Table 2.

Predicted Regulons and Regulogs for 48 Transcription Factors of E. coli, the ECO set

TF	Known members	Predicted members		Sensitivity		PPV		EfREG
		REGULON	REGULOG	REGULON	REGULOG	REGULON	REGULOG
pdhR	4	102	4	0.25	0.25	0.01	0.25	25.5
ilvY	2	182	12	1	1	0.01	0.17	15.17
oxyR	5	137	11	0.6	0.6	0.02	0.27	12.45
torR	4	77	7	1	1	0.05	0.57	11
metR	4	218	21	0.75	0.75	0.01	0.14	10.38
tyrR	11	134	8	0.73	0.55	0.06	0.75	9.42
nagC	10	231	25	0.4	0.4	0.02	0.16	9.24
glpR	8	191	16	1	0.88	0.04	0.44	9.14
iclR	4	249	28	0.5	0.5	0.01	0.07	8.89
malT	10	156	8	0.6	0.4	0.04	0.5	8.67
Irp	29	252	17	0.14	0.1	0.02	0.18	8.34
galR	5	123	18	1	1	0.04	0.28	6.83
modE	3	81	12	1	1	0.04	0.25	6.75
argR	10	182	20	0.7	0.6	0.04	0.3	6.69
cpxR	12	307	50	0.17	0.17	0.01	0.04	6.14
trpR	12	72	3	0.33	0.17	0.06	0.67	6
phoB	23	199	22	0.22	0.17	0.03	0.18	5.79
rpoE	27	144	14	0.26	0.19	0.05	0.36	5.25
fis	22	233	20	0.14	0.09	0.01	0.1	5.18
metJ	5	93	12	1	0.8	0.05	0.33	4.96
fur	20	265	47	0.8	0.7	0.06	0.3	4.32
lexA	16	163	26	0.75	0.56	0.07	0.35	3.53
arcA	56	224	13	0.38	0.16	0.09	0.69	3.16
fadR	6	91	20	0.67	0.5	0.04	0.15	2.56
gcvA	4	110	6	0.75	0.25	0.03	0.17	2.04
ompR	11	152	9	0.27	0.09	0.02	0.11	1.88
flhC	33	95	13	0.24	0.12	0.08	0.31	1.83
fruR	13	193	19	0.77	0.31	0.05	0.21	1.63
narL	48	209	39	0.5	0.27	0.11	0.33	1.57
purR	27	203	48	0.74	0.44	0.1	0.25	1.52
dnaA	3	223	38	0.67	0.33	0.01	0.03	1.47
fnr	72	229	39	0.26	0.12	0.08	0.23	1.32
rpoN	26	265	38	0.27	0.12	0.03	0.08	1.28
soxS	7	206	23	0.43	0.14	0.01	0.04	1
ada	4	195	15	0	0	0	0	1
marR	8	185	24	0.38	0.12	0.02	0.04	0.86
crp	156	542	106	0.45	0.16	0.13	0.24	0.65
araC	9	132	13	0.56	0.11	0.04	0.08	0.41
hns	12	166	15	0.17	0	0.01	0	0
cynR	4	137	12	0.25	0	0.01	0	0
cytR	10	154	4	0.4	0	0.03	0	0
hipB	2	140	10	1	0	0.01	0	0
cysB	17	114	6	0.12	0	0.02	0	0
lacl	3	180	10	0.33	0	0.01	0	0
cspA	2	107	7	0.5	0	0.01	0	0
fhlA	14	169	6	1	0	0.08	0	0
melR	3	55	3	0.67	0	0.04	0	0
deoR	6	118	11	0.33	0	0.02	0	0

The predicted regulons were obtained by applying a site search to the genome of E. coli with a threshold score of P < 0.05. The regulogs were obtained by applying Regulogger to the obtained regulons. The genomes that were used for filtering were Yersinia pestis, Pseudomonas aeruginosa, Haemophilus influenzae, and Vibrio cholerae. The efficiency of Regulogger (Ef_REG) was calculated by comparing the specificity and sensitivity of the regulon and regulog predictions as described in the text.