Tools and best practices for data processing in allelic expression analysis

Abstract

Allelic expression analysis has become important for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. We analyze the properties of allelic expression 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 such errors, show that our quality control measures improve the detection of relevant allelic expression, and introduce tools for the high-throughput production of allelic expression data from RNA-sequencing data.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0762-6) contains supplementary material, which is available to authorized users.

Genetic effects on gene expression across human tissues

“…For each sample, allele-specific RNA-seq read counts were generated at all heterozygous variants with the GATK ASEReadCounter tool 75 . Only uniquely mapping reads with a base quality ≥10 at the variant were counted, and only those variants with coverage of at least eight reads were reported.…”

“…Only uniquely mapping reads with a base quality ≥10 at the variant were counted, and only those variants with coverage of at least eight reads were reported. Variants that met any of the following criteria were flagged and removed from downstream analyses: 1) UCSC 50-mer mappability of <1; 2) simulation-based evidence of mapping bias 76 ; and 3) heterozygous genotype not supported by RNA-seq data across all samples for that donor and no significant (FDR > 1%) evidence that the variant is monoallelic in expression data 75 . Gene level measurements of haplotype expression were calculated by aggregating counts per sample across all heterozygous variants with ASE data within the gene using population phasing.…”

“…Gene-level measurements of haplotype expression were calculated by aggregating counts per sample across all heterozygous variants with ASE data within the gene using population phasing. The following filters were applied on ASE data: total coverage ≥8 reads, no mapping bias in simulations 76 , UCSC mappability > 50, and no significant (FDR > 1%) evidence that variant is monoallelic in expression data 75 . b , log 2 transformed cis -eQTL effect size ( x -axis) versus log 2 transformed ASE effect size ( y -axis) for whole blood (Spearman’s ρ =0.82) and c , subcutaneous adipose (Spearman’s ρ =0.74).…”

A genome-wide interactome of DNA-associated proteins in the human liver

“…Allele-specific expression was calculated for each expressed heterozygous SNP using the GATK "ASEReadCounter" function according to previously described best practices (Castel et al 2015).…”

Discovering Single Nucleotide Polymorphisms Regulating Human Gene Expression Using Allele Specific Expression from RNA-seq Data

“…ASE measured using microarrays and RNA-seq has been used for mapping variants associated with gene expression (Tao et al 2006;Bjornsson et al 2008;Serre et al 2008;Bell and Beck 2009;Degner et al 2009;Ge et al 2009;Palacios et al 2009;Daelemans et al 2010;Gregg et al 2010;Heap et al 2010;Pastinen 2010;Ritchie et al 2010;Sun et al 2010;Wagner et al 2010;Hill et al 2011;Sun 2011;Wolff et al 2011;Castel et al 2015;van de Geijn et al 2015).…”