MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences

Wang, Dianhui; Tapan, Sarwar

doi:10.1186/1752-0509-6-S2-S4

BMC Systems Biology

Table 3 Sep(R, R_l) score comparison for different local block length w in R_l

From: MISCORE: a new scoring function for characterizing DNA regulatory motifs in promoter sequences

	Sep(R, R_l) ± E{std} using 5000 random models
TF	w = O (k /3)	w = max{ O (k /3), 3}	w = min{ O (k /2), 5}	w = O (k /2)
data group 1 (dg₁)
CREB	0.022 ± 0.047	0.022 ± 0.047	-0.016 ± 0.049	-0.016 ± 0.049
SRF	-0.022 ± 0.034	-0.022 ± 0.034	-0.030 ± 0.035	-0.030 ± 0.035
TBP	0.125 ± 0.020	0.128 ± 0.020	0.128 ± 0.020	0.128 ± 0.020
MEF2	0.358 ± 0.041	0.358 ± 0.041	0.367 ± 0.041	0.367 ± 0.041
MYOD	0.066 ± 0.037	-0.089 ± 0.045	-0.089 ± 0.045	-0.089 ± 0.045
ERE	-0.008 ± 0.028	-0.008 ± 0.028	-0.081 ± 0.031	-0.210 ± 0.038
E2F	0.110 ± 0.027	0.110 ± 0.027	0.127 ± 0.026	0.136 ± 0.026
CRP	0.052 ± 0.028	0.052 ± 0.028	0.110 ± 0.024	-0.110 ± 0.039
avg	0.088 ± 0.033	0.069 ± 0.034	0.065 ± 0.034	0.022 ± 0.037
data group 2 (dg₂)
dm01g	0.101 ± 0.035	0.101 ± 0.035	0.105 ± 0.036	0.100 ± 0.037
dm04m	0.053 ± 0.033	0.053 ± 0.033	0.051 ± 0.035	0.051 ± 0.035
hm02r	0.219 ±0.043	0.219 ± 0.043	0.146 ± 0.050	0.146 ± 0.050
hm03r	0.135 ± 0.037	0.135 ± 0.037	0.146 ± 0.037	0.146 ± 0.037
hm06g	0.139 ± 0.051	0.062 ± 0.058	0.062 ± 0.058	0.062 ± 0.058
hm08m	0.084 ± 0.041	0.091 ± 0.041	0.088 ± 0.042	0.088 ± 0.042
hm09g	0.114 ± 0.075	0.114 ± 0.075	0.141 ± 0.074	0.141 ± 0.074
hm10m	0.134 ± 0.038	0.134 ± 0.038	0.129 ± 0.040	0.129 ± 0.040
hm11g	0.168 ± 0.045	0.168 ± 0.045	0.191 ± 0.044	0.191 ± 0.044
hm16g	0.140 ± 0.077	0.140 ± 0.077	0.007 ± 0.098	0.007 ± 0.098
hm17g	0.065 ± 0.045	0.065 ± 0.045	0.026 ± 0.049	0.026 ± 0.049
hm20r	0.322 ± 0.023	0.322 ± 0.023	0.299 ± 0.024	0.299 ± 0.024
hm21g	0.064 ± 0.051	0.064 ± 0.051	0.060 ± 0.054	0.060 ± 0.054
hm24m	0.107 ± 0.042	0.107 ± 0.042	0.081 ± 0.045	0.081 ± 0.045
hm26m	0.265 ± 0.044	0.265 ± 0.044	0.216 ± 0.049	0.216 ± 0.049
mus02r	0.004 ± 0.119	0.004 ± 0.119	-0.273 ± 0.198	-0.273 ± 0.198
mus10g	0.350 ± 0.056	0.354 ± 0.056	0.354 ± 0.056	0.354 ± 0.056
mus11m	0.340 ± 0.042	0.340 ± 0.042	0.329 ± 0.043	0.329 ± 0.043
yst08r	0.131 ± 0.045	0.131 ± 0.045	0.118 ± 0.047	0.107 ± 0.047
yst09g	0.353 ± 0.056	0.353 ± 0.056	0.337 ± 0.058	0.333 ± 0.059
avg	0.164 ± 0.050	0.161 ± 0.050	0.131 ± 0.057	0.130 ± 0.057
data group 3 (dg₃)
CREB	0.072 ± 0.042	0.072 ± 0.042	0.049 ± 0.043	0.049 ± 0.043
SRF	-0.026 ± 0.028	-0.026 ± 0.028	-0.032 ± 0.029	-0.032 ± 0.029
TBP	0.129 ± 0.019	0.133 ± 0.019	0.133 ± 0.019	0.133 ± 0.019
MEF2	0.372 ± 0.042	0.372 ± 0.042	0.380 ± 0.042	0.380 ± 0.042
MYOD	0.088 ± 0.034	-0.076 ± 0.042	-0.076 ± 0.042	-0.076 ± 0.042
avg	0.127 ± 0.033	0.095 ± 0.035	0.091 ± 0.035	0.091 ± 0.035
Result summary:	E{Sep(R, R_l)} ± E{std} on each data group
dg ₁	0.088 ±0.033	0.069 ± 0.034	0.065 ± 0.034	0.022 ± 0.037
dg ₂	0.164 ±0.050	0.161 ± 0.050	0.131 ± 0.057	0.130 ± 0.057
dg ₃	0.127 ±0.033	0.095 ± 0.035	0.091 ± 0.035	0.091 ± 0.035
avg	0.126 ±0.039	0.108 ± 0.040	0.095 ± 0.042	0.081 ± 0.043

Remark: O(*) is a rounding operator and k is the length of k-mers. Sep(R, R_l) is computed on each dataset using 5000 random set of k-mers generated from each dataset. The result summary shows that w = O(k/3) criterion is likely to produce a better separability performance; hence it can be generally applied in the localization approach.

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ISSN: 1752-0509

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