Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features.
Genomics
; 112(6): 4342-4347, 2020 11.
Article
en En
| MEDLINE
| ID: mdl-32721444
ABSTRACT
N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https//github.com/MapFM/m7g_model.git.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
ARN
/
Análisis de Secuencia de ARN
/
Guanosina
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Genomics
Asunto de la revista:
GENETICA
Año:
2020
Tipo del documento:
Article