A new approach to bias correction in RNA-Seq.

ABSTRACT:

Motivation

Quantification of sequence abundance in RNA-Seq experiments is often conflated by protocol-specific sequence bias. The exact sources of the bias are unknown, but may be influenced by polymerase chain reaction amplification, or differing primer affinities and mixtures, for example. The result is decreased accuracy in many applications, such as de novo gene annotation and transcript quantification.

Results

We present a new method to measure and correct for these influences using a simple graphical model. Our model does not rely on existing gene annotations, and model selection is performed automatically making it applicable with few assumptions. We evaluate our method on several datasets, and by multiple criteria, demonstrating that it effectively decreases bias and

SUBMITTER: Jones DC 

PROVIDER: S-EPMC3315719 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature