REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events.

IF 6.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Briefings in bioinformatics Pub Date : 2025-03-04 DOI:10.1093/bib/bbaf107

Adriano Fonzino, Pietro Luca Mazzacuva, Adam Handen, Domenico Alessandro Silvestris, Annette Arnold, Riccardo Pecori, Graziano Pesole, Ernesto Picardi

引用次数: 0

Abstract

A-to-I ribonucleic acid (RNA) editing detection is still a challenging task. Current bioinformatics tools rely on empirical filters and whole genome sequencing or whole exome sequencing data to remove background noise, sequencing errors, and artifacts. Sometimes they make use of cumbersome and time-consuming computational procedures. Here, we present REDInet, a temporal convolutional network-based deep learning algorithm, to profile RNA editing in human RNA sequencing (RNAseq) data. It has been trained on REDIportal RNA editing sites, the largest collection of human A-to-I changes from >8000 RNAseq data of the genotype-tissue expression project. REDInet can classify editing events with high accuracy harnessing RNAseq nucleotide frequencies of 101-base windows without the need for coupled genomic data.

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A-I核糖核酸（RNA）编辑检测仍然是一项具有挑战性的任务。目前的生物信息学工具依赖经验过滤器和全基因组测序或全外显子组测序数据来去除背景噪声、测序错误和人工痕迹。有时，它们会使用繁琐耗时的计算程序。在此，我们介绍一种基于时序卷积网络的深度学习算法 REDInet，用于分析人类 RNA 测序（RNAseq）数据中的 RNA 编辑。该算法在 REDIportal RNA 编辑位点上进行了训练，REDIportal 是基因型-组织表达项目中超过 8000 个 RNAseq 数据中最大的人类 A 到 I 变化集合。REDInet 可利用 RNAseq 101 个碱基窗口的核苷酸频率对编辑事件进行高精度分类，而无需耦合基因组数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Briefings in bioinformatics 生物-生化研究方法

CiteScore

13.20

自引率

13.70%

发文量

549

审稿时长

6 months

期刊介绍： Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.