Alignment of optical maps.
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 likelihoods. The size error model is derived through the application of Central Limit Theorem and validated by residual plots collected from real data. Missing cuts and false cuts are modeled as Bernoulli and Poisson events, respectively, as suggested by previous studies. Likelihoods are used to derive an alignment score through calculation of likelihood ratios for a certain hypothesis test. This allows us to achieve maximal descriminative power for the alignment score. Our scoring method is naturally embedded within a well known DP framework for finding optimal alignments.
Valouev
Anton
A
Department of Mathematics, University of Southern California, Los Angeles, 90089-1113, USA. valouev@usc.edu
Li
Lei
L
Liu
Yu-Chi
YC
Schwartz
David C
DC
Yang
Yi
Y
Zhang
Yu
Y
Waterman
Michael S
MS
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D016678:Genome; D016013:Likelihood Functions; D015233:Models, Statistical; D015183:Restriction Mapping / Q000706:statistics & numerical data*