Analysis of differential splicing suggests different modes of short-term splicing regulation

TL;DR

This paper introduces a Gaussian process-based method to analyze differential splicing in RNA-seq time series, revealing distinct modes of splicing regulation and their independence from gene expression changes.

Contribution

It presents a novel statistical approach for identifying differential splicing modes using RNA-seq time series data, advancing understanding of splicing regulation mechanisms.

Findings

Genes with splicing changes independent of expression levels

Identification of genes with transcript-specific regulation

Different modes of splicing regulation observed during estrogen response

Abstract

Motivation: Alternative splicing is an important mechanism in which the regions of pre-mRNAs are differentially joined in order to form different transcript isoforms. Alternative splicing is involved in the regulation of normal physiological functions but also linked to the development of diseases such as cancer. We analyse differential expression and splicing using RNA-seq time series in three different settings: overall gene expression levels, absolute transcript expression levels and relative transcript expression levels. Results: Using estrogen receptor $\alpha$ signalling response as a model system, our Gaussian process (GP)-based test identifies genes with differential splicing and/or differentially expressed transcripts. We discover genes with consistent changes in alternative splicing independent of changes in absolute expression and genes where some transcripts change while others stay constant in absolute level. The results suggest classes of genes with different modes of alternative splicing regulation during the experiment. Availability: R and Matlab codes implementing the method are available at https://github.com/PROBIC/diffsplicing . An interactive browser for viewing all model fits is available at http://users.ics.aalto.fi/hande/splicingGP/ .