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dc.contributor.authorZhang, Zhang
dc.contributor.authorLópez-Giráldez, Francesc Francisco
dc.contributor.authorTownsend, Jeffrey P.
dc.date.accessioned2015-08-02T09:12:54Z
dc.date.available2015-08-02T09:12:54Z
dc.date.issued2010-06-10
dc.identifier.issn13674803
dc.identifier.pmid20538728
dc.identifier.doi10.1093/bioinformatics/btq303
dc.identifier.urihttp://hdl.handle.net/10754/561501
dc.description.abstractSummary: We present LOX (Level Of eXpression) that estimates the Level Of gene eXpression from high-throughput-expressed sequence datasets with multiple treatments or samples. Unlike most analyses, LOX incorporates a gene bias model that facilitates integration of diverse transcriptomic sequencing data that arises when transcriptomic data have been produced using diverse experimental methodologies. LOX integrates overall sequence count tallies normalized by total expressed sequence count to provide expression levels for each gene relative to all treatments as well as Bayesian credible intervals. © The Author 2010. Published by Oxford University Press. All rights reserved.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905554
dc.titleLOX: Inferring level of expression from diverse methods of census sequencing
dc.typeArticle
dc.contributor.departmentDesert Agriculture Initiative
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalBioinformatics
dc.identifier.pmcidPMC2905554
dc.contributor.institutionDepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, United States
dc.contributor.institutionProgram in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, United States
kaust.personZhang, Zhang
dc.date.published-online2010-06-10
dc.date.published-print2010-08-01


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