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Bioinformatics

Tools for computational analysis of biological data, including genomics, proteomics, and other molecular biology data. Enables access to bioinformatics pipelines, algorithms, and databases.

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    Enables AI-driven pharmacogenomic analysis by querying structured genetic variant, drug response, and disease risk data. Supports natural language questions about medications, traits, and health risks based on user genome data, with privacy-first local execution.
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    MIT
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    Reads raw lab-instrument files (microscopy, mass spectrometry, chromatography, flow cytometry, NMR, electrophysiology, spectroscopy, plate readers, qPCR) without vendor software, returning metadata, images, spectra and traces as JSON. It also checks files for damage, runs common analyses, and exports to open formats such as OME-TIFF, OME-Zarr, mzML, Parquet and NWB.
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    138 PyPI
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    Apache 2.0
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    Provides seamless access to the Protein Data Bank in Europe (PDBe) API and search capabilities, enabling AI clients to query protein structures, perform advanced searches, and retrieve structural biology data.
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    Enables interaction with the CEDAR metadata repository, including fetching templates, searching BioPortal ontology terms, and managing template instances.
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    Enables MCP clients to annotate and classify single human genetic variants in any notation, returning an auditable points-based ACMG verdict with its criteria, gnomAD frequencies, in-silico predictors, ClinVar data, and per-transcript consequences. It is read-only, requires no account or API key, and keeps answers compact while linking back to the source.
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    Retrieve genomic data from EGA, ENA, ENCODE, GEO and NCBI, read bounded regions from indexed BAM/CRAM, VCF and bigWig files, and query source-attributed reference evidence. Runs locally with 23 MCP tools. MIT-licensed; research use.
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    MIT
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    Enables editing and querying of Gene Ontology Causal Activity Models (GO-CAMs) through the Barista API. Supports model creation, individual and fact management, evidence addition, and causal pathway construction for biological knowledge representation.
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    Enables AI assistants to search, browse, analyze, and export biological pathway data from Reactome through natural language.
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    Enables Claude Desktop to read and drive analyses on the active MilliMap session, including datasets, clusters, annotations, and markers.
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    MIT
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    Enables AI agents and applications to search, retrieve, and analyze chemical compounds, substances, and bioassays from PubChem's vast chemical information database through comprehensive tools for chemical research and discovery.
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    Enables AI-assisted molecular biology experiment design with tools for qPCR primer design, cloning strategy optimization, TaqMan probe design, and multiplex compatibility analysis.
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    An MCP server that grounds protein research in the UniProt SPARQL endpoint, providing tools for querying proteins, sequences, variants, diseases, and more via intent-named tools and raw SPARQL.
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    MIT
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    Enables AI coding agents to search academic papers, resolve biomedical entities, mine relations, and traverse citation graphs using Semantic Scholar and PubTator3, with local caching for reproducibility.
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    MIT
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    Enables AI assistants to rapidly gather and synthesize structural, chemical, conservation, and literature data about protein binding pockets for drug-target triage. Useful as a reconnaissance step before computational binder design.
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    Enables LLM agents to query the CZ CELLxGENE Census single-cell atlas with ontology-aware filters, cost caps, and full provenance, allowing natural language questions about cell types, tissues, and gene expression.
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    MIT
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    Enables researchers to query public ENA and BioSamples genomics data in plain English through any MCP client, including counting records, searching samples, retrieving sample details, and checking metadata quality against project requirements.
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    Converts messy metabolite names into standard database identifiers (KEGG, HMDB, ChEBI, PubChem, InChIKey) and performs crosswalking to Mouse-GEM for metabolic model input, with deterministic tools and an LLM reasoning layer for identity disambiguation.
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    MIT
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    Grounds gene-nomenclature work in the HUGO Gene Nomenclature Committee (HGNC) dataset, enabling resolution of gene symbols and IDs to canonical HGNC identifiers, plus cross-references and batch operations.
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    MIT
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    MCP server for polygenic risk score analysis, wrapping just-prs to enable catalog search, PRS computation, VCF normalization, percentile and risk estimation, and quality assessment.
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    MIT

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