MetaGenomics: Shotgun Metagenomics & 16S rRNA Analysis Engine

Pure NumPy/SciPy/scikit-learn metagenomics analysis — no QIIME2, no mothur, no HUMAnN3.

Quick Start

pip install numpy scipy pandas scikit-learn plotly matplotlib requests --break-system-packages -q

python metagenomics.py

Open metagenomics_output/metagenomics.html for the interactive dashboard.

Demo Conditions

• condition="IBD" — inflammatory bowel disease vs. healthy
• condition="CRC" — colorectal cancer vs. healthy
• condition="obesity" — obesity vs. lean
• condition="T2D" — type 2 diabetes vs. healthy

Modules

Module	Description
Taxonomic Profiling	Rarefaction, relative abundance, barplots
Alpha Diversity	Shannon, Simpson, Chao1, Faith's PD, rarefaction curves
Beta Diversity	Bray-Curtis, Aitchison, PCoA, PERMANOVA, ANOSIM
Differential Abundance	LEfSe, ALDEx2, DESeq2-inspired, ANCOM-BC
Functional Profiling	COG, KEGG pathway, ARG detection
Co-occurrence Network	SparCC-inspired correlation network

Citation

If useful, cite: https://biotender.online/MetaGenomics/