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MDhewei
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mutation-analysis-for-gene

Summarize gene mutations from TCGA cohorts via cBioPortal or local MAF files.

64|12|Updated Apr 7, 2026
Search Tags:#visualization#genomics#mutations#tcga#cbioportal#maf#lollipop-plot
One-click install
npx skills add https://github.com/MDhewei/bioinfor-claw --skill mutation-analysis-for-gene
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mutation-analysis-for-gene
Source: https://github.com/MDhewei/bioinfor-claw/tree/main/gene-centered-analysis/mutation-analysis-for-gene
Command: npx skills add https://github.com/MDhewei/bioinfor-claw --skill mutation-analysis-for-gene

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Enables researchers to identify and quantify somatic mutations in a specified gene across TCGA cohorts, providing mutation frequencies, hotspot residues, and mutation-type classifications alongside publication-ready visualizations.

Core Features & Use Cases

  • Cross-cohort mutation profiling: Aggregate somatic mutations for a gene across TCGA PanCancer Atlas studies.
  • Hotspot detection: Identify recurrent amino-acid positions and summarize mutation types at hotspots.
  • Multi-format outputs: Generate a mutation_summary.tsv, lollipop_plot.png, mutation_frequency.png, mutation_types.png, and hotspot_details.tsv for downstream analysis.
  • Flexible data sources: Query cBioPortal by default and optionally consume a local MAF file as a fallback or supplement.
  • Use Case: A researcher analyzes TP53 mutations to compare mutation burden and hotspot distribution between breast cancer and lung cancer cohorts, with ready-to-publish charts.

Quick Start

Run the mutation analysis by specifying a gene and optional mutation file to generate mutation summaries and visualizations.

Frequently Asked Questions about mutation-analysis-for-gene

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

FAQPage Schema
Can I use a local MAF file to analyze gene mutations instead of querying cBioPortal?▼

To identify mutation hotspots for a gene, you need to aggregate somatic mutation records to detect recurrent amino-acid positions. This Skill processes TCGA PanCancer Atlas data to pinpoint these hotspots and outputs a detailed hotspot_details.tsv file.

What outputs are generated when characterizing gene mutations across TCGA?▼

Yes, you can use a local MAF file to analyze gene mutations by providing it as an optional fallback or supplement to online cBioPortal queries. This allows mutation profiling even without direct internet access to the TCGA PanCancer Atlas.

Do I need Python packages like pandas and matplotlib installed to generate mutation lollipop plots?▼

Characterizing gene mutations across TCGA generates a mutation_summary.tsv, lollipop_plot.png, mutation_frequency.png, mutation_types.png, and hotspot_details.tsv. These files provide mutation frequencies, classifications, and visualizations for downstream analysis.

How do I identify mutation hotspots for a gene using TCGA PanCancer Atlas data?▼

Yes, generating mutation lollipop plots requires Python packages like numpy, pandas, and matplotlib installed in your environment. These dependencies handle the data parsing, aggregation, and visualization required for the mutation analysis outputs.

How do I identify mutation hotspots for a gene using TCGA PanCancer Atlas data?▼

To identify mutation hotspots for a gene, you need to aggregate somatic mutation records to detect recurrent amino-acid positions. This Skill processes TCGA PanCancer Atlas data to pinpoint these hotspots and outputs a detailed hotspot_details.tsv file.

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