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PMID: 17179938 Published · ppublish English Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Accurate phylogenetic classification of variable-length DNA fragments.

Nature methods ·Vol. 4 ·No. 1 ·2007-01-00 ·Pages 63-72

McHardy AC, Martín HG, Tsirigos A, Hugenholtz P, Rigoutsos I

Abstract

Metagenome studies have retrieved vast amounts of sequence data from a variety of environments leading to new discoveries and insights into the uncultured microbial world. Except for very simple communities, the encountered diversity has made fragment assembly and the subsequent analysis a challenging problem. A taxonomic characterization of metagenomic fragments is required for a deeper understanding of shotgun-sequenced microbial communities, but success has mostly been limited to sequences containing phylogenetic marker genes. Here we present PhyloPythia, a composition-based classifier that combines higher-level generic clades from a set of 340 completed genomes with sample-derived population models. Extensive analyses on synthetic and real metagenome data sets showed that PhyloPythia allows the accurate classification of most sequence fragments across all considered taxonomic ranks, even for unknown organisms. The method requires no more than 100 kb of training sequence for the creation of accurate models of sample-specific populations and can assign fragments >or=1 kb with high specificity.

MeSH Terms
Animals Archaea/genetics Arthropods/genetics Ascomycota/genetics Bacteria/genetics Chordata/genetics DNA/chemistry,classification,genetics Eukaryotic Cells Genome Genomics/methods Industrial Waste Phylogeny Sargassum/microbiology Software Validation
Chemicals
Industrial Waste DNA
Authors & Affiliations
5 authors, click to expand affiliations / ORCID
McHardy Alice Carolyn
Bioinformatics and Pattern Discovery Group, IBM Thomas J Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, New York 10598, USA.
Martín Héctor García
Tsirigos Aristotelis
Hugenholtz Philip
Rigoutsos Isidore
Article Info
Journal
Nature methods
Abbr.
Nat Methods
ISSN
1548-7091
Published
2007-01-00
Epub
2006-00-10
Pages
63-72
Language
English
Region
United States
NLM ID
101215604
Subset
IM
Analysis Services
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