Abstract
RNA-binding proteins (RBPs) play critical roles in regulating gene expression by modulating splicing, RNA stability, and protein translation. Stimulus-induced alterations in RBP function contribute to global changes in gene expression, but identifying which RBPs are responsible for the observed changes remains an unmet need. Here, we present Transite, a computational approach that systematically infers RBPs influencing gene expression through changes in RNA stability and degradation. As a proof of principle, we apply Transite to RNA expression data from human patients with non-small-cell lung cancer whose tumors were sampled at diagnosis or after recurrence following treatment with platinum-based chemotherapy. Transite implicates known RBP regulators of the DNA damage response and identifies hnRNPC as a new modulator of chemotherapeutic resistance, which we subsequently validated experimentally. Transite serves as a framework for the identification of RBPs that drive cell-state transitions and adds additional value to the vast collection of publicly available gene expression datasets.