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
University of Gothenburg
Technical University of Munich
Émilie D. Tremblay
Canadian Food Inspection Agency (CFIA)
Kaisa Thorell
Karolinska University Hospital
Kärt Kanger
University of Tartu
Martin Eriksson
Chalmers, Mechanics and Maritime Sciences (M2)
Chalmers, Centre for Environment and Sustainability (GMV)
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
Gothenburg Global Biodiversity Centre
University of Gothenburg
Bioinformatics
1367-4803 (ISSN) 13674811 (eISSN)
Vol. 34 23 4027-4033Subject Categories
Bioinformatics (Computational Biology)
Bioinformatics and Systems Biology
Genetics
DOI
10.1093/bioinformatics/bty482
PubMed
29912385