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 ofa marker gene (barcode) to a specific lineage using a reference database that contains named sequences in a knowntaxonomy. This classification is important for assessing the complexity of biological systems. Taxonomic classificationfaces two inherent challenges: first, accuracy is critical as errors can propagate to downstream analysis results; andsecond, the classification time requirements can limit study size and study design, in particular when consideringthe constantly growing reference databases. To address these two challenges, we introduce raxtax, an efficient, noveltaxonomic classification tool that uses common k-mers between all pairs of query and reference sequences. We alsointroduce two novel uncertainty scores which take into account the fundamental biases of reference 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 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)