Sequence Comparison.pdf

114 6 Anatomy of Spaced Seeds
Table 6.5 The hit probability of the best spaced seeds on a region of length 64 for each weight and
a similarity level. This is reproduced from the paper [48] of Choi, Zeng, and Zhang; reprinted with
permission of Oxford University Press.
W
Best spaced seeds
Similarity
(%)
Sensitivity
9 11*11*1*1***111 65
70
80
90
10 11*11***11*1*111 65
70
80
90
11 111*1**1*1**11*111
65
70
111**1*11**1*1*111
80
90
12 111*1*11*1**11*111 65
70
80
90
13 111*11*11**1*1*1111
111*1*11**11**1*1111
111*1**11*1**111*111
14 1111*1*1*11**11*1111
111*111**1*11**1*1111
65
70
80
90
65
70
80
90
0.52215
0.72916
0.97249
0.99991
0.38093
0.59574
0.93685
0.99957
0.26721
0.46712
0.88240
0.99848
0.18385
0.35643
0.81206
0.99583
0.12327
0.26475
0.73071
0.99063
0.08179
0.19351
0.66455
0.98168
Empirical studies show that transition seeds have a good trade-off between sensitivity
and specificity for homology search in both coding and non-coding regions.
6.6.2 Multiple Spaced Seeds
The idea of spaced seeds is equivalent to using multiple similar word patterns to
increase the sensitivity. Naturally, multiple spaced seeds are employed to optimize
the sensitivity. In this case, a set of spaced seeds are selected first; then all the hits
generated by these seeds are examined to produce local alignments.
The empirical studies by several research groups show that doubling the number
of seeds gets better sensitivity than reducing the single seed by one bit. Moreover,
for DNA homology search, the former roughly doubles the number of hits, whereas