KATMAP: Inferring splicing factor activity and regulatory targets from knockdown data

Abstract

Typical RNAseq experiments uncover hundreds of splicing changes, reflecting underlying changes in splicing factor (SF) activity. Understanding transcriptomic variation in terms of SF activity requires elucidating the rules by which each SF impacts splicing. Here we present an interpretable regression model, KATMAP, which models splicing changes transcriptome-wide in terms of changes in SF binding and resulting altered regulation. The regulatory principles KATMAP learns generalize to predict the SFs regulation at individual exons, with potential for design of splice-switching antisense oligonucleotides and inference of the displaced factor. We also discover cooperative splicing regulation by QKI and RFBOX proteins. KATMAP interprets RNAseq data by uncovering the factors responsible for transcriptomic changes, distinguishing direct SF targets from indirect effect, and infers relevant SFs from clinical RNAseq data.