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dc.contributor.author Orlando, DA
dc.contributor.author Brady, SM
dc.contributor.author Fink, TM
dc.contributor.author Benfey, PN
dc.contributor.author Ahnert, SE
dc.coverage.spatial England
dc.date.accessioned 2011-06-21T17:29:33Z
dc.date.issued 2010-06-16
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/20565716
dc.identifier 1471-2164-11-381
dc.identifier.citation BMC Genomics, 2010, 11 pp. 381 - ?
dc.identifier.uri http://hdl.handle.net/10161/4345
dc.description.abstract BACKGROUND: Biological processes occur on a vast range of time scales, and many of them occur concurrently. As a result, system-wide measurements of gene expression have the potential to capture many of these processes simultaneously. The challenge however, is to separate these processes and time scales in the data. In many cases the number of processes and their time scales is unknown. This issue is particularly relevant to developmental biologists, who are interested in processes such as growth, segmentation and differentiation, which can all take place simultaneously, but on different time scales. RESULTS: We introduce a flexible and statistically rigorous method for detecting different time scales in time-series gene expression data, by identifying expression patterns that are temporally shifted between replicate datasets. We apply our approach to a Saccharomyces cerevisiae cell-cycle dataset and an Arabidopsis thaliana root developmental dataset. In both datasets our method successfully detects processes operating on several different time scales. Furthermore we show that many of these time scales can be associated with particular biological functions. CONCLUSIONS: The spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast. Using similar large-scale expression datasets, the identification of biological processes acting at multiple time scales in many organisms is now possible.
dc.format.extent 381 - ?
dc.language eng
dc.language.iso en_US en_US
dc.relation.ispartof BMC Genomics
dc.relation.isversionof 10.1186/1471-2164-11-381
dc.subject Arabidopsis
dc.subject Benchmarking
dc.subject Cell Cycle
dc.subject Gene Expression Profiling
dc.subject Plant Roots
dc.subject Saccharomyces cerevisiae
dc.subject Time Factors
dc.subject Transcription, Genetic
dc.title Detecting separate time scales in genetic expression data.
dc.title.alternative en_US
dc.type Journal Article
dc.description.version Version of Record en_US
duke.date.pubdate 2010-6-16 en_US
duke.description.endpage 381 en_US
duke.description.issue en_US
duke.description.startpage 381 en_US
duke.description.volume 11 en_US
dc.relation.journal Bmc Genomics en_US
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/20565716
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Biology
pubs.publication-status Published online
pubs.volume 11
dc.identifier.eissn 1471-2164

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