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Nucleic Acids Research Advance Access published online on November 11, 2009
Nucleic Acids Research, doi:10.1093/nar/gkp1001
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© The Author(s) 2009. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Database Issue

MetaBioME: a database to explore commercially useful enzymes in metagenomic datasets

Vineet K. Sharma, Naveen Kumar, Tulika Prakash and Todd D. Taylor*

MetaSystems Research Team, Computational Systems Biology Research Group, Advanced Computational Sciences Department, Advanced Science Institute, RIKEN, Yokohama, Kanagawa 230-0045, Japan

*To whom correspondence should be addressed. Tel: +81-45-503-9285; Fax: +81-45-503-9176; Email: taylor{at}riken.jp

Received August 15, 2009. Revised October 8, 2009. Accepted October 17, 2009.

Microbial enzymes have many known applications as biocatalysts in biotechnology, agriculture, medical and other industries. However, only a few enzymes are currently employed for such commercial applications. In this scenario, the current onslaught of metagenomic data provides a new unexplored treasure trove of genomic wealth that can not only enhance the enzyme repertoire by the discovery of novel commercially useful enzymes (CUEs) but can also reveal better functional variants for existing CUEs. We prepared a catalogue of CUEs using text mining of PubMed abstracts and other publicly available information, and manually curated the data to identify 510 CUEs. Further, in order to identify novel homologues of these CUEs, we identified potential ORFs in publicly available metagenomic datasets from 10 diverse sources. Using this strategy, we have developed a resource called MetaBioME (http://metasystems.riken.jp/metabiome/) that comprises (i) a database of CUEs and (ii) a comprehensive platform to facilitate homology-based computational identification of novel homologous CUEs from metagenomic and bacterial genomic datasets. Using MetaBioME, we have identified several novel homologues to known CUEs that can potentially serve as leads for further experimental verification.


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