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

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well in its current form, but their approach is fundamentally more sound and likely to give
better results in the long run with suitable modifications.
Purpose: Another consideration that should drive the choice of score function is the purpose
of alignment. Most existing score functions only attempt to account for mismatches
due to optical map noise. However, for the iterative assembly scheme described in Section
1.3.5, a score function will be more useful if it is designed to tolerate minor differences in
the underlying genome as well. Another situation where the choice of score function may be
important is local alignment. Separate local alignments of an optical map to different regions
of the reference may help identify translocations, in a manner similar to the end-sequence
profiling technique of Volik et al. (2003). In our investigations so far, the SOMA score has
not proven very useful for this purpose. As research continues, more situations are likely to
arise where new types of alignment scores will be required.
Software: Investigation of new score functions is currently somewhat hindered by the lack
of an easily extensible software platform to perform optical map alignment. Fortunately, the
SOMA software suite already implements the relevant algorithms and it should be relatively
simple to design a more powerful user interface around it.
5.1.2 Scale errors
Another possible enhancement to the current alignment scheme is to account for scaling
errors. Additive score functions used with dynamic programming assume that fragment
lengths are independent. However, as Figure 1.4 illustrates, the reported length of fragments
within a map may be scaled up or down together due to variability in estimating the scale
factor, causing the fragment lengths to be correlated. To explore this, recall notation from
Section 2.1.2 and consider the standardized errors
ǫ i =
X i − µ i
√
σ2 (τ 2 + 1)µ i + τ 2 µ 2 i