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Workflows for RNA-seq, GWAS, and variant calling in genomic research

skills/domains/biomedical/genomics-analysis-guide/SKILL.md

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# Genomics Analysis Guide

## Overview

Genomic data analysis is the computational backbone of modern molecular biology. From identifying disease-associated variants through Genome-Wide Association Studies (GWAS) to quantifying gene expression with RNA-seq, these workflows transform raw sequencing data into biological insights that drive discoveries in medicine, agriculture, and evolutionary biology.

This guide covers the three most common genomic analysis workflows: RNA-seq differential expression analysis, GWAS for variant-trait associations, and variant calling from whole-genome sequencing (WGS) data. Each workflow is described with tool recommendations, command-line examples, and downstream analysis steps in R and Python.

The emphasis is on reproducibility and best practices. Genomic analyses involve many sequential steps, and errors in early stages propagate through the entire pipeline. Following standardized workflows -- like those from the Broad Institute, ENCODE, and Bioconductor -- reduces the risk of methodological errors.

## RNA-seq Analysis Pipeline

### Workflow Overview
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