- 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