On the Analysis of Optical Mapping Data - University of Wisconsin ...

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ii ACKNOWLEDGMENTS I would like to thank the many people without whom this work would not have been possible. The optical mapping system, which forms the basis for this thesis, was pioneered by Prof David Schwartz and continues to be developed under his leadership at the Laboratory for Molecular and Computational Genomics at the University of Wisconsin – Madison. Several people at the lab have helped me understand various aspects of the system: primarily Steve Goldstein, as well as Rod Runnheim, Chris Churas, Shiguo Zhou and Gus Potamousis. The optical map data used in this thesis were collected by Alex Lim, Susan Reslewic and Jill Herschleb. Prof Paul Lizardi’s lab at Yale provided some supplementary data and analysis. Finally, my advisor Prof Michael Newton provided many important insights throughout the last few years and helped immensely in shaping the final manuscript.
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Page 1 and 2: ON THE ANALYSIS OF OPTICAL MAPPING
Page 3: To my parents. i
Page 7 and 8: iv Page 3.3 Results . . . . . . . .
Page 9 and 10: v LIST OF TABLES Table Page 1.1 Sum
Page 11 and 12: vi LIST OF FIGURES Figure Page 1.1
Page 13 and 14: ON THE ANALYSIS OF OPTICAL MAPPING
Page 15 and 16: 1 Chapter 1 Overview of Optical Map
Page 17 and 18: 3 hard, do not always have a unique
Page 19 and 20: 5 for microbial and other small gen
Page 21 and 22: 7 Figure 1.2 Close-up of a typical
Page 23 and 24: 9 0.96 0.98 1.00 1.02 1.04 Offset a
Page 25 and 26: 11 direct glimpse at the underlying
Page 27 and 28: 13 which is not surprising since we
Page 29 and 30: 15 Gapped alignments: The above des
Page 31 and 32: Figure 1.5 A visualization of align
Page 33 and 34: 19 Assembly: For these examples, th
Page 35 and 36: 21 Chapter 2 Modeling Optical Map D
Page 37 and 38: 23 Alternatively, it can be thought
Page 39 and 40: 25 and V (X i ) = E(V (Y i R i |R i
Page 41 and 42: 27 affect inference. If necessary,
Page 43 and 44: 29 Quantiles of fragment lengths (K
Page 45 and 46: 31 as a function of the parameters.
Page 47 and 48: 33 by rejecting maps that do not al
Page 49 and 50: 35 30 0.700 − 0.005 0 50 100 150
Page 51 and 52: 37 Chapter 3 Significance of Optica
Page 53 and 54: 39 using optical mapping data from
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41 1.0 0.8 0.6 0.4 0.2 0.0 1.0 0.8
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43 3.3.2 Simplifications Direct app
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45 Mean spurious score 0 −10 −2
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47 3.3.3 Simulation Given a generat
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49 3.4 Discussion 3.4.1 Uses Alignm
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51 The ability to simulate from the
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53 maps, where the separation betwe
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Figure 3.10 Schematic representatio
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57 especially a short noisy one, to
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59 Test statistics: Variability due
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61 in sequence assembly and validat
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63 1988). However, due to sampling
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65 and rate parameters Λ i = E(N i
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67 with mean µ k for the k th stat
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69 Estimated Copy Number in simulat
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71 Posterior probabilities 1.0 0.8
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73 (a) Observed counts and decoded
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75 Conclusion: Copy number alterati
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77 well in its current form, but th
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79 Change in score 15 10 5 0 0.9 1.
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81 will rarely be homozygous. It ma
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83 E.T. Dimalanta, A. Lim, R. Runnh
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85 Appendix A: Score functions for
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87 Appendix B: Hidden Markov Model
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89 which can be shown to have highe
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