Data for raxtax: A k-mer-based non-Bayesian Taxonomic Classifier

This repository contains the input databases and summarized results for evaluating our preprint: raxtax: A k-mer-based non-Bayesian Taxonomic Classifier (BioRxiv) Abstract Motivation: Taxonomic classification in biodiversity studies is the process of assigning the anonymous sequences of amarker gene (barcode) or whole genomes (metagenomics) to a specific lineage using a reference database that containsnamed sequences in a known taxonomy. This classification is important for assessing the diversity of biological systems.Taxonomic classification faces two main challenges: first, accuracy is critical as errors can propagate to downstreamanalysis results; and second, the classification time requirements can limit study size and study design, in particularwhen considering the constantly growing reference databases. To address these two challenges, we introduce raxtax,an efficient, novel taxonomic classification tool for barcodes that uses common k-mers between all pairs of query andreference sequences. We also introduce two novel uncertainty scores which take into account the fundamental biases ofreference databases.Results: We validate raxtax on three widely used empirical reference databases and show that it is 2.7-100 times fasterthan competing state-of-the-art tools on the largest database while being equally accurate. In particular, raxtax exhibitsincreasing speedups with growing query and reference sequence numbers compared to existing tools (for 100,000 and1,000,000 query and reference sequences overall, it is 1.3 and 2.9 times faster, respectively), and therefore alleviates thetaxonomic classification scalability challenge.Availability and Implementation: raxtax is available at https://github.com/noahares/raxtax under a CC-NC-BY-SA license. The raxtax source code, scripts and summary metrics used in our analyses are available at https://github.com/noahares/raxtax_paper_scripts. Original Data Sources UNITE: https://doi.plutof.ut.ee/doi/10.15156/BIO/2959332 Greengenes: http://ftp.microbio.me/greengenes_release/gg_13_5/ BOLD: https://boldsystems.org/ (exact database version no longer available) Empirical Insect OTUs: https://www.ebi.ac.uk/ena/browser/view/PRJEB71324