Skill UI

BioServices provides Python wrappers for 40+ bioinformatics services, allowing cross-database queries, identifier mapping, pathway exploration, and sequence or compound research within consistent workflows.

Programmatic access to the COSMIC cancer mutation database for downloading mutations, Cancer Gene Census, mutational signatures, fusions, copy number, expression, and resistance data to fuel oncology research or bioinformatics pipelines after authentication.

Datamol wraps RDKit with Pythonic APIs tailored for drug discovery, offering SMILES/SELFIES/InChI parsing, standardization, descriptor and fingerprint calculation, clustering, 3D conformer workflows, and parallel/cloud-aware batch I/O while returning native rdkit.Chem.Mol objects.

Access the European Nucleotide Archive via REST/FTP to fetch DNA/RNA sequences, raw reads, assemblies, annotations, and taxonomy for research or bioinformatics pipelines.

RDKit delivers a Python-based cheminformatics toolkit for parsing SMILES/SDF, computing descriptors, generating 2D/3D coordinates, fingerprinting, searching substructures, managing reactions, and visualizing molecules, ideal for drug discovery and computational chemistry workflows.

scikit-bio provides a Python-native biological data toolkit for reading/writing FASTA/Newick files, manipulating sequences, building alignments and phylogenies, and calculating diversity metrics for microbiome and ecology workflows.

Provides direct REST API access to UniProt, the premier resource for protein sequence and functional information. This skill enables users to search proteins by name, gene, or accession number, retrieve sequences in FASTA format, perform cross-database ID mapping (e.g., Ensembl, RefSeq), and analyze functional annotations (GO terms) for building comprehensive bioinformatics pipelines.

AnnData is a core Python framework designed for managing and analyzing annotated matrices, essential in single-cell genomics and high-dimensional data science. It provides a standardized structure (X, obs, var) to store experimental measurements alongside rich metadata. Users can read/write various formats (h5ad, zarr), perform subsetting, concatenation, and complex data manipulation, making it indispensable for modern bioinformatics workflows.

Biopython is a comprehensive Python toolkit for computational molecular biology. It supports essential bioinformatics tasks, including sequence manipulation, file parsing (FASTA, GenBank, PDB), phylogenetic analysis, sequence alignment (BLAST/Multiple), and programmatic access to major databases like NCBI using Bio.Entrez. Ideal for building custom, robust bioinformatics pipelines and conducting research in genomics and structural biology.

A comprehensive Python package providing programmatic access to over 40 major bioinformatics web services and databases (e.g., UniProt, KEGG, ChEMBL, PubChem). It facilitates complex workflows such as cross-database ID mapping, metabolic pathway analysis, protein sequence retrieval, and sequence similarity searching in a single, consistent API.

Provides programmatic and standardized access to massive, versioned datasets of single-cell and spatial transcriptomics from CZ CELLxGENE. This skill allows users to query millions of cells, analyze gene expression, and access pre-calculated embeddings across various tissues, diseases, and species without downloading entire datasets. Essential for large-scale bioinformatics analysis, machine learning model training, and comprehensive cross-dataset comparison.

Datamol is a high-level, Pythonic abstraction layer built over RDKit, designed for computational chemistry and drug discovery. It simplifies complex molecular operations, offering sensible defaults for tasks like SMILES standardization, descriptor calculation, fingerprint generation (ECFP4), 3D conformer analysis, and molecular clustering. It supports efficient parallel processing and cloud storage I/O, making it ideal for handling large-scale chemical datasets.