cosmic-database
COSMIC Somatic Cancer Mutations Database
Overview
COSMIC (Catalogue Of Somatic Mutations In Cancer) is the world's largest expert-curated database of somatic mutations in cancer, covering 6.7M+ coding mutations, 40,000+ cancer samples, 19,000+ genes across all cancer types. It includes the Cancer Gene Census (critical cancer genes), mutational signatures (SBS, DBS, ID), drug resistance variants, copy number data, gene expression, and methylation. The REST API v3.1 enables programmatic queries; most features are freely accessible after registration.
When to Use
- Checking whether a specific somatic variant in a cancer gene is annotated in COSMIC (frequency, cancer type distribution)
- Retrieving all somatic mutations in a gene of interest across COSMIC cancer samples
- Accessing COSMIC Cancer Gene Census classifications (Tier 1/2, role: oncogene/TSG/fusion)
- Looking up mutational signature attributions for samples or cancer types
- Identifying drug resistance variants (pharmacogenomic data) from COSMIC drug resistance database
- Building cancer driver gene lists for bioinformatic pipelines
- For germline/inherited variants use
clinvar-database; for drug-target associations useopentargets-database
Prerequisites
More from jaechang-hits/sciagent-skills
scientific-brainstorming
Structured ideation methods: SCAMPER, Six Thinking Hats, Morphological Analysis, TRIZ, Biomimicry, plus more. Decision framework for picking methods by challenge type (stuck, improving, systematic exploration, contradiction). Use when generating research ideas or exploring interdisciplinary connections.
12snakemake-workflow-engine
Python-based workflow management system for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake automatically determines the execution order and parallelism. Supports local, SLURM, LSF, AWS, and Google Cloud execution via profiles; per-rule conda/Singularity environments. Use for bioinformatics NGS pipelines, ML training workflows, and any multi-step file-processing analysis. Use Nextflow instead for Groovy-based dataflow pipelines or when nf-core ecosystem integration is required.
11esm-protein-language-model
Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and protein embeddings. Use when designing novel proteins, extracting sequence representations for downstream ML, or predicting structure from sequence. Local GPU or EvolutionaryScale Forge cloud API. For traditional structure prediction use AlphaFold; for small-molecule cheminformatics use RDKit.
11biopython-sequence-analysis
Biopython sequence analysis: parse FASTA/FASTQ/GenBank/GFF (SeqIO), NCBI Entrez (esearch/efetch/elink), remote/local BLAST, pairwise/MSA alignment (PairwiseAligner, MUSCLE/ClustalW), phylogenetic trees (Phylo). Use for gene family studies, phylogenomics, comparative genomics, NCBI pipelines. For PCR/restriction/cloning use biopython-molecular-biology; for SAM/BAM use pysam.
11shap-model-explainability
>-
11archs4-database
Query ARCHS4 REST API for uniformly processed RNA-seq expression, tissue patterns, co-expression across 1M+ human/mouse samples. Retrieve z-scores, co-expressed genes, samples by metadata, HDF5 matrices. For variant population genetics use gnomad-database; for pathway enrichment use gget-genomic-databases (Enrichr).
11