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 a
marker gene (barcode) or whole genomes (metagenomics) to a specific lineage using a reference database that contains
named 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 downstream
analysis results; and second, the classification time requirements can limit study size and study design, in particular
when 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 and
reference sequences. We also introduce 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 faster
than competing state-of-the-art tools on the largest database while being equally accurate. In particular, raxtax exhibits
increasing speedups with growing query and reference sequence numbers compared to existing tools (for 100,000 and
1,000,000 query and reference sequences overall, it is 1.3 and 2.9 times faster, respectively), and therefore alleviates the
taxonomic 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