title: EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments.
abstract: BACKGROUND
  Messenger RNA expression is important in normal development and differentiation, as well as in manifestation of disease. RNA-seq experiments allow for the identification of differentially expressed (DE) genes and their corresponding isoforms on a genome-wide scale. However, statistical methods are required to ensure that accurate identifications are made. A number of methods exist for identifying DE genes, but far fewer are available for identifying DE isoforms. When isoform DE is of interest, investigators often apply gene-level (count-based) methods directly to estimates of isoform counts. Doing so is not recommended. In short, estimating isoform expression is relatively straightforward for some groups of isoforms, but more challenging for others. This results in estimation uncertainty that varies across isoform groups. Count-based methods were not designed to accommodate this varying uncertainty, and consequently, application of them for isoform inference results in reduced power for some classes of isoforms and increased false discoveries for others.
  
  
  RESULTS
  Taking advantage of the merits of empirical Bayesian methods, we have developed EBSeq for identifying DE isoforms in an RNA-seq experiment comparing two or more biological conditions. Results demonstrate substantially improved power and performance of EBSeq for identifying DE isoforms. EBSeq also proves to be a robust approach for identifying DE genes.
  
  
  METHODS
  An R package containing examples and sample datasets is available at http://www.biostat.wisc.edu/kendzior/EBSEQ/.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: Department of Statistics, University of Wisconsin, Madison, WI 53706, USA.
authors: Ning
authors: N
authors: Leng
authors: John A
authors: JA
authors: Dawson
authors: James A
authors: JA
authors: Thomson
authors: Victor
authors: V
authors: Ruotti
authors: Anna I
authors: AI
authors: Rissman
authors: Bart M G
authors: BM
authors: Smits
authors: Jill D
authors: JD
authors: Haag
authors: Michael N
authors: MN
authors: Gould
authors: Ron M
authors: RM
authors: Stewart
authors: Christina
authors: C
authors: Kendziorski
mesh_terms: D001499:Bayes Theorem; D002460:Cell Line; D053595:Embryonic Stem Cells / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D016678:Genome; D015233:Models, Statistical; D059368:RNA Isoforms / Q000378:metabolism*; D012333:RNA, Messenger / Q000378:metabolism; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
chemical_list: D059368:RNA Isoforms; D012333:RNA, Messenger