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

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Chapter 2
Modeling Optical Map Data
The first step in the analysis of optical mapping data is to understand its inherent variability.
Unlike traditional restriction mapping techniques, optical mapping obviates the need
to reconstruct the order of restriction fragments. However, the orientations of optical maps
are unknown, fragment lengths are not measured accurately, not all cuts are correctly identified,
and small fragments may desorb and not be seen at all. Further, some maps identified
by image processing may not represent any real restriction maps; e.g., chimeric maps caused
by crossing over of maps in the image, marked up as one. In this chapter we discuss how
these sources of noise can be modeled. Section 2.1, which describes models for optical map
errors, is mostly a review. Later sections consider the estimation of model parameters from
optical map data. Many of the ideas presented there are new and often take advantage of
an in silico reference map. In particular, we outline a non-parametric approach to estimate
desorption rate, use alignments of optical maps to a reference to estimate sizing and scaling
error parameters, and discuss the use of simulation to develop diagnostic plots that can be
used to assess goodness of fit.
2.1 A stochastic model
2.1.1 Origin
Underlying restriction map: It is natural to model optical maps as being generated
from an underlying ‘true’ restriction map associated with the genome under study. This
restriction map can be thought of as a fixed but unknown (high-dimensional) parameter.