Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11579
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dc.contributor.authorFerdous, MM-
dc.contributor.authorVinciotti, V-
dc.contributor.authorLiu, X-
dc.contributor.authorWilson, P-
dc.contributor.editorGammerman, A-
dc.contributor.editorVovk, V-
dc.contributor.editorPapadopoulos, H-
dc.coverage.spatialRoyal Holloway Univ London, Egham, ENGLAND-
dc.coverage.spatialRoyal Holloway Univ London, Egham, ENGLAND-
dc.date.accessioned2015-11-10T15:29:19Z-
dc.date.available2015-01-01-
dc.date.available2015-11-10T15:29:19Z-
dc.date.issued2015-
dc.identifier.citationLecture Notes in Computer Science, 9047: pp. 214 - 222, (2015)en_US
dc.identifier.issn0302-9743-
dc.identifier.urihttp://link.springer.com/chapter/10.1007%2F978-3-319-17091-6_16-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11579-
dc.description.abstractChIP-sequencing experiments are routinely used to study genome-wide chromatin marks. Due to the high-cost and complexity associated with this technology, it is of great interest to investigate whether the low-cost option of microarray experiments can be used in combination with ChIP-seq experiments. Most integrative analyses do not consider important features of ChIP-seq data, such as spatial dependencies and ChIP-efficiencies. In this paper, we address these issues by applying a Markov random field model to ChIP-seq data on the protein Brd4, for which both ChIP-seq and microarray data are available on the same biological conditions. We investigate the correlation between the enrichment probabilities around transcription start sites, estimated by the Markov model, and microarray gene expression values. Our preliminary results suggest that binding of the protein is associated with lower gene expression, but differential binding across different conditions does not show an association with differential expression of the associated genes.en_US
dc.format.extent214 - 222 (9)-
dc.language.isoenen_US
dc.publisherSpringer International Publishingen_US
dc.source3rd International Symposium on Statistical Learning and Data Sciences (SLDS)-
dc.source3rd International Symposium on Statistical Learning and Data Sciences (SLDS)-
dc.subjectProtein bindingen_US
dc.subjectGene regulationen_US
dc.subjectMarkov random fielden_US
dc.titleExploring the link between gene expression and protein binding by integrating mRNA microarray and ChIP-seq dataen_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-17091-6_16-
dc.relation.isPartOfSTATISTICAL LEARNING AND DATA SCIENCES-
pubs.finish-date2015-04-23-
pubs.finish-date2015-04-23-
pubs.publication-statusPublished-
pubs.publication-statusPublished-
pubs.start-date2015-04-20-
pubs.start-date2015-04-20-
pubs.volume9047-
Appears in Collections:Dept of Computer Science Research Papers

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