MetaQuest 2.0.0a1

MetaQuest 2.0.0a1 is the first stabilized research-use alpha of the 2.0 runtime.

Highlights:

• complete short-read preprocessing, taxonomy, assembly, gene prediction, and functional annotation pipeline
• reproducible metadata, safe resume behavior, and stable identifiers
• descriptive JSON, text, offline HTML, tables, and publication-format plots
• validated wheel and source distribution published as metaquest-bio
• Python 3.10 through 3.12 and Linux/WSL support

Install the Python distribution with:

python -m pip install metaquest-bio

The installed command remains metaquest. External tools and databases are not bundled; use the documented Conda environment for the complete runtime.

This is alpha, research-use-only software and does not provide clinical or pathogen-risk conclusions.

v1.2.0

MetaQuest v1.2.0 - Major Feature Release

Release Date: October 2025
Status: Stable
License: TBD

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🎉 Overview

MetaQuest v1.2.0 represents a significant leap forward in metagenomics analysis capabilities. This release introduces enhanced functional annotation, advanced gene prediction controls, professional logging systems, and a comprehensive reporting engine - transforming MetaQuest into a complete solution for both clinical diagnostics and research applications.

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🚀 What's New in v1.2.0

1. Enhanced Functional Annotation System

Dual-Database Annotation

• COG Database Integration: Comprehensive functional categories and orthologous groups for detailed pathway analysis
• SwissProt Database Integration: High-quality, manually curated protein annotations for accurate functional characterization
• Combined Annotation Strategy: Dramatically increased coverage and annotation depth compared to single-database approaches
• Mobile Genetic Element Analysis: Specialized tracking of IS families, transposases, and other mobile elements

Key Benefits:

• Significantly higher annotation coverage (typically 60-80% vs 30-50% with single databases)
• More detailed functional category distributions
• Enhanced pathway and process enrichment insights
• Better detection of horizontally transferred genes

2. Advanced Gene Prediction Controls

Customizable Contig Filtering

• Default Behavior: Automatic filtering of contigs <1000bp before annotation for optimal performance
• Custom Thresholds: --min-contig-length flag for user-defined filtering criteria
• Complete Flexibility: --no-filter-contigs option to annotate all sequences regardless of length
• Performance Optimization: Balanced approach between annotation quality and computational efficiency

Enhanced tbl2asn Management

• Automatic Timeout Control: Default 300-second timeout prevents hung processes
• Auto-Kill Functionality: Stuck tbl2asn processes are automatically terminated and recovery initiated
• Configurable Timeouts: --tbl2asn-timeout allows customization for large datasets
• Override Option: --no-kill-tbl2asn for datasets requiring unlimited processing time

Threading Optimization

• Parallel Processing: --annotation-threads flag for optimal resource utilization
• Scalable Performance: Adjust thread count based on available system resources
• Improved Speed: Significantly faster annotation on multi-core systems

3. Professional Logging System

Standard Mode (Default)

• Clean, user-friendly progress tracking with visual indicators
• Formatted section headers with clear visual separators
• Progress spinners for long-running operations
• Color-coded status messages (✓ success, ✗ error, ⚠ warning)
• Time-formatted completion summaries for all operations

Debug Mode (--debug flag)

• Complete diagnostic output for troubleshooting
• Full command-line invocations for all external tools
• Complete stdout/stderr streams from programs
• Detailed error traces with stack information
• Performance metrics and timing data
• Essential for development and issue resolution

Structured Logging

• Consistent format across all pipeline operations
• Automatic logging to metaquest.log in output directory
• Timestamps and operation context for all events
• Hierarchical message organization

4. Comprehensive Reporting Engine

Enhanced Text Reports

Taxonomic Reports

• Clinical Summary: Immediate actionable insights for healthcare professionals
• Researcher View: Detailed taxonomic breakdown with statistical metrics
• Diversity Metrics: Shannon, Simpson, Chao1, and Observed Species indices
• Abundance Profiles: Hierarchical taxonomic composition from phylum to species

Functional Reports (NEW)

• COG Category Analysis: Detailed functional category distributions with percentages
• Mobile Element Tracking: IS family classification and transposase detection
• Annotation Quality Metrics: Coverage scores, identity percentages, functional diversity
• Pathway Enrichment: Identification of over-represented biological processes
• Protein Function Summary: Top annotated proteins with functional descriptions

Pathogen Risk Reports

• Three-Tier Risk Assessment: High/Medium/Low risk stratification
• Integrated Risk Scoring: Combined pathogenicity, AMR, and virulence scores
• Clinical Interpretation: Actionable recommendations for healthcare providers
• Evidence-Based Assessment: Citations to pathogen and resistance databases

Professional Formatting

• Emoji indicators for quick visual scanning (🦠 🔬 ⚠️ ✓)
• Clear section headers and structured organization
• Role-based content tailored to clinicians vs researchers
• Quality metrics prominently displayed

5. Improved Architecture & Maintainability

Code Refactoring

• Enhanced modular architecture in reporting/ module
• Separation of concerns between data processing and presentation
• Reusable base classes for consistent formatting
• Improved error handling and validation

Enhanced Visualization System

• Modular visualization components for easier maintenance
• Specialized plotters for different analysis types
• Modern color schemes and publication-ready aesthetics
• Responsive design for various output formats

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🔧 Key Improvements

Performance Enhancements

• Faster Annotation: Optimized COG and SwissProt database queries
• Memory Efficiency: Reduced memory footprint for large dataset processing
• Parallel Processing: Better utilization of multi-core systems
• Database Integration: Combined databases reduce redundant searches

Usability Improvements

• Clearer Progress Tracking: Real-time feedback on operation status
• Better Error Messages: Actionable guidance for common issues
• Flexible Configuration: Extensive command-line options for customization
• Comprehensive Documentation: Updated guides and examples

Robustness & Reliability

• Enhanced Error Handling: Graceful failure recovery mechanisms
• Input Validation: Comprehensive checks before processing
• Process Management: Automatic handling of stuck or failed external tools
• Quality Assurance: Built-in checks for annotation quality

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📊 Example Usage

Basic Analysis with Enhanced Features

# Standard FASTQ analysis with dual-database annotation
metaquest analyze fastq --single sample.fastq.gz -o results/

# View comprehensive reports
cat results/taxonomic_report.txt
cat results/functional_report.txt
cat results/pathogen_risk_report.txt

Advanced Annotation Controls

# Custom contig filtering (500bp threshold)
metaquest analyze fastq --single reads.fq --min-contig-length 500 -o results/

# Annotate all contigs without filtering
metaquest analyze fasta assembly.fasta --no-filter-contigs -o results/

# Extended timeout for large datasets
metaquest analyze fastq --paired R1.fq R2.fq --tbl2asn-timeout 600 -o results/

# Maximum parallelization
metaquest analyze fasta genome.fasta --annotation-threads 16 -o results/

Debug Mode for Troubleshooting

# Run with complete diagnostic output
metaquest --debug analyze fastq --single sample.fq -o debug_results/

# Debug output includes:
# - Full command invocations for all tools
# - Complete tool output streams
# - Detailed error traces
# - Performance timing information

Skip Annotation for Rapid Taxonomic Analysis

# Fast taxonomic-only analysis
metaquest analyze fastq --single sample.fastq.gz --skip-annotation -o fast_results/

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🛠 Installation & Updates

New Installation

# Clone repository
git clone https://github.com/your-org/metaquest.git
cd metaquest

# Create environment
conda env create -f environment.yml
conda activate metaquest

# Install MetaQuest
pip install -e .

# Download COG and SwissProt databases
./scripts/setup_databases.sh

# Verify installation
metaquest check

Updating from v1.1.0

# Update repository
cd metaquest
git pull origin main

# Update environment (if needed)
conda env update -f environment.yml

# Reinstall package
pip install -e .

# Download new databases
./scripts/setup_databases.sh --cog --swissprot

# Verify update
metaquest check

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📚 Updated Documentation

• Installation Guide - Updated with database setup instructions
• Usage Guide - Comprehensive examples for new features
• Annotation Guide - NEW: COG and SwissProt database information
• Interactive Help: metaquest --help for complete command reference

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🔬 System Requirements

Minimum Requirements (Unchanged)

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

Updated Database Requirements

• Kraken2: ~8GB disk space
• Pathogen DBs (CARD + VFDB): ~500MB disk space
• COG Database: ~1GB disk space (NEW)
• SwissProt Database: ~1GB disk space (NEW)
• Total Database Storage: ~11GB

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🎯 Use Cases

Clinical Applications

• Pathogen Identification: Enhanced functional characterization for better diagnosis
• AMR Profiling: Mobile genetic element tracking for resistance gene detection
• Outbreak Investigation: Comprehensive functional comparison across samples
• Quality Control: Professional logging for compliance and auditing

Research Applications

• Microbiome Studies: Detailed functional annotation for pathway analysis
• **Comparat...

v1.1.0

MetaQuest v1.1.0 - Official Release

Release Date: October 2025
Status: Stable
License: TBD

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🎉 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.

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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

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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.

v1.0.0

MetaQuest v1.0.0 - First Stable Release

A Comprehensive Metagenomics Analysis Pipeline

This is the official stable release of MetaQuest - an integrated bioinformatics pipeline that addresses the complex challenges of metagenomic data analysis.

🚀 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

📊 Statistical Testing & Machine Learning

• Alpha Diversity: Shannon, Simpson, Chao1, and Observed Species metrics with statistical testing
• Beta Diversity: Bray-Curtis dissimilarity with PCoA visualization
• Statistical Tests: PERMANOVA and ANOSIM for group comparisons
• 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
• 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

Basic Usage Examples

# Validate files
metaquest validate fastq --single sample.fastq.gz
metaquest validate fasta genome.fasta

# Run analysis
metaquest analyze fastq --single sample.fastq.gz -o results/
metaquest analyze fasta genome.fasta -o results/ -s 100

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

🔬 System Requirements

• Linux/macOS operating system
• Conda package manager
• Minimum 8GB RAM (16GB recommended)
• 50GB available disk space for databases

📚 Documentation

• [Installation Guide](installation.md) - Detailed setup instructions
• [Usage Guide](usage.md) - Comprehensive usage examples and command reference
• Interactive help system: metaquest --help

🎯 Target Applications

• Clinical Diagnostics: Pathogen detection and antimicrobial resistance screening
• Research Applications: Microbiome analysis and comparative genomics
• Public Health: Outbreak investigation and surveillance
• Environmental Studies: Microbial community characterization

🔮 Coming Soon (Future Releases)

• Virulence Factor Analysis (Q3 2025)
• Enhanced AMR Analysis (Q4 2025)
• Additional ML models and statistical methods
• Extended database support

🤝 Contributing

We welcome contributions! See our contributing guidelines for:

• Machine learning model enhancement
• Clinical validation studies
• Additional statistical methods
• Documentation improvements

📞 Support

• Bug Reports: Submit via GitHub issues
• Feature Requests: Use GitHub discussions
• Documentation: [installation.md](installation.md) and [usage.md](usage.md)

🏆 Acknowledgments

MetaQuest Development Team - Advancing metagenomics through integrated computational solutions

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Release Date: August 2025
Version: 1.0.0 (Stable)
License: TBD
Citation: Information will be provided upon publication

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.