MetaBioME: A Comprehensive Metagenomic BioMining Engine
MetaBioME is a web resource to find novel homologs for known Commercially Useful Enzymes (CUEs) in metagenomic datasets and completed bacterial genomes .
Looking for CUEs in Metagenomic Datasets
CUEs are defined as 'Commercially Useful Enzymes', which have known applications in several industries like biotechnology, agriculture, pharmaceutics, etc. Metagenomic data provide a unique resource for discovering novel homologs for CUEs from yet unidentified microbes belonging to complex microbial communities from diverse ecosystems.
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. We classified these CUEs into nine broad categories based on their area of application. Further, in order to identify novel homologs for these CUEs, we used our (currently) in-house metagenomic analysis pipeline 'iMetaSys' to identify potential ORFs in publicly available metagenomic datasets from ten diverse sources and 971 complete bacterial genomes.
Using this strategy, we developed this resource, called MetaBioME, which comprises,
- a database of CUEs classified into nine application categories, and
- a comprehensive platform to facilitate homology-based computational identification of novel homologs for known CUEs from metagenomic datasets and completed bacterial genomes.
To our knowledge, this is the first comprehensive effort to curate a freely available database of CUEs and the first such resource for widely exploring them in metagenomic and bacterial genomic data. Using MetaBioME, we have already identified several novel homologs to the curated set of CUEs which can serve as leads for further experimental verification.
The information and data contained in MetaBioME is strictly for academic use only. Please cite the paper, using the following citation information, if you use MetaBioME. The following article can be accessed freely from NCBI PubMed.
How to cite:
MetaBioME: a database to explore commercially useful enzymes in metagenomic datasets. Vineet K. Sharma, Naveen Kumar, Tulika Prakash, Todd D. Taylor. Nucleic Acids Research 2010 Jan;38(Database issue):D468-72. Epub 2009 Nov 11.