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Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists.
Quillet, Aurélien; Anouar, Youssef; Lecroq, Thierry; Dubessy, Christophe.
Afiliación
  • Quillet A; Normandie Université, UNIROUEN, INSERM, Laboratoire Différenciation et Communication Neuronale et Neuroendocrine, 76000 Rouen, France.
  • Anouar Y; Normandie Université, UNIROUEN, INSERM, Laboratoire Différenciation et Communication Neuronale et Neuroendocrine, 76000 Rouen, France.
  • Lecroq T; Normandie Université, UNIROUEN, UNIHAVRE, INSA Rouen, Laboratoire d'Informatique du Traitement de l'Information et des Systèmes, 76000 Rouen, France.
  • Dubessy C; Normandie Université, UNIROUEN, INSERM, Laboratoire Différenciation et Communication Neuronale et Neuroendocrine, 76000 Rouen, France.
Comput Struct Biotechnol J ; 19: 5811-5825, 2021.
Article en En | MEDLINE | ID: mdl-34765096
ABSTRACT
MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Because of their wide network of interactions, miRNAs have become the focus of many studies over the past decade, particularly in animal species. To streamline the number of potential wet lab experiments, the use of miRNA target prediction tools is currently the first step undertaken. However, the predictions made may vary considerably depending on the tool used, which is mostly due to the complex and still not fully understood mechanism of action of miRNAs. The discrepancies complicate the choice of the tool for miRNA target prediction. To provide a comprehensive view of this issue, we highlight in this review the main characteristics of miRNA-target interactions in bilaterian animals, describe the prediction models currently used, and provide some insights for the evaluation of predictor performance.
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Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Struct Biotechnol J Año: 2021 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Struct Biotechnol J Año: 2021 Tipo del documento: Article País de afiliación: Francia