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

Autor:	Nicolas Scalzitti, Arnaud Kress, Romain Orhand, Thomas Weber, Luc Moulinier, Anne Jeannin-Girardon, Pierre Collet, Olivier Poch, Julie D. Thompson
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Splice site prediction Genome annotation Data quality Deep learning Convolutional neural network Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Zdroj:	BMC Bioinformatics, Vol 22, Iss 1, Pp 1-26 (2021)
Druh dokumentu:	article
ISSN:	1471-2105 17599644
DOI:	10.1186/s12859-021-04471-3
Popis:	Abstract 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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/dae123c3bbd74e8ba53ff17599644662 Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.