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

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 d...

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Autores principales:	Nicolas Scalzitti, Arnaud Kress, Romain Orhand, Thomas Weber, Luc Moulinier, Anne Jeannin-Girardon, Pierre Collet, Olivier Poch, Julie D. Thompson
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	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
Acceso en línea:	https://doaj.org/article/dae123c3bbd74e8ba53ff17599644662
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id	oai:doaj.org-article:dae123c3bbd74e8ba53ff17599644662
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spelling	oai:doaj.org-article:dae123c3bbd74e8ba53ff175996446622021-11-28T12:11:07ZSpliceator: multi-species splice site prediction using convolutional neural networks10.1186/s12859-021-04471-31471-2105https://doaj.org/article/dae123c3bbd74e8ba53ff175996446622021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04471-3https://doaj.org/toc/1471-2105Abstract 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.Nicolas ScalzittiArnaud KressRomain OrhandThomas WeberLuc MoulinierAnne Jeannin-GirardonPierre ColletOlivier PochJulie D. ThompsonBMCarticleSplice site predictionGenome annotationData qualityDeep learningConvolutional neural networkComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-26 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	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
spellingShingle	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 Nicolas Scalzitti Arnaud Kress Romain Orhand Thomas Weber Luc Moulinier Anne Jeannin-Girardon Pierre Collet Olivier Poch Julie D. Thompson Spliceator: multi-species splice site prediction using convolutional neural networks
description	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.
format	article
author	Nicolas Scalzitti Arnaud Kress Romain Orhand Thomas Weber Luc Moulinier Anne Jeannin-Girardon Pierre Collet Olivier Poch Julie D. Thompson
author_facet	Nicolas Scalzitti Arnaud Kress Romain Orhand Thomas Weber Luc Moulinier Anne Jeannin-Girardon Pierre Collet Olivier Poch Julie D. Thompson
author_sort	Nicolas Scalzitti
title	Spliceator: multi-species splice site prediction using convolutional neural networks
title_short	Spliceator: multi-species splice site prediction using convolutional neural networks
title_full	Spliceator: multi-species splice site prediction using convolutional neural networks
title_fullStr	Spliceator: multi-species splice site prediction using convolutional neural networks
title_full_unstemmed	Spliceator: multi-species splice site prediction using convolutional neural networks
title_sort	spliceator: multi-species splice site prediction using convolutional neural networks
publisher	BMC
publishDate	2021
url	https://doaj.org/article/dae123c3bbd74e8ba53ff17599644662
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Spliceator: multi-species splice site prediction using convolutional neural networks

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