AlterNet: Alternative splicing-aware gene regulatory network inference

Abstract. Gene regulatory networks (GRNs) help decode biological systems by identifying how genes interact and regulate cellular processes. However, conventional GRN inference methods operate at the gene-level, overlooking transcript-level variability introduced by alternative splicing (AS). In this work, we present AlterNet, the first GRN inference and annotation pipeline which produces transcript-resolved and AS-aware GRNs. AlterNet builds on the GRNBoost2 inference algorithm, and includes a transcript plausibility and annotation workflow. We applied AlterNet to expression data from heart tissue, including samples from donors with normal heart function and from patients with different types of cardiomyopathy. The resulting isoform-level GRNs uncovered highly relevant regulatory interactions not detectable at the gene-level. Overall, AlterNet infers transcript-level regulatory network which enable the discovery of novel, biologically relevant regulatory interactions that remain hidden in gene-level GRNs. The source code of AlterNet is available on GitHub https://github.com/bionetslab/AlterNet, and can be installed as a Python package.