Please use this identifier to cite or link to this item: https://doi.org/10.1093/bioinformatics/bti387
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dc.titlePromoter modeling: The case study of mammalian histone promoters
dc.contributor.authorChowdhary, R.
dc.contributor.authorAli, R.A.
dc.contributor.authorAlbig, W.
dc.contributor.authorDoenecke, D.
dc.contributor.authorBajic, V.B.
dc.date.accessioned2014-10-28T05:14:32Z
dc.date.available2014-10-28T05:14:32Z
dc.date.issued2005-06-01
dc.identifier.citationChowdhary, R., Ali, R.A., Albig, W., Doenecke, D., Bajic, V.B. (2005-06-01). Promoter modeling: The case study of mammalian histone promoters. Bioinformatics 21 (11) : 2623-2628. ScholarBank@NUS Repository. https://doi.org/10.1093/bioinformatics/bti387
dc.identifier.issn13674803
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105314
dc.description.abstractMotivation: Histone proteins play important roles in chromosomal functions. They are significantly evolutionarily conserved across species, which suggests similarity in their transcription regulation. The abundance of experimental data on histone promoters provides an excellent background for the evaluation of computational methods. Our study addresses the issue of how well computational analysis can contribute to unveiling the biologically relevant content of promoter regions for a large number of mammalian histone genes taken across several species, and suggests the consensus promoter models of different histone groups. Results: This is the first study to unveil the detailed promoter structures of all five mammalian histone groups and their subgroups. This is also the most comprehensive computational analysis of histone promoters performed to date. The most exciting fact is that the results correlate very well with the biologically known facts and experimental data. Our analysis convincingly demonstrates that computational approach can significantly contribute to elucidation of promoter content (identification of biologically relevant signals) complementing tedious wet-lab experiments. We believe that this type of analysis can be easily applied to other functional gene classes, thus providing a general framework for modelling promoter groups. These results also provide the basis to hunt for genes co-regulated with histone genes across mammalian genomes. © The Author 2005. Published by Oxford University Press. All rights reserved.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1093/bioinformatics/bti387
dc.description.sourcetitleBioinformatics
dc.description.volume21
dc.description.issue11
dc.description.page2623-2628
dc.description.codenBOINF
dc.identifier.isiut000229441500008
Appears in Collections:Staff Publications

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