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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...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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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. |
format | Text |
id | pubmed-2872879 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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