{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T03:13:18Z","timestamp":1780110798907,"version":"3.54.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Chromatin immunoprecipitation followed by hybridization to a genomic tiling microarray (ChIP-chip) is a routinely used protocol for localizing the genomic targets of DNA-binding proteins. The resolution to which binding sites in this assay can be identified is commonly considered to be limited by two factors: (1) the resolution at which the genomic targets are tiled in the microarray and (2) the large and variable lengths of the immunoprecipitated DNA fragments.<\/jats:p>\n               <jats:p>Results: We have developed a generative model of binding sites in ChIP-chip data and an approach, MeDiChI, for efficiently and robustly learning that model from diverse data sets. We have evaluated MeDiChI's performance using simulated data, as well as on several diverse ChIP-chip data sets collected on widely different tiling array platforms for two different organisms (Saccharomyces cerevisiae and Halobacterium salinarium NRC-1). We find that MeDiChI accurately predicts binding locations to a resolution greater than that of the probe spacing, even for overlapping peaks, and can increase the effective resolution of tiling array data by a factor of 5\u00d7 or better. Moreover, the method's performance on simulated data provides insights into effectively optimizing the experimental design for increased binding site localization accuracy and efficacy.<\/jats:p>\n               <jats:p>Availability: \u00a0MeDiChI is available as an open-source R package, including all data, from http:\/\/baliga.systemsbiology.net\/medichi.<\/jats:p>\n               <jats:p>Contact: \u00a0dreiss@systemsbiology.org<\/jats:p>\n               <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm592","type":"journal-article","created":{"date-parts":[[2007,12,2]],"date-time":"2007-12-02T01:14:36Z","timestamp":1196558076000},"page":"396-403","source":"Crossref","is-referenced-by-count":43,"title":["Model-based deconvolution of genome-wide DNA binding"],"prefix":"10.1093","volume":"24","author":[{"given":"David J.","family":"Reiss","sequence":"first","affiliation":[{"name":"Institute for Systems Biology, 1441 N. 34th St. Seattle, WA 98103-8904, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc T.","family":"Facciotti","sequence":"additional","affiliation":[{"name":"Institute for Systems Biology, 1441 N. 34th St. Seattle, WA 98103-8904, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nitin S.","family":"Baliga","sequence":"additional","affiliation":[{"name":"Institute for Systems Biology, 1441 N. 34th St. Seattle, WA 98103-8904, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2007,12,1]]},"reference":[{"key":"2023061011434023100_B1","doi-asserted-by":"crossref","first-page":"R36","DOI":"10.1186\/gb-2006-7-5-r36","article-title":"The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo","volume":"7","author":"Bonneau","year":"2006","journal-title":"Genome Biol"},{"key":"2023061011434023100_B2","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1016\/j.cell.2005.08.020","article-title":"Core transcriptional regulatory circuitry in human embryonic stem cells","volume":"122","author":"Boyer","year":"2005","journal-title":"Cell"},{"key":"2023061011434023100_B3","doi-asserted-by":"crossref","first-page":"R97","DOI":"10.1186\/gb-2005-6-11-r97","article-title":"ChIPOTle: a user-friendly tool for the analysis of ChIP-chip data [Evaluation Studies]","volume":"6","author":"Buck","year":"2005","journal-title":"Genome Biol"},{"key":"2023061011434023100_B4","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1186\/1471-2105-8-146","article-title":"Analysis of probe level patterns in Affymetrix microarray data [Comparative Study]","volume":"8","author":"Cambon","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023061011434023100_B5","doi-asserted-by":"crossref","first-page":"3385","DOI":"10.1021\/ac052212q","article-title":"Automatic deconvolution of isotope-resolved mass spectra using variable selection and quantized peptide mass distribution","volume":"78","author":"Du","year":"2006","journal-title":"Anal. 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