REDInet: a TCN-based classifier for A-to-I RNA editing detection harnessing million known events

Abstract A-to-I RNA editing detection is still a challenging task. Current bioinformatics tools rely on empirical filters and WGS/WES data to remove background noise, sequencing errors, and artifacts. Sometimes they make use of cumbersome and time-consuming computational procedures. We present here REDInet, a TCN-based Deep Learning algorithm, to profile RNA editing in human RNAseq data. It has been trained on REDIportal RNA editing sites, the largest collection of human A-to-I changes from > 8000 GTEx RNAseq data. REDInet can classify editing events with high accuracy harnessing RNAseq nucleotide frequencies of windows of 101 bases without the need for coupled genomic data.