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Bio-MCP FastQC Server

from bio-mcp

Provides quality control for biological sequence data using the FastQC and MultiQC tools.

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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

🔧 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

📊 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