A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes.
A common biological pathway reconstruction approach -- as implemented by many automatic biological pathway services (such as the KAAS and RAST servers) and the functional annotation of metagenomic sequences -- starts with the identification of protein functions or families (e.g., KO families for the KEGG database and the FIG families for the SEED database) in the query sequences, followed by a direct mapping of the identified protein families onto pathways. Given a predicted patchwork of individual biochemical steps, some metric must be applied in deciding what pathways actually exist in the genome or metagenome represented by the sequences. Commonly, and straightforwardly, a complete biological pathway can be identified in a dataset if at least one of the steps associated with the pathway is found. We report, however, that this naïve mapping approach leads to an inflated estimate of biological pathways, and thus overestimates the functional diversity of the sample from which the DNA sequences are derived. We developed a parsimony approach, called MinPath (Minimal set of Pathways), for biological pathway reconstructions using protein family predictions, which yields a more conservative, yet more faithful, estimation of the biological pathways for a query dataset. MinPath identified far fewer pathways for the genomes collected in the KEGG database -- as compared to the naïve mapping approach -- eliminating some obviously spurious pathway annotations. Results from applying MinPath to several metagenomes indicate that the common methods used for metagenome annotation may significantly overestimate the biological pathways encoded by microbial communities.
Ye
Yuzhen
Y
School of Informatics, Indiana University, Bloomington, IN, USA. yye@indiana.edu
Doak
Thomas G
TG
D000465:Algorithms; D030541:Databases, Genetic; D004926:Escherichia coli / Q000235:genetics; D053263:Gene Regulatory Networks; D016680:Genome, Bacterial; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics
Nucleic Acids Res. 2005 Oct 07;33(17):5691-702
16214803
Bioinformation. 2006 Dec 29;1(8):335-8
17597916
Bioinformatics. 2007 Jul 1;23(13):i359-66
17646318
Curr Opin Chem Biol. 2003 Apr;7(2):238-51
12714058
J Bacteriol. 2008 Apr;190(8):2790-803
18192387
Trends Microbiol. 2005 Nov;13(11):550-8
16169729
Environ Microbiol. 2004 Jun;6(6):543-5
15142241
Annu Rev Biomed Eng. 2009;11:49-79
19400705
Nature. 2008 Apr 3;452(7187):629-32
18337718
Proc Natl Acad Sci U S A. 2002 Sep 3;99(18):11969-74
12163647
Nucleic Acids Res. 2000 Jan 1;28(1):27-30
10592173
Genomics. 2008 Nov;92(5):255-64
18703132
Trends Plant Sci. 2005 Nov;10(11):516-8
16202638
J Comput Biol. 2004;11(1):27-52
15072687
Cell. 2006 Dec 15;127(6):1265-81
17174899
BMC Bioinformatics. 2008 Sep 19;9:386
18803844
Science. 2008 Nov 14;322(5904):1108-9
19008447
Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1374-9
19164758
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W182-5
17526522
Nature. 2009 Jan 22;457(7228):480-4
19043404
Bioinformatics. 2005 Jun;21 Suppl 1:i478-86
15961494
Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W423-6
18477636
J Bioinform Comput Biol. 2003 Apr;1(1):95-117
15290783
BMC Genomics. 2008 Feb 08;9:75
18261238
Bioinformatics. 2004 Aug 4;20 Suppl 1:i186-93
15262798
Protein Sci. 2006 Jun;15(6):1527-9
16731984