DOE JGI sets 'gold standard' for metagenomic data analysis

WALNUT CREEK, CA -- With the advent of more powerful and economical DNA sequencing technologies, gene discovery and characterization is transitioning from single-organism studies to revealing the potential biotechnology applications embedded in communities of microbial genomes, or metagenomes. The field of metagenomics is still in its infancy -- the equivalent of the early days of the California Gold Rush, with labs vying to stake their claim.

Amidst the prospecting, the call has been issued for methods to separate fool's gold from the real nuggets. Such a gold standard has now been provided through work led by the U.S. Department of Energy Joint Genome Institute (DOE JGI) with colleagues from Oak Ridge National Laboratory and IBM's T.J. Watson Research Center. Their results are published in the May edition of Nature Methods.

"DOE JGI and our collaborators have pioneered the use of DNA sequencing-based technologies to understand microbial communities through a combination of computational and experimental methods," said Konstantinos Mavrommatis, lead author of the paper and a post-doctoral fellow in DOE JGI's Genome Biology Program. "We are now exploring ways to analyze metagenomic data to enable accurate classification of sequence fragments into their corresponding species populations. The goal is to reconstruct metabolic pathways by comparing with reference isolate genomes, so that we can model ecosystem dynamics using metabolic reconstructions of metagenomic data. "However, so far all the methods that have been developed were aimed toward analyzing data coming from single, isolate genomes. In this instance, the situation is simple; we know what gene belongs to which organism. In metagenomes, it's much more challenging, because you have sequences from many different organisms all mixed up, and moreover, you don't have enough sequence from each to capture an accurate picture of the entire community, so you only get a glimpse of the ide

Contact: David Gilbert
DOE/Joint Genome Institute

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