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Nucleic Acids Research Advance Access published online on October 5, 2006
Nucleic Acids Research, doi:10.1093/nar/gkl723
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© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational Biology

MetaGene: prokaryotic gene finding from environmental genome shotgun sequences

Hideki Noguchi*, Jungho Park and Toshihisa Takagi

Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo Kashiwa, Chiba 277-8562, Japan

*To whom correspondence should be addressed. Tel: +81 4 7136 3973; Fax: +81 4 7136 4100; Email: hide{at}cb.k.u-tokyo.ac.jp

Received March 18, 2006. Revised September 1, 2006. Accepted September 19, 2006.

Exhaustive gene identification is a fundamental goal in all metagenomics projects. However, most metagenomic sequences are unassembled anonymous fragments, and conventional gene-finding methods cannot be applied. We have developed a prokaryotic gene-finding program, MetaGene, which utilizes di-codon frequencies estimated by the GC content of a given sequence with other various measures. MetaGene can predict a whole range of prokaryotic genes based on the anonymous genomic sequences of a few hundred bases, with a sensitivity of 95% and a specificity of 90% for artificial shotgun sequences (700 bp fragments from 12 species). MetaGene has two sets of codon frequency interpolations, one for bacteria and one for archaea, and automatically selects the proper set for a given sequence using the domain classification method we propose. The domain classification works properly, correctly assigning domain information to more than 90% of the artificial shotgun sequences. Applied to the Sargasso Sea dataset, MetaGene predicted almost all of the annotated genes and a notable number of novel genes. MetaGene can be applied to wide variety of metagenomic projects and expands the utility of metagenomics.


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