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Related Experiment Video

Updated: Sep 16, 2025

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

APAV: An advanced pangenome analysis and visualization toolkit.

Xiaorui Dong¹, Du Jiao¹, Hongzhang Xue¹

¹Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Plos Computational Biology

|July 7, 2025

Summary

APAV is a new toolkit for pangenome analysis, enhancing the detection of gene presence/absence variations (PAVs) at the element level. This approach uncovers more biologically relevant genetic variations for improved phenotype-genotype association studies.

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Area of Science:

Genomics
Bioinformatics

Background:

Traditional pangenome analysis, focusing on gene presence/absence variations (PAVs), has limitations in detecting small mutations and intergenic variations.
Manual inspection of PAVs is time-consuming and hinders efficient result interpretation.

Purpose of the Study:

To introduce APAV, an advanced toolkit for comprehensive pangenome analysis and visualization.
To enable gene element-level PAV analysis and analysis of arbitrary genomic regions.
To provide interactive visualization and reporting for PAV profiles.

Main Methods:

APAV integrates gene element-level PAV analysis and analysis for user-defined genomic regions.
It generates interactive HTML reports for visualizing PAV profiles, including read depth and coverage.
The toolkit supports subsequent analyses such as statistics, clustering, genome size estimation, and phenotype association.

Main Results:

Element-level PAV analysis provides more accurate variation data and identifies more variations than gene-level analysis.
In rice genomes, over 20,000 genes and 50,000 genetic elements were identified.
Tumor genome analysis using APAV revealed approximately three times more phenotype-related genes compared to gene-level analysis.

Conclusions:

APAV enhances pangenome analysis by enabling element-level PAV detection and interactive visualization.
Shifting the PAV unit from genes to smaller elements yields greater biological insights and improves phenotype-genotype association studies.