Pardiff: Inference of Differential Expression at Base-Pair Level from RNA-Seq Experiments

  • Bogdan Mirauta
  • Pierre Nicolas
  • Hugues Richard
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8158)

Abstract

In the field of RNA-Seq transcriptomics, detecting differences in expression levels between two data-sets remains a challenging question. Most current methods consider only point estimates of the expression levels, and thus neglect the uncertainty of these estimates. Further, testing for differential expression is often done on predefined regions. Here, we propose Pardiff, a method that reconstructs the profile of differential expression at a base-pair resolution and incorporate uncertainty via the use of a Bayesian framework. This method is built on our approach, Parseq, to infer the transcriptional landscape from RNA-seq data.

A program, named Pardiff, implements this strategy and will be made available at: http://www.lgm.upmc.fr/parseq/.

Keywords

Fold Change Positive Predictive Value Posterior Distribution Fold Change Threshold Transcriptional Landscape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bogdan Mirauta
    • 1
  • Pierre Nicolas
    • 2
  • Hugues Richard
    • 1
  1. 1.Génomique des microorganismes, UPMC and CNRS UMR7238ParisFrance
  2. 2.Mathématique Informatique et Génome, INRA UR1077, Jouy-en-JosasFrance

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