GC-content normalization for RNA-Seq data.

ABSTRACT:

Background

Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof.

Results

We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs

SUBMITTER: Risso D 

PROVIDER: S-EPMC3315510 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature