Evidence-ranked motif identification.
cERMIT is a computationally efficient motif discovery tool based on analyzing genome-wide quantitative regulatory evidence. Instead of pre-selecting promising candidate sequences, it utilizes information across all sequence regions to search for high-scoring motifs. We apply cERMIT on a range of direct binding and overexpression datasets; it substantially outperforms state-of-the-art approaches on curated ChIP-chip datasets, and easily scales to current mammalian ChIP-seq experiments with data on thousands of non-coding regions.
Program for Computational Biology and Bioinformatics, Duke University, 102 North Building, Durham, NC 27708, USA.
Stoyan
S
Georgiev
Alan P
AP
Boyle
Karthik
K
Jayasurya
Xuan
X
Ding
Sayan
S
Mukherjee
Uwe
U
Ohler
D000818:Animals; D019295:Computational Biology; D004271:DNA, Fungal / Q000235:genetics; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D017422:Sequence Analysis, DNA / Q000379:methods*
D004271:DNA, Fungal