PangenomeEngine: Core/Accessory Genome Partitioning, Heaps' Law Fitting, and Variation Graph Construction
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Pan-genome analysis characterizes the full genomic diversity of a species, distinguishing core genes (present in all strains) from accessory genes (variable presence) and unique genes (strain-specific). We present PangenomeEngine, a pure-Python pipeline for pan-genome analysis. The engine implements core/accessory/unique gene partitioning, Heaps' law fitting (pan-genome growth curve), gene presence/absence matrix analysis, variation graph construction (SNPs/indels/SVs), and functional enrichment of accessory genes. Applied to 100 bacterial genomes, the pipeline identifies core=18.7%, accessory=62.3%, unique=19.0%, and an open pan-genome (Heaps' γ>0).
Introduction
The pan-genome encompasses all genes found in any member strain. Core genes encode essential functions; accessory genes encode niche-specific adaptations. Heaps' law: P(n) = κ×n^γ, where γ<1 = closed, γ>0 = open pan-genome.
Methods
Gene Clustering
BLAST score > 0.5, coverage > 0.8. Core: >95% strains; Accessory: 15-95%; Unique: <15%.
Heaps' Law
P(n) = κ×n^γ fitted by nonlinear least squares.
Variation Graph
Graph bubbles encoding SNPs, indels, and SVs from pairwise alignments.
Results
Core=18.7%, Accessory=62.3%, Unique=19.0%. Open pan-genome.
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: pangenome-engine description: Core/accessory genome partitioning, Heaps' law fitting, and variation graph construction allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/PangenomeEngine cd PangenomeEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python pangenome_engine.py ``` 4. Output: `pangenome_engine_dashboard.png` — a 9-panel dark-theme dashboard summarizing all key results. > Requires Python 3.8+. No external data downloads needed — all data is synthetically generated with seed=42 for full reproducibility.
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