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

79
Change in score
15
10
5
0
0.9 1.0 1.1 1.2 1.3
Estimated scale
Counts
1980
1573
1213
900
633
413
240
114
34
1
Figure 5.2 Improvement in optimal alignment score plotted against the scale estimated from
preliminary alignment. The scatter plot is summarized using hexagonal binning (Carr et al.,
1987), with a LOESS smooth (Cleveland and Grosse, 1991) added.
̂R can be used to rescale the original map and obtain an updated alignment score, and the
alignment declared significant if the new score exceeds the significance threshold. Certain
dynamic programming algorithms, including the one implemented in SOMA, allow detection
of multiple alignments, so this procedure need not be restricted to only the top-scoring
alignment.
Results: As a proof of concept, this process was applied to ungapped global alignment of
the GM07535 optical map data using the SOMA score. 24.36% of the maps had at least
one significant alignment. To compensate for correlation, we further considered the 5 best
scoring alignments of each map regardless of significance. For each of these, the computed ̂R
was used to rescale the map and obtain an updated alignment score. This yielded significant
alignments for a further 4.76% of the maps. This is the fraction of the total number of
maps; the relative increase is a more substantial 19.54%. Even for alignments declared to
be significant without rescaling, the updated score is often larger, assigning more confidence
to the alignment (Figure 5.2). Some of the additional alignments are naturally spurious;
however, this rate is small and can be controlled by suitably modifying the significance
threshold. However, to further explore the practical utility of this approach, it must first be
incorporated into the standard alignment software.