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Dr.
Ananth Kalyanaraman
.jpg) 509-335-6760
ananth@eecs.wsu.edu
Assistant Professor, School of Electrical Engineering and Computer
Science. Ph.D. 2006, Iowa State University.
Research
My
primary research interests lie at the intersection of computer
science and biology, more specifically genomics. I am primarily
interested in the design and development of efficient algorithms
and scalable software tools for the analysis of genomic data.
The problem areas and applications of interest are as follows:
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Genome
and repetitive pattern discovery: Assembling
a genome from its numerous shreds and fragments is a computationally
challenging task. Plant genomes are particularly challenging
because of their highly complex genomic structure and evolutionary
history. We have developed a software system called PaCE, which
can efficiently exploit thousands of processors and their
memory for the clustering and assembly of millions of genomic
fragments. The software was successfully applied for gene-enriched
maize genome assembly, with the time to solution drastically
reduced from tens of days to a matter of hours. It is also
used in the clustering of millions of Expressed Sequence
Tags. I am also involved in the development of pattern discovery
tools for the de novo identification of structurally
categorized and unknown (novel) repetitive substructures
within genomes.
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Comparative
genomics: Comparing
multiple genomes and multiple genomic loci provides valuable
insights into the genomic differentiators and similarities
across organisms. I am interested in studying synteny and
genome rearrangements among genomes from a diverse set of
species. In collaboration with the Dr. Amit Dhingra’s
(WSU) laboratory, we are developing new comparative techniques
in the context of enabling PCR-based sequencing for organellar
genomes.
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Gene
to function (association) mapping: Identifying
the gene(s) responsible for a key functional trait is a fundamental
problem in genomics. In collaboration with Dr. Kulvinder
Gill’s (WSU) laboratory, we have been looking at wheat
marker data to identify statistically significant correlations
that may exist between genes/marker data and observed functional
traits.
-
Metagenomic
analysis: Metagenome
is a collective term representing the pool of microbial genomes
collected from environment samples. I am interested in developing
new analytical and computational capabilities that would
enable the profiling and understanding the genomic content
of community data.
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High-performance
computing: With
every new breakthrough in sequencing and other wetlab technologies,
there has been an avalanche of biological data deposited
in public databases. Computational tools are therefore becoming
an indispensable resource for automated hypothesis testing,
modeling and discovery. If analysis has to keep pace with
the data generation then the development of high-performance
computing (HPC) solutions becomes imperative. To this end,
a general emphasis in my research is to develop HPC solutions
suited for exploiting the high compute power and memory capacities
of the state-of-the-art supercomputing technologies.
Selected
Publications
A.
Kalyanaraman,
S.J. Emrich, P.S. Schnable, S. Aluru. 2007. Assembling
genomes on large-scale parallel computers. Journal
of Parallel and Distributed Computing (JPDC), In press.
C.
Wu, A. Kalyanaraman, A. Dhingra. 2007. An
efficient computational framework for amplifying arbitrarily
long conserved DNA sequences. Proc. LSS Computational Systems
Bioinformatics (CSB'07), UC San Diego, August 13-17.
W.
Davis,
A. Kalyanaraman, D. Cook. 2007. An information theoretic approach
for the discovery of irregular and repetitive patterns in genomic
data. Proc. LSS Computational Systems Bioinformatics (CSB'07),
UC San Diego, August 13-17.
A.
Kalyanaraman,
S. Aluru. 2006. Efficient
algorithms and software for detection of full-length LTR retrotransposons. Journal
of Bioinformatics and Computational Biology (JBCB), 4(2):197-216.
A.
Kalyanaraman,
S. Aluru, P.S. Schnable. 2006. Turning
repeats to advantage: Scaffolding genomic contigs using LTR retrotransposons. Proc.
LSS Computational Systems Bioinformatics (CSB'06), 167-178.
P.
Ko, M. Narayanan, A. Kalyanaraman, S. Aluru. 2004. Space-conserving
optimal DNA-protein alignment. Proc. IEEE
Computational Systems Bioinformatics Conference (CSB'04), 77-85.
M.
Mitreva, A.A. Elling, M. Dante, A.P. Kloek, A. Kalyanaraman,
S. Aluru, S.W. Clifton, D.M. Bird, T.J. Baum, J.P. McCarter. 2004. A
survey of SL1-spliced transcripts from the root-lesion nematode
Pratylenchus penetrans. Molecular Genetics and Genomics
(MGG), 272:138-148.
A.
Kalyanaraman,
S. Aluru, S. Kothari, V. Brendel. 2003. Efficient
clustering of large EST data sets on parallel computers.
Nucleic Acids Research (NAR), 31(11):2963-2974.
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