depmap

Identify cancer-specific gene dependencies and synthetic lethal partners from DepMap datasets.

74|5|Updated Dec 10, 2025
One-click install
npx skills add https://github.com/dralkh/seerai --skill depmap-dralkh
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: depmap
Source: https://github.com/dralkh/seerai/tree/main/skills/depmap
Command: npx skills add https://github.com/dralkh/seerai --skill depmap-dralkh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps researchers identify cancer-specific gene dependencies, synthetic lethal partners, and drug response signals from DepMap data without manually piecing together multiple datasets.

Core Features & Use Cases

  • Gene dependency analysis: Compare Chronos or RNAi scores across cell lines to find essential genes and selective vulnerabilities.
  • Biomarker discovery: Test whether mutations, expression, or copy number changes predict dependency on a target gene.
  • Systematic review support: Use DepMap annotations and analysis workflows to prioritize targets for oncology research and validate hypotheses across cancer lineages.
  • Use case: A researcher studying KRAS-mutant lung cancer can quickly find which genes are most selectively essential in that lineage and cross-check them against mutation and expression patterns.

Quick Start

Use the depmap skill to identify the most selective gene dependencies for a chosen cancer lineage and summarize the top candidate vulnerabilities.

Frequently Asked Questions about depmap

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

FAQPage Schema
How do I identify synthetic lethal partners using DepMap CRISPR and RNAi data?

To identify synthetic lethal partners using DepMap CRISPR and RNAi data, compare Chronos dependency scores across cell lines to find selectively essential genes. This pinpoints cancer-specific vulnerabilities by statistically contrasting mutant and wild-type groups.

What is gene dependency analysis for cancer target validation?

Gene dependency analysis for cancer target validation is the process of evaluating Chronos scores to determine if a gene is selectively essential in specific cancer lineages. It validates oncology research hypotheses by aligning cell line metadata with dependency patterns.

Can I discover biomarkers for drug sensitivity from gene expression and copy number data?

Yes, you can discover biomarkers for drug sensitivity from gene expression and copy number data. The workflow involves testing whether mutations, expression changes, or copy number alterations predict dependency on a specific target gene across cancer lineages.

Does pan-cancer essentiality analysis require statistical comparison of mutant versus wild-type groups?

Yes, pan-cancer essentiality analysis requires statistical comparison of mutant versus wild-type groups. It relies on Chronos-based dependency interpretation and cell line metadata alignment to systematically prioritize targets and validate hypotheses across cancer lineages.

What is the best way to find cancer-specific gene dependencies without manually piecing together datasets?

The best way to find cancer-specific gene dependencies without manually piecing together datasets is to apply integrated analysis workflows across CRISPR, RNAi, mutation, and expression data. This systematically identifies selective vulnerabilities and drug response signals.