Rank-Statistics Based Enrichment-Site Prediction Algorithm Developed for Chromatin Immunoprecipitation on Chip Experiments

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Rank-Statistics Based Enrichment-Site Prediction Algorithm Developed for Chromatin Immunoprecipitation on Chip Experiments

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dc.contributor.author Ghosh, Srinka
dc.contributor.author Hirsch, Heather A
dc.contributor.author Sekinger, Edward
dc.contributor.author Gingeras, Thomas R
dc.contributor.author Struhl, Kevin
dc.date.accessioned 2011-04-28T07:27:53Z
dc.date.issued 2006
dc.identifier.citation Ghosh, Srinka, Heather A. Hirsch, Edward Sekinger, Kevin Struhl, and Thomas R. Gingeras. 2006. Rank-statistics based enrichment-site prediction algorithm developed for chromatin immunoprecipitation on chip experiments. BMC Bioinformatics 7: 434. en_US
dc.identifier.issn 1471-2105 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4878951
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. Tiling arrays are increasingly used in chromatin immunoprecipitation (IP) experiments (ChIP on chip). ChIP on chip facilitates the generation of genome-wide maps of in-vivo interactions between DNA-associated proteins including transcription factors and DNA. Analysis of the hybridization of an immunoprecipitated sample to a tiling array facilitates the identification of ChIP-enriched segments of the genome. These enriched segments are putative targets of antibody assayable regulatory elements. The enrichment response is not ubiquitous across the genome. Typically 5 to 10% of tiled probes manifest some significant enrichment. Depending upon the factor being studied, this response can drop to less than 1%. The detection and assessment of significance for interactions that emanate from non-canonical and/or un-annotated regions of the genome is especially challenging. This is the motivation behind the proposed algorithm. Results: We have proposed a novel rank and replicate statistics-based methodology for identifying and ascribing statistical confidence to regions of ChIP-enrichment. The algorithm is optimized for identification of sites that manifest low levels of enrichment but are true positives, as validated by alternative biochemical experiments. Although the method is described here in the context of ChIP on chip experiments, it can be generalized to any treatment-control experimental design. The results of the algorithm show a high degree of concordance with independent biochemical validation methods. The sensitivity and specificity of the algorithm have been characterized via quantitative PCR and independent computational approaches. Conclusion: The algorithm ranks all enrichment sites based on their intra-replicate ranks and inter-replicate rank consistency. Following the ranking, the method allows segmentation of sites based on a meta p-value, a composite array signal enrichment criterion, or a composite of these two measures. The sensitivities obtained subsequent to the segmentation of data using a meta p-value of 10-5, an array signal enrichment of 0.2 and a composite of these two values are 88%, 87% and 95%, respectively. en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.isversionof doi:10.1186/1471-2105-7-434 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1615882/pdf/ en_US
dash.license LAA
dc.title Rank-Statistics Based Enrichment-Site Prediction Algorithm Developed for Chromatin Immunoprecipitation on Chip Experiments 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 2011-04-28T07:27:53Z
dash.affiliation.other HMS^Biological Chemistry and Molecular Pharmacology en_US

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