A new strategy to reduce allelic bias in RNA-Seq readmapping

Abstract

Accurate estimation of expression levels from RNA-Seq data entails precise mapping of the sequence reads to a reference genome. Because the standard reference genome contains only one allele at any given locus, reads overlapping polymorphic loci that carry a non-reference allele are at least one mismatch away from the reference and, hence, are less likely to be mapped. This bias in read mapping leads to inaccurate estimates of allele-specific expression (ASE). To address this read-mapping bias, we propose the construction of an enhanced reference genome that includes the alternative alleles at known polymorphic loci. We show that mapping to this enhanced reference reduced the read-mapping biases, leading to more reliable estimates of ASE. Experiments on simulated data show that the proposed strategy reduced the number of loci with mapping bias by ≥63% when compared with a previous approach that relies on masking the polymorphic loci and by ≥18% when compared with the standard approach that uses an unaltered reference. When we applied our strategy to actual RNA-Seq data, we found that it mapped up to 15% more reads than the previous approaches and identified many seemingly incorrect inferences made by them.

No excess of cis -regulatory variation associated with intra-specific selection in wild pearl millet ( Cenchrus americanus )

“…Several approaches have been proposed to circumvent the mapping bias issue such as SNP-masked genome at known heterozygous positions (Degner et al. 2009), including known polymorphism data in an enhanced reference genome (Vijaya Satya et al. 2012) or mapping reads on the two parental genomes (McManus et al.…”

RNA Sequencing and Analysis

“…To assess reference-allele mapping bias, the number of mismatches in reads containing the nonreference allele should be assessed as increased bias is observed with greater sequence divergence between alleles (Stevenson et al 2013). To correct for read-mapping bias, an enhanced reference genome can be constructed that masks all SNP positions or includes the alternative alleles at polymorphic loci (Degner et al 2009; Satya et al 2012). Statistical methods to better address these technical biases are under active development and are expected to foster further improvements in ASE detection.…”

Chromatin Accessibility Data Sets Show Bias Due to Sequence Specificity of the DNase I Enzyme

“…Sequencing technologies do not behave like idealized models and, over the past few years, several groups have reported sequence composition bias in the reads observed from ChIP-seq and RNA-seq experiments and they have presented different models for normalization of such data[13]–[15]. To the best of our knowledge there has not been any report of sequence bias in DNase-seq data.…”