iMapSplice: Alleviating Reference Bias Through Personalized RNA-seq Alignment

Genomic variants in both coding and non-coding sequences can have functionally important and sometimes deleterious effects on exon splicing of gene transcripts. For transcriptome profiling using RNA-seq, the accurate alignment of reads across exon junctions is a critical step. Existing algorithms that utilize a standard reference genome as a template sometimes have difficulty in mapping reads that carry genomic variants. These problems can lead to allelic ratio biases and the failure to detect splice variants created by splice site polymorphisms. To improve RNA-seq read alignment, we have developed a novel approach called iMapSplice that enables personalized mRNA transcriptome profiling. The algorithm makes use of personal genomic information and performs an unbiased alignment towards genome indices carrying both reference and alternative bases. Importantly, this breaks the dependency on reference genome splice site dinucleotide motifs and enables iMapSplice to discover personal splice junctions created through splice site polymorphisms. We report comparative analyses using a number of simulated and real datasets. Besides general improvements in read alignment and splice junction discovery, iMapSplice greatly alleviates allelic ratio biases and unravels many previously uncharacterized splice junctions created by splice site polymorphisms, with minimal overhead in computation time and storage. Software download URL: https://github.com/LiuBioinfo/iMapSplice

Methods For Robust Quantification Of Rna Alternative Splicing In Heterogeneous Rna-Seq Datasets

RNA alternative splicing is primarily responsible for transcriptome diversity and is relevant to human development and disease. However, current approaches to splicing quantication make simplifying assumptions which are violated when RNA sequencing data are heterogeneous. Influences from genetic and environmental background contribute to variability within a group of samples purported to represent the same biological condition. This work describes three methods which account for data heterogeneity when detecting differential RNA splicing between sample groups. First, a robust model is implemented for outlier detection within a group of purported replicates. Next, large RNA-seq datasets with high within-group variability are addressed with a statistical approach which retains power to detect changing splice junctions without sacricing specicity. Finally, applying these tools to call sQTLs in GTEx tissues has identified splicing variations associated with risk loci for cardiovascular disease and anomalous skeletal development. Each of these methods correctly handles the properties of heterogeneous RNA-seq data to improve precision and reduce false discovery rate