An Alignment-Free Regression Approach for Estimating Allele-Specific Expression Using RNA-Seq Data

RNA-seq technology enables large-scale studies of allele-specific expression (ASE), or the expression difference between maternal and paternal alleles. Here, we study ASE in animals for which parental RNA-seq data are available. While most methods for determining ASE rely on read alignment, read alignment either leads to reference bias or requires knowledge of genomic variants in each parental strain. When RNA-seq data are available for both parental strains of a hybrid animal, it is possible to infer ASE with minimal reference bias and without knowledge of parental genomic variants. Our approach first uses parental RNA-seq reads to discover maternal and paternal versions of transcript sequences. Using these alternative transcript sequences as features, we estimate abundance levels of transcripts in the hybrid animal using a modified lasso linear regression model.We tested our methods on synthetic data from the mouse transcriptome and compared our results with those of Trinity, a state-of-the-art de novo RNA-seq assembler. Our methods achieved high sensitivity and specificity in both identifying expressed transcripts and transcripts exhibiting ASE. We also ran our methods on real RNA-seq mouse data from two F1 samples with wild-derived parental strains and were able to validate known genes exhibiting ASE, as well as confirm the expected maternal contribution ratios in all genes and genes on the X chromosome.