Bias detection and correction in RNA-Sequencing data.

ABSTRACT:

Background

High throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low background, and ability to identify novel transcripts. Moreover, for genes with multiple isoforms, expression of each isoform may be estimated from RNA-Seq data. Despite these advantages, recent work revealed that base level read counts from RNA-Seq data may not be randomly distributed and can be affected by local nucleotide composition. It was not clear though how the base level read count bias may affect gene level expression estimates.

Results

In this paper, by using five published RNA-Seq data sets from different biological sources and wit

SUBMITTER: Zheng W 

PROVIDER: S-EPMC3149584 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature