Genome Biol. 2010;11(2):R19. Epub 2010 Feb 15.

Evidence-ranked motif identification.

Georgiev S, Boyle AP, Jayasurya K, Ding X, Mukherjee S, Ohler U.

Source

Program for Computational Biology and Bioinformatics, Duke University, 102 North Building, Durham, NC 27708, USA.

Abstract

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.

PMID:: 20156354; [PubMed - indexed for MEDLINE]
PMCID: PMC2872879

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DNA, Fungal

Grant Support

P50GM081883/GM/NIGMS NIH HHS/United States

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