Journal Article

A Novel Bioinformatics Strategy for Function Prediction of Poorly-Characterized Protein Genes Obtained from Metagenome Analyses

Takashi Abe, Shigehiko Kanaya, Hiroshi Uehara and Toshimichi Ikemura

in DNA Research

Published on behalf of Kazusa DNA Research Institute

Volume 16, issue 5, pages 287-297
Published in print October 2009 | ISSN: 1340-2838
Published online October 2009 | e-ISSN: 1756-1663 | DOI: http://dx.doi.org/10.1093/dnares/dsp018

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As a result of remarkable progresses of DNA sequencing technology, vast quantities of genomic sequences have been decoded. Homology search for amino acid sequences, such as BLAST, has become a basic tool for assigning functions of genes/proteins when genomic sequences are decoded. Although the homology search has clearly been a powerful and irreplaceable method, the functions of only 50% or fewer of genes can be predicted when a novel genome is decoded. A prediction method independent of the homology search is urgently needed. By analyzing oligonucleotide compositions in genomic sequences, we previously developed a modified Self-Organizing Map ‘BLSOM’ that clustered genomic fragments according to phylotype with no advance knowledge of phylotype. Using BLSOM for di-, tri- and tetrapeptide compositions, we developed a system to enable separation (self-organization) of proteins by function. Analyzing oligopeptide frequencies in proteins previously classified into COGs (clusters of orthologous groups of proteins), BLSOMs could faithfully reproduce the COG classifications. This indicated that proteins, whose functions are unknown because of lack of significant sequence similarity with function-known proteins, can be related to function-known proteins based on similarity in oligopeptide composition. BLSOM was applied to predict functions of vast quantities of proteins derived from mixed genomes in environmental samples.

Keywords: batch learning SOM; oligopeptide frequency; protein function; metagenome; alignment-free clustering

Journal Article.  5881 words.  Illustrated.

Subjects: Genetics and Genomics

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