Evidence-ranked motif identification.
| dc.contributor.author | Georgiev, S | |
| dc.contributor.author | Boyle, AP | |
| dc.contributor.author | Jayasurya, K | |
| dc.contributor.author | Ding, X | |
| dc.contributor.author | Mukherjee, Sayan | |
| dc.contributor.author | Ohler, Uwe | |
| dc.coverage.spatial | England | |
| dc.date.accessioned | 2011-06-21T17:30:29Z | |
| dc.date.accessioned | 2015-09-03T06:36:26Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | 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. | |
| dc.description.version | Version of Record | |
| dc.identifier | ||
| dc.identifier | gb-2010-11-2-r19 | |
| dc.identifier.eissn | 1474-760X | |
| dc.identifier.uri | ||
| dc.language | eng | |
| dc.language.iso | en_US | |
| dc.relation.ispartof | Genome Biol | |
| dc.relation.isversionof | 10.1186/gb-2010-11-2-r19 | |
| dc.relation.journal | Genome biology | |
| dc.relation.replaces | ||
| dc.relation.replaces | 10161/4393 | |
| dc.subject | Animals | |
| dc.subject | Computational Biology | |
| dc.subject | DNA, Fungal | |
| dc.subject | Genome, Human | |
| dc.subject | Genome-Wide Association Study | |
| dc.subject | Humans | |
| dc.subject | Mice | |
| dc.subject | Oligonucleotide Array Sequence Analysis | |
| dc.subject | Sequence Analysis, DNA | |
| dc.title | Evidence-ranked motif identification. | |
| dc.title.alternative | ||
| dc.type | Journal article | |
| duke.date.pubdate | 2010-00-00 | |
| duke.description.issue | 2 | |
| duke.description.volume | 11 | |
| pubs.author-url | ||
| pubs.begin-page | R19 | |
| pubs.issue | 2 | |
| pubs.organisational-group | Basic Science Departments | |
| pubs.organisational-group | Biostatistics & Bioinformatics | |
| pubs.organisational-group | Duke | |
| pubs.organisational-group | School of Medicine | |
| pubs.publication-status | Published | |
| pubs.volume | 11 |
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