title: Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.
abstract: BACKGROUND
  RNA sequencing (RNA-Seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next-generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-Seq, substantial biases and uncertainty in short read alignment pose challenges for data analysis. In particular, large base-specific variation and between-base dependence make simple approaches, such as those that use averaging to normalize RNA-Seq data and quantify gene expressions, ineffective.
  
  
  RESULTS
  In this study, we propose a Poisson mixed-effects (POME) model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Since the proposed model is capable of incorporating base-specific variation as well as between-base dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level.
  
  
  METHODS
  POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html.
  
  
  BACKGROUND
  yuzhu@purdue.edu; zhaohui.qin@emory.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Hu
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhu
authors.forename: Jeremy M G
authors.initials: JM
authors.lastname: Taylor
authors.forename: Jun S
authors.initials: JS
authors.lastname: Liu
authors.forename: Zhaohui S
authors.initials: ZS
authors.lastname: Qin
mesh_terms: D045744:Cell Line, Tumor; D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008297:Male; D046228:Microarray Analysis; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*; D059467:Transcriptome
references.citation: Nucleic Acids Res. 2010 Sep;38(17):e170
references.pmid: 20671027
references.citation: Cell. 2008 May 2;133(3):523-36
references.pmid: 18423832
references.citation: Genome Res. 2008 Apr;18(4):610-21
references.pmid: 18285502
references.citation: Nat Methods. 2008 Jul;5(7):613-9
references.pmid: 18516046
references.citation: Genome Res. 2008 Sep;18(9):1509-17
references.pmid: 18550803
references.citation: Biostatistics. 2007 Apr;8(2):158-83
references.pmid: 16809429
references.citation: Nat Methods. 2008 Jul;5(7):621-8
references.pmid: 18516045
references.citation: Proc Natl Acad Sci U S A. 2001 Jan 2;98(1):31-6
references.pmid: 11134512
references.citation: Nat Biotechnol. 1996 Dec;14(13):1675-80
references.pmid: 9634850
references.citation: Nat Biotechnol. 2010 May;28(5):511-5
references.pmid: 20436464
references.citation: BMC Bioinformatics. 2010 Feb 18;11:94
references.pmid: 20167110
references.citation: Nature. 2008 Jun 26;453(7199):1239-43
references.pmid: 18488015
references.citation: Genome Biol. 2010;11(5):R50
references.pmid: 20459815
references.citation: Biometrics. 1987 Sep;43(3):671-81
references.pmid: 3663823
references.citation: Nat Biotechnol. 2011 Jul 31;29(8):742-9
references.pmid: 21804560
references.citation: PLoS One. 2011 Mar 01;6(3):e17305
references.pmid: 21390249
references.citation: Stat Methods Med Res. 2005 Feb;14(1):35-59
references.pmid: 15690999
references.citation: Science. 1995 Oct 20;270(5235):467-70
references.pmid: 7569999
references.citation: Nature. 2009 Mar 5;458(7234):97-101
references.pmid: 19136943
references.citation: Science. 2008 Jun 6;320(5881):1344-9
references.pmid: 18451266
references.citation: Nat Rev Genet. 2009 Jan;10(1):57-63
references.pmid: 19015660