Discriminative motif analysis of high-throughput dataset | Zendy

Zizhen Yao | Zendy; Kyle L. MacQuarrie | Zendy; Abraham Fong | Zendy; Stephen J. Tapscott | Zendy; Walter L. Ruzzo | Zendy; Robert Gentleman | Zendy

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Discriminative motif analysis of high-throughput dataset

Author(s) -

Zizhen Yao,

Kyle L. MacQuarrie,

Abraham Fong,

Stephen J. Tapscott,

Walter L. Ruzzo,

Robert Gentleman

Publication year - 2013

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btt615

Subject(s) - bioconductor , computer science , discriminative model , sequence motif , scalability , data mining , computational biology , motif (music) , encode , machine learning , biology , dna , genetics , database , gene , acoustics , physics

High-throughput ChIP-seq studies typically identify thousands of peaks for a single transcription factor (TF). It is common for traditional motif discovery tools to predict motifs that are statistically significant against a naïve background distribution but are of questionable biological relevance.

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