461333 Seq2Flux: A Web-Based Platform to Bridge High-Throughput Sequencing and Metabolic Flux Analysis

Wednesday, November 16, 2016: 1:06 PM
Continental 8 (Hilton San Francisco Union Square)
Weihua Guo and Xueyang Feng, Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA

High-throughput sequencing techniques, such as whole-genome sequencing and RNA-Seq, have been widely applied on over 3,000 species for uncovering the mysteries of biological systems. With the rapid advancement of the sequencing techniques, the cost of sequencing service has been dramatically decreased to $50-60/genome. Currently, with the access to over 100 TB sequencing data, the interpretation of the sequencing data, however, becomes the bottleneck for developing advanced sequencing service. This is mainly because of the fact that few detailed phenotypic readouts, e.g., metabolic flux distribution, could be directly linked with sequencing data for intuitive interpretation of the sequencing results. To this end, we developed Seq2Flux as a web-based platform to bridge the high-throughput sequencing data and metabolic flux analysis. We implemented Seq2Flux for a proof-of-concept study to predict metabolism of E. coli from customized genome sequencing. In general, we used the whole-genome sequencing data of E. coli strain provided by users as the input, and run the logic operation of the gene-protein-reaction association to map the gene annotation into a genome-scale metabolic model of E. coli (iJO1366). We next developed a constraint-based flux analysis algorithm to integrate large-scale fluxomics data with the sequence-curated metabolic models to achieve accurate prediction of metabolic fluxes as well as other phenotypes (e.g., growth rate). Taken together, Seq2Flux could reveal valuable metabolic information directly from genome sequencing data, which paves the ways for advanced applications of high-throughput sequencing data such as personalized medicine.

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