BioNext-MCP: Intelligent Bioinformatics Analysis Assistant

The simplest way to perform bioinformatics analysis through Claude Desktop - just chat in natural language, no programming required!

中文版 | English

🎯 What is this?

BioNext-MCP allows you to perform complex bioinformatics analysis through natural language conversations with Claude Desktop, without writing any code!

Simply put:

• 🗣️ Tell Claude what data you want to analyze in plain English
• 🤖 Claude automatically generates professional Python analysis scripts
• ⚡ System automatically executes scripts and displays results
• 📊 Get beautiful HTML reports and visualization charts

✨ Key Features

🧬 Supported Analysis Types

• Single-cell RNA sequencing (scRNA-seq) - Cell clustering, differential expression, trajectory analysis
• Genomics - Variant analysis, annotation, functional enrichment
• Transcriptomics - Differential expression, pathway analysis, co-expression networks
• Proteomics - Protein identification, quantitative analysis
• Multi-omics integration - Data fusion, correlation analysis

🎨 Smart Features

• Automatic environment setup - Detects Python, auto-installs required packages (pandas, numpy, matplotlib, etc.)
• UTF-8 encoding support - Perfect support for international characters
• Visualization-first - Automatically generates charts and displays them in HTML reports
• Quality assurance - Focuses on code completeness and analysis accuracy
• Error handling - Smart diagnosis of issues with solution suggestions

🚀 Quick Start

Step 1: Install Python Environment

#### Recommended: Official Website Installation

1. Visit https://www.python.org/downloads/
2. Download Python 3.9 or higher
3. Make sure to check "Add Python to PATH" during installation

#### Verify Installation Open command prompt and type:

python --version

If you see version information, installation was successful!

Step 2: Install BioNext-MCP

1. Download Project

git clone https://github.com/your-username/BioNext-mcp.git
cd BioNext-mcp

1. Install Dependencies

npm install
npm run build

Step 3: Configure Claude Desktop

1. Find Configuration File

• Windows: %APPDATA%\Claude\claude_desktop_config.json

1. Add Configuration

{
  "mcpServers": {
    "bioinformatics-workflow": {
      "command": "node",
      "args": ["D:\\path\\to\\BioNext-mcp\\dist\\index.js"],
      "cwd": "D:\\path\\to\\BioNext-mcp",
      "env": {
        "PROJECT_PATH": "D:\\path\\to\\your\\analysis\\directory"
      }
    }
  }
}

Important:

• Replace paths with your actual installation paths
• Set analysis directory to where you want results saved

1. Restart Claude Desktop

💡 How to Use

Basic Conversation Flow

1. Describe Your Analysis Needs

I have a single-cell RNA sequencing data file data.h5ad, and I want to perform cell clustering analysis and differential expression analysis

1. Claude will generate analysis scripts and execute them automatically
2. Get detailed HTML reports including:

• Execution results and statistics
• Generated charts and visualizations
• Complete analysis logs

Practical Examples

#### 🧪 Single-cell Analysis

Please help me analyze this scRNA-seq data:
- File: C:\data\pbmc3k.h5ad
- Need: quality control, normalization, clustering, marker gene identification
- Output: UMAP plot, clustering heatmap, differential expression gene list

#### 🧬 Gene Expression Analysis

I have RNA-seq expression matrices from two groups:
- Control group: control_samples.csv
- Treatment group: treatment_samples.csv
- Analysis: differential expression, GO enrichment, KEGG pathway analysis
- Visualization: volcano plot, heatmap, pathway diagrams

#### 📊 Data Exploration

Help me explore this gene expression dataset:
- File: gene_expression.csv
- Need: data overview, correlation analysis, PCA analysis
- Generate: statistical summary, correlation heatmap, PCA plot

🎨 Beautiful Reports

HTML Report Features

• 📊 Visualization Gallery - Automatically detects and displays generated images
• 🔍 Interactive Viewing - Click images to zoom and view
• 📝 Detailed Logs - Complete execution process records
• 📈 Statistical Summary - Script execution status and performance metrics

Automatic Browser Opening

• Reports automatically open in browser after analysis completion
• If not auto-opened, manually open the generated HTML file

🛠️ Common Issues

Python-related

Q: "Python not found" error? A: Ensure Python is installed and added to PATH environment variable

Q: Package installation fails? A: System will automatically retry, or manually run pip install package_name

Analysis-related

Q: Script execution fails? A:

• Check if data file paths are correct
• Confirm data format meets requirements
• Check error logs for detailed information

Q: No HTML report generated? A: HTML reports are only generated when all scripts execute successfully, fix execution errors first

Data Formats

Q: What data formats are supported? A:

• CSV, TSV, Excel files
• HDF5 format (.h5, .h5ad)
• FASTA, FASTQ sequence files
• VCF variant files
• Other common bioinformatics formats

🎯 Usage Tips

1. Clear Description of Needs

✅ Good description:
"Analyze single-cell data, perform quality control (filter low-quality cells), normalization, dimensionality reduction (PCA+UMAP), clustering (leiden algorithm), find marker genes for each cluster"

❌ Vague description:
"Analyze this data"

2. Provide Complete File Paths

✅ Use absolute paths:
"C:\Users\username\data\sample.h5ad"

❌ Relative paths may fail:
"./data/sample.h5ad"

3. Specify Output Requirements

✅ Clear output:
"Generate UMAP plot, heatmap, save results to CSV file"

❌ Unclear:
"Do some visualization"

4. Step-by-step Analysis

For complex analyses, break into multiple conversations:

1. First: Data loading and quality control
2. Second: Normalization and dimensionality reduction
3. Third: Clustering and visualization
4. Fourth: Differential analysis

🎉 Start Your Bioinformatics Journey

You're ready now! Open Claude Desktop, tell it what data you want to analyze, and let AI handle the complex bioinformatics analysis for you!

📞 Get Help

• GitHub Issues: Report problems or suggest improvements
• Documentation: View detailed usage documentation
• Examples: Reference example analysis cases

Remember: Describe your analysis needs in natural language, Claude will handle all the technical details for you! 🚀