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AI EngineeringPython

Bio-Research Start

Set up your bio-research environment and explore available tools. Use when first getting oriented, checking which MCP servers are connected, or surveying available analysis skills before starting a new project.

23.1K stars2.8K forksAdded 2026/07/19

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README

From the original repository

Bio-Research Start

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

You are helping a biological researcher get oriented with the bio-research plugin. Walk through the following steps in order.

Step 1: Welcome

Display this welcome message:

Bio-Research Plugin

Your AI-powered research assistant for the life sciences. This plugin brings
together literature search, data analysis pipelines,
and scientific strategy — all in one place.

Step 2: Check Available MCP Servers

Test which MCP servers are connected by listing available tools. Group the results:

Literature & Data Sources:

~~literature database — biomedical literature search
~~literature database — preprint access (biology and medicine)
~~journal access — academic publications
~~data repository — collaborative research data (Sage Bionetworks)

Drug Discovery & Clinical:

~~chemical database — bioactive compound database
~~drug target database — drug target discovery platform
ClinicalTrials.gov — clinical trial registry
~~clinical data platform — clinical trial site ranking and platform help

Visualization & AI:

~~scientific illustration — create scientific figures and diagrams
~~AI research platform — AI for biology (histopathology, drug discovery)

Report which servers are connected and which are not yet set up.

Step 3: Survey Available Skills

List the analysis skills available in this plugin:

Skill	What It Does
Single-Cell RNA QC	Quality control for scRNA-seq data with MAD-based filtering
scvi-tools	Deep learning for single-cell omics (scVI, scANVI, totalVI, PeakVI, etc.)
Nextflow Pipelines	Run nf-core pipelines (RNA-seq, WGS/WES, ATAC-seq)
Instrument Data Converter	Convert lab instrument output to Allotrope ASM format
Scientific Problem Selection	Systematic framework for choosing research problems

Step 4: Optional Setup — Binary MCP Servers

Mention that two additional MCP servers are available as separate installations:

~~genomics platform — Access cloud analysis data and workflows Install: Download txg-node.mcpb from https://github.com/10XGenomics/txg-mcp/releases
~~tool database (Harvard MIMS) — AI tools for scientific discovery Install: Download tooluniverse.mcpb from https://github.com/mims-harvard/ToolUniverse/releases

These require downloading binary files and are optional.

Step 5: Ask How to Help

Ask the researcher what they're working on today. Suggest starting points based on common workflows:

Literature review — "Search ~~literature database for recent papers on [topic]"
Analyze sequencing data — "Run QC on my single-cell data" or "Set up an RNA-seq pipeline"
Drug discovery — "Search ~~chemical database for compounds targeting [protein]" or "Find drug targets for [disease]"
Data standardization — "Convert my instrument data to Allotrope format"
Research strategy — "Help me evaluate a new project idea"

Wait for the user's response and guide them to the appropriate tools and skills.

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