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The Computer Journal Advance Access originally published online on March 6, 2007
The Computer Journal 2008 51(1):26-38; doi:10.1093/comjnl/bxm003
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© The Author 2008. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Innovative Computational Methods for Transcriptomic Data Analysis: A Case Study in the Use of FPT for Practical Algorithm Design and Implementation1

Michael A. Langston2,*, Andy D. Perkins2, Arnold M. Saxton3, Jon A. Scharff2 and Brynn H. Voy4

2 Department of Computer Science, University of Tennessee, Knoxville, TN 37996-3450, USA
3 Department of Animal Science, University of Tennessee, Knoxville, TN 37996-4574. USA
4 Mammalian Genetics and Genomics, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6124, USA

* Corresponding author: langston{at}cs.utk.edu

Received 18 May 2006; revised 12 January 2007

Tools of molecular biology and the evolving tools of genomics can now be exploited to study the genetic regulatory mechanisms that control cellular responses to a wide variety of stimuli. These responses are highly complex, and involve many genes and gene products. The main objectives of this paper are to describe a novel research program centered on understanding these responses by

  1. developing powerful graph algorithms that exploit the innovative principles of fixed parameter tractability in order to generate distilled gene sets;
  2. producing scalable, high performance parallel and distributed implementations of these algorithms utilizing cutting-edge computing platforms and auxiliary resources;
  3. employing these implementations to identify gene sets suggestive of co-regulation; and
  4. performing sequence analysis and genomic data mining to examine, winnow and highlight the most promising gene sets for more detailed investigation.
As a case study, we describe our work aimed at elucidating genetic regulatory mechanisms that control cellular responses to low-dose ionizing radiation (IR). A low-dose exposure, as defined here, is an exposure of at most 10 cGy (rads). While the consequences of high doses of radiation are well known, the net outcome of low-dose exposures continues to be debated, with support in the literature for both detrimental and beneficial effects. We use genome-scale gene expression data collected in response to low-dose IR exposure in vivo to identify the pathways that are activated or repressed as a tissue responds to the radiation insult. The driving motivation is that knowledge of these pathways will help clarify and interpret physiological responses to IR, which will advance our understanding of the health consequences of low-dose radiation exposures.

Key Words: transcriptomic data analysis • fixed-parameter tractability • graph algorithms

1 A preliminary version of a portion of this paper was presented at the ACM Symposium on Applied Computing, held in Dijon, France, in April 2006.


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