A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments.

ABSTRACT:

Background

High-throughput RNA sequencing (RNA-seq) offers unprecedented power to capture the real dynamics of gene expression. Experimental designs with extensive biological replication present a unique opportunity to exploit this feature and distinguish expression profiles with higher resolution. RNA-seq data analysis methods so far have been mostly applied to data sets with few replicates and their default settings try to provide the best performance under this constraint. These methods are based on two well-known count data distributions: the Poisson and the negative binomial. The way to properly calibrate them with large RNA-seq data sets is not trivial for the non-expert bioinformatics user.

Results

Here we show that expression profiles produced by extensively-replicat

SUBMITTER: Esnaola M 

PROVIDER: S-EPMC3849762 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature