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BMC Bioinformatics. 2009 Oct 27;10:355. doi: 10.1186/1471-2105-10-355.

Stratification of co-evolving genomic groups using ranked phylogenetic profiles.

Author information

1
The Blavatnik School of Computer Sciences and School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel. shiri.freilich@gmail.com

Abstract

BACKGROUND:

Previous methods of detecting the taxonomic origins of arbitrary sequence collections, with a significant impact to genome analysis and in particular metagenomics, have primarily focused on compositional features of genomes. The evolutionary patterns of phylogenetic distribution of genes or proteins, represented by phylogenetic profiles, provide an alternative approach for the detection of taxonomic origins, but typically suffer from low accuracy. Herein, we present rank-BLAST, a novel approach for the assignment of protein sequences into genomic groups of the same taxonomic origin, based on the ranking order of phylogenetic profiles of target genes or proteins across the reference database.

RESULTS:

The rank-BLAST approach is validated by computing the phylogenetic profiles of all sequences for five distinct microbial species of varying degrees of phylogenetic proximity, against a reference database of 243 fully sequenced genomes. The approach - a combination of sequence searches, statistical estimation and clustering - analyses the degree of sequence divergence between sets of protein sequences and allows the classification of protein sequences according to the species of origin with high accuracy, allowing taxonomic classification of 64% of the proteins studied. In most cases, a main cluster is detected, representing the corresponding species. Secondary, functionally distinct and species-specific clusters exhibit different patterns of phylogenetic distribution, thus flagging gene groups of interest. Detailed analyses of such cases are provided as examples.

CONCLUSION:

Our results indicate that the rank-BLAST approach can capture the taxonomic origins of sequence collections in an accurate and efficient manner. The approach can be useful both for the analysis of genome evolution and the detection of species groups in metagenomics samples.

PMID:
19860884
PMCID:
PMC2775751
DOI:
10.1186/1471-2105-10-355
[Indexed for MEDLINE]
Free PMC Article
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