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

12
restriction map can be derived in silico by identifying the enzyme recognition pattern in
the reference sequence, and the primary goal of optical mapping is to determine how the
genome under study differs from the reference copy in terms of their respective restriction
maps. Such differences can be due to errors in the sequence, especially in the early stages
of sequencing, but more importantly, they can reflect real biological variation. In either
case, these broad goals are often tackled by breaking them down into smaller, more tractable
problems.
Algorithmic challenges: Optical mapping has been very successful in obtaining restriction
maps of relatively small genomes (e.g. microbes). A critical component of this success
has been algorithmic research in the 1990’s specifically aimed at optical mapping data, notably
the work of Anantharaman et al. (1999) leading to the Gentig assembly software. With
recent technological advances, the focus has shifted to larger genomes. The primary challenge
introduced by this shift is scalability. Computational methods that work well for microbial
genomes may fail for large genomes due to memory and speed limits of existing computational
systems. Since mammalian genomes differ in size from microbial genomes by several orders
of magnitude, the relative coverage may be far less. Careful statistical analysis is thus critical
in making full use of the available data. New methods are also required to take advantage of
in silico maps when they are available. It should be noted that restriction maps have many
fundamental similarities with sequence data, and algorithms developed for sequence analysis
can often be adapted to work with optical maps (e.g. Huang and Waterman, 1992).
Validation: Due to the nature of optical mapping data, it is rarely possible to know the
true answer except in very special circumstances. It is therefore natural to use simulation to
validate algorithmic techniques. While this has been implicitly acknowledged in much of the
algorithmic work on optical mapping, we think that the stochastic model used in simulation
itself deserves closer attention. With the large data sets that are now available, we can also
hope to use the data to validate models, at least in some limited ways. In particular, we
have found graphical diagnostics to be particularly useful in model checking (see Section 2.3),