Efficient and Accurate Quantitative Profiling of Alternative Splicing Patterns of Any Complexity on a Laptop

Abstract

Alternative splicing (AS) is a widespread process underlying the generation of transcriptomic and proteomic diversity and is frequently misregulated in human disease. Accordingly, an important goal of biomedical research is the development of toolscapable of comprehensively, accurately, and efficiently profiling AS. Here, we describe Whippet, an easy-to-use RNA-seq analysis method that rapidly-with hardware requirements compatible with a laptop-models and quantifies AS events of any complexity without loss of accuracy. Using an entropic measure of splicing complexity, Whippet reveals that one-third of human protein coding genes produce transcripts with complex AS events involving co-expression of two or more principal splice isoforms. We observe that high-entropy AS events are more prevalent in tumor relative to matched normal tissues and correlate with increased expression of proto-oncogenic splicing factors. Whippet thus affords the rapid and accurate analysis of AS events of any complexity, and as such will facilitate future biomedical research.