Agent skill

star-rna-seq-aligner

Splice-aware RNA-seq aligner producing sorted BAM and splice junction tables. Builds genome index, runs two-pass alignment for better junctions. Outputs sorted BAM, junctions (SJ.out.tab), stats (Log.final.out), optional gene counts. Use Salmon for fast pseudoalignment; STAR when a BAM is needed for variant calling, IGV, or ENCODE pipelines.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill star-rna-seq-aligner --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 14 KB
Bundled scripts: none
Path: skills/sciagent/star-rna-seq-aligner/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 144
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# STAR — Spliced RNA-seq Aligner ## Overview STAR (Spliced Transcripts Alignment to a Reference) aligns RNA-seq reads to a genome in a splice-aware manner, identifying novel and annotated splice junctions in a single pass. It generates coordinate-sorted BAM files compatible with samtools, IGV, deeptools, and GATK. STAR's 2-pass mode re-aligns reads using junctions discovered in the first pass, improving sensitivity for novel splice sites. With `--quantMode GeneCounts`, STAR simultaneously produces gene-level read count tables without requiring a separate featureCounts or HTSeq step. ## When to Use - Aligning bulk RNA-seq reads to a reference genome when downstream tools require a BAM file (variant calling, visualization, deeptools) - Running ENCODE-compliant RNA-seq pipelines that mandate genome alignment - Discovering novel splice junctions and alternative splicing events in the dataset - Generating gene count tables alongside BAM alignment in a single step with `--quantMode GeneCounts` - Processing long reads or reads with high mismatch rates by tuning `--outFilterMismatchNmax` - Use **Salmon** instead when you only need transcript/gene quantification and do not need a BAM file —

What's inside
Steps it walks through
  1. Overview
  2. When to Use
  3. Prerequisites
  4. Quick Start
  5. Workflow
  6. Step 1: Prepare Reference Files
  7. Step 2: Generate Genome Index
  8. Step 3: Align RNA-seq Reads
  9. Step 4: Run 2-Pass Alignment for Improved Sensitivity
  10. Step 5: Check Alignment Statistics
  11. Step 6: Generate Gene Count Tables
  12. Key Parameters
  13. Common Recipes
  14. Recipe 1: Batch Align All Samples
Commands it runs
Install with conda (recommended)
conda install -c bioconda star
Verify
STAR --version
STAR_2.7.11a
Or compile from source
git clone https://github.com/alexdobin/STAR
cd STAR/source && make STAR
STAR --runMode genomeGenerate \
STAR --runThreadN 8 \
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About this skill
What does the star-rna-seq-aligner skill do?

Splice-aware RNA-seq aligner producing sorted BAM and splice junction tables. Builds genome index, runs two-pass alignment for better junctions. Outputs sorted BAM, junctions (SJ.out.tab), stats (Log.final.out), optional gene counts. Use Salmon for fast pseudoalignment; STAR when a BAM is needed for variant calling, IGV, or ENCODE pipelines.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill star-rna-seq-aligner --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 BioTender-max/awesome-bio-agent-skills, a repository with 144 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.

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