Date(s) - 26 Feb 2014
10:00 AM - 11:30 AM
3043 ECpE Building Addition
Title: Scalable Graph Heuristics for Functional Characterization of Large-scale Metagenomics Data
Speaker: Ananth Kalyanaraman, Associate Professor, Washington State University
Abstract: Biological data naturally lend themselves to graph-based representations. Among other uses, graph-based representations can be used to reveal tightly-knit communities within data that share common characteristics such as homology or function. Consequently, clustering formulations are prevalent in a number of biological applications, including that of (but not limited to) determining complexes in protein-protein interactions and functional characterization of metagenomics data. Performing these operations at a large-scale, however, still remains significantly challenging. In this talk, I will report on the on-going development of new parallel approaches for modeling the interplay among metagenomics data into graphs, and subsequently identifying sequence clusters from them. The results of clustering can be used for metabolic pathway identification and functional annotation of microbial communities. The talk will cover novel graph-theoretic heuristics for clustering weighted and unweighted graphs, and their parallelization on shared and distributed memory machines. Experimental results on subsets of a medium-scale ocean metagenomics input (containing ~11M vertices and 640M edges) demonstrate significant qualitative and performance improvements in the reported clustering. Time permitting, I will also discuss how our approach ideas can be extended to implement other big data analytics within computational biology and associated challenges.
Speaker Bio: Ananth Kalyanaraman is an Associate Professor at the School of Electrical Engineering and Computer Science in Washington State University. He received his Bachelor of Engineering from Visvesvaraya National Institute Technology in Nagpur (India) in 1998, and his MS and PhD from Iowa State University in 2002 and 2006, respectively. His main area of research interest is in high performance computational biology, with focus on developing algorithms that use high-performance computing for data-intensive problems originating from the areas of computational genomics and metagenomics. Ananth is a recipient of a DOE Early Career Award, Early Career Impact Award from Iowa State University, and two best paper awards. He has organized workshops and mini-symposia relating to high performance computational biology at IEEE, ACM and SIAM conferences, and regularly serves on a number of program committees and proposal panels. His research is currently funded by DOE, NSF, and USDA. Ananth is a member of AAAS, ACM, IEEE-CS, and ISCB.