GRISOTTO: A greedy approach to improve combinatorial algorithms for motif discovery with prior knowledge

Table 2 Comparison of GRISOTTO with state-of-the-art methods over ChiP-chip data.

Algorithm	Description	Successes	%	Ref
PhyloCon	Local alignment of conserved regions	19	12%	[29]
PhyME	Alignment-based with EM	21	13%	[30]
MEME:OOPS	MEME with OOPS model	36	23%	[31]
MEME:ZOOPS	MEME with ZOOPS model	39	25%	[31]
MEME-c	MEME without conserved bases masked	49	31%	[28]
PhyloGibbs	Alignment-based with Gibbs Sampling	54	35%	[32]
Kellis et al.	Alignment-based	56	36%	[33]
CompareProspector	Alignment-based with Gibbs sampling	64	41%	[34]
Converge	Alignment-based with EM	68	44%	[35]
MEME:OOPS-	MEME with OOPS model and priors	73	47%	[17]
PRIORITY-	Gibbs sampler with priors	77	49%	[16]
MEME:ZOOP-	MEME with ZOOPS model and priors	81	52%	[17]
GRISOTTO-	GRISOTTO with priors	83	53%	-
PRIORITY-	Gibbs sampler with priors	70	45%	[15]
GRISOTTO-	GRISOTTO with priors	80	51%	-
PRIORITY-	Gibbs sampler with priors	70	45%	[11]
GRISOTTO-	GRISOTTO with priors	77	49%	-
GRISOTTO-	GRISOTTO with combined priors	93	60%	-

The results of motif discoverers were taken from R. Gordân et al. [16] and T. L. Bailey et al. [17].
All priors used were devised by R. Gordân, A. J. Hartemink and L. Narlikar [11, 14–16].

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