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Evidence-ranked motif identification

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 dir...

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Detalles Bibliográficos
Autores principales: Georgiev, Stoyan, Boyle, Alan P, Jayasurya, Karthik, Ding, Xuan, Mukherjee, Sayan, Ohler, Uwe
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2872879/
https://www.ncbi.nlm.nih.gov/pubmed/20156354
http://dx.doi.org/10.1186/gb-2010-11-2-r19
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author Georgiev, Stoyan
Boyle, Alan P
Jayasurya, Karthik
Ding, Xuan
Mukherjee, Sayan
Ohler, Uwe
author_facet Georgiev, Stoyan
Boyle, Alan P
Jayasurya, Karthik
Ding, Xuan
Mukherjee, Sayan
Ohler, Uwe
author_sort Georgiev, Stoyan
collection PubMed
description 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.
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spelling pubmed-28728792010-05-20 Evidence-ranked motif identification Georgiev, Stoyan Boyle, Alan P Jayasurya, Karthik Ding, Xuan Mukherjee, Sayan Ohler, Uwe Genome Biol Method 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. BioMed Central 2010 2010-02-15 /pmc/articles/PMC2872879/ /pubmed/20156354 http://dx.doi.org/10.1186/gb-2010-11-2-r19 Text en Copyright ©2010 Georgiev et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Georgiev, Stoyan
Boyle, Alan P
Jayasurya, Karthik
Ding, Xuan
Mukherjee, Sayan
Ohler, Uwe
Evidence-ranked motif identification
title Evidence-ranked motif identification
title_full Evidence-ranked motif identification
title_fullStr Evidence-ranked motif identification
title_full_unstemmed Evidence-ranked motif identification
title_short Evidence-ranked motif identification
title_sort evidence-ranked motif identification
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2872879/
https://www.ncbi.nlm.nih.gov/pubmed/20156354
http://dx.doi.org/10.1186/gb-2010-11-2-r19
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