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Analysis & spreadsheets51k · #1859

depmap

Helps your agent access and analyze data on genetic dependencies in cancer. It can identify genes affecting cancer growth and drug sensitivity. Useful for oncology research.

What it does

This tool helps find information about dependencies in cancer cells. It analyzes data from genetic and drug experiments to suggest which genes or treatments might be important for cancer.

When to use it

  • ✓To rank which genes are most critical for cancer survival.
  • ✓To compare how different cancer groups respond to treatments.
  • ✓To find potential drug combinations that work better together.

Example of what to ask your AI

“What genes are most important for breast cancer, according to the latest data?”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/depmap/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/K-Dense-AI/scientific-agent-skills/blob/main/skills/depmap/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

The tool accesses a database called DepMap, which contains information from cancer cell experiments. It searches and processes this data to find patterns and relationships between genes, cancer models, and drug responses.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add K-Dense-AI/scientific-agent-skills --skill depmap

Or download just the file:

curl -L https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/main/skills/depmap/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

DepMap — Cancer Dependency Map

When to use

Use this skill to rank dependencies in cancer models, compare prespecified biomarker cohorts, inspect co-essentiality, or relate genetic dependencies to PRISM compound response. These analyses generate target and synthetic-lethality hypotheses; they do not establish clinical efficacy, a therapeutic window, or a causal interaction.

DepMap integrates Broad and Sanger CRISPR screens and hosts independent RNAi and drug-screen datasets. Match each question to its assay and pinned release. RNAi DEMETER2 is a different perturbation modality, not an older CRISPR scoring method.

1. Discover a release, then obtain its files

Use DepMap Downloads and the selected release's README and release notes. On review, the live catalogue's latest DepMap Public release was 26Q1, dated 2026-04-01. Rediscover before new work; a release name or expected quarter is not evidence a release is available.

Verified public endpoint

GET https://depmap.org/portal/api/no-captcha/download/files returns a complete CSV catalogue, with no request body, API key, or pagination parameters. Required metadata columns are release, release_date, filename, and md5_hash. The live response also has url: older files can have links, while recent release rows have blank links. All 85 entries for 26Q1 had blank url values at review. Treat availability per row, not per endpoint.

The staff announcement introduced this metadata route in July 2026. The older https://depmap.org/portal/api/download/files catalogue can supply download links but returned an HTML verification page during review, including with HTTP 200. Validate content before parsing. Obtain current files through the portal when verification is required. Refresh expiring signed links immediately before use; do not fabricate storage URLs or assume every new release is on Figshare.

No supported contract was verified for the former /api/gene?gene_id=... or /api/data/gene_dependency examples. Use local release matrices for gene slices. Do not assume a Python package named depmap is an official portal client.

From the skill root, use the bundled helpers:

from scripts.depmap_data import fetch_catalogue, select_file, verify_md5

catalogue = fetch_catalogue()
public = catalogue[catalogue["release"].str.startswith("DepMap Public ")]
print(public[["release", "release_date"]].drop_duplicates()
      .sort_values("release_date", ascending=False).head(12))

# Explicit example pin; inspect discovery output before selecting your release.
release = "DepMap Public 26Q1"
record = select_file(catalogue, release, "CRISPRGeneEffect.csv")
print(record[["release", "filename", "md5_hash"]])
# After obtaining this file from the portal:
# verify_md5("CRISPRGeneEffect.csv", record["md5_hash"])

Save the selected metadata, retrieval date, release citation, local checksum, and README alongside analysis outputs. Record source dataset terms separately from this skill's license; newer release terms differ from older Figshare releases. For a missing published checksum, retain a local SHA-256 and explicitly state that it was not compared with a publisher checksum.

2. Inspect file contracts before joining

The following names were verified in the 26Q1 catalogue. Check that release's README for units and identifier level before treating a file as a numeric matrix.

File Use and important contract
CRISPRGeneEffect.csv Integrated, model-level Chronos effects; rows are ModelID, columns preserve Symbol (EntrezID)
CRISPRGeneEffectUncorrected.csv Uncorrected effects; not interchangeable with corrected effects
CRISPRGeneDependency.csv Release-specific dependency statistics; confirm probability/FDR definition and direction in the README
Model.csv Model metadata keyed by ModelID
ModelCondition.csv Growth/treatment conditions keyed by ModelConditionID, mapping to ModelID
ScreenGeneEffect.csv, ScreenSequenceMap.csv, CRISPRScreenMap.csv Screen-level effects and mappings; multiple screens can belong to one model
OmicsExpressionTPMLogp1HumanProteinCodingGenes.csv Expression output with sequencing/model metadata and default-entry flags; exclude metadata columns from expression calculations

…

FAQ

What are cancer 'dependencies'?

Dependencies are genes or processes that cancer cells need to survive. If you block a dependency, the cancer might die.

What is DepMap?

DepMap is a project that collects and organizes data from experiments on thousands of cancer cell lines to understand their dependencies.

Can this tool tell me which treatment to use?

No, this tool generates hypotheses and suggestions for research. It does not confirm treatment effectiveness in patients or determine the best treatment to use.

Details

Original description by the author

Retrieves and analyzes Cancer Dependency Map (DepMap) release data, including CRISPR Chronos gene effects, cancer model annotations, omics biomarkers, and PRISM drug sensitivity. Supports cancer-selective dependency, co-essentiality, and candidate synthetic-lethality analyses with release-aware identifiers and statistical checks.

repo: K-Dense-AI/scientific-agent-skills

Popularity

GitHub stars
51k
Ranking position
#1859
License
MIT
Updated
10/15/2026

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