Download CLC Genomics Workbench

CHAPTER 2. HIGH-THROUGHPUT SEQUENCING
112
Figure 2.118: Peak refinement settings.
the region where a peak in coverage is found. A center of sequencing intensity is defined for all
forward reads as the median value of the center points of all forward reads and likewise for all
reverse reads. The "refined peak" is thus defined as the region between these two points.
One of the advantages of including this boundary refinement is that shorter regions can be given
as input to subsequent pattern discovery analysis.
By checking the Filter peaks based on difference in read orientation counts the algorithm will
calculate the normalized difference in the number of forward and reverse reads within a peak as
|count forward reads - count reverse reads|
count forward reads + count reverse reads
The desired maximum value of this parameter can be set in the Normalized difference of read
counts field and any candidate peak with a value above this will then be dismissed. Setting a
low value will ensure that peaks are only called if there is a well balanced number of forward and
reverse reads.
As an example if you have 15 forward reads and 5 reverse reads, you will end up with a value of
0.5. With the default limit set to 0.4, a peak like that would be excluded.
By checking the Filter peaks based on spatial distribution of read orientation the algorithm
will evaluate how clearly separated the location of forward and reverse reads are within a
peak. This is done via the Wilcoxon rank-sum test (see http://en.wikipedia.org/wiki/
Mann-Whitney-Wilcoxon_test). The null hypothesis here is that the positions of forward
and reverse reads within a peak are drawn from the same distribution i.e. that their locations
are not significantly different and the alternative hypothesis is that the forward reads have a
sum of ranked positions that is shifted to lower positions than the reverse reads. Peaks will
be dismissed if the probability of the null hypothesis exceeds the value set in the Maximum
probability field.
Setting a low Maximum probability will ensure that peaks are only called if there is a clear
signature distribution where forward reads are found upstream of reverse reads within the peak.
A general comment about peak filtering is that the relevant statistics are all reported in the peak