bio-rna-quantification-featurecounts-counting
Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR.
npx skills add BioTender-max/awesome-bio-agent-skills --skill featurecounts-counting --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.
## Version Compatibility Reference examples tested with: DESeq2 1.42+, HISAT2 2.2.1+, STAR 2.7.11+, Subread 2.0+, edgeR 4.0+, pandas 2.2+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # featureCounts Counting **"Count reads per gene from my BAM files"** → Assign aligned reads to genomic features using a GTF annotation to produce a gene-by-sample count matrix for DE analysis. - CLI: `featureCounts -a genes.gtf -o counts.txt sample1.bam sample2.bam` Count reads mapping to genomic features (genes, exons) from BAM files. ## Basic Usage ```bash # Single sample featureCounts -a annotation.gtf -o counts.txt aligned.bam # Multiple samples (recommended - single matrix output) featureCounts -a annotation.gtf -o counts.txt sample1.bam sample2.bam sample3.bam # All BAMs in di
- Version Compatibility
- Basic Usage
- Paired-End Data
- Strand-Specific Libraries
- Feature Types
- Multi-Mapping Reads
- Overlapping Features
- Performance Options
- Output Files
- Extract Count Matrix
- Python Processing
- R Processing
- Common Issues
- Related Skills
Single sample featureCounts -a annotation.gtf -o counts.txt aligned.bam Multiple samples (recommended - single matrix output) featureCounts -a annotation.gtf -o counts.txt sample1.bam sample2.bam sample3.bam All BAMs in directory featureCounts -a annotation.gtf -o counts.txt *.bam Count fragments, not reads (required for paired-end) featureCounts -p --countReadPairs -a annotation.gtf -o counts.txt *.bam Check proper pairs only featureCounts -p --countReadPairs -B -C -a annotation.gtf -o counts.txt *.bam
What does the bio-rna-quantification-featurecounts-counting skill do?
Count reads per gene from aligned BAM files using Subread featureCounts. Use when processing BAM files from STAR/HISAT2 to generate gene-level counts for DESeq2/edgeR.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill featurecounts-counting --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 135 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.