Introduction

Quantification of sequence abundance in RNA-Seq experiments is often conflated by protocol-specific sequence bias. The exact sources of the bias are unknown, but may be influenced by polymerase chain reaction amplification, or differing primer affinities and mixtures, for example. The result is decreased accuracy in many applications, such as de novo gene annotation and transcript quantification.We present a new method to measure and correct for these influences using a simple graphical model. Our model does not rely on existing gene annotations, and model selection is performed automatically making it applicable with few assumptions. We evaluate our method on several datasets, and by multiple criteria, demonstrating that it effectively decreases bias and increases uniformity. Additionally, we provide theoretical and empirical results showing that the method is unlikely to have any effect on unbiased data, suggesting it can be applied with little risk of spurious adjustment.The method is implemented in the seqbias R/Bioconductor package, available freely under the LGPL license from http://bioconductor.orgdcjones@cs.washington.eduSupplementary data are available at Bioinformatics online.

Publications

  1. A new approach to bias correction in RNA-Seq.
    Cite this
    Jones DC, Ruzzo WL, Peng X, Katze MG, 2012-04-01 - Bioinformatics (Oxford, England)

Credits

  1. Daniel C Jones
    Developer

    Department of Computer Science and Engineering, University of Washington, United States of America

  2. Walter L Ruzzo
    Developer

  3. Xinxia Peng
    Developer

  4. Michael G Katze
    Investigator

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Summary
AccessionBT001336
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByMichael G Katze