MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html. CONTACT: yuzhu@purdue.edu; zhaohui.qin@emory.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. AVAILABILITY AND IMPLEMENTATION: POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html. CONTACT: yuzhu@purdue.edu; zhaohui.qin@emory.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Nicole Cloonan; Alistair R R Forrest; Gabriel Kolle; Brooke B A Gardiner; Geoffrey J Faulkner; Mellissa K Brown; Darrin F Taylor; Anita L Steptoe; Shivangi Wani; Graeme Bethel; Alan J Robertson; Andrew C Perkins; Stephen J Bruce; Clarence C Lee; Swati S Ranade; Heather E Peckham; Jonathan M Manning; Kevin J McKernan; Sean M Grimmond Journal: Nat Methods Date: 2008-05-30 Impact factor: 28.547
Authors: Ryan Lister; Ronan C O'Malley; Julian Tonti-Filippini; Brian D Gregory; Charles C Berry; A Harvey Millar; Joseph R Ecker Journal: Cell Date: 2008-05-02 Impact factor: 41.582
Authors: Brian T Wilhelm; Samuel Marguerat; Stephen Watt; Falk Schubert; Valerie Wood; Ian Goodhead; Christopher J Penkett; Jane Rogers; Jürg Bähler Journal: Nature Date: 2008-05-18 Impact factor: 49.962