Highly scalable algorithms for robust string barcoding

Authors:
Bhaskar DasGupta;Kishori M. Konwar;Ion I. Mandoiu;Alex A. Shvartsman
Affiliations:
Department of Computer Science,University of Illinois at Chicago, Chicago, IL 60607-7053, USA.;Computer Science and Engineering Department, University of Connecticut, 371 Fairfield Rd., 2155 Unit, Storrs, CT 06269-2155, USA.;Computer Science and Engineering Department, University of Connecticut, 371 Fairfield Rd., 2155 Unit, Storrs, CT 06269-2155, USA.;Computer Science and Engineering Department, University of Connecticut, 371 Fairfield Rd., 2155 Unit, Storrs, CT 06269-2155, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2005

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Abstract

String barcoding is a recently introduced technique for genomic based identification of microorganisms. In this paper, we describe the engineering of highly scalable algorithms for robust string barcoding. Our methods enable distinguisher selection based on whole genomic sequences of hundreds of microorganisms of up to bacterial size, on a well equipped workstation. Experimental results on both randomly generated and NCBI genomic data show that whole-genome based selection results in a number of distinguishers nearly matching the information theoretic lower bounds for the problem.