De novo ChIP-seq analysis
dc.citation.epage | 10 | en_US |
dc.citation.issueNumber | 205 | en_US |
dc.citation.spage | 1 | en_US |
dc.citation.volumeNumber | 16 | en_US |
dc.contributor.author | He, X. | en_US |
dc.contributor.author | Cicek, A. E. | en_US |
dc.contributor.author | Wang, Y. | en_US |
dc.contributor.author | Schulz, M. H. | en_US |
dc.contributor.author | Le, Hai-Son | en_US |
dc.contributor.author | Bar-Joseph, Z. | en_US |
dc.date.accessioned | 2016-02-08T09:38:30Z | |
dc.date.available | 2016-02-08T09:38:30Z | |
dc.date.issued | 2015 | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.description.abstract | Methods for the analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data start by aligning the short reads to a reference genome. While often successful, they are not appropriate for cases where a reference genome is not available. Here we develop methods for de novo analysis of ChIP-seq data. Our methods combine de novo assembly with statistical tests enabling motif discovery without the use of a reference genome. We validate the performance of our method using human and mouse data. Analysis of fly data indicates that our method outperforms alignment based methods that utilize closely related species. | en_US |
dc.identifier.doi | 10.1186/s13059-015-0756-4 | en_US |
dc.identifier.eissn | 1474-760X | en_US |
dc.identifier.issn | 1474-7596 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/20948 | en_US |
dc.language.iso | English | en_US |
dc.publisher | BioMed Central Ltd. | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1186/s13059-015-0756-4 | en_US |
dc.source.title | Genome Biology: biology for the post-genomic era | en_US |
dc.subject | Hide markov model | en_US |
dc.subject | Reference genome | en_US |
dc.subject | Peak calling | en_US |
dc.subject | Correct motif | en_US |
dc.subject | Jaspar database | en_US |
dc.title | De novo ChIP-seq analysis | en_US |
dc.type | Article | en_US |
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