"Resources and Information on Data Analysis" matching MCP servers:

This server enables multi-agent conversations for interacting with Cirro's biological data platform through its OpenAPI interface, auto-generated using AG2's MCP builder.

Parses MS-DIAL binary lipidomics outputs and runs standard analyses server-side, returning compact summaries so an LLM can drive full lipidomics analysis without exposing raw matrices.

MCP server providing access to the BERtron API for aggregating genomic and environmental data from multiple BER data sources, enabling geospatial search and health checks.

Enables analysis of bulk RNA-seq data using natural language queries, executing R and Python in a Docker container with automatic sample anonymization and privacy controls.

MCP server and Claude Plugin for a full ENCODE Project genomic data and analysis toolkit — search, download, track, and analyze functional genomics experiments

Enables querying the WormBase database for C. elegans and nematode genomics data, including gene information, protein sequences, phenotypes, interactions, and expression patterns through natural language.

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.

Enables interaction with MGnify metagenomics resources and tools through the Model Context Protocol. Provides access to MGnify's API for querying and analyzing metagenomic datasets and related biological information.

Enables AI agents to conversationally interact with genomics research networks for data analysis and discovery across multiple Omics AI Explorer platforms. It provides tools for exploring data collections, examining table schemas, and executing SQL queries against datasets like Viral AI and Neuroscience AI.

A Model Context Protocol (MCP) server that provides access to the Protein Data Bank (PDB) - the worldwide repository of information about the 3D structures of proteins, nucleic acids, and complex assemblies.

Intelligent Genomic Data Server Provides high-quality gene information queries, homologous gene analysis, and evolutionary research functions via the MCP protocol.

Enables querying genomics data from the Alliance of Genome Resources across model organisms including human, mouse, rat, zebrafish, fly, worm, yeast, and xenopus. Supports gene searches, disease associations, expression data, orthologs, phenotypes, and molecular interactions through natural language.

A comprehensive Model Context Protocol (MCP) server that provides access to the AlphaFold Protein Structure Database through a rich set of tools and resources for protein structure prediction analysis.

Enables AI assistants to access protein information directly from UniProt, allowing retrieval of protein names, functions, sequences, and organism data by accession number.

An MCP server that provides standardized access to biomedical knowledge bases and resources, enabling AI systems to retrieve verified information from sources like bioRxiv, EuropePMC, and various protein/gene databases.

Enables comprehensive access to PubChem's chemical database with over 110 million compounds. Supports chemical searches, structure analysis, bioactivity data, safety information, and molecular property calculations through 30 specialized tools.

Enables AI assistants to query genetic variant data, gene constraints, and population genetics information from the gnomAD (Genome Aggregation Database) through its GraphQL API. Supports searching for genes and variants, retrieving constraint scores, analyzing population frequencies, and accessing genomic coverage data.

A server that enables AI assistants to interact with cancer genomics data from cBioPortal, allowing users to explore cancer studies, access genomic data, and retrieve mutations and clinical information.

An MCP server that enables single-cell RNA sequencing analysis through natural language, supporting data processing, visualization, and analysis tasks without requiring coding knowledge.

Enables AI assistants to perform quality control analysis on high-throughput sequencing data using FastQC and MultiQC. It supports single-file and batch processing of FASTQ/FASTA files and generates comprehensive, interactive summary reports.