Exascale Solutions for Microbiome Analysis

Microbiomes are integral to the environment, agriculture, health and biomanufacturing, yet we've sequenced less than one percent of the millions of species in the world. 

The Exabiome Project is tackling that challenge. 

Microorganisms are central players in climate change, environmental remediation, food production, and human health.  They occur naturally as “microbiomes,” cooperative communities of microbes. 

These communities are made up of hundreds or thousands of microbial species of varying abundance and diversity, each contributing to the function of the whole.  Less than one percent of the millions of species of microbes in the world have been isolated and cultivated in the laboratory, and only a small fraction of those have been sequenced.  Meanwhile, vast troves of microbial data are growing exponentially, representing an untapped wealth of information that could be used to help remediate environmental problems or to manufacture novel chemicals and medicines. 

“Metagenomics” is a powerful method for studying microbiomes. First scientists sequence the DNA in microbiomes using high-throughput genome sequencing technologies. To find families of genes that work together and to compare across metagenomes, researchers perform a kind of data analysis akin to sorting puzzle pieces out of a jumbled mound of several puzzles and reassembling them. It requires a lot of computing power. More than we have today. As a result, vast treasure troves of metagenomic data are piling up, just waiting to be analyzed and probed for information that will require computing power many times greater than is available today and smarter ways of putting those powerful machines to work.

 The ExaBiome team from Berkeley Lab, Los Alamos, and the Joint Genome Institute is developing exascale algorithms and software to address just these challenges and will work with the vendor community to co-design systems that have the necessary network and memory features to address these and other large scale analytics problems. 

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