{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T05:20:18Z","timestamp":1674710418282},"reference-count":36,"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":1490,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: High-throughput nucleotide sequencing technologies provide large amounts of quantitative genomic data at nucleotide resolution, which are important for the present and future biomedical researches; for example differential analysis of base-level RNA expression data will improve our understanding of transcriptome, including both coding and non-coding genes. However, most studies of these data have relied on existing genome annotations and thus are limited to the analysis of known transcripts.<\/jats:p>\n               <jats:p>Results: In this article, we propose a novel method based on a marked point process model to find differentially expressed genomic regions of arbitrary length without using genome annotations. The presented method conducts a statistical test for differential analysis in regions of various lengths at each nucleotide and searches the optimal configuration of the regions by using a Monte Carlo simulation. We applied the proposed method to both synthetic and real genomic data, and their results demonstrate the effectiveness of our method.<\/jats:p>\n               <jats:p>Availability: The program used in this study is available at https:\/\/sites.google.com\/site\/hiroshihatsuda\/.<\/jats:p>\n               <jats:p>Contact: \u00a0H.Hatsuda@warwick.ac.uk<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts371","type":"journal-article","created":{"date-parts":[[2012,9,7]],"date-time":"2012-09-07T20:35:22Z","timestamp":1347050122000},"page":"i633-i639","source":"Crossref","is-referenced-by-count":1,"title":["Finding differentially expressed regions of arbitrary length in quantitative genomic data based on marked point process model"],"prefix":"10.1093","volume":"28","author":[{"given":"Hiroshi","family":"Hatsuda","sequence":"first","affiliation":[{"name":"Department of Statistics, the University of Warwick, Coventry CV4 7AL, UK"}]}],"member":"286","published-online":{"date-parts":[[2012,9,3]]},"reference":[{"issue":"Database issue","key":"2023012513063178400_B1","first-page":"49","article-title":"MachiBase: a Drosophila melanogaster 5\u2032-end mRNA transcription database","volume":"37","author":"Ahsan","year":"2008","journal-title":"Nucleic Acids Res."},{"key":"2023012513063178400_B2","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol."},{"key":"2023012513063178400_B3","first-page":"185","article-title":"Case studies in spatial point process modeling","volume-title":"Springer Lecture Notes in Statistics","author":"Baddeley","year":"2006"},{"issue":"6","key":"2023012513063178400_B4","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1038\/ng1789","article-title":"Genome-wide analysis of mammalian promoter architecture and evolution","volume":"38","author":"Carninci","year":"2006","journal-title":"Nat. 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