Towards optimally multiplexed applications of universal DNA tag systems

  • Authors:
  • Amir Ben-Dor;Tzvika Hartman;Benno Schwikowski;Roded Sharan;Zohar Yakhini

  • Affiliations:
  • Agilent Laboratories;Weizmann Institute;Institute for Systems Biology, Seattle, WA;International Computer Science Institute, Berkeley CA;Agilent Laboratories and Technion

  • Venue:
  • RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
  • Year:
  • 2003

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Abstract

We study a design and optimization problem that occurs, for example, when single nucleotide polymorphisms (SNPs) are to be genotyped using a universal DNA tag array. The problem of optimizing the universal array to avoid disruptive cross-hybridization between universal components of the system was addressed in a previous work. However, cross-hybridization can also occur assay-specifically, due to unwanted complementarity involving assay-specific components. Here we examine the problem of identifying the most economic experimental configuration of the assay-specific components that avoids cross-hybridization. Our formalization translates this problem into the problem of covering the vertices of one side of a bipartite graph by a minimum number of balanced subgraphs of maximum degree 1. We show that the general problem is NP-complete. However, in the real biological setting the vertices that need to be covered have degrees bounded by d. We exploit this restriction and develop an O(d)-approximation algorithm for the problem. We also give an O(d)-approximation for a variant of the problem in which the covering subgraphs are required to be vertex-disjoint. In addition, we propose a stochastic model for the input data and use it to prove a lower bound on the cover size. We complement our theoretical analysis by implementing two heuristic approaches and testing their performance on simulated and real SNP data.