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@dpatel511 dpatel511 released this 09 Oct 11:59
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v1.1.0
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MetaQuest v1.1.0 - Official Release

Release Date: October 2025
Status: Stable
License: TBD

🎉 Overview

This is the official stable release of MetaQuest - a comprehensive metagenomics analysis pipeline that addresses the complex challenges of metagenomic data analysis. MetaQuest integrates taxonomic classification, pathogen detection, machine learning, and statistical analysis into a unified, production-ready platform.

🚀 Key Features

Core Analysis Capabilities

  • Advanced File Validation: Comprehensive FASTQ/FASTA quality control with contamination detection and N50 metrics
  • Taxonomic Classification: Species-level taxonomic profiling with diversity metrics for both FASTQ and FASTA inputs
  • Pathogen Detection: Clinical-grade pathogen screening with risk assessment and comprehensive recommendations
  • Machine Learning Integration: Pre-trained ML models for pathogen prediction with feature extraction and model artifacts
  • Statistical Analysis: Alpha/beta diversity analysis with PERMANOVA, ANOSIM, and differential abundance testing

Analysis Workflows

  • FASTQ Analysis: Rapid Kraken2/Bracken classification optimized for clinical applications
  • FASTA Analysis: High-accuracy BLAST classification with ML enhancement for research applications
  • Comparative Analysis: Multi-sample statistical comparison with publication-ready visualizations

🔧 Key Improvements in v1.1.0

This release focuses on improving the internal architecture and robustness of the pipeline for better performance and future extensibility.

Architecture Enhancements

  • Modular Reporting Engine: Complete refactoring of the reporting module for improved maintainability and easier addition of new visualization types and report formats
  • Enhanced FASTQ Processing: Switched from seqkit split to reformat.sh from BBMap suite for de-interleaving paired-end FASTQ files, providing more robust, memory-efficient, and faster handling of large sequencing files

Performance Improvements

  • Optimized memory usage during file processing
  • Faster database integration with combined SwissProt+COG database
  • Improved error handling and validation across all modules

📊 Statistical Testing & Machine Learning

Diversity Metrics

  • Alpha Diversity: Shannon, Simpson, Chao1, and Observed Species metrics with statistical testing
  • Beta Diversity: Bray-Curtis dissimilarity with PCoA visualization

Statistical Methods

  • Group Comparisons: PERMANOVA and ANOSIM tests
  • Differential Abundance: Mann-Whitney U tests with FDR and Bonferroni correction
  • ML Biomarker Discovery: Random Forest classification with cross-validation and feature importance

📈 Reporting & Visualization

  • Interactive HTML dashboards with dynamic visualizations
  • Alpha diversity box plots with statistical significance indicators
  • Beta diversity PCoA plots with group clustering
  • Differential abundance volcano plots
  • Interactive taxonomic heatmaps and abundance bar plots
  • Comprehensive statistical comparison summaries

🛠 Installation & Usage

Quick Installation

git clone https://github.com/your-org/metaquest.git
cd metaquest
conda env create -f environment.yml
conda activate metaquest
pip install -e .
metaquest check

Database Setup

# Download all databases (recommended for first-time setup)
./scripts/setup_databases.sh --all

# Or download specific databases
./scripts/setup_databases.sh --kraken
./scripts/setup_databases.sh --pathogen
./scripts/setup_databases.sh --swissprot

Basic Usage Examples

File Validation

# Validate FASTQ files
metaquest validate fastq --single sample.fastq.gz
metaquest validate fastq --paired sample_R1.fastq.gz sample_R2.fastq.gz

# Validate FASTA files
metaquest validate fasta genome.fasta

Run Analysis

# FASTQ analysis (clinical workflow)
metaquest analyze fastq --single sample.fastq.gz -o results/

# FASTA analysis (research workflow)
metaquest analyze fasta genome.fasta -o results/ -s 100

# Paired-end FASTQ analysis
metaquest analyze fastq --paired sample_R1.fastq.gz sample_R2.fastq.gz -o results/

Comparative Analysis

# Compare multiple samples
metaquest compare -i sample1_results/ sample2_results/ sample3_results/ \
    -m metadata.tsv -o comparison/

🔬 System Requirements

Minimum Requirements

  • Operating System: Linux or macOS
  • RAM: 8GB (16GB recommended for large datasets)
  • Disk Space: 50GB available for databases and analysis results
  • Software: Conda package manager

Database Requirements

  • Kraken2: ~8GB disk space
  • Pathogen DBs (CARD + VFDB): ~500MB disk space
  • SwissProt+COG: ~2GB disk space

📚 Documentation

  • [Installation Guide](installation.md) - Detailed setup instructions and troubleshooting
  • [Usage Guide](usage.md) - Comprehensive usage examples and command reference
  • Interactive Help: Run metaquest --help for command-line assistance

🎯 Target Applications

Clinical & Public Health

  • Pathogen detection and identification
  • Antimicrobial resistance (AMR) screening
  • Outbreak investigation and surveillance
  • Clinical diagnostics

Research & Environmental

  • Microbiome characterization and analysis
  • Comparative genomics studies
  • Environmental microbial community profiling
  • Biodiversity assessments

🔮 Roadmap (Future Releases)

Planned Features

  • Virulence Factor Analysis (Q3 2025) - Comprehensive virulence gene detection and annotation
  • Enhanced AMR Analysis (Q4 2025) - Extended resistance mechanism characterization
  • Additional ML Models - Expanded pathogen prediction and biomarker discovery
  • Statistical Methods - Additional multivariate analysis options
  • Extended Database Support - Integration with additional reference databases

🐛 Known Issues

No critical issues identified in this stable release. For bug reports or feature requests, please use GitHub issues.

🤝 Contributing

We welcome contributions from the community! Areas of interest include:

  • Machine learning model enhancement and validation
  • Clinical validation studies and benchmarking
  • Additional statistical methods and tests
  • Documentation improvements and tutorials
  • Database integration and updates

Please see our contributing guidelines for more information.

📞 Support & Contact

  • Bug Reports: Submit via GitHub issues
  • Feature Requests: Use GitHub discussions
  • Documentation: See [installation.md](installation.md) and [usage.md](usage.md)
  • General Questions: Open a GitHub discussion

🏆 Acknowledgments

MetaQuest Development Team - Advancing metagenomics through integrated computational solutions

Special thanks to all contributors, testers, and early adopters who provided valuable feedback during development.

📄 Citation

Citation information will be provided upon publication. If you use MetaQuest in your research, please check back for citation details.

Version History

v1.1.0 (October 2025) - Current Release

  • Modular reporting engine refactoring
  • Enhanced FASTQ processing with BBMap integration
  • Combined SwissProt+COG database for improved functional annotation
  • Performance optimizations and bug fixes
  • Production-ready stable release

v1.0.0 (Initial Release)

  • Core pipeline implementation
  • Basic taxonomic classification and pathogen detection
  • Initial ML integration

Note: This release represents a major milestone with all core functionality complete and thoroughly tested. MetaQuest is ready for production use in both clinical and research environments.

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