A high-performance interface for querying the Cancer Dependency Map (DepMap) to identify cancer-specific genetic vulnerabilities and validate drug targets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install erobeng-master
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install erobeng-master using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is DepMap: Cancer Dependency & Target Validation?
The DepMap skill enables AI agents to interface with the Broad Institute's Cancer Dependency Map project. By leveraging large-scale CRISPR, RNAi, and compound sensitivity datasets, this tool helps researchers identify which genes are essential for survival in specific cancer types. It is a vital component for those building oncology-focused applications with Openclaw Skills, allowing for the systematic discovery of therapeutic targets and synthetic lethal interactions across hundreds of human cancer cell lines.
This skill provides access to Chronos dependency scores, where highly negative values indicate stronger essentiality. By integrating cell line metadata and omics data, it transforms raw screening results into actionable biological insights, making it indispensable for modern computational oncology and drug discovery workflows.
DepMap: Cancer Dependency & Target Validation Use Cases
- Target Validation: Confirm if a gene is essential for survival in cancer cell lines with specific mutations (e.g., KRAS-mutant).
- Biomarker Discovery: Identify genomic features that predict sensitivity to the knockout of a specific gene.
- Synthetic Lethality: Find genes that become selectively essential only when another specific gene is mutated or deleted.
- Drug Sensitivity Analysis: Determine which cell line features predict a positive response to specific chemical compounds.
- Pan-Cancer Screening: Differentiate between broadly essential genes (toxic targets) and selectively essential genes (safe therapeutic targets).
How DepMap: Cancer Dependency & Target Validation Works
- Initialize the connection to the DepMap Portal API or load local datasets such as the CRISPR Chronos gene effect matrix.
- Query specific gene dependency slices or cell line annotations using unique DepMap_ID identifiers.
- Filter cell lines based on lineage, primary disease, or mutation status to isolate specific cancer subtypes.
- Perform statistical comparisons (e.g., Mann-Whitney U tests) to evaluate if mutations predict dependency.
- Calculate co-essentiality by correlating dependency profiles across the entire genome to find shared biological pathways.
- Generate structured reports or data frames for integration into larger Openclaw Skills research pipelines.
DepMap: Cancer Dependency & Target Validation Setup
To integrate this capability into your Openclaw Skills environment, ensure you have the necessary Python data stack installed:
pip install pandas requests scipy numpy
For large-scale local analysis, download the primary datasets (CRISPRGeneEffect.csv and sample_info.csv) from the DepMap Portal download page. For real-time queries, ensure your network allows outbound traffic to https://depmap.org/portal/api.
DepMap: Cancer Dependency & Target Validation Data Schema & Taxonomy
The skill organizes data around the DepMap_ID (e.g., ACH-000001) as the primary key. Below is the metadata taxonomy used:
| Data Type | Source File | Description |
|---|---|---|
| Gene Dependency | CRISPRGeneEffect.csv |
Chronos scores; -1 represents the median of common essential genes. |
| Cell Metadata | sample_info.csv |
Includes lineage, primary disease, and cell line names. |
| Omics Data | OmicsCNGene.csv |
Copy number data per gene for assessing genomic amplification. |
| Mutations | OmicsSomaticMutationsMatrixDamaging.csv |
Binary matrix of damaging somatic mutations. |
| Expression | OmicsExpressionProteinCodingGenesTPMLogp1.csv |
mRNA expression levels (TPM). |
DepMap: Cancer Dependency & Target Validation Advanced Features
- Automated Biomarker Screening: Programmatically test if mutations in one gene predict dependency on another.
- Co-Essentiality Mapping: Identify functional gene modules by finding the most correlated dependency profiles genome-wide.
- Compound Sensitivity Integration: Correlate PRISM drug screening data with genomic features for drug-target discovery.
- Multi-Omics Filtering: Cross-reference dependency scores with expression and copy number data to eliminate false positives.
- Selectivity Scoring: Calculate the variance of gene effects to distinguish between pan-essential and cancer-selective targets within Openclaw Skills automation.
SKILL.md
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