Introduction

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.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.POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html.yuzhu@purdue.edu; zhaohui.qin@emory.eduSupplementary data are available at Bioinformatics online.

Publications

  1. Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq.
    Cite this
    Hu M, Zhu Y, Taylor JM, Liu JS, Qin ZS, 2012-01-01 - Bioinformatics (Oxford, England)

Credits

  1. Ming Hu
    Developer

    Department of Statistics, Harvard University, United States of America

  2. Yu Zhu
    Developer

  3. Jeremy M G Taylor
    Developer

  4. Jun S Liu
    Developer

  5. Zhaohui S Qin
    Investigator

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Summary
AccessionBT000262
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByZhaohui S Qin