rare-disease-rnaseq

name: rare-disease-rnaseqdescription: Blood RNA-seq expression-outlier detection for rare-disease diagnostics. Cases scored against a control reference panel; outliers ranked and filtered by a haploinsufficient disease-gene panel.license: MITmetadata:   openclaw:     requires:       bins:       - python3      env: null      config: null    always: false    emoji: 🩸    homepage: https://github.com/ClawBio/ClawBio    os:     - darwin    - linux    install:     - kind: uv      package: pandas      bins: null    - kind: uv      package: numpy      bins: null    - kind: uv      package: matplotlib      bins: null  tags:   - rna-seq  - rare-disease  - outlier-detection  - OUTRIDER  - FRASER  - diagnostic  - blood  - transcriptomics  - haploinsufficiency  trigger_keywords:   - rare disease rnaseq  - expression outlier  - OUTRIDER  - FRASER  - blood rna-seq diagnostic  - NGRL  - undiagnosed  - candidate diagnosis  version: 0.1.0

🩸 Rare-Disease Blood RNA-seq Outlier Detection

Reproduces the diagnostic principle of the Genomics England NGRL paper (Blood-based RNA-Seq of 5,412 individuals, medRxiv 2026.03.19.26348811). For each case sample, scores per-gene expression against a control reference panel and flags candidates falling in a curated dosage-sensitive disease-gene panel.

When To Use

• A WGS-negative or WGS-VUS rare-disease patient with a paired blood RNA-seq sample
• A clinical bioinformatician triaging candidate diagnoses before MDT review
• A population-biobank team building an ancestry-matched control reference for outlier calling (e.g. Qatar Biobank for Sidra paediatric cases)

Method

Per-gene robust outlier scoring on log2(CPM+1):

1. Library-size normalise (CPM), log-transform
2. For each gene: compute median and MAD across the control panel
3. For each case-gene cell: modified z = 0.6745 (x − median) / MAD
4. Flag |z| ≥ threshold (default 3) and gene in disease panel
5. Rank by |z|, separate down-outliers (haploinsufficiency-consistent) from up-outliers

This implements the diagnostic principle of OUTRIDER (per-gene outlier vs control panel) without the autoencoder, so it runs in seconds with no R/Bioconductor stack. For clinical-grade calls swap to the full DROP pipeline (gagneurlab/drop) which adds OUTRIDER's denoising autoencoder, FRASER2 splicing outliers, and confounder correction. The skill's I/O contract is the same so the upgrade is drop-in.

Input Contract

• Counts matrix (.csv or .tsv): rows = genes (HGNC symbol), columns = sample IDs
• Cases file (.txt): one case sample ID per line
• Controls file (.txt): one control sample ID per line (typically n ≥ 50)
• Disease panel (optional, .csv with gene and mechanism columns): defaults to a built-in 50-gene haploinsufficient panel

Output Structure

rdoutlier_report/
├── report.md                     # per-case candidate diagnoses + clinical narrative
├── result.json                   # standard ClawBio envelope
├── figures/
│   └── case_outlier_heatmap.png  # z-scores across cases × top genes
├── tables/
│   ├── outlier_calls.csv         # all flagged outliers with z-score, direction, mechanism
│   └── per_gene_stats.csv        # control median + MAD per gene
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Demo

python clawbio.py run rdoutlier --demo

Generates 100 synthetic Gulf-ancestry control samples + 2 cases with injected outliers (FBN1 down, NF1 up) across a 200-gene panel. Demonstrates the diagnostic loop end-to-end in seconds.

Production Path (Sidra / QBB Reference)

Safety

• Local-only processing, no network calls in core pipeline
• Compatible with secure research environments (Genomics England RE pattern; Sidra clinical genomics environment)
• Disclaimer required on every report

Disclaimer

ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions.