Metaxa2 Database Builder: enabling taxonomic identification from metagenomic or metabarcoding data using any genetic marker
Journal article, 2018

Motivation: Correct taxonomic identification of DNA sequences is central to studies of biodiversity using both shotgun metagenomic and metabarcoding approaches. However, no genetic marker gives sufficient performance across all the biological kingdoms, hampering studies of taxonomic diversity in many groups of organisms. This has led to the adoption of a range of genetic markers for DNA metabarcoding. While many taxonomic classification software tools can be re-trained on these genetic markers, they are often designed with assumptions that impair their utility on genes other than the SSU and LSU rRNA. Here, we present an update to Metaxa2 that enables the use of any genetic marker for taxonomic classification of metagenome and amplicon sequence data. Results: We evaluated the Metaxa2 Database Builder on 11 commonly used barcoding regions and found that while there are wide differences in performance between different genetic markers, our software performs satisfactorily provided that the input taxonomy and sequence data are of high quality. Availability and implementation: Freely available on the web as part of the Metaxa2 package at http://microbiology.se/software/metaxa2/. Supplementary information: Supplementary data are available at Bioinformatics online.

Author

Johan Bengtsson-Palme

University of Wisconsin Madison

University of Gothenburg

Rodney T. Richardson

Ohio Agricultural Research and Development Center

Marco Meola

Forschungsanstalt Agroscope Changins-Wadenswil

Christian Wurzbacher

Technical University of Munich

University of Gothenburg

Émilie D. Tremblay

Canadian Food Inspection Agency (CFIA)

Kaisa Thorell

Karolinska University Hospital

Kärt Kanger

University of Tartu

Martin Eriksson

GMV

Chalmers, Mechanics and Maritime Sciences

Guillaume J. Bilodeau

Canadian Food Inspection Agency (CFIA)

Reed M. Johnson

Ohio Agricultural Research and Development Center

Martin Hartmann

Swiss Federal Institute of Technology in Zürich (ETH)

Eidgenossische Forschungsanstalt fur Wald, Schnee Und Landschaft Eth-Bereichs

R. Henrik Nilsson

University of Gothenburg

Gothenburg Global Biodiversity Centre

Bioinformatics

1367-4803 (ISSN) 1367-4811 (eISSN)

Vol. 34 23 4027-4033

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

Genetics

DOI

10.1093/bioinformatics/bty482

PubMed

29912385

More information

Latest update

3/11/2019