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    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.
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    A Model Context Protocol server that interfaces with Biomart databases, allowing models to discover biological datasets, explore attributes/filters, retrieve biological data, and translate between different biological identifiers.
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    MIT
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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 enables AI coding assistants to interact with Rosetta, PyRosetta, and Biotite for running RosettaScripts, validating XML, translating between Rosetta and Biotite, scoring structures, and querying documentation.
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    Enables AI assistants to query the Ubergraph biomedical ontology SPARQL endpoint with tools for custom SPARQL queries, term lookup, search, and hierarchy traversal.
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    MIT
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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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    Provides LLMs with structured access to critical biomedical databases including PubTator3 (PubMed/PMC), ClinicalTrials.gov, and MyVariant.info through the Model Context Protocol.
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    Enables bioinformatics analysis through natural language conversations with Claude Desktop, automatically generating and executing Python scripts to produce HTML reports and visualizations.
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    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.
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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