Spliceator: multi-species splice site prediction using convolutional neural networks - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue BMC Bioinformatics Année : 2021

Spliceator: multi-species splice site prediction using convolutional neural networks

Résumé

Background Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking. Results We developed Spliceator to predict splice sites in a wide range of species, including model and non-model organisms. Spliceator uses a convolutional neural network and is trained on carefully validated data from over 100 organisms. We show that Spliceator achieves consistently high accuracy (89–92%) compared to existing methods on independent benchmarks from human, fish, fly, worm, plant and protist organisms. Conclusions Spliceator is a new Deep Learning method trained on high-quality data, which can be used to predict splice sites in diverse organisms, ranging from human to protists, with consistently high accuracy.
Fichier principal
Vignette du fichier
view-2.pdf (3.58 Mo) Télécharger le fichier
Origine : Publication financée par une institution

Dates et versions

hal-03464474 , version 1 (07-12-2021)

Identifiants

Citer

Nicolas Scalzitti, Arnaud Kress, Romain Orhand, Thomas Weber, Luc Moulinier, et al.. Spliceator: multi-species splice site prediction using convolutional neural networks. BMC Bioinformatics, 2021, 22, pp.561. ⟨10.1186/s12859-021-04471-3⟩. ⟨hal-03464474⟩
94 Consultations
36 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More