Research

This skill enables querying the Reactome database for biological pathway analysis. It supports pathway enrichment analysis on gene/protein lists, expression data analysis, gene-to-pathway mapping, and visualization of results in the Reactome Pathway Browser for systems biology research.

A comprehensive reference skill for searching the PubMed biomedical literature database. Provides guidance on constructing advanced search queries using Boolean operators, MeSH terms, and field tags, plus documentation for programmatic access via the NCBI E-utilities API.

This skill enables querying the PubChem database, the world's largest freely available chemical database with 110M+ compounds. It supports searching compounds by name, structure (SMILES/InChI), or formula, retrieving molecular properties, performing similarity and substructure searches, and accessing bioactivity data from screening assays.

This skill enables access to the RCSB Protein Data Bank, the worldwide repository for 3D structural data of biological macromolecules. It supports searching for protein and nucleic acid structures by text, sequence, or structural similarity, downloading coordinate files in various formats, and retrieving metadata for structural biology and drug discovery workflows.

This skill enables querying the Open Targets Platform for drug target discovery, allowing users to search for gene targets, disease associations, known drugs, and evidence data. It provides Python helper functions to access tractability assessments, safety liabilities, clinical precedence, and genetic evidence for therapeutic target prioritization.

This skill provides comprehensive documentation for accessing the NIH Metabolomics Workbench REST API, enabling queries for metabolite structures, study metadata, RefMet nomenclature standardization, mass spectrometry m/z searches, and gene/protein associations across 4,200+ metabolomics studies.

This skill enables querying the KEGG (Kyoto Encyclopedia of Genes and Genomes) bioinformatics database through REST API calls. It supports pathway analysis, gene-pathway mapping, metabolic pathway exploration, drug interaction checking, and ID conversion between KEGG and external databases like UniProt and PubChem.

This skill provides comprehensive guidance for accessing the Human Metabolome Database (HMDB), a freely available resource containing detailed information about 220,000+ human metabolites. It covers web-based searches, spectral matching, data downloads, and common research workflows for metabolomics, biomarker discovery, and metabolite identification.

This skill provides guidance for querying the NHGRI-EBI GWAS Catalog, a comprehensive database of genome-wide association studies. It includes documentation on searching for SNP-trait associations, retrieving study metadata, accessing summary statistics, and integrating with Python for genetic epidemiology research and polygenic risk score development.

This skill enables access to the NCBI Gene Expression Omnibus (GEO), a public repository containing over 264,000 gene expression studies. It provides tools to search for datasets, download microarray and RNA-seq data, parse SOFT/matrix files, and perform expression analysis including differential expression and meta-analysis across studies.

This skill enables querying the NCBI Gene database for gene annotation and functional analysis. It provides Python scripts to search genes by symbol or ID, retrieve detailed gene metadata including RefSeqs, GO annotations, chromosomal locations, and phenotypes, and perform efficient batch lookups with built-in rate limiting.

This skill enables querying FDA regulatory databases through the openFDA public API. It provides access to drug adverse events, device safety data, food recalls, veterinary reports, and substance information for regulatory research and safety analysis.

This skill enables querying the Ensembl genome database, a comprehensive resource for vertebrate genomic data maintained by EMBL-EBI. It supports gene information retrieval, DNA/protein sequence fetching, variant effect prediction using VEP, ortholog discovery, and coordinate mapping between genome assemblies for over 250 species.

This skill provides comprehensive guidance for accessing the European Nucleotide Archive (ENA), a public repository for nucleotide sequence data. It documents REST APIs for searching samples, studies, and assemblies, retrieving FASTQ files and genome sequences, querying taxonomic information, and bulk downloading datasets via FTP or Aspera for genomics research pipelines.

This skill provides programmatic access to COSMIC (Catalogue of Somatic Mutations in Cancer), the world's largest cancer mutation database. It allows downloading mutation data, Cancer Gene Census lists, mutational signatures, gene fusions, and drug resistance information for cancer research and bioinformatics workflows.

A comprehensive guide for accessing NCBI's ClinVar database to query clinical significance of human genetic variants. Provides documentation on searching variants by gene/position, interpreting pathogenicity classifications (ACMG/AMP guidelines), downloading bulk data via FTP, and annotating VCF files with clinical significance.

This skill provides access to the ClinPGx pharmacogenomics database, which contains clinical pharmacogenetics information including gene-drug interactions, CPIC guidelines, allele functions, and drug labels. It enables querying for precision medicine applications such as genotype-guided dosing decisions and identifying genetic risk factors for drug toxicity.

This skill enables querying the ClinicalTrials.gov database to search for clinical trials by condition, drug, location, status, or phase. It provides functions to retrieve detailed trial information, extract eligibility criteria, and export data for clinical research and patient matching workflows.

This skill enables querying the ChEMBL database, a manually curated collection of over 2 million bioactive molecules maintained by the European Bioinformatics Institute. It supports compound searches, target information retrieval, bioactivity data queries (IC50, Ki, EC50), similarity/substructure searches, and drug discovery workflows for medicinal chemistry research.

This skill provides access to the BRENDA enzyme database, the world's most comprehensive enzyme information system. It enables researchers to retrieve kinetic parameters (Km, kcat, Vmax), reaction equations, substrate specificities, and optimal conditions for over 45,000 enzymes. The skill also supports metabolic pathway construction and retrosynthetic analysis for enzyme engineering applications.

This skill enables access to the AlphaFold Protein Structure Database, providing code examples and documentation for retrieving AI-predicted 3D protein structures. It supports querying by UniProt ID, downloading coordinate files (PDB/mmCIF), analyzing confidence metrics (pLDDT, PAE), and bulk data access via Google Cloud for structural biology and drug discovery workflows.

TorchDrug is a documentation skill that provides comprehensive guidance for using the TorchDrug PyTorch library in drug discovery and molecular science. It covers graph neural networks for molecules and proteins, including molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, and retrosynthesis planning with 40+ curated datasets and 20+ model architectures.

A comprehensive reference skill for the statsmodels Python library, covering statistical modeling techniques including linear regression, generalized linear models, discrete choice models, time series analysis, and statistical diagnostics. Provides code examples, best practices, and detailed explanations for econometrics and rigorous statistical inference.

A comprehensive documentation skill for scvi-tools, a Python framework for probabilistic deep generative models in single-cell genomics. It provides guidance on models for RNA-seq, ATAC-seq, multimodal data integration, spatial transcriptomics, and specialized modalities like methylation and cytometry analysis.

A comprehensive reference skill for scikit-bio, a Python bioinformatics library. Provides documentation and code examples for biological sequence manipulation, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination analysis (PCoA, CCA), statistical tests (PERMANOVA, ANOSIM), and microbiome data processing with FASTA/BIOM file format support.

This skill provides a comprehensive toolkit for analyzing single-cell RNA-seq data using the scanpy library. It enables quality control, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, marker gene identification, cell type annotation, and publication-quality visualizations.

RDKit is a comprehensive cheminformatics skill that guides users through molecular analysis tasks including reading/writing chemical structures (SMILES, SDF), calculating molecular descriptors (LogP, TPSA, molecular weight), generating fingerprints for similarity searching, and performing substructure matching. It includes ready-to-use Python scripts for drug-likeness screening, similarity searches, and functional group filtering.

A comprehensive reference skill for QuTiP (Quantum Toolbox in Python), providing documentation for simulating open quantum systems, master equations, and quantum optics. Includes detailed guides for time evolution solvers, visualization tools, and advanced features like Floquet theory and HEOM methods.

This skill provides extensive guidance for working with IBM's Qiskit quantum computing framework. It covers building quantum circuits, running simulations, executing on real IBM Quantum hardware, and implementing quantum algorithms like VQE, QAOA, and Grover's search. The skill includes reference documentation for setup, primitives, transpilation, visualization, and various quantum applications in chemistry, optimization, and machine learning.

PyTDC (Therapeutics Data Commons) provides AI-ready datasets and benchmarks for drug discovery and development. It offers curated datasets spanning ADME, toxicity, drug-target interactions, and molecular generation with standardized evaluation metrics and meaningful data splits for therapeutic machine learning applications.