Terrestrial microbial communities play critical roles in global carbon cycling, but are poorly represented in climate models. The activities of microbial communities can be understood by studying their genetics and their expression products using metagenomics (reconstruction of microbial genomes) and metaproteomics (analysis of the protein products of genes). The increased throughput of metagenomics and metaproteomics has enabled systematic sampling and measurement of a large number of soil microbial communities across space and time in a terrestrial ecosystem. However, a large amount of sequencing data and mass spectrometry data generated in these metagenomics and metaproteomics studies poses a tremendous computational challenge. This project supports using a set of scalable algorithms on Titan to perform high-resolution data analytics for these integrated omics experiments. The results will provide a mechanistic understanding of the carbon turnover processes by soil microbial communities and can be used to inform climate models.
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