EditPredict: Prediction of RNA editable sites with convolutional neural network.
Genomics
; 113(6): 3864-3871, 2021 11.
Article
em En
| MEDLINE
| ID: mdl-34562567
ABSTRACT
RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to predict human RNA editing events in both Alu regions and non-Alu regions. With a validation dataset resulting from CRISPR/Cas9 knockout of the ADAR1 enzyme, the validation accuracies reached 99.5% and 93.6% for Alu and non-Alu regions, respectively. We ported our CNN models in a web service named EditPredict. EditPredict not only works on reference genome sequences but can also take into consideration single nucleotide variants in personal genomes. In addition to the human genome, EditPredict tackles other model organisms including bumblebee, fruitfly, mouse, and squid genomes. EditPredict can be used stand-alone to predict novel RNA editing and it can be used to assist in filtering for candidate RNA editing detected from RNA-Seq data.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Edição de RNA
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
Genomics
Assunto da revista:
GENETICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos