bio-splicing-pipeline
End-to-end alternative splicing analysis from FASTQ to differential splicing results for short-read bulk RNA-seq. Aligns with STAR 2-pass cohort-style, performs junction QC (RSeQC, MaxEntScan, SpliceAI), runs rMATS-turbo and leafcutter for concordant differential analysis, optionally MAJIQ V3 for complex events / heterogeneous cohorts, isoform-switching with NMD/ORF/domain consequences (IsoformSwitchAnalyzeR v2 + DRIMSeq+DEXSeq+stageR DTU), and sashimi visualizations. Use when performing comprehensive splicing analysis from raw bulk RNA-seq data; for variant-driven splice prediction see splice
npx skills add BioTender-max/awesome-bio-agent-skills --skill splicing-pipeline --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: STAR 2.7.11+, fastp 0.23+, numpy 1.26+, 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 - 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. # Alternative Splicing Analysis Pipeline **"Analyze alternative splicing from my RNA-seq data"** → Orchestrate STAR alignment, PSI quantification (rMATS-turbo/SUPPA2), differential splicing detection, isoform switching analysis (IsoformSwitchAnalyzeR), sashimi plot visualization, and junction QC. Complete workflow from raw RNA-seq to differential splicing results. ## Pipeline Overview ``` FASTQ → Read QC → STAR 2-pass → Junction QC → rMATS-turbo → Results → Visualization ↓ (Optional) IsoformSwitchAnalyzeR ``` ## Step 1: Read Quality Control ```bash # fastp for adapter trimming and quality filtering fastp \ -i sample_R1.fastq.
- Version Compatibility
- Pipeline Overview
- Step 1: Read Quality Control
- Step 2: STAR 2-Pass Alignment
- Step 3: Junction QC Checkpoint
- Step 4: Differential Splicing with rMATS-turbo
- Step 5: Filter Results
- Step 6: Optional Isoform Switching
- Step 7: Sashimi Visualization
- Complete Pipeline Script
- When NOT to Use This Pipeline (Pipeline Variants)
- Related Skills
fastp for adapter trimming and quality filtering fastp \ First pass to detect novel junctions STAR \ Generate new index with discovered junctions cat *_SJ.out.tab > combined_SJ.out.tab Second pass with combined junctions Create sample list files rmats.py \ set -e
What does the bio-splicing-pipeline skill do?
End-to-end alternative splicing analysis from FASTQ to differential splicing results for short-read bulk RNA-seq. Aligns with STAR 2-pass cohort-style, performs junction QC (RSeQC, MaxEntScan, SpliceAI), runs rMATS-turbo and leafcutter for concordant differential analysis, optionally MAJIQ V3 for complex events / heterogeneous cohorts, isoform-switching with NMD/ORF/domain consequences (IsoformSwitchAnalyzeR v2 + DRIMSeq+DEXSeq+stageR DTU), and sashimi visualizations. Use when performing comprehensive splicing analysis from raw bulk RNA-seq data; for variant-driven splice prediction see splice
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill splicing-pipeline --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.