{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T17:10:04Z","timestamp":1764349804782},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2005,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Tiling array is a new type of microarray that can be used to survey genomic transcriptional activities and transcription factor binding sites at high resolution. The goal of this paper is to develop effective statistical tools to identify genomic loci that show transcriptional or protein binding patterns of interest.<\/jats:p>\n               <jats:p>Results: A two-step approach is proposed and is implemented in TileMap. In the first step, a test-statistic is computed for each probe based on a hierarchical empirical Bayes model. In the second step, the test-statistics of probes within a genomic region are used to infer whether the region is of interest or not. Hierarchical empirical Bayes model shrinks variance estimates and increases sensitivity of the analysis. It allows complex multiple sample comparisons that are essential for the study of temporal and spatial patterns of hybridization across different experimental conditions. Neighboring probes are combined through a moving average method (MA) or a hidden Markov model (HMM). Unbalanced mixture subtraction is proposed to provide approximate estimates of false discovery rate for MA and model parameters for HMM.<\/jats:p>\n               <jats:p>Availability: TileMap is freely available at http:\/\/biogibbs.stanford.edu\/~jihk\/TileMap\/index.htm<\/jats:p>\n               <jats:p>Contact: \u00a0whwong@stanford.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0http:\/\/biogibbs.stanford.edu\/~jihk\/TileMap\/index.htm (includes coloured versions of all figures)<\/jats:p>","DOI":"10.1093\/bioinformatics\/bti593","type":"journal-article","created":{"date-parts":[[2005,7,27]],"date-time":"2005-07-27T02:34:03Z","timestamp":1122431643000},"page":"3629-3636","source":"Crossref","is-referenced-by-count":188,"title":["TileMap: create chromosomal map of tiling array hybridizations"],"prefix":"10.1093","volume":"21","author":[{"given":"Hongkai","family":"Ji","sequence":"first","affiliation":[]},{"given":"Wing Hung","family":"Wong","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2005,7,26]]},"reference":[{"key":"2023060912064068900_B1","doi-asserted-by":"crossref","unstructured":"Baldi, P. and Long, A.D. 2001A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics17509\u2013519","DOI":"10.1093\/bioinformatics\/17.6.509"},{"key":"2023060912064068900_B2","unstructured":"Bolstad, B.M., et al. 2003A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics19185\u2013193"},{"key":"2023060912064068900_B3","doi-asserted-by":"crossref","unstructured":"Cawley, S., et al. 2004Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell116499\u2013509","DOI":"10.1016\/S0092-8674(04)00127-8"},{"key":"2023060912064068900_B4","doi-asserted-by":"crossref","unstructured":"Kampa, D., et al. 2004Novel RNAs identified from an in-depth analysis of the transcriptome of human chromosomes 21 and 22. Genome Res.14331\u2013342","DOI":"10.1101\/gr.2094104"},{"key":"2023060912064068900_B5","doi-asserted-by":"crossref","unstructured":"Kapranov, P., et al. 2002Large-scale transcriptional activity in chromosomes 21 and 22. Science296916\u2013919","DOI":"10.1126\/science.1068597"},{"key":"2023060912064068900_B6","unstructured":"Kapranov, P., et al. 2003Beyond expression profiling: next generation uses of high density oligonucleotide arrays. Brief. Funct. Genomic. Proteomic.247\u201356"},{"key":"2023060912064068900_B7","unstructured":"Keles, S., van der Laan, M.J., Dudoit, S., Cawley, S.E. 2004Multiple testing methods for ChIP-Chip high density oligonucleotide array data. Working Paper Series, Paper 147 , Berkeley, CA  U.C. Berkeley Division of Biostatistics, University of California"},{"key":"2023060912064068900_B8","doi-asserted-by":"crossref","unstructured":"Li, W., et al. 2005A hidden Markov model for analyzing ChIP-chip experiments on genome tiling arrays and its application to p53 binding sequences. Bioinformatics21Suppl. 1, i274\u2013i282","DOI":"10.1093\/bioinformatics\/bti1046"},{"key":"2023060912064068900_B9","doi-asserted-by":"crossref","unstructured":"Morris, C.N. 1983Natural exponential families with quadratic variance functions: statistical theory. The Annals of Statistics11515\u2013529","DOI":"10.1214\/aos\/1176346158"},{"key":"2023060912064068900_B10","unstructured":"Newton, M.A., et al. 2004Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics5155\u2013176"},{"key":"2023060912064068900_B11","doi-asserted-by":"crossref","unstructured":"Smyth, G.K. 2004Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol.3 Article 3","DOI":"10.2202\/1544-6115.1027"},{"key":"2023060912064068900_B12","unstructured":"Storey, J.D. and Tibshirani, R. 2003Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA1009440\u20139445"},{"key":"2023060912064068900_B13","unstructured":"Tusher, V.G., et al. 2001Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA985116\u20135121"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/21\/18\/3629\/50554892\/bioinformatics_21_18_3629.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/21\/18\/3629\/50554892\/bioinformatics_21_18_3629.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T12:07:17Z","timestamp":1686312437000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/21\/18\/3629\/202354"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2005,7,26]]},"references-count":13,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2005,9,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bti593","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2005,9]]},"published":{"date-parts":[[2005,7,26]]}}}