ShaKer: RNA SHAPE prediction using graph kernel.
Bioinformatics
; 35(14): i354-i359, 2019 07 15.
Article
en En
| MEDLINE
| ID: mdl-31510707
ABSTRACT
SUMMARY:
SHAPE experiments are used to probe the structure of RNA molecules. We present ShaKer to predict SHAPE data for RNA using a graph-kernel-based machine learning approach that is trained on experimental SHAPE information. While other available methods require a manually curated reference structure, ShaKer predicts reactivity data based on sequence input only and by sampling the ensemble of possible structures. Thus, ShaKer is well placed to enable experiment-driven, transcriptome-wide SHAPE data prediction to enable the study of RNA structuredness and to improve RNA structure and RNA-RNA interaction prediction. For performance evaluation, we use accuracy and accessibility comparing to experimental SHAPE data and competing methods. We can show that Shaker outperforms its competitors and is able to predict high quality SHAPE annotations even when no reference structure is provided. AVAILABILITY AND IMPLEMENTATION ShaKer is freely available at https//github.com/BackofenLab/ShaKer.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Programas Informáticos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Alemania