rare-disease-rnaseq

Detect expression outliers in blood RNA-seq data against a disease gene panel.

Updated May 10, 2026

Search Tags:#diagnostic #rna-seq #outlier detection #rare disease #blood

Ready to run

npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill rare-disease-rnaseq

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rare-disease-rnaseq
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/rare-disease-rnaseq
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill rare-disease-rnaseq

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, and includes scripts (resource) components.

What problem does it solve?

This Skill provides blood RNA-seq expression outlier detection, aiding in the diagnosis of rare diseases by identifying potential disease markers in RNA expression profiles.

Core Features & Use Cases

Expression Outlier Detection: Identifies genes with unusual expression levels in blood RNA-seq data.
Disease Gene Panel: Filters outliers against a curated list of haploinsufficient disease genes.
Use Case: Ideal for diagnosing rare diseases in patients with undiagnosed conditions, especially when combined with WGS-negative or WGS-VUS data.

Quick Start

Run the rare-disease-rnaseq skill with the 'rdoutlier' command, providing a counts matrix, cases file, controls file, and disease panel.

Frequently Asked Questions about rare-disease-rnaseq

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect expression outliers in blood RNA-seq for rare disease diagnostics?▼

To detect expression outliers in blood RNA-seq, you run the 'rdoutlier' command with a counts matrix, cases file, controls file, and disease panel to identify unusual gene expression profiles against a reference.

What is RNA-seq expression outlier detection and when is it needed for rare disease?▼

RNA-seq expression outlier detection identifies genes with unusual expression levels in blood transcriptomics. It is needed for diagnosing rare diseases, especially when WGS-negative or WGS-VUS data requires further investigation.

Do I need a specific disease gene panel to run blood RNA-seq outlier detection?▼

Yes, you need a disease gene panel to filter outliers against a curated list of haploinsufficient disease genes, and a controls file to establish the reference baseline for your cases.

Can I use pandas and numpy for rare disease RNA-seq analysis?▼

Yes, this rare disease RNA-seq analysis requires pandas and numpy for data processing, and matplotlib for visualizing expression outliers, operating directly on your counts matrix.

What is the best way to analyze undiagnosed rare disease cases using blood transcriptomics?▼

The best way to analyze undiagnosed rare disease cases is comparing blood RNA-seq expression profiles against a control reference panel and a curated haploinsufficient disease gene panel.