Parallel Methods in Computational Genomics
Mr. Ananth Kalyanaraman
Wednesday, April 19, 2006
1:30PM - CSB-232
Abstract
Analyzing molecular sequence data is a key computational step in the
process of making biological discovery at a molecular level. Due to
rapid advancements in sequencing technologies, sequence databases have grown phenomenally in both size and complexity. This has rendered the task of
analyzing them on a serial computer ineffective due to both time and
memory constraints. The focus of this talk will be on the design and
development of parallel algorithms and software for analyzing very large scale sequence data. I will describe a parallel clustering algorithm that we developed for the following two applications: (i) clustering Expressed Sequence Tags (ESTs) which are a type of DNA sequence data used in gene-related
studies, and (ii) clustering genomic data for assembling the maize genome. The novelty of our approach lies in its space and time efficiency and its capability to exploit the vast computing power and memory easily available through distributed memory parallel computers. This work has
significantly enhanced the problem size reach while also drastically reducing the time to solution.
Short Bio
Ananth Kalyanaraman is a doctoral candidate in Computer Engineering at
Iowa State University. He received his M.S. in Computer Science from Iowa State University in 2002, and B.E. in Computer Science and Engineering from Visvesvaraya National Institute of Technology, Nagpur, India in 1998. His research interests include bioinformatics and computational biology, parallel computing, and string algorithms. Ananth is a recipient of IBM and Pioneer Hi-Bred graduate research fellowships. Recently, two of his publications were recognized with best paper awards (in conferences IPDPS'06 and CSB'05). He is a member of ACM, IEEE, and SIAM. More information can be found at his homepage:
http://www.public.iastate.edu/~ananthk
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