GraphProt2: A novel deep learning-based method for predicting binding sites of RNA-binding proteins

CLIP-seq is the state-of-the-art technique to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression which can be highly variable between conditions, and thus cannot provide a complete picture of the RBP binding landscape. This necessitates the use of computational methods to predict missing binding sites. Here we present GraphProt2, a computational RBP binding site prediction method based on graph convolutional neural networks (GCN). In contrast to current CNN methods, GraphProt2 supports variable length input as well as the possibility to accurately predict nucleotide-wise binding profiles. We demonstrate its superior performance compared to GraphProt and a CNN-based method on single as well as combined CLIP-seq datasets.