Spliceator: multi-species splice site prediction using convolutional neural networks.
Convolutional neural network
Data quality
Deep learning
Genome annotation
Splice site prediction
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
23 Nov 2021
23 Nov 2021
Historique:
received:
04
07
2021
accepted:
09
11
2021
entrez:
24
11
2021
pubmed:
25
11
2021
medline:
26
11
2021
Statut:
epublish
Résumé
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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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.
Identifiants
pubmed: 34814826
doi: 10.1186/s12859-021-04471-3
pii: 10.1186/s12859-021-04471-3
pmc: PMC8609763
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
561Subventions
Organisme : agence nationale de la recherche
ID : ANR-11-INBS-0013
Organisme : agence nationale de la recherche
ID : GA-676559
Organisme : agence nationale de la recherche
ID : ANR-18-RAR3-0006-02
Informations de copyright
© 2021. The Author(s).
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