REDInet: a TCN-based classifier for A-to-I RNA editing detection harnessing million known events-Reference-Cited by-同舟云学术

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

Published:2024-09-10 Issue: Volume: Page:
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Author:

Fonzino Adriano¹,Mazzacuva Pietro Luca²,Handen Adam³,Silvestris Domenico Alessandro¹,Arnold Annette⁴,Pecori Riccardo⁴,Pesole Graziano¹,Picardi Ernesto¹

Affiliation:

1. University of Bari Aldo Moro

2. Institute of Biomembranes, Bioenergetics and Molecular Biotechnology - IBIOM-CNR

3. University of Chicago

4. German Cancer Research Center

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.

Publisher

Springer Science and Business Media LLC

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