Semi-supervised deep learning with graph neural network for cross-species regulatory sequence prediction

Abstract

Genome-wide association studies have systematically identified thousands of single nucleotide polymorphisms (SNPs) associated with complex genetic diseases. However, the majority of those SNPs were found in non-coding genomic regions, preventing the understanding of the underlying causal mechanism. Predicting molecular processes based on the DNA sequence represents a promising approach to understand the role of those non-coding SNPs. Over the past years, deep learning was successfully applied to regulatory sequence prediction. Such method required DNA sequences associated with functional data for training. However, the human genome has a finite size which strongly limits the amount of DNA sequence with functional data available for training. Conversely, the amount of mammalian DNA sequences is exponentially increasing due to ongoing large sequencing projects, but without functional data in most cases. Here, we propose a semi-supervised learning approach based on graph neural network which allows to borrow information from homologous mammal sequences during training. Our approach can be plugged into any existing deep learning model and showed improvements in many different situations, including classification and regression, and for different types of functional data.