WEVOTE: Weighted Voting Taxonomic Identification Method of Microbial Sequences.
BACKGROUND
Metagenome shotgun sequencing presents opportunities to identify organisms that may prevent or promote disease. The analysis of sample diversity is achieved by taxonomic identification of metagenomic reads followed by generating an abundance profile. Numerous tools have been developed based on different design principles. Tools achieving high precision can lack sensitivity in some applications. Conversely, tools with high sensitivity can suffer from low precision and require long computation time.


METHODS
In this paper, we present WEVOTE (WEighted VOting Taxonomic idEntification), a method that classifies metagenome shotgun sequencing DNA reads based on an ensemble of existing methods using k-mer-based, marker-based, and naive-similarity based approaches. Our evaluation on fourteen benchmarking datasets shows that WEVOTE improves the classification precision by reducing false positive annotations while preserving a high level of sensitivity.


CONCLUSIONS
WEVOTE is an efficient and automated tool that combines multiple individual taxonomic identification methods to produce more precise and sensitive microbial profiles. WEVOTE is developed primarily to identify reads generated by MetaGenome Shotgun sequencing. It is expandable and has the potential to incorporate additional tools to produce a more accurate taxonomic profile. WEVOTE was implemented using C++ and shell scripting and is available at www.github.com/aametwally/WEVOTE.
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States of America.|Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States of America.
Ahmed A
AA
Metwally
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States of America.
Yang
Y
Dai
Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States of America.
Patricia W
PW
Finn
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States of America.|Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States of America.
David L
DL
Perkins
Bioinformatics. 2013 Sep 15;29(18):2253-60
23828782
Nat Methods. 2012 Jun 10;9(8):811-4
22688413
Syst Biol. 2012 Jan;61(1):90-106
22139466
J Mol Evol. 2001 Jun;52(6):540-2
11443357
Bioinformatics. 2011 Jan 1;27(1):127-9
21062764
J Comput Biol. 2000 Feb-Apr;7(1-2):203-14
10890397
Bioinformatics. 2014 Dec 15;30(24):3548-55
25359891
Nat Commun. 2016 Apr 13;7:11257
27071849
Genome Biol. 2008 Oct 13;9(10):R151
18851752
BMC Genomics. 2015 Mar 25;16:236
25879410
Bioinformatics. 1998;14(9):755-63
9918945
Sci Rep. 2016 Jan 18;6:19233
26778510
Genome Res. 2007 Mar;17(3):377-86
17255551
J Mol Biol. 1990 Oct 5;215(3):403-10
2231712
Genome Biol. 2009;10(3):R25
19261174
Nat Methods. 2009 Sep;6(9):673-6
19648916
Nat Methods. 2015 Oct;12(10):902-3
26418763
Nucleic Acids Res. 2010 Jan;38(Database issue):D355-60
19880382
Nat Methods. 2013 Dec;10(12):1196-9
24141494
Genome Biol. 2014 Mar 03;15(3):R46
24580807
BMC Bioinformatics. 2010 Oct 30;11:538
21034504
BMC Genomics. 2011;12 Suppl 2:S4
21989143