Table of Contents
ISRN Bioinformatics
Volume 2012, Article ID 817508, 8 pages
http://dx.doi.org/10.5402/2012/817508
Research Article

Differential Expression Analysis for RNA-Seq Data

1School of Computational and Integrative Sciences, JNU, New Delhi 110067, India
2CorrZ Technosolutions Pvt. Ltd., Noida 201304, India
3Indian Statistical Institute, New Delhi 110016, India
4School of Life Sciences, JNU, New Delhi 110067, India

Received 22 June 2012; Accepted 9 August 2012

Academic Editors: D. Piquemal and A. Torkamani

Copyright © 2012 Rashi Gupta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

RNA-Seq is increasingly being used for gene expression profiling. In this approach, next-generation sequencing (NGS) platforms are used for sequencing. Due to highly parallel nature, millions of reads are generated in a short time and at low cost. Therefore analysis of the data is a major challenge and development of statistical and computational methods is essential for drawing meaningful conclusions from this huge data. In here, we assessed three different types of normalization (transcript parts per million, trimmed mean of M values, quantile normalization) and evaluated if normalized data reduces technical variability across replicates. In addition, we also proposed two novel methods for detecting differentially expressed genes between two biological conditions: (i) likelihood ratio method, and (ii) Bayesian method. Our proposed methods for finding differentially expressed genes were tested on three real datasets. Our methods performed at least as well as, and often better than, the existing methods for analysis of differential expression.