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Evidence-ranked motif identification.
Georgiev, Stoyan; Boyle, Alan P; Jayasurya, Karthik; Ding, Xuan; Mukherjee, Sayan; Ohler, Uwe.
  • Georgiev S; Program for Computational Biology and Bioinformatics, Duke University, 102 North Building, Durham, NC 27708, USA.
Genome Biol ; 11(2): R19, 2010.
Article en En | MEDLINE | ID: mdl-20156354
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.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Análisis de Secuencia de ADN / Análisis de Secuencia por Matrices de Oligonucleótidos / Estudio de Asociación del Genoma Completo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Animals / Humans Idioma: En Año: 2010 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Análisis de Secuencia de ADN / Análisis de Secuencia por Matrices de Oligonucleótidos / Estudio de Asociación del Genoma Completo Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Animals / Humans Idioma: En Año: 2010 Tipo del documento: Article