bio-outlier-splicing-detection
Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection
npx skills add BioTender-max/awesome-bio-agent-skills --skill outlier-splicing-detection --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: FRASER 2.0 (>=1.99.0), OUTRIDER 1.20+, LeafcutterMD via leafcutter 0.2.9+, DROP 1.4+, R 4.4+, BiocManager 1.30+ Before using code patterns, verify installed versions match. If versions differ: - 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. # Outlier Splicing Detection For clinical RNA-seq diagnostics in rare disease, the question is not "what differs between groups?" but "what is aberrant in this single patient relative to a panel of unaffected samples?". The statistical framework is **single-sample-vs-cohort outlier detection**, fundamentally different from two-group differential splicing. Tools in this space are designed for clinical Mendelian diagnostic settings. ## Tool Taxonomy | Tool | Statistic | Test target | Fails when | |------|-----------|-------------|------------| | FRASER 2.0 | Beta-binomial autoencoder on Intron Jaccard Index | Splicing outliers (per-sample, per-jun
- Version Compatibility
- Tool Taxonomy
- Decision Tree by Diagnostic Scenario
- When to Use Outlier vs Differential
- FRASER 2.0 Workflow
- OUTRIDER for Gene-Level Outlier Expression
- LeafcutterMD for Annotation-Free Outlier Intron Usage
- DROP Pipeline (Integrated Workflow)
- Variant + Outlier Integration
- Cohort Size and Power
- Tissue Choice for Mendelian RNA-seq
- Hyperparameter Tuning
- Per-Tool Failure Modes
- FRASER 2.0: Q Hyperparameter Mistuning
for bam in *.bam; do
regtools junctions extract -a 8 -m 50 -s XS "$bam" -o "${bam%.bam}.junc"
done
ls *.junc > juncfiles.txt
python leafcutter_cluster_regtools.py -j juncfiles.txt -o leafcutter -m 50 -l 500000
leafcutterMD.R \
leafcutter_perind_numers.counts.gz
Install via bioconda (DROP is not on PyPI)
mamba create -n drop_env -c conda-forge -c bioconda drop --override-channels
conda activate drop_envWhat does the bio-outlier-splicing-detection skill do?
Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill outlier-splicing-detection --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.