bio-splicing-qc
Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep,
npx skills add BioTender-max/awesome-bio-agent-skills --skill splicing-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.
## Version Compatibility Reference examples tested with: RSeQC 5.0+, STAR 2.7.11+, samtools 1.19+, pysam 0.22+, regtools 1.0+, maxentpy 0.0.1+, spliceai 1.3+, matplotlib 3.8+, pandas 2.2+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - 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. # Splicing-Specific Quality Control Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice. Failures in any of these silently bias PSI estimates and inflate novel-junction false positives. The decision sequence is: experimental design → library prep → alignment strategy → annotation → diagnostic metrics. Each layer's failure mode is distinct. ## QC Layer Taxonomy | Layer | Target | Tool | Fails when | |-------|--------|------|------------| | Experimental design | Read length, depth, replicates, library type | Pre-sequencing review | <PE 75nt; n<3 vs n<3
- Version Compatibility
- QC Layer Taxonomy
- Decision Tree by Question
- Experimental Design Audit (Before Sequencing)
- STAR 2-Pass Alignment
- Junction Saturation
- Novel-vs-Known Junction Ratio
- Junction Read Overhang and Coverage
- Splice Site Strength (MaxEntScan and SpliceAI)
- Picard CollectRnaSeqMetrics and Gene-Body Coverage
- Strandedness Verification
- Annotation Choice
- rRNA Contamination Check
- Per-Tool Failure Modes
Pass 1: per-sample STAR --runMode alignReads \ Cohort-style 2-pass: collect all SJ.out.tab from pass 1 cat pass1_*_SJ.out.tab | awk '$5 > 0 && $7 >= 3' | sort -u > cohort_novel_SJ.tab Pass 2: re-align with augmented junctions junction_saturation.py \ junction_annotation.py -i sample.bam -r gencode_v45.bed -o sample_junc_annot picard CollectRnaSeqMetrics \ Strandedness conversion (foot-gun): Reverse-stranded (Illumina TruSeq Stranded; NEB Ultra II Directional — both dUTP):
What does the bio-splicing-qc skill do?
Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep,
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill splicing-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 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.