Individual skill
tooluniverse-epigenomics
Genomics and epigenomics analysis: DNA methylation (CpG, 5mC, 5hmC, bisulfite, RRBS), m6A RNA modification (MeRIP-seq), ChIP-seq peaks, ATAC-seq accessibility, histone modifications, chromatin state, multi-omics integration. Combines pandas/scipy/pysam computation with ToolUniverse annotation tools. Use for genome-wide epigenomic statistics, methylation analysis, and chromatin-genome integration.
plugins/tooluniverse/skills/tooluniverse-epigenomics/SKILL.md
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.env.templateasset · 499 bytesANALYSIS_PROCEDURES.mddoc · 5,078 bytesCODE_REFERENCE.mddoc · 33,821 bytesQUICK_START.mddoc · 8,934 bytesSKILL.mdcore · 12,176 bytesSKILL.mdcore · 12,176 bytesSKILL.mdcore · 12,176 bytesTOOLS_REFERENCE.mddoc · 3,733 bytesscripts/methylation_density.pyruns · 8,801 bytes
Read excerpt
# Genomics and Epigenomics Data Processing
## ⚠<U+FE0F> TOP-OF-MIND RULE: long-format methylation CSV — count ROWS, not unique positions
When the input is a long-format methylation CSV (one row per `(sample, CpG_position)`
e.g. columns `Pos, Chromosome, MethylationPercentage`), "how many sites are
removed when filtering" almost always means **rows removed**, NOT unique-position
removals. The two answers differ by a factor of ≈ `n_samples`.
| Question phrasing | What it means |
|---|---|
| "how many sites are removed when filtering …" | **rows removed** (= samples × positions failing the filter) |
| "how many unique CpG sites pass filter" | **unique positions** (dedupe by `Pos` then filter) |
