openbiomcp 0.1.0

BioOpenMCP: Model Context Protocol tools and CLI for bioinformatics workflows.

OpenBioMCP

OpenBioMCP is a Python package for running Model Context Protocol (MCP) tools, including FastQC integration and other bioinformatics utilities with comprehensive background execution and status checking capabilities.

Features

• Modular design - Organized by feature/domain for scalability
• Background execution - Run long-running bioinformatics tools without blocking
• Real-time status monitoring - Check job progress and retrieve results
• Job management - Start, stop, and clean up background jobs
• CLI entry point - Command-line interface for easy access
• MCP integration - Expose tools through Model Context Protocol
• Ready for PyPI distribution

Supported Tools

FastQC

• Quality control analysis for FASTQ files
• Background execution with status monitoring
• HTML report generation

Cutadapt

• Adapter trimming for sequencing data
• Background execution with real-time monitoring
• Flexible command-line argument support

Trim Galore

• Automated adapter and quality trimming
• Background execution with comprehensive monitoring
• Integration with FastQC and Cutadapt
• Quality and length filtering options

STAR Alignment

• RNA-seq alignment tool
• Genome index support
• Background execution with status monitoring
• Configurable thread count for performance optimization

Background Execution

All supported tools can be run in the background, allowing you to:

from openbiomcp.modules.fastqc.fastqc import fastqc_background, get_fastqc_status
from openbiomcp.modules.cutadapt.cutadapt import cutadapt_background
from openbiomcp.modules.trim_galore.trim_galore import trim_galore_background
from openbiomcp.modules.star_alignment.star_alignment import star_alignment_background

# Start FastQC in background
job = fastqc_background("sample.fastq", job_id="qc_001")

# Check status
status = get_fastqc_status("qc_001")
print(f"Status: {status['status']}")

# Start Cutadapt in background
trim_job = cutadapt_background(
    args=["-a", "AGATCGGAAGAGC"],
    input_file="sample.fastq",
    output_file="trimmed.fastq"
)

# Start Trim Galore in background
trim_galore_job = trim_galore_background(
    fastq_path="sample.fastq",
    extra_args="--quality 20 --length 50"
)

# Start STAR alignment in background
star_job = star_alignment_background(
    fastq_path="sample.fastq",
    genome_dir="/path/to/genome/index",
    output_dir="/path/to/alignment",
    threads=8
)

Documentation

• Background Running and Status Checking - Comprehensive guide to background execution
• FastQC Background Usage - FastQC-specific background functionality
• Feature Domain Structure - Project organization and architecture

Installation

pip install openbiomcp

Usage

from openbiomcp.modules.fastqc import fastqc_background, get_fastqc_status
from openbiomcp.modules.cutadapt import cutadapt_background
from openbiomcp.modules.trim_galore import trim_galore_background

# Run quality control
qc_job = fastqc_background("sample.fastq")

# Run adapter trimming
trim_job = cutadapt_background(
    args=["-a", "AGATCGGAAGAGC"],
    input_file="sample.fastq",
    output_file="trimmed.fastq"
)

# Run quality trimming
trim_galore_job = trim_galore_background(
    fastq_path="sample.fastq",
    extra_args="--quality 20 --length 50"
)

# Monitor jobs
status = get_fastqc_status(qc_job['job_id'])