How do I use Bio-MCP FastQC Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Bio-MCP FastQC Server Analyze all FASTQ files in raw_data/ and generate a MultiQC report" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Bio-MCP FastQC Server 🔬

Quality Control Analysis via Model Context Protocol

An MCP server that enables AI assistants to run FastQC and MultiQC quality control analysis on sequencing data. Part of the Bio-MCP ecosystem.

🎯 Purpose

FastQC is essential for quality assessment of high-throughput sequencing data. This MCP server allows AI assistants to:

Analyze single files - Get detailed QC reports for individual FASTQ/FASTA files
Batch process - Run QC on multiple files simultaneously
Generate summary reports - Create MultiQC reports combining multiple analyses
Handle large datasets - Queue system support for computationally intensive jobs

🚀 Quick Start

Prerequisites

Install FastQC and MultiQC:

# Via conda (recommended) conda install -c bioconda fastqc multiqc # Via package managers # Ubuntu/Debian sudo apt-get install fastqc pip install multiqc # macOS brew install fastqc pip install multiqc

Installation

# Clone and install git clone https://github.com/bio-mcp/bio-mcp-fastqc.git cd bio-mcp-fastqc pip install -e . # Or install directly pip install git+https://github.com/bio-mcp/bio-mcp-fastqc.git

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{ "mcpServers": { "bio-fastqc": { "command": "python", "args": ["-m", "src.server"], "cwd": "/path/to/bio-mcp-fastqc" } } }

🔧 Available Tools

Core Analysis Tools

`fastqc_single`

Run FastQC on a single FASTQ/FASTA file.

Parameters:

input_file (required): Path to FASTQ or FASTA file
threads (optional): Number of threads (default: 1)
contaminants (optional): Path to custom contaminants file
adapters (optional): Path to custom adapters file
limits (optional): Path to custom limits file

Example:

User: "Run quality control on my_sample.fastq.gz" AI: [calls fastqc_single] → Returns detailed QC report with pass/warn/fail status for each module

`fastqc_batch`

Run FastQC on multiple files in a directory.

Parameters:

input_dir (required): Directory containing FASTQ/FASTA files
file_pattern (optional): File pattern to match (default: ".fastq")
threads (optional): Number of threads (default: 4)

Example:

User: "Analyze all fastq files in the data/ directory" AI: [calls fastqc_batch] → Processes all files and returns summary statistics

`multiqc_report`

Generate MultiQC report from FastQC results.

Parameters:

input_dir (required): Directory containing FastQC and other analysis results
title (optional): Custom title for the report
comment (optional): Comment to add to the report
template (optional): Report template (default, simple, sections, gathered)

Example:

User: "Create a summary report from all the QC results" AI: [calls multiqc_report] → Generates interactive HTML report combining all analyses

Queue System Tools (when queue enabled)

For large datasets or batch processing:

fastqc_single_async - Queue single file analysis
fastqc_batch_async - Queue batch analysis
multiqc_report_async - Queue report generation
get_job_status - Check job progress
get_job_result - Retrieve completed results
cancel_job - Cancel running jobs

💡 Usage Examples

Basic Quality Assessment

User: "What's the quality of my sequencing data in sample1.fastq.gz?" AI Response: ✅ FastQC Analysis Complete for sample1 Module Status Summary: ✅ Basic Statistics: PASS ✅ Per base sequence quality: PASS ⚠️ Per sequence quality scores: WARN ✅ Per base sequence content: PASS ✅ Per sequence GC content: PASS ✅ Per base N content: PASS ✅ Sequence Length Distribution: PASS ❌ Sequence Duplication Levels: FAIL ✅ Overrepresented sequences: PASS ✅ Adapter Content: PASS Basic Statistics: • Total Sequences: 2,450,000 • Sequences flagged as poor quality: 0 • Sequence length: 150 • %GC: 47

Batch Analysis

User: "Analyze all FASTQ files in my project/raw_data/ folder" AI Response: FastQC Batch Analysis Complete ======================================== Processed 12 files: ✅ sample01_R1.fastq.gz: 8P/2W/0F ✅ sample01_R2.fastq.gz: 8P/2W/0F ⚠️ sample02_R1.fastq.gz: 7P/2W/1F ⚠️ sample02_R2.fastq.gz: 7P/2W/1F ... Overall Summary: • Total PASS: 94 • Total WARN: 24 • Total FAIL: 2 Tip: Run multiqc_report on this directory to generate a combined report!

Complete Workflow

User: "I have a directory of paired-end FASTQ files. Can you run quality control and create a summary report?" AI: I'll run a complete QC workflow on your paired-end data: 1. First, let me analyze all FASTQ files in batch: [runs fastqc_batch on directory] 2. Now I'll generate a MultiQC summary report: [runs multiqc_report on results] ✅ Complete QC workflow finished! Summary: - 24 FASTQ files processed (12 samples, paired-end) - Average quality score: 32.5 - 2 samples have adapter contamination warnings - 1 sample shows high duplication levels - Interactive HTML report generated: multiqc_report.html The MultiQC report provides detailed visualizations of: - Quality score distributions across all samples - GC content comparison - Sequence length distributions - Adapter content analysis - Sample correlation analysis

🐳 Docker Usage

Build and Run

# Build the image docker build -t bio-mcp-fastqc . # Run with data mounting docker run -v /path/to/data:/data bio-mcp-fastqc

Docker Compose (with Queue System)

services: fastqc-server: build: . volumes: - ./data:/data environment: - BIO_MCP_QUEUE_URL=http://queue-api:8000 depends_on: - queue-api

⚙️ Configuration

Environment Variables

BIO_MCP_FASTQC_PATH - Path to FastQC executable (default: "fastqc")
BIO_MCP_MULTIQC_PATH - Path to MultiQC executable (default: "multiqc")
BIO_MCP_MAX_FILE_SIZE - Maximum file size in bytes (default: 10GB)
BIO_MCP_TIMEOUT - Command timeout in seconds (default: 1800)
BIO_MCP_TEMP_DIR - Temporary directory for processing

Queue System Integration

To enable async processing for large datasets:

from src.server_with_queue import FastQCServerWithQueue server = FastQCServerWithQueue(queue_url="http://localhost:8000")

📊 Output Files

FastQC generates several output files:

HTML Report (*_fastqc.html) - Interactive quality report
Data File (fastqc_data.txt) - Raw metrics and statistics
Summary File (summary.txt) - Pass/warn/fail status for each module
Plots - Various quality plots and charts

MultiQC combines these into:

MultiQC Report (multiqc_report.html) - Combined interactive report
Data Directory (multiqc_data/) - Processed data and statistics
General Stats (multiqc_general_stats.txt) - Summary table

🔍 Quality Metrics Explained

FastQC analyzes multiple quality aspects:

Key Modules

Per base sequence quality - Quality scores across read positions
Per sequence quality scores - Distribution of mean quality scores
Per base sequence content - A/T/G/C content across positions
Per sequence GC content - GC% distribution vs expected
Sequence duplication levels - PCR duplication assessment
Adapter content - Contaminating adapter sequences

Status Interpretation

✅ PASS - Analysis indicates no problems
⚠️ WARN - Slightly unusual, may not be problematic
❌ FAIL - Likely problematic, requires attention

🧬 Integration with Bio-MCP Ecosystem

FastQC works seamlessly with other Bio-MCP tools:

User: "Run the complete preprocessing pipeline on my samples" AI Workflow: 1. fastqc_batch → Initial quality assessment 2. trimmomatic → Trim low-quality bases and adapters 3. fastqc_batch → Post-trimming QC 4. multiqc_report → Combined before/after report

🤝 Contributing

We welcome contributions! See the Bio-MCP contributing guide.

Development Setup

git clone https://github.com/bio-mcp/bio-mcp-fastqc.git cd bio-mcp-fastqc pip install -e ".[dev]" pytest

📄 License

MIT License - see LICENSE file.

🙏 Acknowledgments

FastQC by Simon Andrews at Babraham Bioinformatics
MultiQC by Phil Ewels and the MultiQC community
Bio-MCP project and contributors

Part of the Bio-MCP ecosystem - Making bioinformatics accessible to AI assistants.

For more tools: Bio-MCP Organization

Bio-MCP FastQC Server

Bio-MCP FastQC Server 🔬

🎯 Purpose

🚀 Quick Start

Prerequisites

Installation

Claude Desktop Configuration

🔧 Available Tools

Core Analysis Tools

`fastqc_single`

`fastqc_batch`

`multiqc_report`

Queue System Tools (when queue enabled)

💡 Usage Examples

Basic Quality Assessment

Batch Analysis

Complete Workflow

🐳 Docker Usage

Build and Run

Docker Compose (with Queue System)

⚙️ Configuration

Environment Variables

Queue System Integration

📊 Output Files

🔍 Quality Metrics Explained

Key Modules

Status Interpretation

🧬 Integration with Bio-MCP Ecosystem

🤝 Contributing

Development Setup

📄 License

🙏 Acknowledgments

Resources

New MCP Servers

Latest Blog Posts

MCP directory API