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Designing Optimally Multiplexed SNP Genotyping Assays

  • Yonatan Aumann
  • Efrat Manisterski
  • Zohar Yakhini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2812)

Abstract

We consider the task of SNP (Single Nucleotide Polymorphism) genotyping. In many studies, genotyping of a large number of SNP must be performed. Multiple SNPs can be genotyped together in the same assay (a process called multiplexed genotyping), provided they adhere to some constraints. We address the optimization problem of designing assays that maximize the number of SNPs genotyped, subject to the multiplexing constraints. We focus on the SNP genotyping method based on primer extension and mass-spectrometry (PEA/MS). We translate the optimization problem to a graph coloring problem, and provide an essentially optimal heuristics for solving the corresponding coloring problem. In addition, we present a method that enables a dramatic increase in the multiplexing rate by modifying primer masses. In this case, the multiplexing design problem can be modelled as a matching problem in hypergraphs. We analyze the problem from both theoretical and practical aspects, providing theoretical hardness results and practical heuristics. The heuristics are tested using simulation methods, and prove to be close to optimal in practice.

Keywords

Greedy Algorithm Edge Coloring Single Nucleotide Polymorphism Locus Graph Coloring Problem Single Nucleotide Polymorphism Site 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yonatan Aumann
    • 1
  • Efrat Manisterski
    • 1
  • Zohar Yakhini
    • 2
  1. 1.Dept. of Computer ScienceBar-Ilan UniversityRamat GanIsrael
  2. 2.Agilent Laboratories and Departmemt of Computer Sciencethe TechnionHaifaIsrael

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