Algorithms for Molecular Biology

Table 2 Comparison of running time and used space of SUFPREF and AHOPRO programs for PSSM-based patterns of length 12

From: Analysis of pattern overlaps and exact computation of P-values of pattern occurrences numbers: case of Hidden Markov Models

Experiments parameters			Time			Space
Pattern	$Fraction (ℋ)$	Prob Distrib	SufPref	AhoPro	Aho/SP	SufPref	AhoPro	Aho/SP
PSSM(12,9.63)	0.00001	Bernoulli	0.02	0.37	20.39	0.44	0.59	1.36
PSSM(12,8.69)	0.00003	Bernoulli	0.03	0.90	32.00	0.5	0.97	1.94
PSSM(12,7.41)	0.0001	Bernoulli	0.07	2.60	37.64	0.69	1.88	2.74
PSSM(12,5.89)	0.0003	Bernoulli	0.27	7.64	28.10	1.21	4.97	4.11
PSSM(12,4.01)	0.001	Bernoulli	1.27	26.15	20.61	3.01	15.28	5.07
PSSM(12,2.04)	0.003	Bernoulli	4.99	78.37	15.70	7.75	42.61	5.50
PSSM(12,9.63)	0.00001	Markov	0.03	0.38	15.12	0.47	0.62	1.32
PSSM(12,8.69)	0.00003	Markov	0.05	0.91	18.65	0.53	0.97	1.84
PSSM(12,7.41)	0.0001	Markov	0.11	2.64	23.13	0.71	1.91	2.67
PSSM(12,5.89)	0.0003	Markov	0.41	7.74	18.78	1.24	5.02	4.04
PSSM(12,4.01)	0.001	Markov	1.77	26.50	14.95	3.04	15.31	5.04
PSSM(12,2.04)	0.003	Markov	6.67	79.25	11.88	8.36	42.65	4.94

See Table 1 for the general information on the patterns. The intermediate values of $Fraction (ℋ)$ (0.003, 0.0003, etc. instead of more common 0.005, 0.0005, etc.) were chosen to obtain more homogeneous log-scale.

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