TY - JOUR T1 - Tools and best practices for allelic expression analysis JF - bioRxiv DO - 10.1101/016097 SP - 016097 AU - Stephane E. Castel AU - Ami Levy Moonshine AU - Pejman Mohammadi AU - Eric Banks AU - Tuuli Lappalainen Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/03/05/016097.abstract N2 - 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. ER -