genomics-analysis-guide
Workflows for RNA-seq, GWAS, and variant calling in genomic research
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill genomics-analysis-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ``` Raw FASTQ files | v [Quality Control] --> FastQC, MultiQC | v [Trimming] --> Trimmomatic, fastp |
- Overview
- RNA-seq Analysis Pipeline
- Workflow Overview
- Step 1: Quality Control
- Step 2: Read Trimming
- Step 3: Alignment with STAR
- Step 4: Differential Expression with DESeq2
- GWAS Pipeline
- QC with PLINK2
- Association Testing
- Manhattan Plot in Python
- Variant Calling Pipeline
- GATK Best Practices
- Best Practices
Run FastQC on all FASTQ files fastqc -t 8 -o qc_results/ raw_data/*.fastq.gz Aggregate QC reports multiqc qc_results/ -o multiqc_report/ fastp for quality trimming and adapter removal fastp \ Build genome index (one time) STAR --runMode genomeGenerate \ Align reads STAR --runMode alignReads \
What does the genomics-analysis-guide skill do?
Workflows for RNA-seq, GWAS, and variant calling in genomic research
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill genomics-analysis-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.