Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-5-20
pubmed:abstractText
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.
pubmed:grant
pubmed:commentsCorrections
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pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1465-6914
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
11
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
R19
pubmed:dateRevised
2010-9-28
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Evidence-ranked motif identification.
pubmed:affiliation
Program for Computational Biology and Bioinformatics, Duke University, 102 North Building, Durham, NC 27708, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural