Nonparametric methods for analyzing replication origins in genomewide data

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dc.contributor.author Ghosh, Debashis en_US
dc.date.accessioned 2006-09-11T19:34:20Z
dc.date.available 2006-09-11T19:34:20Z
dc.date.issued 2005-01 en_US
dc.identifier.citation Ghosh, Debashis; (2005). "Nonparametric methods for analyzing replication origins in genomewide data." Functional & Integrative Genomics 5(1): 28-31. <http://hdl.handle.net/2027.42/47937> en_US
dc.identifier.issn 1438-7948 en_US
dc.identifier.issn 1438-793X en_US
dc.identifier.uri http://hdl.handle.net/2027.42/47937
dc.identifier.uri http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15599787&dopt=citation en_US
dc.description.abstract Due to the advent of high-throughput genomic technology, it has become possible to monitor cellular activities on a genomewide basis. With these new methods, scientists can begin to address important biological questions. One such question involves the identification of replication origins, which are regions in the chromosomes where DNA replication is initiated. One hypothesis is that their locations are nonrandom throughout the genome. In this article, we analyze data from a recent yeast study in which candidate replication origins were profiled using cDNA microarrays to test this hypothesis. We find no evidence for such clustering. en_US
dc.format.extent 104596 bytes
dc.format.extent 3115 bytes
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso en_US
dc.publisher Springer-Verlag en_US
dc.subject.other Gene Expression en_US
dc.subject.other Density Estimation en_US
dc.subject.other Kernel Smoothing en_US
dc.subject.other Derivative Estimation en_US
dc.subject.other LifeSciences en_US
dc.subject.other Changepoint en_US
dc.title Nonparametric methods for analyzing replication origins in genomewide data en_US
dc.type Original Paper en_US
dc.subject.hlbsecondlevel Genetics en_US
dc.subject.hlbtoplevel Health Sciences en_US
dc.description.peerreviewed Peer Reviewed en_US
dc.contributor.affiliationum Departments of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Room M4057, Ann Arbor, MI, 48109-2029, USA en_US
dc.contributor.affiliationumcampus Ann Arbor en_US
dc.identifier.pmid 15599787 en_US
dc.description.bitstreamurl http://deepblue.lib.umich.edu/bitstream/2027.42/47937/1/10142_2004_Article_122.pdf en_US
dc.identifier.doi http://dx.doi.org/10.1007/s10142-004-0122-1 en_US
dc.identifier.source Functional & Integrative Genomics en_US
dc.owningcollname Interdisciplinary and Peer-Reviewed
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