bio-rnaseq-qc
RNA-seq specific quality control including rRNA contamination detection, strandedness verification, gene body coverage, and transcript integrity metrics. Use when validating RNA-seq libraries before differential expression analysis.
npx skills add majiayu000/claude-skill-registry --skill rnaseq-qc --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.
# RNA-seq Quality Control RNA-seq specific QC metrics beyond general read quality. ## rRNA Contamination Detection High rRNA content indicates failed rRNA depletion or polyA selection. ### SortMeRNA ```bash sortmerna \ --ref rRNA_databases/smr_v4.3_default_db.fasta \ --reads sample.fastq.gz \ --aligned rRNA_reads \ --other non_rRNA_reads \ --fastx \ --threads 8 rrna_count=$(grep -c "^@" rRNA_reads.fastq 2>/dev/null || echo 0) total_count=$(zcat sample.fastq.gz | grep -c "^@") rrna_pct=$(echo "scale=2; $rrna_count / $total_count * 100" | bc) echo "rRNA: ${rrna_pct}%" ``` ### BLAST Against rRNA ```bash seqkit sample -n 10000 sample.fastq.gz | seqkit fq2fa > sample_10k.fasta blastn -query sample_10k.fasta -db rrna_db -outfmt 6 -evalue 1e-10 -max_target_seqs 1 | wc -l ``` ### Expected rRNA Levels | Library Type | Expected rRNA | |--------------|---------------| | PolyA selected | < 5% | | rRNA depleted | < 10% | | Total RNA | 50-80% | ## Strandedness Verification ### RSeQC infer_experiment ```bash infer_experiment.py -i aligned.bam -r genes.bed ``` ### Output Interpretation ``` Fraction of reads explained by "1++,1--,2+-,2-+": 0.9856 # Forward stranded Fraction of reads explained by "1
- rRNA Contamination Detection
- SortMeRNA
- BLAST Against rRNA
- Expected rRNA Levels
- Strandedness Verification
- RSeQC inferexperiment
- Output Interpretation
- Strand Inference
- Salmon Strandedness
- Gene Body Coverage
- RSeQC geneBodycoverage
- Interpretation
- Read Distribution
- RSeQC readdistribution
sortmerna \
echo "rRNA: ${rrna_pct}%"
seqkit sample -n 10000 sample.fastq.gz | seqkit fq2fa > sample_10k.fasta
blastn -query sample_10k.fasta -db rrna_db -outfmt 6 -evalue 1e-10 -max_target_seqs 1 | wc -l
infer_experiment.py -i aligned.bam -r genes.bed
salmon quant -i index -l A -r sample.fastq.gz -o quant/
grep "library_types" quant/lib_format_counts.json
geneBody_coverage.py \
read_distribution.py -i aligned.bam -r genes.bed > distribution.txt
tin.py -i aligned.bam -r genes.bed > tin_scores.txtWhat does the bio-rnaseq-qc skill do?
RNA-seq specific quality control including rRNA contamination detection, strandedness verification, gene body coverage, and transcript integrity metrics. Use when validating RNA-seq libraries before differential expression analysis.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill rnaseq-qc --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 majiayu000/claude-skill-registry, a repository with 534 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.