Highly scalable algorithms for robust string barcoding

Authors:
B. DasGupta;K. M. Konwar;I. I. Măndoiu;A. A. Shvartsman
Affiliations:
Department of Computer Science, University of Illinois at Chicago, Chicago, IL;Computer Science and Engineering Department, University of Connecticut, Storrs, CT;Computer Science and Engineering Department, University of Connecticut, Storrs, CT;Computer Science and Engineering Department, University of Connecticut, Storrs, CT
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
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, and can be easily parallelized to further extend the applicability range to thousands of bacterial size genomes. 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.