Differential analysis for high density tiling microarray data

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Differential analysis for high density tiling microarray data

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dc.contributor.author Ghosh, Srinka
dc.contributor.author Hirsch, Heather A.
dc.contributor.author Sekinger, Edward A.
dc.contributor.author Kapranov, Philipp
dc.contributor.author Struhl, Kevin
dc.contributor.author Gingeras, Thomas R.
dc.date.accessioned 2011-04-18T04:51:30Z
dc.date.issued 2007
dc.identifier.citation Ghosh, Srinka, Heather A. Hirsch, Edward A. Sekinger, Philipp Kapranov, Kevin Struhl, and Thomas R. Gingeras. 2007. Differential analysis for high density tiling microarray data. BMC Bioinformatics 8: 359. en_US
dc.identifier.issn 1471-2105 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4853428
dc.description.abstract Background: High density oligonucleotide tiling arrays are an effective and powerful platform for conducting unbiased genome-wide studies. The ab initio probe selection method employed in tiling arrays is unbiased, and thus ensures consistent sampling across coding and non-coding regions of the genome. These arrays are being increasingly used to study the associated processes of transcription, transcription factor binding, chromatin structure and their association. Studies of differential expression and/or regulation provide critical insight into the mechanics of transcription and regulation that occurs during the developmental program of a cell. The time-course experiment, which comprises an in-vivo system and the proposed analyses, is used to determine if annotated and un-annotated portions of genome manifest coordinated differential response to the induced developmental program. Results: We have proposed a novel approach, based on a piece-wise function – to analyze genome-wide differential response. This enables segmentation of the response based on protein-coding and non-coding regions; for genes the methodology also partitions differential response with a 5' versus 3' versus intra-genic bias. Conclusion: The algorithm built upon the framework of Significance Analysis of Microarrays, uses a generalized logic to define regions/patterns of coordinated differential change. By not adhering to the gene-centric paradigm, discordant differential expression patterns between exons and introns have been identified at a FDR of less than 12 percent. A co-localization of differential binding between RNA Polymerase II and tetra-acetylated histone has been quantified at a p-value < 0.003 it is most significant at the 5' end of genes, at a p-value < 10^{-13}. The prototype R code has been made available as supplementary material [see Additional file 1]. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.isversionof doi:10.1186/1471-2105-8-359 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2231405/pdf/ en_US
dash.license LAA
dc.subject false discovery rate en_US
dc.subject factor-binding sites en_US
dc.subject gene-expression en_US
dc.subject chromatin immunoprecipitation en_US
dc.subject chip-chip en_US
dc.subject genome en_US
dc.subject statistics en_US
dc.subject arrays en_US
dc.subject rates en_US
dc.subject human-chromosome-21 en_US
dc.title Differential analysis for high density tiling microarray data en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal BMC Bioinformatics en_US
dash.depositing.author Struhl, Kevin
dc.date.available 2011-04-18T04:51:30Z
dash.affiliation.other HMS^Biological Chemistry and Molecular Pharmacology en_US

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