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Metagenomics Analysis Toolkit

A comprehensive toolkit for metagenomics data analysis including taxonomic profiling, functional annotation, and diversity analysis.

2 min read
#metagenomics #python #taxonomy #functional-annotation #diversity #microbiome

Metagenomics Analysis Toolkit

A Python-based toolkit for comprehensive metagenomics data analysis, providing streamlined workflows for taxonomic profiling, functional annotation, and microbial diversity analysis.

Overview

The Metagenomics Analysis Toolkit simplifies complex metagenomics workflows by providing a unified interface for common analysis tasks. It integrates popular tools like MetaPhlAn, HUMAnN, and QIIME2 into easy-to-use Python functions.

Key Features

  • Taxonomic Profiling: Species-level identification with confidence scores
  • Functional Annotation: Pathway and gene family analysis
  • Diversity Analysis: Alpha and beta diversity with statistical testing
  • Visualization: Publication-ready plots and interactive dashboards
  • Data Management: Standardized data structures and file formats
  • Reproducibility: Integrated provenance tracking

Core Modules

1. Taxonomic Profiling

from metagenomics import taxonomy

# Run MetaPhlAn analysis
taxa_results = taxonomy.run_metaphlan(
    fastq_files=['sample1_R1.fastq', 'sample1_R2.fastq'],
    output_dir='results/taxonomy'
)

# Generate Krona plot
taxonomy.create_krona_plot(taxa_results, 'krona.html')

2. Functional Analysis

from metagenomics import functional

# Run HUMAnN analysis
func_results = functional.run_humann(
    input_dir='results/taxonomy',
    output_dir='results/functional'
)

# Pathway enrichment analysis
enrichment = functional.pathway_enrichment(
    func_results,
    metadata_file='metadata.tsv'
)

3. Diversity Analysis

from metagenomics import diversity

# Calculate alpha diversity
alpha_div = diversity.alpha_diversity(
    taxa_table='otu_table.tsv',
    method='shannon'
)

# Beta diversity and PCoA
beta_div, pcoa = diversity.beta_diversity(
    taxa_table='otu_table.tsv',
    method='bray-curtis'
)

Installation

# Clone the repository
git clone https://github.com/tamoghnadas12/metagenomics-toolkit
cd metagenomics-toolkit

# Create conda environment
conda env create -f environment.yml
conda activate metagenomics-toolkit

# Install package
pip install -e .

Usage Examples

Basic Workflow

import metagenomics as mg

# Initialize analysis
analysis = mg.MetagenomicsAnalysis(
    input_dir='data/fastq',
    output_dir='results'
)

# Run complete pipeline
analysis.run_taxonomic_profiling()
analysis.run_functional_annotation()
analysis.run_diversity_analysis()

# Generate report
analysis.generate_report()

Custom Analysis

# Load pre-computed results
taxa_df = mg.load_taxa_table('results/taxa.tsv')
func_df = mg.load_functional_table('results/functional.tsv')

# Custom visualization
from metagenomics.visualization import plot_taxa_barplot
plot_taxa_barplot(
    taxa_df,
    top_n=20,
    group_by='treatment',
    output_file='taxa_barplot.png'
)

Technologies Integrated

  • Taxonomic Profiling: MetaPhlAn4, Kraken2, Centrifuge
  • Functional Analysis: HUMAnN3, KEGG, MetaCyc
  • Diversity Analysis: QIIME2, scikit-bio, vegan
  • Visualization: Matplotlib, Seaborn, Plotly, Krona
  • Data Management: Pandas, NumPy, HDF5

Documentation

Contributing

We welcome contributions! Please read our contributing guidelines and code of conduct.

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.

Continue Learning

One Small Win

Try this quick command to get started:

git clone https://github.com/tamoghnadas12/metagenomics-toolkit

Copy and paste this into your terminal to get started immediately.

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Start Your Own Project

Use our battle-tested template to jumpstart your reproducible research workflows. Pre-configured environments, standardized structure, and example workflows included.

Use This Template 
git clone https://github.com/Tamoghna12/bench2bash-starter
cd bench2bash-starter
conda env create -f env.yml
make run