Zeitschrift für Angewandte Bioinformatik und Computational Biology

A Review on Recent Statistical Models for RNA-Seq Data

Mohammad Samir Farooqi, DC Mishra, KK Chaturvedi, Anil Rai, SB Lal, Sanjeev Kumar, Jyotika Bhati and Anu Sharma

The next generation sequencing technology, RNA-sequencing (RNA-seq), has got growing acceptance in transcriptome analyses. Statistical methods used for gene expression analyses with RNAseq provide meaningful inferences of gene expression using counts of reads. There are various statistical models with its pros and cons available for RNA-seq data analysis. There is a need for consistent statistical methods to explore the information from the developing sequencing technologies. The current article gives a review of the statistical methods with their limitations that can be useful for the RNA-seq analysis. The main emphasis is given to the parametric, nonparametric and hybrid models for identifying the genes with differential expression.