Comprehensive and scalable quantification of splicing differences with MntJULiP

Abstract

Alternative splicing of mRNA is an essential gene regulatory mechanism with important roles in development and disease. We present MntJULiP, a method for comprehensive and accurate quantification of splicing differences between two or more conditions. MntJULiP implements novel Dirichlet-multinomial and zero-inflated negative binomial models within a Bayesian framework to detect both changes in splicing ratios and in absolute splicing levels of introns with high accuracy, and can find classes of variation overlooked by reference tools. Additionally, a mixture model allows multiple conditions to be compared simultaneously. Highly scalable, it processed hundreds of GTEx samples in <1 hour to reveal splicing constituents of tissue differentiation.