depmap

Query DepMap Chronos gene dependency scores across cancer cell lines.

21|1|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/OwnLabAI/ownlab --skill depmap-ownlabai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: depmap
Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/depmap
Command: npx skills add https://github.com/OwnLabAI/ownlab --skill depmap-ownlabai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DepMap provides access to gene dependency scores across cancer cell lines to identify essential genes, cancer-selective vulnerabilities, and potential drug targets.

Core Features & Use Cases

  • Dependency scores across hundreds of cancer models (Chronos) for gene-level prioritization.
  • Selectivity analysis to distinguish cancer-specific dependencies from pan-essential genes.
  • Biomarker and synthetic lethality exploration by integrating mutation, copy-number, and expression data.
  • Workflow guidance for target validation and compound sensitivity analyses using DepMap resources.

Quick Start

Query Chronos scores for a gene across DepMap cell lines to identify cancer-selective dependencies.

Frequently Asked Questions about depmap

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

FAQPage Schema
How do I identify cancer-selective gene dependencies using DepMap Chronos scores?

To identify cancer-selective dependencies, query DepMap Chronos gene effect scores across hundreds of cancer cell lines and compare them against pan-essential genes to pinpoint lineage-specific vulnerabilities for target validation.

Can I use DepMap cell line metadata to filter gene dependency data by specific cancer types?

Yes, you can filter Chronos gene dependency data by specific cancer types by cross-referencing DepMap cell line metadata, allowing you to restrict selectivity analysis to defined lineages and genomic contexts.

What's the best way to find biomarker correlations in cancer cell lines?

The best way to find biomarker correlations is to integrate DepMap OmicsExpression and mutation reference datasets with Chronos gene effect scores to map drug sensitivity and dependency patterns across genomic features.

How does Chronos gene effect scoring work for finding essential genes?

Chronos gene effect scoring estimates gene dependency by quantifying cell line proliferation changes after gene knockout, allowing you to distinguish essential genes from non-essential genes across cancer models.

Are there limitations to using cancer cell line dependency data for target validation?

A limitation of using DepMap cancer cell line data for target validation is its reliance on in vitro models, meaning dependencies identified through Chronos scores require further validation in in vivo contexts.