Differential analysis for high density tiling microarray data

Ghosh, S., Hirsch, H. A., Sekinger, E. A., Kapranov, P., Struhl, K., Gingeras, T. R. (2007) Differential analysis for high density tiling microarray data. BMC Bioinformatics, 8. ISSN 14712105 (ISSN)

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URL: http://www.ncbi.nlm.nih.gov/pubmed/17892592
DOI: 10.1186/1471-2105-8-359

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]. © 2007 Ghosh et al; licensee BioMed Central Ltd.

Item Type: Paper
Uncontrolled Keywords: DNA RNA retinoic acid ab initio calculation algorithm analytic method article bioinformatics chromatin structure DNA microarray exon gene control gene expression genome genome analysis intron microarray analysis RNA transcription transcription regulation biological model biology cell strain HL 60 chemistry chromosome map decision theory DNA probe drug effect gene expression profiling gene structure genetic transcription genetics genomics human methodology physiology prediction and forecasting regulatory sequence Tetra Algorithms Chromosome Mapping Computational Biology DNA Probes Gene Components HL-60 Cells Humans Models Genetic Oligonucleotide Array Sequence Analysis Predictive Value of Tests Regulatory Sequences Nucleic Acid Transcription Genetic Tretinoin
Subjects: bioinformatics > genomics and proteomics > Validation and Standardization > Microarray Data Standardization and Validation
bioinformatics > genomics and proteomics > analysis and processing > microarray gene expression processing
CSHL Authors:
Communities: CSHL labs > Gingeras lab
Depositing User: CSHL Librarian
Date: 2007
Date Deposited: 08 Mar 2012 16:05
Last Modified: 12 Jul 2013 20:00
PMCID: PMC2231405
Related URLs:
URI: http://repository.cshl.edu/id/eprint/25316

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