"Information about Docker containerization technology" matching MCP servers:

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.

A framework that integrates Brain-Computer Interface technology with the Model Context Protocol to enable real-time neural signal processing and AI-powered interactions for healthcare, accessibility, and research applications.

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.

Enables users to generate volcano plots by submitting jobs with input files and parameters, supporting local or Docker execution.

Allows to retrieve information about an AnnData object via MCP using the read_lazy function from anndata.

Enables workflow management and Docker image building for Bio-OS platform. Supports WDL workflow submission, validation, and monitoring, along with Docker image building and status tracking for bioinformatics workflows.

Official public, read-only Helena Bioinformatics MCP for source-cited answers about the company and its published products.

Enables querying the gnomAD genome aggregation database for variant, gene, and region information.

An MCP server that enables language models to fetch protein information from the UniProt database, including protein details, sequences, functions, and structures.

Enables protein modeling and design using the Rosetta suite via Docker, including structure refinement, mutation stability analysis, docking, and loop modeling through natural language commands.

Enables AI-powered protein design, including binders, peptides, and custom proteins, with GPU-accelerated Docker deployment and async job management.

Enables protein structure prediction using the Chai-1 model via Docker, with tools for small peptides, FASTA-based predictions, MSA-enhanced predictions, batch processing, and job management.

Enables AI-powered protein structure prediction and variant analysis via Docker, with tools for submitting predictions, batch processing variants, and monitoring jobs.

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 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.

A Model Context Protocol server providing LLMs with access to the Ensembl genomics database, enabling AI assistants to query gene information, sequences, variants, and other genomic data across multiple species.

Provides a programmatic interface to the Genome Aggregation Database (gnomAD) API across versions v2.1.1, v3.1.2, and v4.1.0. It enables users to query gene metadata, variant information, population frequencies, and ClinVar data through a unified schema.

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

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.