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

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5.2 Other topics
Preliminary filtering: The optical mapping pipeline processes massive amounts of data.
This process is helped if maps that are unlikely to contribute to the results are filtered
out beforehand. It is currently standard practice to leave out maps that do not exceed a
minimum length threshold, usually 300 Kb. However, length of an optical map does not
by itself determine the usefulness of a map. As suggested in Section 3.4.2, one alternative
is to use a map-specific information score, derived from alignment score functions, for this
purpose. A similar but more difficult line of research would be to develop a map-specific
confidence score as part of the image processing step, to reflect the likely quality of the map.
Iterative assembly: The iterative assembly scheme described by Reslewic et al. has been
successful in assembling large genomes by taking advantage of an initial reference map. Being
a first attempt, there is naturally scope for improvement. Although the results have been
validated to some extent (e.g. using PCR), the use of Gentig as an assembly tool must
be regarded as heuristic, since the set of input maps is highly selective and not random
as the model expects. Further, the depth of these data sets are lower than the values
suggested by Anantharaman and Mishra (2003) for reliable assemblies, which may explain
alignment problems involving small fragments. Generally speaking, the inherent trade-off
between depth (resources) and accuracy is unavoidable, and low depth may be an acceptable
compromise in many situations. However, a formal study is necessary to quantify this tradeoff.
Also desirable are formal approaches to develop rules to control false positives, which
are currently derived in an ad hoc fashion. One specific goal is to obtain an estimate for the
false positive rate, which is not obvious even in the case where the true genome is known.
General questions: More generally, several algorithmic questions remain unanswered.
The most obvious one is de novo assembly for large genomes, which is required when a draft
sequence is not available. Another consideration is the analysis of heterozygous or mixture
(e.g. cancer) populations. This is particularly important in studying human genomes, which