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depmap

Analyze cancer cell line dependency profiles to identify selective gene vulnerabilities.

Updated Jul 1, 2026
Search Tags:#biomarker discovery#crispr#synthetic lethality#chronos#depmap#gene dependency#cancer cell lines
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill depmap-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: depmap
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/depmap
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill depmap-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you analyze cancer cell line dependency data to identify essential genes, selective vulnerabilities, and druggable targets with confidence.

Core Features & Use Cases

  • Gene dependency analysis: Compare Chronos and gene effect scores across cell lines to find strong or selective dependencies.
  • Biomarker and synthetic lethality workflows: Test whether mutations, expression patterns, or copy-number states predict sensitivity to gene knockout.
  • Drug response and co-essentiality insights: Explore PRISM compound sensitivity and correlated dependency profiles to prioritize targets and validate mechanisms.
  • Use case: A researcher can assess whether a KRAS-related target is selectively essential in lung cancer lines and whether that dependency is associated with a specific mutation or expression signature.

Quick Start

Ask the depmap skill to analyze a target gene across DepMap cell lines and summarize dependency, selectivity, and biomarker associations.

Frequently Asked Questions about depmap

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

FAQPage Schema
How do I identify selective cancer gene dependencies from CRISPR knockout data?▼

You can test for synthetic lethality by analyzing whether specific mutations, expression patterns, or copy-number states predict sensitivity to gene knockout across DepMap cancer cell lines. The analysis applies statistical testing to validate these biomarker associations.

Can I use DepMap RNAi and mutation data together for biomarker discovery?▼

PRISM compound sensitivity data helps prioritize therapeutic targets by exploring drug response alongside co-essentiality insights. You can correlate dependency profiles with compound sensitivity to validate target mechanisms and identify druggable vulnerabilities.

What is the best way to find druggable targets using cancer cell line dependency profiles?▼

The best way to find druggable targets is to analyze cancer cell line dependency profiles by comparing Chronos scores across contexts. Use co-essentiality analysis and PRISM drug response data to validate target selectivity and mechanism confidence.

Do I need to align cell-line metadata before analyzing gene dependency scores?▼

Yes, cell-line metadata alignment is required before analyzing gene dependency scores. Proper metadata alignment ensures accurate interpretation of score thresholds, statistical testing, and FDR-controlled ranking across CRISPR, RNAi, and PRISM workflows.

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