Characterizing alternative splicing effects on protein interaction networks with LINDA
Published in Bioinformatics, 2023
Alternative RNA splicing plays a crucial role in defining protein function. However, despite its relevance, there is a lack of tools that characterize effects of splicing on protein interaction networks in a mechanistic manner (i.e. presence or absence of protein–protein interactions due to RNA splicing). To fill this gap, we present Linear Integer programming for Network reconstruction using transcriptomics and Differential splicing data Analysis (LINDA) as a method that integrates resources of protein–protein and domain–domain interactions, transcription factor targets, and differential splicing/transcript analysis to infer splicing-dependent effects on cellular pathways and regulatory networks. We have applied LINDA to a panel of 54 shRNA depletion experiments in HepG2 and K562 cells from the ENCORE initiative. Through computational benchmarking, we could show that the integration of splicing effects with LINDA can identify pathway mechanisms contributing to known bioprocesses better than other state of the art methods, which do not account for splicing. Additionally, we have experimentally validated some of the predicted splicing effects that the depletion of HNRNPK in K562 cells has on signalling.
Recommended citation: Enio Gjerga, Isabel S. Naarmann-de Vries and Christoph Dieterich. “Characterizing alternative splicing effects on protein interaction networks with LINDA” Bioinformatics Volume 39, Issue Supplement_1, June 2023, Pages i458–i464. https://doi.org/10.1093/bioinformatics/btad224