{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,2,18]],"date-time":"2023-02-18T06:10:54Z","timestamp":1676700654713},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2642,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Individual probes on an Affymetrix tiling array usually behave differently. Modeling and removing these probe effects are critical for detecting signals from the array data. Current data processing techniques either require control samples or use probe sequences to model probe-specific variability, such as with MAT. Although the MAT approach can be applied without control samples, residual probe effects continue to distort the true biological signals.<\/jats:p>\n               <jats:p>Results: We propose TileProbe, a new technique that builds upon the MAT algorithm by incorporating publicly available data sets to remove tiling array probe effects. By using a large number of these readily available arrays, TileProbe robustly models the residual probe effects that MAT model cannot explain. When applied to analyzing ChIP-chip data, TileProbe performs consistently better than MAT across a variety of analytical conditions. This shows that TileProbe resolves the issue of probe-specific effects more completely.<\/jats:p>\n               <jats:p>Availability: \u00a0http:\/\/www.biostat.jhsph.edu\/\u223chji\/cisgenome\/index_files\/tileprobe.htm<\/jats:p>\n               <jats:p>Contact: \u00a0hji@jhsph.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp425","type":"journal-article","created":{"date-parts":[[2009,7,11]],"date-time":"2009-07-11T00:13:56Z","timestamp":1247271236000},"page":"2369-2375","source":"Crossref","is-referenced-by-count":12,"title":["TileProbe: modeling tiling array probe effects using publicly available data"],"prefix":"10.1093","volume":"25","author":[{"given":"Jennifer Toolan","family":"Judy","sequence":"first","affiliation":[{"name":"1 Department of Mental Health and 2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA"}]},{"given":"Hongkai","family":"Ji","sequence":"additional","affiliation":[{"name":"1 Department of Mental Health and 2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA"}]}],"member":"286","published-online":{"date-parts":[[2009,7,9]]},"reference":[{"key":"2023013112114730200_B1","doi-asserted-by":"crossref","first-page":"D760","DOI":"10.1093\/nar\/gkl887","article-title":"NCBI GEO: Mining tens of millions of ex-pression profiles\u2014database and tools update","volume":"35","author":"Barrett","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"2023013112114730200_B2","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.cell.2006.02.041","article-title":"A bivalent chromatin structure marks key developmental genes in embryonic stem cells","volume":"125","author":"Bernstein","year":"2006","journal-title":"Cell"},{"key":"2023013112114730200_B3","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1126\/science.1103388","article-title":"Global identification of human transcribed sequences with genome tiling arrays","volume":"306","author":"Bertone","year":"2004","journal-title":"Science"},{"key":"2023013112114730200_B4","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/bioinformatics\/19.2.185","article-title":"A comparison of normalization methods for high density oligonucleotide array data based on variance and bias","volume":"19","author":"Bolstad","year":"2003","journal-title":"Bioinformatics"},{"key":"2023013112114730200_B5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.cell.2005.05.008","article-title":"Chromosome-wide mapping of estrogen receptor binding reveals long-range regulation requiring the forkhead protein FoxA1","volume":"122","author":"Carroll","year":"2005","journal-title":"Cell"},{"key":"2023013112114730200_B6","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/S0092-8674(04)00127-8","article-title":"Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs","volume":"116","author":"Cawley","year":"2004","journal-title":"Cell"},{"key":"2023013112114730200_B7","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1093\/bioinformatics\/btl289","article-title":"Transcript mapping with high-density oligonucleotide tiling arrays","volume":"22","author":"Huber","year":"2006","journal-title":"Bioinformatics"},{"key":"2023013112114730200_B8","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1093\/biostatistics\/4.2.249","article-title":"Exploration, normalization, and summaries of high density oligonucleotide array probe level data","volume":"4","author":"Irizarry","year":"2003","journal-title":"Biostatistics"},{"key":"2023013112114730200_B9","doi-asserted-by":"crossref","first-page":"3629","DOI":"10.1093\/bioinformatics\/bti593","article-title":"TileMap: create chromosomal map of tiling array hybridizations","volume":"21","author":"Ji","year":"2005","journal-title":"Bioinformatics"},{"key":"2023013112114730200_B10","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1038\/nbt.1505","article-title":"An integrated software system for analyzing ChIP-chip and ChIP-seq data","volume":"26","author":"Ji","year":"2008","journal-title":"Nat. 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