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

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The ability to simulate from the null distribution allows us to try out and choose from
among various sets of parameters. Note that an appropriate choice may depend on the task;
for example, the best score for gapped alignment is always larger than that for ungapped
alignment, so the same set of parameters may not be optimal for both. Indeed, it is tempting
to try and ‘improve’ the scores we have used in our examples and consider ones other than
those reported in Table 3.2; however, we will refrain from doing so since a proper study
requires a systematic effort that is beyond the scope of this discussion.
3.4.2 Information measure
Location-specific cutoff: It is empirically known that different cutoffs for the SOMA
score seem appropriate for alignments to different parts of a reference map, but a formal approach
incorporating this idea has been difficult to formulate. Map-specific cutoffs provide
a perfectly natural explanation for this observation, since an optical map is largely determined
by its origin. However, this does not guard against spurious alignments at similar
(homologous) regions in the genome, which are also a potential concern.
Information measure: A related construct that proves useful in further understanding
optical map score functions is the score obtained by aligning a map with itself, which we
henceforth denote by ψ(M). Given a score function, this can be thought of as an information
measure for the map: if the map had no errors, this would be the score for the correct alignment.
Errors normally reduce the correct alignment score from this perfect score. ψ(M) is of
course higher for longer maps, but is also affected by the lengths of the component fragments
since most score functions reward matches involving longer fragments, which are rarer. Maps
with lower information content are naturally harder to align successfully. However, Figure
3.8 shows that even for maps with high information content, the distributions of spurious
and real SOMA scores are not well separated.
Simulation: In general, any optical map dataset and score function can be summarized
by a plot analogous to Figure 3.8. Figure 3.9 shows such a plot for a set of simulated optical