeker.html - Network accessline 351
- **GitHub:** - **Bioconductor support forum:** https://support.bioconductor.org/ - **Paper:** Yu G, Wang LG, He QY (2015). Bioinformatics, 31(14):2382-2383. - Network accessline 352
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 362
This skill is the **knowledge layer** — when, why, and how to use `chipseeker`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=chipse - Network accessline 364
▶ **[Open `chipseeker` on BioMate →](https://www.biomate.ai?ref=kb&pkg=chipseeker)**
skills/epigenomics/epigrahmm/SKILL.md
- Network accessline 128
## References - Homepage: bioconductor.org/packages/epigrahmm - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/epigrahmm/inst/doc/epigrahmm.html - Network accessline 130
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/epigrahmm/inst/doc/epigrahmm.html
skills/epigenomics/extrachips/SKILL.md
- Network accessline 80
## References - Homepage: bioconductor.org/packages/extraChIPs - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/extraChIPs/inst/doc/extraChIPs.html - Network accessline 82
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/extraChIPs/inst/doc/extraChIPs.html
skills/epigenomics/hicdcplus/SKILL.md
- Network accessline 84
## References - Homepage: bioconductor.org/packages/HiCDCPlus - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/HiCDCPlus/inst/doc/HiCDCPlus.html - Network accessline 86
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/HiCDCPlus/inst/doc/HiCDCPlus.html
skills/epigenomics/hicexperiment/SKILL.md
- Network accessline 64
## References - Homepage: bioconductor.org/packages/HiCExperiment - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/HiCExperiment/inst/doc/HiCExperiment.html - Network accessline 66
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/HiCExperiment/inst/doc/HiCExperiment.html
skills/epigenomics/hicontacts/SKILL.md
- Network accessline 111
## References - Homepage: bioconductor.org/packages/HiContacts - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/HiContacts/inst/doc/HiContacts.html - Network accessline 113
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/HiContacts/inst/doc/HiContacts.html
skills/epigenomics/macsr/SKILL.md
skills/epigenomics/miasim/SKILL.md
skills/epigenomics/minfi/SKILL.md
- Network accessline 10
> **Domain:** DNA Methylation > **Bioconductor:** [minfi](https://bioconductor.org/packages/release/bioc/html/minfi.html) - Network accessline 11
> **Bioconductor:** [minfi](https://bioconductor.org/packages/release/bioc/html/minfi.html) - Network accessline 194
- **Aryee MJ et al. (2014). Bioinformatics 30:2363.** [PMID:24793076](https://pubmed.ncbi.nlm.nih.gov/24793076) - Network accessline 334
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/minfi.html - Network accessline 335
- **Vignette:** https://bioconductor.org/packages/release/bioc/vignettes/minfi/inst/doc/minfi.html - Network accessline 336
- **GitHub:** https://github.com/Bioconductor/minfi - Network accessline 337
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 347
This skill is the **knowledge layer** — when, why, and how to use `minfi`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=minfi)** — - Network accessline 349
▶ **[Open `minfi` on BioMate →](https://www.biomate.ai?ref=kb&pkg=minfi)**
skills/general/basilisk/SKILL.md
- Network accessline 61
## References - Homepage: https://bioconductor.org/packages/basilisk - Network accessline 62
- Homepage: https://bioconductor.org/packages/basilisk - Network accessline 63
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/basilisk/inst/doc/motivation.html - Network accessline 70
This skill is the **knowledge layer** — when, why, and how to use `basilisk`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=basilisk - Network accessline 72
▶ **[Open `basilisk` on BioMate →](https://www.biomate.ai?ref=kb&pkg=basilisk)**
skills/general/biobase/SKILL.md
- Network accessline 62
## References - Homepage: https://bioconductor.org/packages/Biobase - Network accessline 63
- Homepage: https://bioconductor.org/packages/Biobase - Network accessline 64
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf - Network accessline 71
This skill is the **knowledge layer** — when, why, and how to use `biobase`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=biobase)* - Network accessline 73
▶ **[Open `biobase` on BioMate →](https://www.biomate.ai?ref=kb&pkg=biobase)**
skills/general/biovizbase/SKILL.md
- Network accessline 58
## References - Homepage: https://bioconductor.org/packages/biovizBase - Network accessline 59
- Homepage: https://bioconductor.org/packages/biovizBase - Network accessline 60
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/biovizBase/inst/doc/biovizBase.pdf - Network accessline 67
This skill is the **knowledge layer** — when, why, and how to use `biovizbase`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=bioviz - Network accessline 69
▶ **[Open `biovizbase` on BioMate →](https://www.biomate.ai?ref=kb&pkg=biovizbase)**
skills/general/dose/SKILL.md
- Network accessline 44
## References - Homepage: https://bioconductor.org/packages/DOSE - Network accessline 45
- Homepage: https://bioconductor.org/packages/DOSE - Network accessline 46
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/DOSE/inst/doc/DOSE.html - Network accessline 53
This skill is the **knowledge layer** — when, why, and how to use `dose`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=dose)** — fr - Network accessline 55
▶ **[Open `dose` on BioMate →](https://www.biomate.ai?ref=kb&pkg=dose)**
skills/general/experimenthub/SKILL.md
- Network accessline 54
## References - Homepage: https://bioconductor.org/packages/ExperimentHub - Network accessline 55
- Homepage: https://bioconductor.org/packages/ExperimentHub - Network accessline 56
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ExperimentHub/inst/doc/ExperimentHub.html - Network accessline 63
This skill is the **knowledge layer** — when, why, and how to use `experimenthub`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=exp - Network accessline 65
▶ **[Open `experimenthub` on BioMate →](https://www.biomate.ai?ref=kb&pkg=experimenthub)**
skills/general/faers/SKILL.md
- Network accessline 70
## References - Homepage: bioconductor.org/packages/faers - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/faers/inst/doc/FAERS-Pharmacovigilance.html - Network accessline 72
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/faers/inst/doc/FAERS-Pharmacovigilance.html
skills/general/gdsfmt/SKILL.md
- Network accessline 60
This skill is the **knowledge layer** — when, why, and how to use `gdsfmt`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=gdsfmt)** - Network accessline 62
▶ **[Open `gdsfmt` on BioMate →](https://www.biomate.ai?ref=kb&pkg=gdsfmt)**
skills/general/genefilter/SKILL.md
- Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `genefilter`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=genefi - Network accessline 71
▶ **[Open `genefilter` on BioMate →](https://www.biomate.ai?ref=kb&pkg=genefilter)**
skills/general/geneplotter/SKILL.md
- Network accessline 57
## References - Homepage: https://bioconductor.org/packages/geneplotter - Network accessline 58
- Homepage: https://bioconductor.org/packages/geneplotter - Network accessline 59
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/geneplotter/inst/doc/visualizing.pdf - Network accessline 66
This skill is the **knowledge layer** — when, why, and how to use `geneplotter`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=genep - Network accessline 68
▶ **[Open `geneplotter` on BioMate →](https://www.biomate.ai?ref=kb&pkg=geneplotter)**
skills/general/graphite/SKILL.md
- Network accessline 57
## References - Homepage: https://bioconductor.org/packages/graphite - Network accessline 58
- Homepage: https://bioconductor.org/packages/graphite - Network accessline 59
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/graphite/inst/doc/graphite.pdf - Network accessline 66
This skill is the **knowledge layer** — when, why, and how to use `graphite`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=graphite - Network accessline 68
▶ **[Open `graphite` on BioMate →](https://www.biomate.ai?ref=kb&pkg=graphite)**
skills/general/gypsum/SKILL.md
- Network accessline 1
--- name: bioconductor-gypsum description: "Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrat - Network accessline 2
name: bioconductor-gypsum description: "Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrative - Network accessline 3
description: "Client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various adminstrative and management tasks. Chec - Network accessline 62
## References - Homepage: https://bioconductor.org/packages/gypsum - Network accessline 63
- Homepage: https://bioconductor.org/packages/gypsum - Network accessline 64
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/gypsum/inst/doc/userguide.html - Network accessline 71
This skill is the **knowledge layer** — when, why, and how to use `gypsum`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=gypsum)** - Network accessline 73
▶ **[Open `gypsum` on BioMate →](https://www.biomate.ai?ref=kb&pkg=gypsum)**
skills/general/immunotation/SKILL.md
- Network accessline 74
## References - Homepage: bioconductor.org/packages/immunotation - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/immunotation/inst/doc/immunotation.html - Network accessline 76
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/immunotation/inst/doc/immunotation.html
skills/general/mosbi/SKILL.md
- Network accessline 88
## References - Homepage: bioconductor.org/packages/mosbi - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/mosbi/inst/doc/example-workflow.html - Network accessline 90
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/mosbi/inst/doc/example-workflow.html
skills/general/multiassayexperiment/SKILL.md
- Network accessline 56
## References - Homepage: https://bioconductor.org/packages/MultiAssayExperiment - Network accessline 57
- Homepage: https://bioconductor.org/packages/MultiAssayExperiment - Network accessline 58
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html - Network accessline 65
This skill is the **knowledge layer** — when, why, and how to use `multiassayexperiment`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb& - Network accessline 67
▶ **[Open `multiassayexperiment` on BioMate →](https://www.biomate.ai?ref=kb&pkg=multiassayexperiment)**
skills/general/pcamethods/SKILL.md
- Network accessline 65
## References - Homepage: https://bioconductor.org/packages/pcaMethods - Network accessline 66
- Homepage: https://bioconductor.org/packages/pcaMethods - Network accessline 67
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/pcaMethods/inst/doc/pcaMethods.pdf - Network accessline 74
This skill is the **knowledge layer** — when, why, and how to use `pcamethods`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=pcamet - Network accessline 76
▶ **[Open `pcamethods` on BioMate →](https://www.biomate.ai?ref=kb&pkg=pcamethods)**
skills/general/qvalue/SKILL.md
- Network accessline 63
## References - Homepage: https://bioconductor.org/packages/qvalue - Network accessline 64
- Homepage: https://bioconductor.org/packages/qvalue - Network accessline 65
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/qvalue/inst/doc/qvalue.pdf - Network accessline 72
This skill is the **knowledge layer** — when, why, and how to use `qvalue`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=qvalue)** - Network accessline 74
▶ **[Open `qvalue` on BioMate →](https://www.biomate.ai?ref=kb&pkg=qvalue)**
skills/general/residualmatrix/SKILL.md
- Network accessline 55
## References - Homepage: https://bioconductor.org/packages/ResidualMatrix - Network accessline 56
- Homepage: https://bioconductor.org/packages/ResidualMatrix - Network accessline 57
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ResidualMatrix/inst/doc/ResidualMatrix.html - Network accessline 64
This skill is the **knowledge layer** — when, why, and how to use `residualmatrix`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=re - Network accessline 66
▶ **[Open `residualmatrix` on BioMate →](https://www.biomate.ai?ref=kb&pkg=residualmatrix)**
skills/general/seqlogo/SKILL.md
- Network accessline 48
## References - Homepage: https://bioconductor.org/packages/seqLogo - Network accessline 49
- Homepage: https://bioconductor.org/packages/seqLogo - Network accessline 50
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/seqLogo/inst/doc/seqLogo.pdf - Network accessline 57
This skill is the **knowledge layer** — when, why, and how to use `seqlogo`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=seqlogo)* - Network accessline 59
▶ **[Open `seqlogo` on BioMate →](https://www.biomate.ai?ref=kb&pkg=seqlogo)**
skills/general/topgo/SKILL.md
- Network accessline 62
## References - Homepage: https://bioconductor.org/packages/topGO - Network accessline 63
- Homepage: https://bioconductor.org/packages/topGO - Network accessline 64
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/topGO/inst/doc/topGO.pdf - Network accessline 71
This skill is the **knowledge layer** — when, why, and how to use `topgo`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=topgo)** — - Network accessline 73
▶ **[Open `topgo` on BioMate →](https://www.biomate.ai?ref=kb&pkg=topgo)**
skills/genomics/annotate/SKILL.md
- Network accessline 75
- {"pmid": "41317327", "doi": "10.1016/j.xpro.2025.104221", "title": "Protocol to annotate and automate single-cell instance segmentation on stimulated Raman histology using deep learning.", "authors": ["Bhattacharya A", "Landgraf E", "Jian - Network accessline 76
- {'pmid': '41342577', 'doi': '10.1093/gigascience/giaf121', 'title': 'Toward a standardized framework for pangenome graph evaluation: assessing crop plant pangenome variation graph construction from multiple assemblies.', 'authors': ['Kopa - Network accessline 85
This skill is the **knowledge layer** — when, why, and how to use `annotate`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=annotate - Network accessline 87
▶ **[Open `annotate` on BioMate →](https://www.biomate.ai?ref=kb&pkg=annotate)**
skills/genomics/beer/SKILL.md
skills/genomics/biocviews/SKILL.md
- Network accessline 59
## References - Homepage: bioconductor.org/packages/biocViews - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/biocViews/inst/doc/biocViews-HOWTO.pdf - Network accessline 61
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/biocViews/inst/doc/biocViews-HOWTO.pdf - Network accessline 68
This skill is the **knowledge layer** — when, why, and how to use `biocviews`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=biocvie - Network accessline 70
▶ **[Open `biocviews` on BioMate →](https://www.biomate.ai?ref=kb&pkg=biocviews)**
skills/genomics/biostrings/SKILL.md
- Network accessline 63
## References - Homepage: bioconductor.org/packages/Biostrings - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf - Network accessline 65
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf - Network accessline 72
This skill is the **knowledge layer** — when, why, and how to use `biostrings`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=biostr - Network accessline 74
▶ **[Open `biostrings` on BioMate →](https://www.biomate.ai?ref=kb&pkg=biostrings)**
skills/genomics/bsgenome/SKILL.md
- Network accessline 55
## References - Homepage: https://bioconductor.org/packages/BSgenome - Network accessline 56
- Homepage: https://bioconductor.org/packages/BSgenome - Network accessline 57
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/BSgenome/inst/doc/GenomeSearching.pdf - Network accessline 64
This skill is the **knowledge layer** — when, why, and how to use `bsgenome`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=bsgenome - Network accessline 66
▶ **[Open `bsgenome` on BioMate →](https://www.biomate.ai?ref=kb&pkg=bsgenome)**
skills/genomics/clusterprofiler/SKILL.md
- Network accessline 53
## References - Homepage: https://bioconductor.org/packages/clusterProfiler - Network accessline 54
- Homepage: https://bioconductor.org/packages/clusterProfiler - Network accessline 55
- Vignette: https://yulab-smu.github.io/clusterProfiler-book/ - Network accessline 62
This skill is the **knowledge layer** — when, why, and how to use `clusterprofiler`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=c - Network accessline 64
▶ **[Open `clusterprofiler` on BioMate →](https://www.biomate.ai?ref=kb&pkg=clusterprofiler)**
skills/genomics/cogeqc/SKILL.md
- Network accessline 79
## References - Homepage: bioconductor.org/packages/cogeqc - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cogeqc/inst/doc/genome_assembly_annotation_completeness.html - Network accessline 81
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cogeqc/inst/doc/genome_assembly_annotation_completeness.html
skills/genomics/complexheatmap/SKILL.md
- Network accessline 62
## References - Homepage: https://bioconductor.org/packages/ComplexHeatmap - Network accessline 63
- Homepage: https://bioconductor.org/packages/ComplexHeatmap - Network accessline 64
- Vignette: http://jokergoo.github.io/ComplexHeatmap-reference/book/ - Network accessline 71
This skill is the **knowledge layer** — when, why, and how to use `complexheatmap`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=co - Network accessline 73
▶ **[Open `complexheatmap` on BioMate →](https://www.biomate.ai?ref=kb&pkg=complexheatmap)**
skills/genomics/consensusclusterplus/SKILL.md
- Network accessline 65
## References - Homepage: https://bioconductor.org/packages/ConsensusClusterPlus - Network accessline 66
- Homepage: https://bioconductor.org/packages/ConsensusClusterPlus - Network accessline 67
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf - Network accessline 74
This skill is the **knowledge layer** — when, why, and how to use `consensusclusterplus`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb& - Network accessline 76
▶ **[Open `consensusclusterplus` on BioMate →](https://www.biomate.ai?ref=kb&pkg=consensusclusterplus)**
skills/genomics/decipher/SKILL.md
- Network accessline 65
## References - Homepage: https://bioconductor.org/packages/DECIPHER - Network accessline 66
- Homepage: https://bioconductor.org/packages/DECIPHER - Network accessline 67
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf - Network accessline 74
This skill is the **knowledge layer** — when, why, and how to use `decipher`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=decipher - Network accessline 76
▶ **[Open `decipher` on BioMate →](https://www.biomate.ai?ref=kb&pkg=decipher)**
skills/genomics/dnacopy/SKILL.md
- Network accessline 63
## References - Homepage: https://bioconductor.org/packages/DNAcopy - Network accessline 64
- Homepage: https://bioconductor.org/packages/DNAcopy - Network accessline 65
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/DNAcopy/inst/doc/DNAcopy.pdf - Network accessline 72
This skill is the **knowledge layer** — when, why, and how to use `dnacopy`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=dnacopy)* - Network accessline 74
▶ **[Open `dnacopy` on BioMate →](https://www.biomate.ai?ref=kb&pkg=dnacopy)**
skills/genomics/doubletrouble/SKILL.md
- Network accessline 73
## References - Homepage: bioconductor.org/packages/doubletrouble - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/doubletrouble/inst/doc/doubletrouble.html - Network accessline 75
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/doubletrouble/inst/doc/doubletrouble.html
skills/genomics/epialleler/SKILL.md
- Network accessline 137
## References - Homepage: bioconductor.org/packages/epialleler - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/epialleler/inst/doc/epialleler.html - Network accessline 139
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/epialleler/inst/doc/epialleler.html
skills/genomics/genomicalignments/SKILL.md
- Network accessline 60
## References - Homepage: https://bioconductor.org/packages/GenomicAlignments - Network accessline 61
- Homepage: https://bioconductor.org/packages/GenomicAlignments - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `genomicalignments`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg - Network accessline 71
▶ **[Open `genomicalignments` on BioMate →](https://www.biomate.ai?ref=kb&pkg=genomicalignments)**
skills/genomics/genomicfeatures/SKILL.md
- Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `genomicfeatures`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=g - Network accessline 71
▶ **[Open `genomicfeatures` on BioMate →](https://www.biomate.ai?ref=kb&pkg=genomicfeatures)**
skills/genomics/genomicranges/SKILL.md
- Network accessline 10
> **Domain:** Genomics Infrastructure > **Bioconductor:** [GenomicRanges](https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html) - Network accessline 11
> **Bioconductor:** [GenomicRanges](https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html) - Network accessline 208
- **Lawrence M et al. (2013). PLoS Comput Biol 9:e1003118.** [PMID:23950696](https://pubmed.ncbi.nlm.nih.gov/23950696) - Network accessline 212
- **Huber W et al. (2015). Nature Methods 12:115.** [PMID:25633503](https://pubmed.ncbi.nlm.nih.gov/25633503) - Network accessline 490
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html - Network accessline 491
- **Vignette:** https://bioconductor.org/packages/release/bioc/vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html - Network accessline 492
- **GitHub:** https://github.com/Bioconductor/GenomicRanges - Network accessline 493
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 503
This skill is the **knowledge layer** — when, why, and how to use `genomicranges`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=gen - Network accessline 505
▶ **[Open `genomicranges` on BioMate →](https://www.biomate.ai?ref=kb&pkg=genomicranges)**
skills/genomics/ggbio/SKILL.md
- Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `ggbio`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=ggbio)** — - Network accessline 71
▶ **[Open `ggbio` on BioMate →](https://www.biomate.ai?ref=kb&pkg=ggbio)**
skills/genomics/ggmanh/SKILL.md
skills/genomics/ggtree/SKILL.md
- Network accessline 63
## References - Homepage: https://bioconductor.org/packages/ggtree - Network accessline 64
- Homepage: https://bioconductor.org/packages/ggtree - Network accessline 65
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ggtree/inst/doc/ggtree.html - Network accessline 72
This skill is the **knowledge layer** — when, why, and how to use `ggtree`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=ggtree)** - Network accessline 74
▶ **[Open `ggtree` on BioMate →](https://www.biomate.ai?ref=kb&pkg=ggtree)**
skills/genomics/gosemsim/SKILL.md
- Network accessline 36
## Best Practices - Refer to the comprehensive online book (https://yulab-smu.top/biomedical-knowledge-mining-book/) for the full vignette, detailed workflows, and tutorials. - Network accessline 37
- Refer to the comprehensive online book (https://yulab-smu.top/biomedical-knowledge-mining-book/) for the full vignette, detailed workflows, and tutorials. - Network accessline 52
## References - Homepage: https://bioconductor.org/packages/gosemsim - Network accessline 53
- Homepage: https://bioconductor.org/packages/gosemsim - Network accessline 54
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GOSemSim/inst/doc/GOSemSim.html - Network accessline 61
This skill is the **knowledge layer** — when, why, and how to use `gosemsim`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=gosemsim - Network accessline 63
▶ **[Open `gosemsim` on BioMate →](https://www.biomate.ai?ref=kb&pkg=gosemsim)**
skills/genomics/gviz/SKILL.md
- Network accessline 59
## References - Homepage: https://bioconductor.org/packages/Gviz - Network accessline 60
- Homepage: https://bioconductor.org/packages/Gviz - Network accessline 61
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/Gviz/inst/doc/Gviz.pdf - Network accessline 68
This skill is the **knowledge layer** — when, why, and how to use `gviz`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=gviz)** — fr - Network accessline 70
▶ **[Open `gviz` on BioMate →](https://www.biomate.ai?ref=kb&pkg=gviz)**
skills/genomics/keggrest/SKILL.md
- Network accessline 1
--- name: bioconductor-keggrest description: "A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see <https: - Network accessline 2
name: bioconductor-keggrest description: "A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see <https://ww - Network accessline 3
description: "A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see <https://www.kegg.jp/kegg/rest/>). Note - Network accessline 58
## References - Homepage: https://bioconductor.org/packages/KEGGREST - Network accessline 59
- Homepage: https://bioconductor.org/packages/KEGGREST - Network accessline 60
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html - Network accessline 67
This skill is the **knowledge layer** — when, why, and how to use `keggrest`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=keggrest - Network accessline 69
▶ **[Open `keggrest` on BioMate →](https://www.biomate.ai?ref=kb&pkg=keggrest)**
skills/genomics/lintind/SKILL.md
skills/genomics/msa2dist/SKILL.md
- Network accessline 73
## References - Homepage: bioconductor.org/packages/msa2dist - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/msa2dist/inst/doc/MSA2dist.html - Network accessline 75
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/msa2dist/inst/doc/MSA2dist.html
skills/genomics/organismdbi/SKILL.md
- Network accessline 60
## References - Homepage: https://bioconductor.org/packages/OrganismDbi - Network accessline 61
- Homepage: https://bioconductor.org/packages/OrganismDbi - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `organismdbi`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=organ - Network accessline 71
▶ **[Open `organismdbi` on BioMate →](https://www.biomate.ai?ref=kb&pkg=organismdbi)**
skills/genomics/rblast/SKILL.md
skills/genomics/rsamtools/SKILL.md
- Network accessline 62
## References - Homepage: https://bioconductor.org/packages/Rsamtools - Network accessline 63
- Homepage: https://bioconductor.org/packages/Rsamtools - Network accessline 64
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf - Network accessline 71
This skill is the **knowledge layer** — when, why, and how to use `rsamtools`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=rsamtoo - Network accessline 73
▶ **[Open `rsamtools` on BioMate →](https://www.biomate.ai?ref=kb&pkg=rsamtools)**
skills/genomics/rtracklayer/SKILL.md
- Network accessline 60
## References - Homepage: https://bioconductor.org/packages/rtracklayer - Network accessline 61
- Homepage: https://bioconductor.org/packages/rtracklayer - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/rtracklayer/inst/doc/rtracklayer.pdf - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `rtracklayer`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=rtrac - Network accessline 71
▶ **[Open `rtracklayer` on BioMate →](https://www.biomate.ai?ref=kb&pkg=rtracklayer)**
skills/genomics/shortread/SKILL.md
- Network accessline 59
## References - Homepage: https://bioconductor.org/packages/ShortRead - Network accessline 60
- Homepage: https://bioconductor.org/packages/ShortRead - Network accessline 61
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ShortRead/inst/doc/Overview.pdf - Network accessline 68
This skill is the **knowledge layer** — when, why, and how to use `shortread`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=shortre - Network accessline 70
▶ **[Open `shortread` on BioMate →](https://www.biomate.ai?ref=kb&pkg=shortread)**
skills/genomics/supersigs/SKILL.md
- Network accessline 112
## References - Homepage: bioconductor.org/packages/supersigs - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/supersigs/inst/doc/supersigs.html - Network accessline 114
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/supersigs/inst/doc/supersigs.html
skills/genomics/syntenet/SKILL.md
- Network accessline 96
## References - Homepage: bioconductor.org/packages/syntenet - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/syntenet/inst/doc/vignette.html - Network accessline 98
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/syntenet/inst/doc/vignette.html
skills/genomics/tfbstools/SKILL.md
- Network accessline 64
## References - Homepage: https://bioconductor.org/packages/TFBSTools - Network accessline 65
- Homepage: https://bioconductor.org/packages/TFBSTools - Network accessline 66
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/TFBSTools/inst/doc/TFBSTools.html - Network accessline 73
This skill is the **knowledge layer** — when, why, and how to use `tfbstools`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=tfbstoo - Network accessline 75
▶ **[Open `tfbstools` on BioMate →](https://www.biomate.ai?ref=kb&pkg=tfbstools)**
skills/genomics/txdbmaker/SKILL.md
- Network accessline 67
This skill is the **knowledge layer** — when, why, and how to use `txdbmaker`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=txdbmak - Network accessline 69
▶ **[Open `txdbmaker` on BioMate →](https://www.biomate.ai?ref=kb&pkg=txdbmaker)**
skills/imaging/cytoviewer/SKILL.md
- Network accessline 80
## References - Homepage: bioconductor.org/packages/cytoviewer - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cytoviewer/inst/doc/cytoviewer.html - Network accessline 82
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cytoviewer/inst/doc/cytoviewer.html
skills/imaging/ebimage/SKILL.md
- Network accessline 61
## References - Homepage: https://bioconductor.org/packages/EBImage - Network accessline 62
- Homepage: https://bioconductor.org/packages/EBImage - Network accessline 63
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/EBImage/inst/doc/EBImage-introduction.html - Network accessline 70
This skill is the **knowledge layer** — when, why, and how to use `ebimage`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=ebimage)* - Network accessline 72
▶ **[Open `ebimage` on BioMate →](https://www.biomate.ai?ref=kb&pkg=ebimage)**
skills/imaging/flowcore/SKILL.md
- Network accessline 60
## References - Homepage: bioconductor.org/packages/flowcore - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/flowCore/inst/doc/HowTo-flowCore.pdf - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/flowCore/inst/doc/HowTo-flowCore.pdf - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `flowcore`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=flowcore - Network accessline 71
▶ **[Open `flowcore` on BioMate →](https://www.biomate.ai?ref=kb&pkg=flowcore)**
skills/imaging/lisaclust/SKILL.md
- Network accessline 108
## References - Homepage: bioconductor.org/packages/lisaclust - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/lisaclust/inst/doc/lisaClust.html - Network accessline 110
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/lisaclust/inst/doc/lisaClust.html
skills/metabolomics/rgoslin/SKILL.md
- Network accessline 70
## References - Homepage: bioconductor.org/packages/rgoslin - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/rgoslin/inst/doc/introduction.html - Network accessline 72
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/rgoslin/inst/doc/introduction.html
skills/metagenomics/biomformat/SKILL.md
- Network accessline 57
## References - Homepage: https://bioconductor.org/packages/biomformat - Network accessline 58
- Homepage: https://bioconductor.org/packages/biomformat - Network accessline 59
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/biomformat/inst/doc/biomformat.html - Network accessline 66
This skill is the **knowledge layer** — when, why, and how to use `biomformat`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=biomfo - Network accessline 68
▶ **[Open `biomformat` on BioMate →](https://www.biomate.ai?ref=kb&pkg=biomformat)**
skills/metagenomics/dirichletmultinomial/SKILL.md
- Network accessline 52
## Citations - Holmes et al., 2012, PLoS ONE 7(2): 1-15. https://doi.org/10.1371/journal.pone.0030126 - Network accessline 53
- Holmes et al., 2012, PLoS ONE 7(2): 1-15. https://doi.org/10.1371/journal.pone.0030126 - Network accessline 55
## References - Homepage: https://bioconductor.org/packages/DirichletMultinomial - Network accessline 56
- Homepage: https://bioconductor.org/packages/DirichletMultinomial - Network accessline 57
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf - Network accessline 64
This skill is the **knowledge layer** — when, why, and how to use `dirichletmultinomial`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb& - Network accessline 66
▶ **[Open `dirichletmultinomial` on BioMate →](https://www.biomate.ai?ref=kb&pkg=dirichletmultinomial)**
skills/metagenomics/microbiome/SKILL.md
- Network accessline 51
## References - Homepage: https://bioconductor.org/packages/microbiome - Network accessline 52
- Homepage: https://bioconductor.org/packages/microbiome - Network accessline 53
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/microbiome - Network accessline 60
This skill is the **knowledge layer** — when, why, and how to use `microbiome`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=microb - Network accessline 62
▶ **[Open `microbiome` on BioMate →](https://www.biomate.ai?ref=kb&pkg=microbiome)**
skills/metagenomics/phyloseq/SKILL.md
- Network accessline 61
## References - Homepage: https://bioconductor.org/packages/phyloseq - Network accessline 62
- Homepage: https://bioconductor.org/packages/phyloseq - Network accessline 63
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/phyloseq/inst/doc/phyloseq-analysis.html - Network accessline 70
This skill is the **knowledge layer** — when, why, and how to use `phyloseq`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=phyloseq - Network accessline 72
▶ **[Open `phyloseq` on BioMate →](https://www.biomate.ai?ref=kb&pkg=phyloseq)**
skills/proteomics/bandle/SKILL.md
- Network accessline 95
## References - Homepage: bioconductor.org/packages/bandle - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/bandle/inst/doc/vignette1-getting-started.html - Network accessline 97
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/bandle/inst/doc/vignette1-getting-started.html
skills/proteomics/bionar/SKILL.md
skills/proteomics/drugtargetinteractions/SKILL.md
- Network accessline 70
## References - Homepage: bioconductor.org/packages/drugTargetInteractions - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.html - Network accessline 72
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.html
skills/proteomics/matrixqcvis/SKILL.md
- Network accessline 75
## References - Homepage: bioconductor.org/packages/MatrixQCvis - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html - Network accessline 77
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html
skills/proteomics/mixomics/SKILL.md
- Network accessline 60
## References - Homepage: http://www.mixOmics.org - Network accessline 61
- Homepage: http://www.mixOmics.org - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/mixOmics/inst/doc/vignettes.html - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `mixomics`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=mixomics - Network accessline 71
▶ **[Open `mixomics` on BioMate →](https://www.biomate.ai?ref=kb&pkg=mixomics)**
skills/proteomics/msbackendmassbank/SKILL.md
- Network accessline 60
## References - Homepage: bioconductor.org/packages/MsBackendMassbank - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.html - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.html
skills/proteomics/mscoreutils/SKILL.md
- Network accessline 51
## Citations - Sticker et al. "Robust summarization and inference in proteome-wide label-free quantification." https://doi.org/10.1101/668863. - Network accessline 52
- Sticker et al. "Robust summarization and inference in proteome-wide label-free quantification." https://doi.org/10.1101/668863. - Network accessline 55
## References - Homepage: bioconductor.org/packages/MsCoreUtils - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html - Network accessline 57
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html - Network accessline 64
This skill is the **knowledge layer** — when, why, and how to use `mscoreutils`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=mscor - Network accessline 66
▶ **[Open `mscoreutils` on BioMate →](https://www.biomate.ai?ref=kb&pkg=mscoreutils)**
skills/proteomics/msdatahub/SKILL.md
skills/proteomics/msnbase/SKILL.md
- Network accessline 10
> **Domain:** Proteomics / Mass spectrometry > **Bioconductor:** [MSnbase](https://bioconductor.org/packages/release/bioc/html/MSnbase.html) - Network accessline 11
> **Bioconductor:** [MSnbase](https://bioconductor.org/packages/release/bioc/html/MSnbase.html) - Network accessline 191
- **Gatto L, Lilley KS (2012). Bioinformatics 28:288.** [PMID:22113085](https://pubmed.ncbi.nlm.nih.gov/22113085) - Network accessline 213
MSnbase , efficient and elegant R-based processing and visualisation of raw mass spectrometry data . Laurent Gatto, Sebastian Gibb, Johannes Rainer. bioRxiv 2020.04.29.067868; doi: https://doi.org/10.1101/2020.04.29.067868 - Network accessline 214
visualisation of raw mass spectrometry data . Laurent Gatto, Sebastian Gibb, Johannes Rainer. bioRxiv 2020.04.29.067868; doi: https://doi.org/10.1101/2020.04.29.067868 As a benchmarking dataset, we are going to use a subset of an TMT - Network accessline 215
Sebastian Gibb, Johannes Rainer. bioRxiv 2020.04.29.067868; doi: https://doi.org/10.1101/2020.04.29.067868 - Network accessline 355
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/MSnbase.html - Network accessline 356
- **Vignette:** https://bioconductor.org/packages/release/bioc/html/MSnbase.html - Network accessline 357
- **GitHub:** - **Bioconductor support forum:** https://support.bioconductor.org/ - **Paper:** Gatto L, Lilley KS (2012). Bioinformatics, 28(2):288-289. - Network accessline 358
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 368
This skill is the **knowledge layer** — when, why, and how to use `msnbase`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=msnbase)* - Network accessline 370
▶ **[Open `msnbase` on BioMate →](https://www.biomate.ai?ref=kb&pkg=msnbase)**
skills/proteomics/mzid/SKILL.md
- Network accessline 51
## References - Homepage: https://bioconductor.org/packages/mzID - Network accessline 52
- Homepage: https://bioconductor.org/packages/mzID - Network accessline 53
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/mzID/inst/doc/mzID.html - Network accessline 60
This skill is the **knowledge layer** — when, why, and how to use `mzid`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=mzid)** — fr - Network accessline 62
▶ **[Open `mzid` on BioMate →](https://www.biomate.ai?ref=kb&pkg=mzid)**
skills/proteomics/mzr/SKILL.md
- Network accessline 53
## References - Homepage: https://bioconductor.org/packages/mzR - Network accessline 54
- Homepage: https://bioconductor.org/packages/mzR - Network accessline 55
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/mzR/inst/doc/mzR.html - Network accessline 62
This skill is the **knowledge layer** — when, why, and how to use `mzr`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=mzr)** — free - Network accessline 64
▶ **[Open `mzr` on BioMate →](https://www.biomate.ai?ref=kb&pkg=mzr)**
skills/proteomics/phipdata/SKILL.md
skills/proteomics/psmatch/SKILL.md
- Network accessline 62
## References - Homepage: https://bioconductor.org/packages/PSMatch - Network accessline 63
- Homepage: https://bioconductor.org/packages/PSMatch - Network accessline 64
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/PSMatch/inst/doc/PSMatch.html - Network accessline 71
This skill is the **knowledge layer** — when, why, and how to use `psmatch`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=psmatch)* - Network accessline 73
▶ **[Open `psmatch` on BioMate →](https://www.biomate.ai?ref=kb&pkg=psmatch)**
skills/proteomics/qfeatures/SKILL.md
- Network accessline 66
## References - Homepage: https://bioconductor.org/packages/QFeatures - Network accessline 67
- Homepage: https://bioconductor.org/packages/QFeatures - Network accessline 68
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/QFeatures/inst/doc/QFeatures.html - Network accessline 75
This skill is the **knowledge layer** — when, why, and how to use `qfeatures`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=qfeatur - Network accessline 77
▶ **[Open `qfeatures` on BioMate →](https://www.biomate.ai?ref=kb&pkg=qfeatures)**
skills/proteomics/stringdb/SKILL.md
- Network accessline 62
## References - Homepage: bioconductor.org/packages/STRINGdb - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/STRINGdb/inst/doc/STRINGdb.pdf - Network accessline 64
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/STRINGdb/inst/doc/STRINGdb.pdf - Network accessline 71
This skill is the **knowledge layer** — when, why, and how to use `stringdb`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=stringdb - Network accessline 73
▶ **[Open `stringdb` on BioMate →](https://www.biomate.ai?ref=kb&pkg=stringdb)**
skills/proteomics/targetdecoy/SKILL.md
- Network accessline 71
## References - Homepage: https://bioconductor.org/packages/TargetDecoy - Network accessline 72
- Homepage: https://bioconductor.org/packages/TargetDecoy - Network accessline 73
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/TargetDecoy/inst/doc/Introduction_to_TargetDecoy.html
skills/single-cell/cytomem/SKILL.md
- Network accessline 100
## References - Homepage: bioconductor.org/packages/cytoMEM - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.html - Network accessline 102
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.html
skills/single-cell/demuxmix/SKILL.md
- Network accessline 107
## References - Homepage: bioconductor.org/packages/demuxmix - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/demuxmix/inst/doc/demuxmix.html - Network accessline 109
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/demuxmix/inst/doc/demuxmix.html
skills/single-cell/hoodscanr/SKILL.md
- Network accessline 99
## References - Homepage: bioconductor.org/packages/hoodscanR - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/hoodscanR/inst/doc/hoodscanR_introduction.html - Network accessline 101
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/hoodscanR/inst/doc/hoodscanR_introduction.html
skills/single-cell/singlecellexperiment/SKILL.md
- Network accessline 10
> **Domain:** Single-cell > **Bioconductor:** [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) - Network accessline 11
> **Bioconductor:** [SingleCellExperiment](https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html) - Network accessline 185
- **Amezquita RA et al. (2020). Nature Methods 17:137.** [PMID:31792435](https://pubmed.ncbi.nlm.nih.gov/31792435) - Network accessline 190
- **Lun ATL, McCarthy DJ, Marioni JC (2016). F1000Research 5:2122.** [PMID:27909575](https://pubmed.ncbi.nlm.nih.gov/27909575) - Network accessline 398
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html - Network accessline 399
- **Vignette:** https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html - Network accessline 400
- **GitHub:** - **Bioconductor support forum:** https://support.bioconductor.org/ - **Paper:** Amezquita RA et al. (2020). Nature Methods, 17:137-145. - Network accessline 401
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 411
This skill is the **knowledge layer** — when, why, and how to use `singlecellexperiment`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb& - Network accessline 413
▶ **[Open `singlecellexperiment` on BioMate →](https://www.biomate.ai?ref=kb&pkg=singlecellexperiment)**
skills/single-cell/treesummarizedexperiment/SKILL.md
- Network accessline 66
## References - Homepage: https://bioconductor.org/packages/TreeSummarizedExperiment - Network accessline 67
- Homepage: https://bioconductor.org/packages/TreeSummarizedExperiment - Network accessline 68
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSE.html - Network accessline 75
This skill is the **knowledge layer** — when, why, and how to use `treesummarizedexperiment`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref - Network accessline 77
▶ **[Open `treesummarizedexperiment` on BioMate →](https://www.biomate.ai?ref=kb&pkg=treesummarizedexperiment)**
skills/single-cell/zellkonverter/SKILL.md
- Network accessline 58
## References - Homepage: https://bioconductor.org/packages/zellkonverter - Network accessline 59
- Homepage: https://bioconductor.org/packages/zellkonverter - Network accessline 60
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/zellkonverter/inst/doc/zellkonverter.html - Network accessline 67
This skill is the **knowledge layer** — when, why, and how to use `zellkonverter`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=zel - Network accessline 69
▶ **[Open `zellkonverter` on BioMate →](https://www.biomate.ai?ref=kb&pkg=zellkonverter)**
skills/transcriptomics/affy/SKILL.md
- Network accessline 63
## References - Homepage: https://bioconductor.org/packages/affy - Network accessline 64
- Homepage: https://bioconductor.org/packages/affy - Network accessline 65
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/affy/inst/doc/builtinMethods.pdf - Network accessline 72
This skill is the **knowledge layer** — when, why, and how to use `affy`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=affy)** — fr - Network accessline 74
▶ **[Open `affy` on BioMate →](https://www.biomate.ai?ref=kb&pkg=affy)**
skills/transcriptomics/apeglm/SKILL.md
- Network accessline 67
## References - Homepage: https://bioconductor.org/packages/apeglm - Network accessline 68
- Homepage: https://bioconductor.org/packages/apeglm - Network accessline 69
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/apeglm/inst/doc/apeglm.html - Network accessline 76
This skill is the **knowledge layer** — when, why, and how to use `apeglm`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=apeglm)** - Network accessline 78
▶ **[Open `apeglm` on BioMate →](https://www.biomate.ai?ref=kb&pkg=apeglm)**
skills/transcriptomics/aucell/SKILL.md
- Network accessline 60
## References - Homepage: https://bioconductor.org/packages/AUCell - Network accessline 61
- Homepage: https://bioconductor.org/packages/AUCell - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/AUCell/inst/doc/AUCell.html - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `aucell`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=aucell)** - Network accessline 71
▶ **[Open `aucell` on BioMate →](https://www.biomate.ai?ref=kb&pkg=aucell)**
skills/transcriptomics/awst/SKILL.md
skills/transcriptomics/batchelor/SKILL.md
- Network accessline 10
> **Domain:** Single-cell / batch correction > **Bioconductor:** [batchelor](https://bioconductor.org/packages/release/bioc/html/batchelor.html) - Network accessline 11
> **Bioconductor:** [batchelor](https://bioconductor.org/packages/release/bioc/html/batchelor.html) - Network accessline 175
- **Haghverdi L et al. (2018). Nat Biotechnol 36:421.** [PMID:30911987](https://pubmed.ncbi.nlm.nih.gov/30911987) - Network accessline 178
- **Luecken MD et al. (2022). Nature Methods 19:41.** [PMID:34535456](https://pubmed.ncbi.nlm.nih.gov/34535456) - Network accessline 402
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/batchelor.html - Network accessline 403
- **Vignette:** https://bioconductor.org/packages/release/bioc/html/batchelor.html - Network accessline 404
- **GitHub:** - **Bioconductor support forum:** https://support.bioconductor.org/ - **Paper:** Haghverdi L et al. (2018). Nature Biotechnology, 36:421-427. - Network accessline 405
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 415
This skill is the **knowledge layer** — when, why, and how to use `batchelor`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=batchel - Network accessline 417
▶ **[Open `batchelor` on BioMate →](https://www.biomate.ai?ref=kb&pkg=batchelor)**
skills/transcriptomics/bindingsitefinder/SKILL.md
- Network accessline 124
## References - Homepage: https://bioconductor.org/packages/bindingsitefinder - Network accessline 125
- Homepage: https://bioconductor.org/packages/bindingsitefinder - Network accessline 126
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/bindingsitefinder/inst/doc/Definition_of_binding_sites_from_iCLIP_signal.html
skills/transcriptomics/bloodgen3module/SKILL.md
- Network accessline 1
--- name: bioconductor-bloodgen3module description: The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or indivi - Network accessline 2
name: bioconductor-bloodgen3module description: The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual - Network accessline 3
description: The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization - Network accessline 127
## References - Homepage: https://bioconductor.org/packages/bloodgen3module - Network accessline 128
- Homepage: https://bioconductor.org/packages/bloodgen3module - Network accessline 129
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/bloodgen3module/inst/doc/BloodGen3Module.html
skills/transcriptomics/busseq/SKILL.md
- Network accessline 106
## References - Homepage: bioconductor.org/packages/busseq - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/busseq/inst/doc/BUSseq_user_guide.html - Network accessline 108
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/busseq/inst/doc/BUSseq_user_guide.html
skills/transcriptomics/cageminer/SKILL.md
- Network accessline 88
## References - Homepage: bioconductor.org/packages/cageminer - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cageminer/inst/doc/cageminer.html - Network accessline 90
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cageminer/inst/doc/cageminer.html
skills/transcriptomics/cardelino/SKILL.md
- Network accessline 115
## References - Homepage: bioconductor.org/packages/cardelino - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cardelino/inst/doc/vignette-cloneid.html - Network accessline 117
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cardelino/inst/doc/vignette-cloneid.html
skills/transcriptomics/cbnplot/SKILL.md
skills/transcriptomics/cellbarcode/SKILL.md
- Network accessline 104
## References - Homepage: bioconductor.org/packages/cellbarcode - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cellbarcode/inst/doc/CellBarcode.html - Network accessline 106
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cellbarcode/inst/doc/CellBarcode.html
skills/transcriptomics/cellxgenedp/SKILL.md
- Network accessline 1
--- name: bioconductor-cellxgenedp description: The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summa - Network accessline 2
name: bioconductor-cellxgenedp description: The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries - Network accessline 3
description: The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provi - Network accessline 14
The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an altern - Network accessline 77
## References - Homepage: bioconductor.org/packages/cellxgenedp - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cellxgenedp/inst/doc/Discovery_and_retrieval.html - Network accessline 79
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cellxgenedp/inst/doc/Discovery_and_retrieval.html
skills/transcriptomics/condiments/SKILL.md
skills/transcriptomics/cotan/SKILL.md
skills/transcriptomics/crisprbase/SKILL.md
skills/transcriptomics/dada2/SKILL.md
- Network accessline 64
## References - Homepage: https://bioconductor.org/packages/dada2 - Network accessline 65
- Homepage: https://bioconductor.org/packages/dada2 - Network accessline 66
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/dada2/inst/doc/dada2-intro.html - Network accessline 73
This skill is the **knowledge layer** — when, why, and how to use `dada2`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=dada2)** — - Network accessline 75
▶ **[Open `dada2` on BioMate →](https://www.biomate.ai?ref=kb&pkg=dada2)**
skills/transcriptomics/deconvr/SKILL.md
- Network accessline 104
## References - Homepage: bioconductor.org/packages/deconvR - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/deconvR/inst/doc/deconvR.html - Network accessline 106
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/deconvR/inst/doc/deconvR.html
skills/transcriptomics/deseq2/SKILL.md
- Network accessline 10
> **Domain:** RNA-seq > **Bioconductor:** [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) - Network accessline 11
> **Bioconductor:** [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) - Network accessline 268
- **Love MI, Huber W, Anders S (2014). Genome Biol 15:550.** [PMID:25516281](https://pubmed.ncbi.nlm.nih.gov/25516281) - Network accessline 271
- **Rapaport F et al. (2013). Genome Biol 14:R95.** [PMID:24651052](https://pubmed.ncbi.nlm.nih.gov/24651052) - Network accessline 274
- **Love MI et al. (2015). F1000Research 4:1070.** [PMID:29481549](https://pubmed.ncbi.nlm.nih.gov/29481549) - Network accessline 305
- [DiffBind normalization error: invalid argument type (list) - cannot make it work everything seems co](https://www.biostars.org/p/9610880/) - Network accessline 306
- [DESeq2: error in checkFullRank(modelMatrix)](https://www.biostars.org/p/9605748/) - Network accessline 307
- [Are unequal sample sizes in differential gene expression (DGE) analysis a problem for edgeR DESeq2 a](https://www.biostars.org/p/9605688/) - Network accessline 308
- [Contrasts for comparing condition within tissue](https://www.biostars.org/p/9605190/) - Network accessline 309
- [PyDeseq2 - InvalidIndexError](https://www.biostars.org/p/9604809/) - Network accessline 348
Any and all DESeq2 questions should be posted to the Bioconductor support site , which serves as a searchable knowledge base of questions and answers: https://support.bioconductor.org - Network accessline 349
https://support.bioconductor.org - Network accessline 380
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/DESeq2.html - Network accessline 381
- **Vignette:** https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html - Network accessline 382
- **GitHub:** https://github.com/mikelove/DESeq2 - Network accessline 383
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 393
This skill is the **knowledge layer** — when, why, and how to use `deseq2`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=deseq2)** - Network accessline 395
▶ **[Open `deseq2` on BioMate →](https://www.biomate.ai?ref=kb&pkg=deseq2)**
skills/transcriptomics/despace/SKILL.md
- Network accessline 100
## References - Homepage: bioconductor.org/packages/DESpace - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/DESpace/inst/doc/DESpace.html - Network accessline 102
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/DESpace/inst/doc/DESpace.html
skills/transcriptomics/dexma/SKILL.md
skills/transcriptomics/dino/SKILL.md
skills/transcriptomics/dropletutils/SKILL.md
- Network accessline 10
> **Domain:** Single-cell / droplet > **Bioconductor:** [DropletUtils](https://bioconductor.org/packages/release/bioc/html/DropletUtils.html) - Network accessline 11
> **Bioconductor:** [DropletUtils](https://bioconductor.org/packages/release/bioc/html/DropletUtils.html) - Network accessline 181
- **Lun ATL et al. (2019). Genome Biol 20:63.** [PMID:30902100](https://pubmed.ncbi.nlm.nih.gov/30902100) - Network accessline 390
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/DropletUtils.html - Network accessline 391
- **Vignette:** https://bioconductor.org/packages/release/bioc/html/DropletUtils.html - Network accessline 392
- **GitHub:** - **Bioconductor support forum:** https://support.bioconductor.org/ - **Paper:** Lun ATL et al. (2019). Genome Biology, 20:63. - Network accessline 393
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 403
This skill is the **knowledge layer** — when, why, and how to use `dropletutils`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=drop - Network accessline 405
▶ **[Open `dropletutils` on BioMate →](https://www.biomate.ai?ref=kb&pkg=dropletutils)**
skills/transcriptomics/easier/SKILL.md
skills/transcriptomics/edger/SKILL.md
- Network accessline 10
> **Domain:** RNA-seq / ChIP-seq / ATAC-seq > **Bioconductor:** [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html) - Network accessline 11
> **Bioconductor:** [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html) - Network accessline 229
- **Robinson MD, McCarthy DJ, Smyth GK (2010). Bioinformatics 26:139.** [PMID:19910308](https://pubmed.ncbi.nlm.nih.gov/19910308) - Network accessline 232
- **Law CW et al. (2014). Genome Biol 15:R29.** [PMID:24926665](https://pubmed.ncbi.nlm.nih.gov/24926665) - Network accessline 251
- [Are unequal sample sizes in differential gene expression (DGE) analysis a problem for edgeR DESeq2 a](https://www.biostars.org/p/9605688/) - Network accessline 252
- [Contrasts for comparing condition within tissue](https://www.biostars.org/p/9605190/) - Network accessline 253
- [Seeking Advice on Using `edgeR` and `variancePartition` for RNA-seq Data with Multiple Tissues and P](https://www.biostars.org/p/9601556/) - Network accessline 254
- [DEG analysis of RNA-seq data across multiple tissues and two conditions](https://www.biostars.org/p/9592889/) - Network accessline 255
- [Error in edgeR/Deseq2 Analysis](https://www.biostars.org/p/9592725/) - Network accessline 297
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/edgeR.html - Network accessline 298
- **Vignette:** https://bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf - Network accessline 299
- **GitHub:** https://github.com/Bioconductor/edgeR - Network accessline 300
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 310
This skill is the **knowledge layer** — when, why, and how to use `edger`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=edger)** — - Network accessline 312
▶ **[Open `edger` on BioMate →](https://www.biomate.ai?ref=kb&pkg=edger)**
skills/transcriptomics/enhancedvolcano/SKILL.md
- Network accessline 56
## Citations - Blighe, K, S Rana, and M Lewis. 2018. “EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling.” https://github.com/kevinblighe/EnhancedVolcano. - Network accessline 57
- Blighe, K, S Rana, and M Lewis. 2018. “EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling.” https://github.com/kevinblighe/EnhancedVolcano. - Network accessline 59
## References - Homepage: https://bioconductor.org/packages/EnhancedVolcano - Network accessline 60
- Homepage: https://bioconductor.org/packages/EnhancedVolcano - Network accessline 61
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.pdf - Network accessline 68
This skill is the **knowledge layer** — when, why, and how to use `enhancedvolcano`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=e - Network accessline 70
▶ **[Open `enhancedvolcano` on BioMate →](https://www.biomate.ai?ref=kb&pkg=enhancedvolcano)**
skills/transcriptomics/epistack/SKILL.md
- Network accessline 23
# 1. Load peak files path_peaks <- c( peak1 = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/master/lab-5-chipseq/EpigeneticsCSAMA/inst/bedfiles/Rep1_peaks_ucsc_chr6.bed", - Network accessline 24
path_peaks <- c( peak1 = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/master/lab-5-chipseq/EpigeneticsCSAMA/inst/bedfiles/Rep1_peaks_ucsc_chr6.bed", peak2 = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/mast - Network accessline 25
peak1 = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/master/lab-5-chipseq/EpigeneticsCSAMA/inst/bedfiles/Rep1_peaks_ucsc_chr6.bed", - Network accessline 26
peak2 = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/master/lab-5-chipseq/EpigeneticsCSAMA/inst/bedfiles/Rep2_peaks_ucsc_chr6.bed" - Network accessline 49
# 3. Load aligned reads path_reads <- c( rep1 = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/master/lab-5-chipseq/EpigeneticsCSAMA/inst/bedfiles/H3K27ac_rep1_filtered_ucsc_chr6.bed", - Network accessline 50
path_reads <- c( rep1 = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/master/lab-5-chipseq/EpigeneticsCSAMA/inst/bedfiles/H3K27ac_rep1_filtered_ucsc_chr6.bed", rep2 = "https://raw.githubusercontent.com/Bioconductor/CSAMA - Network accessline 51
rep1 = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/master/lab-5-chipseq/EpigeneticsCSAMA/inst/bedfiles/H3K27ac_rep1_filtered_ucsc_chr6.bed", - Network accessline 52
rep2 = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/master/lab-5-chipseq/EpigeneticsCSAMA/inst/bedfiles/H3K27ac_rep2_filtered_ucsc_chr6.bed", - Network accessline 53
input = "https://raw.githubusercontent.com/Bioconductor/CSAMA2016/master/lab-5-chipseq/EpigeneticsCSAMA/inst/bedfiles/ES_input_filtered_ucsc_chr6.bed" - Network accessline 202
## References - Homepage: https://bioconductor.org/packages/epistack - Network accessline 203
- Homepage: https://bioconductor.org/packages/epistack - Network accessline 204
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/epistack/inst/doc/epistack.html
skills/transcriptomics/fgsea/SKILL.md
- Network accessline 36
- {'assumption': 'Gene identifiers are unique within the ranked genes file (no duplicates)', 'violation_context': 'Duplicate gene identifiers may lead to unpredictable behavior or incorrect enrichment calculations', 'quote': 'Gene identifie - Network accessline 37
- {'assumption': 'The ranking statistic (e.g., t-statistic, log fold-change) is continuous and reflects biological effect size or significance', 'violation_context': 'Categorical or non-quantitative rankings may produce meaningless enrichme - Network accessline 38
- {'assumption': 'Gene sets are biologically meaningful and appropriately curated', 'violation_context': 'Poorly curated or irrelevant gene sets may produce spurious enrichment results', 'evidence_url': 'http://www.broadinstitute.org/gsea/m - Network accessline 75
- {'title': 'fgsea Bioconductor package'} - {'title': 'MSigDB collections', 'url': 'http://www.broadinstitute.org/gsea/msigdb'} - Network accessline 76
- {'title': 'MSigDB collections', 'url': 'http://www.broadinstitute.org/gsea/msigdb'} - Network accessline 77
- {'title': 'GMT format specification', 'url': 'http://www.broadinstitute.org/gsea/msigdb'} - Network accessline 84
This skill is the **knowledge layer** — when, why, and how to use `fgsea`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=fgsea)** — - Network accessline 86
▶ **[Open `fgsea` on BioMate →](https://www.biomate.ai?ref=kb&pkg=fgsea)**
skills/transcriptomics/gatom/SKILL.md
- Network accessline 62
# 1. Read the declared user input file from the working directory met.de.lipids <- data.table::fread("https://artyomovlab.wustl.edu/publications/supp_materials/GATOM/Ctrl.vs.HighFat.lipid.de.csv") - Network accessline 63
met.de.lipids <- data.table::fread("https://artyomovlab.wustl.edu/publications/supp_materials/GATOM/Ctrl.vs.HighFat.lipid.de.csv") - Network accessline 65
# 2. Download/load the required network and annotation databases network.lipids <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/network.rhea.lipids.rds")) - Network accessline 66
network.lipids <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/network.rhea.lipids.rds")) - Network accessline 67
met.lipids.db <- readRDS(url("http://artyomovlab.wustl.edu/publications/supp_materials/GATOM/met.lipids.db.rds"))
skills/transcriptomics/genomicinstability/SKILL.md
- Network accessline 102
## References - Homepage: bioconductor.org/packages/genomicInstability - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/genomicInstability/inst/doc/genomicInstability.html - Network accessline 104
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/genomicInstability/inst/doc/genomicInstability.html
skills/transcriptomics/geoquery/SKILL.md
- Network accessline 59
## References - Homepage: https://bioconductor.org/packages/GEOquery - Network accessline 60
- Homepage: https://bioconductor.org/packages/GEOquery - Network accessline 61
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GEOquery/inst/doc/GEOquery.html - Network accessline 68
This skill is the **knowledge layer** — when, why, and how to use `geoquery`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=geoquery - Network accessline 70
▶ **[Open `geoquery` on BioMate →](https://www.biomate.ai?ref=kb&pkg=geoquery)**
skills/transcriptomics/geotcgadata/SKILL.md
- Network accessline 163
## References - Homepage: bioconductor.org/packages/GeoTcgaData - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GeoTcgaData/inst/doc/GeoTcgaData.html - Network accessline 165
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GeoTcgaData/inst/doc/GeoTcgaData.html
skills/transcriptomics/glmgampoi/SKILL.md
- Network accessline 59
## References - Homepage: https://bioconductor.org/packages/glmGamPoi - Network accessline 60
- Homepage: https://bioconductor.org/packages/glmGamPoi - Network accessline 61
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/glmGamPoi/inst/doc/glmGamPoi.html - Network accessline 68
This skill is the **knowledge layer** — when, why, and how to use `glmgampoi`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=glmgamp - Network accessline 70
▶ **[Open `glmgampoi` on BioMate →](https://www.biomate.ai?ref=kb&pkg=glmgampoi)**
skills/transcriptomics/granulator/SKILL.md
skills/transcriptomics/gseabase/SKILL.md
- Network accessline 61
## References - Homepage: https://bioconductor.org/packages/GSEABase - Network accessline 62
- Homepage: https://bioconductor.org/packages/GSEABase - Network accessline 63
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GSEABase - Network accessline 70
This skill is the **knowledge layer** — when, why, and how to use `gseabase`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=gseabase - Network accessline 72
▶ **[Open `gseabase` on BioMate →](https://www.biomate.ai?ref=kb&pkg=gseabase)**
skills/transcriptomics/gsva/SKILL.md
- Network accessline 60
## References - Homepage: https://bioconductor.org/packages/GSVA - Network accessline 61
- Homepage: https://bioconductor.org/packages/GSVA - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/GSVA/inst/doc/GSVA.html - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `gsva`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=gsva)** — fr - Network accessline 71
▶ **[Open `gsva` on BioMate →](https://www.biomate.ai?ref=kb&pkg=gsva)**
skills/transcriptomics/harmonizr/SKILL.md
- Network accessline 73
## References - Homepage: bioconductor.org/packages/HarmonizR - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.html - Network accessline 75
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.html
skills/transcriptomics/illuminaio/SKILL.md
- Network accessline 50
## References - Homepage: https://bioconductor.org/packages/illuminaio - Network accessline 51
- Homepage: https://bioconductor.org/packages/illuminaio - Network accessline 52
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/illuminaio/inst/doc/Description_of_Encrypted_IDAT_Format.pdf - Network accessline 59
This skill is the **knowledge layer** — when, why, and how to use `illuminaio`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=illumi - Network accessline 61
▶ **[Open `illuminaio` on BioMate →](https://www.biomate.ai?ref=kb&pkg=illuminaio)**
skills/transcriptomics/intercellar/SKILL.md
- Network accessline 43
## Common Pitfalls - *App does not open automatically*: If the browser does not launch, manually copy and navigate to the local address (e.g., `http://127.0.0.1:6134`) shown in the R console. - Network accessline 44
- *App does not open automatically*: If the browser does not launch, manually copy and navigate to the local address (e.g., `http://127.0.0.1:6134`) shown in the R console. - Network accessline 58
## References - Homepage: https://bioconductor.org/packages/intercellar - Network accessline 59
- Homepage: https://bioconductor.org/packages/intercellar - Network accessline 60
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/intercellar/inst/doc/vignette.html
skills/transcriptomics/limma/SKILL.md
- Network accessline 10
> **Domain:** Microarray / RNA-seq > **Bioconductor:** [limma](https://bioconductor.org/packages/release/bioc/html/limma.html) - Network accessline 11
> **Bioconductor:** [limma](https://bioconductor.org/packages/release/bioc/html/limma.html) - Network accessline 223
- **Ritchie ME et al. (2015). Nucleic Acids Res 43:e47.** [PMID:25605792](https://pubmed.ncbi.nlm.nih.gov/25605792) - Network accessline 226
- **Law CW et al. (2014). Genome Biol 15:R29.** [PMID:24926665](https://pubmed.ncbi.nlm.nih.gov/24926665) - Network accessline 239
- [Contrasts for comparing condition within tissue](https://www.biostars.org/p/9605190/) - Network accessline 240
- [Deseq2 Limma - the paired test seems to be incorrect - is it known problem ?](https://www.biostars.org/p/9596357/) - Network accessline 241
- [limma eBayes error in analysis of Illumina HumanHT-12 V4.0 expression beadchip data](https://www.biostars.org/p/9588697/) - Network accessline 272
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47. doi:10.1093/nar/gkv007 Phipson B, Lee S, Majewski IJ - Network accessline 273
Phipson B, Lee S, Majewski IJ, Alexander WS, and Smyth GK (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Annals of Applied Statistics 10, 946-963. doi:10.1 - Network accessline 274
Law CW, Chen Y, Shi W, Smyth GK (2014). Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15, R29. doi:10.1186/gb-2014-15-2-r29 . See also the Preprint Version at https://gksmyth.github.io/pu - Network accessline 279
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/limma.html - Network accessline 280
- **Vignette:** https://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf - Network accessline 281
- **GitHub:** https://github.com/Bioconductor/limma - Network accessline 282
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 292
This skill is the **knowledge layer** — when, why, and how to use `limma`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=limma)** — - Network accessline 294
▶ **[Open `limma` on BioMate →](https://www.biomate.ai?ref=kb&pkg=limma)**
skills/transcriptomics/mast/SKILL.md
- Network accessline 61
## References - Homepage: https://bioconductor.org/packages/MAST - Network accessline 62
- Homepage: https://bioconductor.org/packages/MAST - Network accessline 63
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MAST/inst/doc/MAST-Intro.html - Network accessline 70
This skill is the **knowledge layer** — when, why, and how to use `mast`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=mast)** — fr - Network accessline 72
▶ **[Open `mast` on BioMate →](https://www.biomate.ai?ref=kb&pkg=mast)**
skills/transcriptomics/mastr/SKILL.md
skills/transcriptomics/metabocoreutils/SKILL.md
- Network accessline 60
## References - Homepage: https://bioconductor.org/packages/MetaboCoreUtils - Network accessline 61
- Homepage: https://bioconductor.org/packages/MetaboCoreUtils - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.html - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `metabocoreutils`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=m - Network accessline 71
▶ **[Open `metabocoreutils` on BioMate →](https://www.biomate.ai?ref=kb&pkg=metabocoreutils)**
skills/transcriptomics/metapod/SKILL.md
- Network accessline 57
## References - Homepage: https://bioconductor.org/packages/metapod - Network accessline 58
- Homepage: https://bioconductor.org/packages/metapod - Network accessline 59
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/metapod/inst/doc/metapod.html - Network accessline 66
This skill is the **knowledge layer** — when, why, and how to use `metapod`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=metapod)* - Network accessline 68
▶ **[Open `metapod` on BioMate →](https://www.biomate.ai?ref=kb&pkg=metapod)**
skills/transcriptomics/mirtarrnaseq/SKILL.md
- Network accessline 159
## References - Homepage: https://bioconductor.org/packages/mirtarrnaseq - Network accessline 160
- Homepage: https://bioconductor.org/packages/mirtarrnaseq - Network accessline 161
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/mirtarrnaseq/inst/doc/mirTarRnaSeq.html
skills/transcriptomics/monocle/SKILL.md
- Network accessline 53
## Citations - Trapnell C, Cacchiarelli D, et al. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudo-temporal ordering of single cells. Nature Biotechnology, 32:381-386. [PMID:24658644](https://pubmed.ncbi.nlm - Network accessline 54
- Trapnell C, Cacchiarelli D, et al. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudo-temporal ordering of single cells. Nature Biotechnology, 32:381-386. [PMID:24658644](https://pubmed.ncbi.nlm.nih.gov/2465 - Network accessline 55
- Qiu X, Hill A, et al. (2017). Single-cell mRNA quantification and differential analysis with Census. Nature Methods, 14:309-315. [PMID:28114287](https://pubmed.ncbi.nlm.nih.gov/28114287) - Network accessline 57
## References - Homepage: https://bioconductor.org/packages/monocle - Network accessline 58
- Homepage: https://bioconductor.org/packages/monocle - Network accessline 59
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/monocle/inst/doc/monocle-vignette.pdf - Network accessline 66
This skill is the **knowledge layer** — when, why, and how to use `monocle`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=monocle)* - Network accessline 68
▶ **[Open `monocle` on BioMate →](https://www.biomate.ai?ref=kb&pkg=monocle)**
skills/transcriptomics/msa/SKILL.md
- Network accessline 60
## References - Homepage: https://bioconductor.org/packages/msa - Network accessline 61
- Homepage: https://bioconductor.org/packages/msa - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/msa/inst/doc/msa.pdf - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `msa`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=msa)** — free - Network accessline 71
▶ **[Open `msa` on BioMate →](https://www.biomate.ai?ref=kb&pkg=msa)**
skills/transcriptomics/msstatslip/SKILL.md
- Network accessline 99
## References - Homepage: bioconductor.org/packages/MSstatsLiP - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html - Network accessline 101
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html
skills/transcriptomics/nanotube/SKILL.md
- Network accessline 78
## References - Homepage: bioconductor.org/packages/NanoTube - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/NanoTube/inst/doc/NanoTube.html - Network accessline 80
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/NanoTube/inst/doc/NanoTube.html
skills/transcriptomics/nnsvg/SKILL.md
skills/transcriptomics/omicsviewer/SKILL.md
skills/transcriptomics/ompbam/SKILL.md
skills/transcriptomics/orfhunter/SKILL.md
- Network accessline 17
trans <- system.file("extdata", "Set.trans_sequences.fasta", package = "ORFhunteR") model <- "http://www.sstcenter.com/download/ORFhunteR/classRFmodel_1.rds" - Network accessline 18
model <- "http://www.sstcenter.com/download/ORFhunteR/classRFmodel_1.rds" - Network accessline 50
# Load pseudo-ORFs (lncRNAs) and true ORFs (mRNAs) fileORFLncRNAs <- "http://www.sstcenter.com/download/ORFhunteR/NCBI_RefSeq_release_109_GRCh38.p12_ORF_candidates_sequences_lncRNAs.fasta.gz" - Network accessline 51
fileORFLncRNAs <- "http://www.sstcenter.com/download/ORFhunteR/NCBI_RefSeq_release_109_GRCh38.p12_ORF_candidates_sequences_lncRNAs.fasta.gz" - Network accessline 54
fileORFmRNAs <- "http://www.sstcenter.com/download/ORFhunteR/NCBI_RefSeq_release_109_GRCh38.p12_ORFs_true_sequences_mRNAs.fasta.gz" - Network accessline 73
trans <- system.file("extdata", "Set.trans_sequences.fasta", package = "ORFhunteR") model <- "http://www.sstcenter.com/download/ORFhunteR/classRFmodel_1.rds" - Network accessline 74
model <- "http://www.sstcenter.com/download/ORFhunteR/classRFmodel_1.rds" - Network accessline 139
## References - Homepage: https://bioconductor.org/packages/orfhunter - Network accessline 140
- Homepage: https://bioconductor.org/packages/orfhunter - Network accessline 141
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/orfhunter/inst/doc/ORFhunteR.html
skills/transcriptomics/pathview/SKILL.md
- Network accessline 72
- Luo W, Brouwer C. (2013). Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics, 29(14):1830-1831. [PMID:23740750](https://pubmed.ncbi.nlm.nih.gov/23740750) doi:10.1093/bioinformatics/btt - Network accessline 83
This skill is the **knowledge layer** — when, why, and how to use `pathview`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=pathview - Network accessline 85
▶ **[Open `pathview` on BioMate →](https://www.biomate.ai?ref=kb&pkg=pathview)**
skills/transcriptomics/proteodisco/SKILL.md
- Possible obfuscationline 100
testSeq1 <- Biostrings::DNAString('ATGACCGCGTCCTCCTCCAGCGACTATGGACAGACTTCCAAGATGAGCCCACGCGTCCCTCAGCAGGATTGGCTGTCTCAACCCCCAGCCAGGGTCACCATCAAAATGGAATGTAACCCTAGCCAGGTGAATGGCTCAAGGAACTCTCCTGATGAATGCAGTGTGGCCAAAGGCGGGAAGATGGTGGGCAGCCCAGACACCGTTG - Network accessline 162
## References - Homepage: bioconductor.org/packages/proteodisco - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/proteodisco/inst/doc/Overview_of_Proteodisco.html - Network accessline 164
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/proteodisco/inst/doc/Overview_of_Proteodisco.html
skills/transcriptomics/ptairms/SKILL.md
- Network accessline 117
## References - Homepage: bioconductor.org/packages/ptairms - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ptairms/inst/doc/ptairMS.html - Network accessline 119
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ptairms/inst/doc/ptairMS.html
skills/transcriptomics/ribodipa/SKILL.md
- Network accessline 174
## References - Homepage: bioconductor.org/packages/ribodipa - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ribodipa/inst/doc/RiboDiPA.html - Network accessline 176
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ribodipa/inst/doc/RiboDiPA.html
skills/transcriptomics/scanmir/SKILL.md
skills/transcriptomics/scannotatr/SKILL.md
- Network accessline 135
## References - Homepage: bioconductor.org/packages/scannotatr - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/scannotatr/inst/doc/scannotatr.html - Network accessline 137
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/scannotatr/inst/doc/scannotatr.html
skills/transcriptomics/scarray/SKILL.md
skills/transcriptomics/scater/SKILL.md
- Network accessline 10
> **Domain:** Single-cell / QC > **Bioconductor:** [scater](https://bioconductor.org/packages/release/bioc/html/scater.html) - Network accessline 11
> **Bioconductor:** [scater](https://bioconductor.org/packages/release/bioc/html/scater.html) - Network accessline 204
- **McCarthy DJ et al. (2017). Bioinformatics 33:1179.** [PMID:28088785](https://pubmed.ncbi.nlm.nih.gov/28088785) - Network accessline 225
To demonstrate the use of the various scater functions, we will load in the classic Zeisel dataset: Note: A more comprehensive description of the use of scater (along with other packages) in a scRNA-seq analysis workflow is available at htt - Network accessline 227
Note: A more comprehensive description of the use of scater (along with other packages) in a scRNA-seq analysis workflow is available at https://osca.bioconductor.org . - Network accessline 399
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/scater.html - Network accessline 400
- **Vignette:** https://bioconductor.org/packages/release/bioc/html/scater.html - Network accessline 401
- **GitHub:** - **Bioconductor support forum:** https://support.bioconductor.org/ - **Paper:** McCarthy DJ et al. (2017). Bioinformatics, 33(8):1179-1186. - Network accessline 402
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 412
This skill is the **knowledge layer** — when, why, and how to use `scater`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=scater)** - Network accessline 414
▶ **[Open `scater` on BioMate →](https://www.biomate.ai?ref=kb&pkg=scater)**
skills/transcriptomics/sccomp/SKILL.md
- Network accessline 159
## Citations - Mangiola, Stefano, et al. 2023. "Sccomp: Robust Differential Composition and Variability Analysis for Single-Cell Data." Proceedings of the National Academy of Sciences 120 (33): e2203828120. https://doi.org/10.1073/pnas.2203 - Network accessline 160
- Mangiola, Stefano, et al. 2023. "Sccomp: Robust Differential Composition and Variability Analysis for Single-Cell Data." Proceedings of the National Academy of Sciences 120 (33): e2203828120. https://doi.org/10.1073/pnas.2203828120 - Network accessline 162
## References - Homepage: bioconductor.org/packages/sccomp - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/sccomp/inst/doc/vignettes.html - Network accessline 164
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/sccomp/inst/doc/vignettes.html
skills/transcriptomics/scdblfinder/SKILL.md
- Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `scdblfinder`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=scdbl - Network accessline 71
▶ **[Open `scdblfinder` on BioMate →](https://www.biomate.ai?ref=kb&pkg=scdblfinder)**
skills/transcriptomics/scdesign3/SKILL.md
- Network accessline 110
## References - Homepage: bioconductor.org/packages/scDesign3 - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/scDesign3/inst/doc/scDesign3-quickstart-vignette.html - Network accessline 112
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/scDesign3/inst/doc/scDesign3-quickstart-vignette.html
skills/transcriptomics/scran/SKILL.md
- Network accessline 10
> **Domain:** Single-cell RNA-seq > **Bioconductor:** [scran](https://bioconductor.org/packages/release/bioc/html/scran.html) - Network accessline 11
> **Bioconductor:** [scran](https://bioconductor.org/packages/release/bioc/html/scran.html) - Network accessline 200
- **Lun ATL, McCarthy DJ, Marioni JC (2016). Genome Biol 17:75.** [PMID:27122128](https://pubmed.ncbi.nlm.nih.gov/27122128) - Network accessline 221
including cell cycle phase assignment, variance modelling and testing for marker genes and gene-gene correlations. This vignette provides brief descriptions of these methods and some toy examples to demonstrate their use. Note: A more compr - Network accessline 223
Note: A more comprehensive description of the use of scran (along with other packages) in a scRNA-seq analysis workflow is available at https://osca.bioconductor.org . - Network accessline 403
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/scran.html - Network accessline 404
- **Vignette:** https://bioconductor.org/packages/release/bioc/vignettes/scran/inst/doc/scran.html - Network accessline 405
- **GitHub:** https://github.com/Bioconductor/scran - Network accessline 406
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 416
This skill is the **knowledge layer** — when, why, and how to use `scran`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=scran)** — - Network accessline 418
▶ **[Open `scran` on BioMate →](https://www.biomate.ai?ref=kb&pkg=scran)**
skills/transcriptomics/scuttle/SKILL.md
- Network accessline 61
## References - Homepage: https://bioconductor.org/packages/scuttle - Network accessline 62
- Homepage: https://bioconductor.org/packages/scuttle - Network accessline 63
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/scuttle/inst/doc/QC.html - Network accessline 70
This skill is the **knowledge layer** — when, why, and how to use `scuttle`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=scuttle)* - Network accessline 72
▶ **[Open `scuttle` on BioMate →](https://www.biomate.ai?ref=kb&pkg=scuttle)**
skills/transcriptomics/sechm/SKILL.md
skills/transcriptomics/sgcp/SKILL.md
skills/transcriptomics/siggenes/SKILL.md
- Network accessline 63
## References - Homepage: https://bioconductor.org/packages/siggenes - Network accessline 64
- Homepage: https://bioconductor.org/packages/siggenes - Network accessline 65
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/siggenes/inst/doc/siggenes.pdf - Network accessline 72
This skill is the **knowledge layer** — when, why, and how to use `siggenes`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=siggenes - Network accessline 74
▶ **[Open `siggenes` on BioMate →](https://www.biomate.ai?ref=kb&pkg=siggenes)**
skills/transcriptomics/simbu/SKILL.md
skills/transcriptomics/simpleseg/SKILL.md
- Network accessline 88
## References - Homepage: bioconductor.org/packages/simpleSeg - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/simpleSeg/inst/doc/simpleSeg.html - Network accessline 90
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/simpleSeg/inst/doc/simpleSeg.html
skills/transcriptomics/singler/SKILL.md
- Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `singler`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=singler)* - Network accessline 71
▶ **[Open `singler` on BioMate →](https://www.biomate.ai?ref=kb&pkg=singler)**
skills/transcriptomics/spatialde/SKILL.md
- Network accessline 125
## References - Homepage: bioconductor.org/packages/spatialDE - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/spatialDE/inst/doc/spatialDE.html - Network accessline 127
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/spatialDE/inst/doc/spatialDE.html
skills/transcriptomics/spatialexperiment/SKILL.md
- Network accessline 59
## References - Homepage: bioconductor.org/packages/SpatialExperiment - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html - Network accessline 61
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html - Network accessline 68
This skill is the **knowledge layer** — when, why, and how to use `spatialexperiment`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg - Network accessline 70
▶ **[Open `spatialexperiment` on BioMate →](https://www.biomate.ai?ref=kb&pkg=spatialexperiment)**
skills/transcriptomics/splicewiz/SKILL.md
- Network accessline 187
## References - Homepage: bioconductor.org/packages/splicewiz - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/splicewiz/inst/doc/vignette_0_cc3952e3.html - Network accessline 189
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/splicewiz/inst/doc/vignette_0_cc3952e3.html
skills/transcriptomics/spotclean/SKILL.md
- Network accessline 106
## Citations - Ni, Z., Prasad, A., Chen, S. et al. SpotClean adjusts for spot swapping in spatial transcriptomics data. Nat Commun 13, 2971 (2022). https://doi.org/10.1038/s41467-022-30587-y - Network accessline 107
- Ni, Z., Prasad, A., Chen, S. et al. SpotClean adjusts for spot swapping in spatial transcriptomics data. Nat Commun 13, 2971 (2022). https://doi.org/10.1038/s41467-022-30587-y - Network accessline 109
## References - Homepage: bioconductor.org/packages/spotclean - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/spotclean/inst/doc/SpotClean.html - Network accessline 111
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/spotclean/inst/doc/SpotClean.html
skills/transcriptomics/spotsweeper/SKILL.md
- Network accessline 115
## References - Homepage: bioconductor.org/packages/spotsweeper - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/spotsweeper/inst/doc/getting_started.html - Network accessline 117
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/spotsweeper/inst/doc/getting_started.html
skills/transcriptomics/standr/SKILL.md
- Network accessline 92
## References - Homepage: bioconductor.org/packages/standr - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/standr/inst/doc/standR_introduction.html - Network accessline 94
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/standr/inst/doc/standR_introduction.html
skills/transcriptomics/statial/SKILL.md
- Network accessline 101
## References - Homepage: bioconductor.org/packages/Statial - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/Statial/inst/doc/Statial.html - Network accessline 103
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/Statial/inst/doc/Statial.html
skills/transcriptomics/sva/SKILL.md
- Network accessline 67
## References - Homepage: https://bioconductor.org/packages/sva - Network accessline 68
- Homepage: https://bioconductor.org/packages/sva - Network accessline 69
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/sva/inst/doc/sva.pdf - Network accessline 76
This skill is the **knowledge layer** — when, why, and how to use `sva`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=sva)** — free - Network accessline 78
▶ **[Open `sva` on BioMate →](https://www.biomate.ai?ref=kb&pkg=sva)**
skills/transcriptomics/tanggle/SKILL.md
skills/transcriptomics/tdbasedufeadv/SKILL.md
- Network accessline 156
## Citations - Taguchi, Y-H. 2020. Unsupervised Feature Extraction Applied to Bioinformatics. Springer International Publishing. https://doi.org/10.1007/978-3-030-22456-1 - Network accessline 157
- Taguchi, Y-H. 2020. Unsupervised Feature Extraction Applied to Bioinformatics. Springer International Publishing. https://doi.org/10.1007/978-3-030-22456-1 - Network accessline 160
## References - Homepage: bioconductor.org/packages/TDbasedUFEadv - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/TDbasedUFEadv/inst/doc/Enrichment.html - Network accessline 162
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/TDbasedUFEadv/inst/doc/Enrichment.html
skills/transcriptomics/trajectoryutils/SKILL.md
- Network accessline 60
## References - Homepage: https://bioconductor.org/packages/TrajectoryUtils - Network accessline 61
- Homepage: https://bioconductor.org/packages/TrajectoryUtils - Network accessline 62
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/TrajectoryUtils/inst/doc/TrajectoryUtils.html - Network accessline 69
This skill is the **knowledge layer** — when, why, and how to use `trajectoryutils`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=t - Network accessline 71
▶ **[Open `trajectoryutils` on BioMate →](https://www.biomate.ai?ref=kb&pkg=trajectoryutils)**
skills/transcriptomics/transformgampoi/SKILL.md
- Network accessline 89
## References - Homepage: bioconductor.org/packages/transformGamPoi - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/transformGamPoi/inst/doc/transformGamPoi_Quickstart.html - Network accessline 91
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/transformGamPoi/inst/doc/transformGamPoi_Quickstart.html
skills/transcriptomics/treeio/SKILL.md
- Network accessline 33
## Best Practices - **Consult External Documentation**: Go to `https://yulab-smu.top/treedata-book/` for the full vignette and comprehensive workflow instructions. - Network accessline 34
- **Consult External Documentation**: Go to `https://yulab-smu.top/treedata-book/` for the full vignette and comprehensive workflow instructions. - Network accessline 50
## References - Homepage: bioconductor.org/packages/treeio - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/treeio/inst/doc/treeio.html - Network accessline 52
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/treeio/inst/doc/treeio.html - Network accessline 59
This skill is the **knowledge layer** — when, why, and how to use `treeio`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=treeio)** - Network accessline 61
▶ **[Open `treeio` on BioMate →](https://www.biomate.ai?ref=kb&pkg=treeio)**
skills/transcriptomics/treekor/SKILL.md
skills/transcriptomics/tximport/SKILL.md
- Network accessline 65
- Soneson C, Love MI, Robinson MD. (2015). Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research, 4:1521. [PMID:26925227](https://pubmed.ncbi.nlm.nih.gov/26925227) doi:10.12688/f1000resea - Network accessline 76
This skill is the **knowledge layer** — when, why, and how to use `tximport`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=tximport - Network accessline 78
▶ **[Open `tximport` on BioMate →](https://www.biomate.ai?ref=kb&pkg=tximport)**
skills/transcriptomics/visse/SKILL.md
skills/transcriptomics/voyager/SKILL.md
- Network accessline 102
## References - Homepage: bioconductor.org/packages/Voyager - Vignette: https://bioconductor.org/packages/release/bioc/vignettes/Voyager/inst/doc/voyager.html - Network accessline 104
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/Voyager/inst/doc/voyager.html
skills/transcriptomics/zenith/SKILL.md
skills/variant-calling/annotationhub/SKILL.md
- Network accessline 10
> **Domain:** Annotation / Genomics > **Bioconductor:** [AnnotationHub](https://bioconductor.org/packages/release/bioc/html/AnnotationHub.html) - Network accessline 11
> **Bioconductor:** [AnnotationHub](https://bioconductor.org/packages/release/bioc/html/AnnotationHub.html) - Network accessline 157
- **Morgan M, Shepherd L (2022). Bioconductor Annotation Infrastructure.** [Bioconductor Workflows](https://bioconductor.org/help/course-materials/) - Network accessline 161
- **Gentleman RC et al. (2004). Genome Biol 5:R80.** [PMID:15461798](https://pubmed.ncbi.nlm.nih.gov/15461798) - Network accessline 223
proxy <- "http://my_user:my_password@myproxy:8080" - Network accessline 232
As mentioned previously, There is no global option like httr::set_config to set configuration options when using httr2. A config argument may be passed to [[ and cache functions. This argument is a R list object that will be passed to httr2 - Network accessline 233
set configuration options when using httr2. A config argument may be passed to [[ and cache functions. This argument is a R list object that will be passed to httr2::req_options . The names of the items should be valid curl options as defin - Network accessline 234
to httr2::req_options . The names of the items should be valid curl options as - Network accessline 301
- **Bioconductor page:** https://bioconductor.org/packages/release/bioc/html/AnnotationHub.html - Network accessline 302
- **Vignette:** https://bioconductor.org/packages/release/bioc/html/AnnotationHub.html - Network accessline 303
- **GitHub:** - **Bioconductor support forum:** https://support.bioconductor.org/ - **Paper:** Morgan M et al. (2021). Bioconductor package. - Network accessline 304
- **Bioconductor support forum:** https://support.bioconductor.org/ - Network accessline 314
This skill is the **knowledge layer** — when, why, and how to use `annotationhub`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=ann - Network accessline 316
▶ **[Open `annotationhub` on BioMate →](https://www.biomate.ai?ref=kb&pkg=annotationhub)**
skills/variant-calling/snpstats/SKILL.md
- Network accessline 63
## References - Homepage: https://bioconductor.org/packages/snpStats - Network accessline 64
- Homepage: https://bioconductor.org/packages/snpStats - Network accessline 65
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/snpStats/inst/doc/snpStats-vignette.pdf - Network accessline 72
This skill is the **knowledge layer** — when, why, and how to use `snpstats`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=snpstats - Network accessline 74
▶ **[Open `snpstats` on BioMate →](https://www.biomate.ai?ref=kb&pkg=snpstats)**
skills/variant-calling/variantannotation/SKILL.md
- Network accessline 15
- **Depends:** BiocGenerics, MatrixGenerics, Seqinfo, GenomicRanges, SummarizedExperiment, Rsamtools - **Imports:** DBI, Biobase, S4Vectors, IRanges, XVector, Biostrings, AnnotationDbi, rtracklayer, BSgenome, GenomicFeatures, curl - **Syste - Network accessline 16
- **Imports:** DBI, Biobase, S4Vectors, IRanges, XVector, Biostrings, AnnotationDbi, rtracklayer, BSgenome, GenomicFeatures, curl - **System requirements:** GNU make - **Install:** `BiocManager::install("VariantAnnotation")` - Network accessline 61
## References - Homepage: https://bioconductor.org/packages/VariantAnnotation - Network accessline 62
- Homepage: https://bioconductor.org/packages/VariantAnnotation - Network accessline 63
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf - Network accessline 70
This skill is the **knowledge layer** — when, why, and how to use `variantannotation`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg - Network accessline 72
▶ **[Open `variantannotation` on BioMate →](https://www.biomate.ai?ref=kb&pkg=variantannotation)**
skills/variant-calling/vsn/SKILL.md
- Network accessline 65
## References - Homepage: https://bioconductor.org/packages/vsn - Network accessline 66
- Homepage: https://bioconductor.org/packages/vsn - Network accessline 67
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/vsn/inst/doc/vsn.pdf - Network accessline 74
This skill is the **knowledge layer** — when, why, and how to use `vsn`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=vsn)** — free - Network accessline 76
▶ **[Open `vsn` on BioMate →](https://www.biomate.ai?ref=kb&pkg=vsn)**
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Signal confidence: 1 fact
- Network access in prose of the SKILL.mdbiomart/SKILL.md:399Low confidence
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Frontmatter field user-invocable in skills/annotation/biomart/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/annotation/biomart/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/annotation/biomart/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/annotation/biomart/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/annotation/biomart/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/annotation/biomart/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.
