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By News Staff | February 7th 2008 11:00 PM | Track Comments
The Joint Genome Institute (DOE JGI) has released an upgraded version of the IMG/M metagenome data management and analysis system, providing tools for analyzing the functional capability of microbial communities based on their metagenome DNA sequence in the context of reference isolate genomes.

The new version includes five additional metagenome datasets generated from microbial community samples that were the subject of recently published studies, including the metagenomic and functional analysis of termite hindgut microbiota (Nature 450, 560-565, 22 November 2007) and the single cell genetic analysis of TM7, a rare and uncultivated microbe from the human mouth (PNAS, July 17, 2007, vol. 104, no. 29, 11889-11894).

IMG/M contains all isolate genomes in version 2.4 of DOE JGI's Integrated Microbial Genomes (IMG) system, which represents an increase of 1,339 reference genomes from the previous version of IMG/M. Now, IMG/M contains 2,953 isolate genomes consisting of 819 bacterial, 50 archaeal, 40 eukaryotic genomes, and 2,044 viruses.

IMG/M provides new tools for analyzing metagenome datasets in the context of reference isolate genomes, such as the Reference Genome Context Viewer and Protein Recruitment Plot that allow the examination of metagenomes in the context of individual reference isolate genomes. New Abundance Comparison and Functional Category Comparison tools enable pairwise function analysis (COG, Pfam, Enzyme, TIGRfam) and functional category (e.g., COG category) abundance comparisons, respectively, between a metagenome dataset and one or several reference metagenomes or genomes, and test whether the differences in abundance are statistically significant.

IMG/M has been developed jointly by the DOE JGI's Genome Biology Program (GBP) and Lawrence Berkeley National Laboratory (LBNL) Biological Data Management and Technology Center (BDMTC). The large-scale pairwise gene similarity computations for all the genomes included in IMG/M have been carried out using ScalaBLAST by the Computational Biology and Bioinformatics Group of the Computational Sciences and Mathematics Division at Pacific Northwest National Laboratory, using the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL) Molecular Sciences Computing Facility supercomputer.

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