XtractPAV: an automated pipeline for identifying presence-absence variations across multiple genomes

Presence-absence variations (PAVs) significantly influence phenotypic diversity across and within species by modulating functional modules associated with stress responsiveness, adaptation, and developmental processes. This modulation ultimately contributes to genetic diversity at both inter- and intra-species levels. However, existing tools available for detecting PAVs in assembled genomes possess limitations that hinder comprehensive analyses. These limitations include the absence of scalable workflows for multi-genome analysis, the imposition of stringent parameters regarding coverage, PAV length, and sequence identity, as well as the frequent necessity for manual integration. To address these challenges, we developed XtractPAV, an end-to-end pipeline that automates the extraction, annotation, and interactive visualization of PAVs across assembled genomes. XtractPAV was evaluated using assembled genomes from both eukaryotic and prokaryotic organisms, including Pyrus communis, Arabidopsis thaliana, Mus musculus, and Salmonella enterica, to assess its capability to detect presence-absence variations across diverse species. The performance of XtractPAV was benchmarked against other established pipelines, demonstrating an optimized workflow that allows comprehensive extraction and annotation of PAVs. To further validate PAVs, representative XtractPAV-identified PAVs in A. thaliana and P. communis were independently confirmed using WGS paired-end read mapping, demonstrating consistent query-specific insertion and deletion signatures at predicted loci in the reference genome. Notably, the pipeline successfully identified PAVs from the reference set and also revealed novel PAV regions overlapping with genes. Furthermore, the automated report generation feature of XtractPAV produces publication-ready summaries of PAV distributions alongside diverse interactive figures. The XtractPAV webpage is available at its project page https://sherazahmadd.github.io/XtractPAV/ and its GitHub repository https://github.com/SherazAhmadd/XtractPAV .