CNOGpro

CNOGpro detects and quantifies copy number variations (CNVs) in prokaryotic whole-genome sequencing (WGS) data using read coverage analysis and statistical modeling.

Key Features:

• Sliding Window Read Coverage Analysis: Aligns WGS reads to a reference genome and counts reads within sliding windows to estimate genomic coverage.
• GC Content Bias Normalization: Normalizes read counts to correct biases caused by variations in genomic GC content.
• Hidden Markov Model Analysis: Applies a Hidden Markov Model (HMM) to coverage profiles to infer genomic states associated with copy number variations.
• Bootstrapping-Based Detection: Uses bootstrapping with repeated sampling on individual genes to detect CNVs without explicit breakpoint determination.

Scientific Applications:

• Prokaryotic Genomics: Identifies and quantifies copy number variations in bacterial and archaeal genomes from WGS data.
• Genomic Variation Studies: Supports analysis of genetic diversity, genome evolution, and adaptation in prokaryotic organisms.
• Sequencing Data Analysis: Enables CNV detection in both real and simulated prokaryotic whole-genome sequencing datasets.

Methodology:

CNOGpro aligns sequencing reads to a reference genome, calculates read coverage in sliding windows, normalizes coverage for GC content bias, and applies Hidden Markov Model analysis and gene-level bootstrapping to detect copy number variations.