Chapter

Research in Computational Molecular Biology

Volume 3500 of the series Lecture Notes in Computer Science pp 489-504

Alignment of Optical Maps

  • Anton ValouevAffiliated withCarnegie Mellon UniversityDepartment of Mathematics, University of Southern California
  • , Lei LiAffiliated withCarnegie Mellon UniversityDepartment of Mathematics, University of Southern California
  • , Yu-Chi LiuAffiliated withCarnegie Mellon UniversityMolecular and Computational Biology Program, Department of Biological Sciences, University of Southern California
  • , David C. SchwartzAffiliated withCarnegie Mellon UniversityLaboratory for Molecular and Computational Genomics, Departments of Genetics and Chemistry, University of Wisconsin-Madison
  • , Yi YangAffiliated withCarnegie Mellon UniversityDepartment of Mathematics, University of Southern California
  • , Yu ZhangAffiliated withCarnegie Mellon UniversityDepartment of Statistics, Harvard University
  • , Michael S. WatermanAffiliated withCarnegie Mellon UniversityDepartment of Mathematics, University of Southern California

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Abstract

We introduce a new scoring method for calculation of alignments of optical maps. Missing cuts, false cuts and sizing errors present in optical maps are addressed by our alignment score through calculation of corresponding likelihood ratios. The Sizing error model is derived through the application of CLT and validated by residual plots collected from real data. Missing cuts and false cuts are modeled as Bernoulli and Poisson events respectively. This probabilistic framework is used to derive an alignment score through calculation of likelihood ratio. Consequently, this allows to achieve maximal descriminative power for alignment calculation. The proposed scoring method is naturally embedded within a well known DP framework for finding optimal alignments.