TaxonReportViewer: Parsing and Visualizing Taxonomic Hierarchies in Metagenomic Datasets

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TaxonReportViewer: Parsing and Visualizing Taxonomic Hierarchies in Metagenomic Datasets

Authors

Razzolini, E.; de Souza, C. R.

Abstract

Shotgun metagenomics enables comprehensive analysis of microbial communities by classifying sequencing reads directly from environmental samples, yet interpreting the resulting taxonomic classification reports, especially those generated by tools like Kraken2, remains a challenge due to their hierarchical, text-based format. Existing visualization tools such as Krona and Pavian either rely on web infrastructure or provide limited interactivity and flexibility for comparative analysis. To address this gap, we developed TaxonReportViewer (TRV), an open-source, cross-platform software with a graphical user interface that facilitates the exploration, comparison, and visualization of Kraken2 classification outputs. Implemented in Python 3, TRV includes a robust parser capable of accurately reconstructing taxonomic hierarchies even from inconsistently formatted reports. The tool offers dynamic search and filtering of taxa, exportable abundance matrices, one-click generation of comparative bar charts and heatmaps, and direct links to the NCBI Taxonomy database. TRV supports analysis of multiple samples in parallel, allowing users to identify taxonomic patterns across conditions without requiring programming skills. Its visualization features enable immediate recognition of taxon-specific abundance shifts and clustering of samples by taxonomic profile. Tested on a range of Kraken2 reports, TRV demonstrated consistent performance, rapid search capabilities, and accurate representation of lineage structures. It operates efficiently on standard consumer hardware and requires no installation beyond Python, ensuring accessibility for researchers in diverse computational environments. TaxonReportViewer fills a critical niche between raw taxonomic classification and biological interpretation, empowering researchers to derive meaningful insights from complex metagenomic datasets with ease.

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