"How to execute custom code in a Linux Docker container" matching MCP servers:

Enables genomic sequence analysis through the Evo 2 model, supporting DNA sequence scoring, embedding, generation, and variant effect prediction with multiple model checkpoints (7B, 40B, 1B parameters).

Enables variant annotation and effect prediction using the Ensembl VEP API, with support for batch and single queries.

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.

Enables AI assistants to query the Ubergraph biomedical ontology SPARQL endpoint with tools for custom SPARQL queries, term lookup, search, and hierarchy traversal.

Leverages large language models to analyze users' WeGene genetic testing reports, providing access to report data via custom URI schemes and enabling profile and report management through OAuth authentication and API utilization.

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

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.

Enables AI assistants to perform NCBI BLAST sequence similarity searches through natural language, supporting nucleotide and protein searches, custom database creation, and multiple output formats.

MCP server for interacting with Galaxy bioinformatics platform, enabling AI assistants to connect to Galaxy instances, search and execute tools, manage workflows, and access other features.

Control PyMOL from Cursor via natural language. Enables describing molecular structures and styles in plain language to execute PyMOL commands through XML-RPC.

Enables LLMs and AI agents to query a biomedical knowledge graph stored in RedisGraph, with tools for concept search, synonym enrichment, and study variable discovery through semantic relationships.

Enables Claude Code to interact with a TACC or SLURM HPC cluster for bioinformatics pipelines, allowing job management, log reading, file browsing, remote script execution, and job submission through natural language.

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 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.

Enables language models to search biomedical literature, fetch sequences, and follow links across Entrez databases through eleven read-only tools wrapping the nine NCBI Entrez E-utilities. It handles URL building, pacing, redirects, response caps, and API-key redaction so queries can be answered without a browser or scraping.

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.

Enables AI assistants to clean PDB structures and run initial-stage energy relaxation for biomolecular modeling. It provides a structured workflow for preparing structures before production molecular dynamics or coarse-graining pipelines.