Gregor, Ivan, Dröge, Johannes, Schirmer, Melanie, Quince, Christopher and McHardy, Alice C. (2016) PhyloPythiaS+ : a self-training method for the rapid reconstruction of low-ranking taxonomic bins from metagenomes. PeerJ, 4 . e1603. doi:10.7717/peerj.1603

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Abstract

Background. Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. This is often achieved by a combination of sequence assembly and binning, where sequences are grouped into ‘bins’ representing taxa of the underlying microbial community. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for species bins recovery from deep-branching phyla is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in the model and identifies ‘training’ sequences based on marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area do not have.

Results. We have developed PhyloPythiaS+, a successor to our PhyloPythia(S) software. The new (+) component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated the simultaneous counting of 4–6-mers used for taxonomic binning 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion. PhyloPythiaS+ was compared to MEGAN, taxator-tk, Kraken and the generic PhyloPythiaS model. The results showed that PhyloPythiaS+ performs especially well for samples originating from novel environments in comparison to the other methods.
Availability. PhyloPythiaS+ in a virtual machine is available for installation under Windows, Unix systems or OS X on: https://github.com/algbioi/ppsp/wiki.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history Q Science > QR Microbiology
Divisions:	Faculty of Medicine > Warwick Medical School > Biomedical Sciences Faculty of Medicine > Warwick Medical School > Biomedical Sciences > Microbiology & Infection Faculty of Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH):	Metagenomics -- Computer programs, Microbiology -- Classification -- Research
Journal or Publication Title:	PeerJ
Publisher:	PeerJ, Ltd.
ISSN:	2167-8359
Official Date:	2 August 2016
Dates:	Date Event 2 August 2016 Published 24 December 2015 Accepted 14 October 2015 Submitted
Date of first compliant deposit:	17 January 2017
Volume:	4
Article Number:	e1603
DOI:	10.7717/peerj.1603
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Max-Planck-Gesellschaft zur Förderung der Wissenschaften [Max Planck Society for the Advancement of Science], Heinrich-Heine-Universität Düsseldorf, Helmholtz-Zentrum für Infektionsforschung, Unilever (Firm), Engineering and Physical Sciences Research Council (EPSRC)
Grant number:	EP/H003851/1 (EPSRC)

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