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 
increases uniformity. Additionally, we provide theoretical and empirical results 
showing that the method is unlikely to have any effect on unbiased data, 
suggesting it can be applied with little risk of spurious adjustment.
AVAILABILITY: The method is implemented in the seqbias R/Bioconductor package, 
available freely under the LGPL license from http://bioconductor.org
CONTACT: dcjones@cs.washington.edu
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics 
online.