RT Journal Article
SR Electronic
T1 Tools and best practices for allelic expression analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 016097
DO 10.1101/016097
A1 Stephane E. Castel
A1 Ami Levy Moonshine
A1 Pejman Mohammadi
A1 Eric Banks
A1 Tuuli Lappalainen
YR 2015
UL http://biorxiv.org/content/early/2015/03/05/016097.abstract
AB Allelic expression (AE) analysis has become an important tool for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. In this paper, we systematically analyze the properties of AE read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting and filtering for such errors, and show that the resulting AE data has extremely low technical noise. Finally, we introduce novel software for high-throughput production of AE data from RNA-sequencing data, implemented in the GATK framework. These improved tools and best practices for AE analysis yield higher quality AE data by reducing technical bias. This provides a practical framework for wider adoption of AE analysis by the genomics community.