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Evidence-ranked motif identification

Item Type:Article
Title:Evidence-ranked motif identification
Creators Name:Georgiev, S. and Boyle, A.P. and Jayasurya, K. and Ding, X. and Mukherjee, S. and Ohler, U.
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.
Keywords:Computational Biology, Fungal, Genome-Wide Association Study, Human Genome, Oligonucleotide Array Sequence Analysis, DNA Sequence Analysis, Animals, Mice
Source:Genome Biology
ISSN:1474-760X
Publisher:BioMed Central (U.K.)
Volume:11
Number:2
Page Range:R19
Date:15 February 2010
Official Publication:https://doi.org/10.1186/gb-2010-11-2-r19
PubMed:View item in PubMed

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