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

BMC Bioinformatics

Romain Orhand¹  Thomas Weber¹  Julie D. Thompson¹  Anne Jeannin-Girardon¹  Pierre Collet¹  Nicolas Scalzitti¹  Olivier Poch¹  Arnaud Kress²  Luc Moulinier² 

[1] Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France;Complex Systems and Translational Bioinformatics (CSTB), ICube Laboratory, UMR7357, University of Strasbourg, 1 rue Eugène Boeckel, 67000, Strasbourg, France;BiGEst-ICube Platform, ICube Laboratory, UMR7357, 1 rue Eugène Boeckel, 67000, Strasbourg, France;

关键词: Splice site prediction;    Genome annotation;    Data quality;    Deep learning;    Convolutional neural network;   

DOI  :  10.1186/s12859-021-04471-3

来源: Springer

【 摘 要 】

BackgroundAb 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.ResultsWe 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.ConclusionsSpliceator 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.

【 授权许可】

CC BY