Flipper: An advanced framework for identifyingdifferential RNA binding behavior with eCLIP data

Abstract

Motivation: Crosslinking and immunoprecipitation (CLIP) methods are widely used to identify RNA binding protein (RBP) binding sites and assess how treatments alter RBP binding patterns and regulatory activity. However, current tools for differential RBP binding analysis lack core features required for rigorous statistical inference, including proper normalization and appropriate handling of replicate experiments. Furthermore, existing approaches cannot adequately separate expression or splicing driven effects from true changes in RBP binding. Results: Here we present Flipper, an application purpose-built for the analysis of differential RBP binding. Flipper introduces several innovations that adapt the DESeq2 framework for eCLIP data. These include the use of input controls to account for expression and splicing driven effects and hierarchical normalization that adjusts for technical variation without confounding signal to noise ratios. We demonstrate that Flipper exhibits high specificity when applied to real differential eCLIP data while also providing deeper biological insights. In addition, analyses of both real and simulated data indicate that Flipper outperforms existing approaches. Together, these results highlight Flipper as an effective framework for differential RBP binding analysis.