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Spliceator: multi-species splice site prediction using convolutional neural networks
- Source :
- BMC Bioinformatics, BMC Bioinformatics, 2021, 22 (1), pp.561. ⟨10.1186/s12859-021-04471-3⟩, BMC Bioinformatics, 2021, 22, pp.561. ⟨10.1186/s12859-021-04471-3⟩, BMC Bioinformatics, BioMed Central, 2021, 22, pp.561. ⟨10.1186/s12859-021-04471-3⟩, BMC Bioinformatics, Vol 22, Iss 1, Pp 1-26 (2021)
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
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.
- Subjects :
- QH301-705.5
Computer science
Computer applications to medicine. Medical informatics
ved/biology.organism_classification_rank.species
R858-859.7
Convolutional neural network
Computational biology
[INFO] Computer Science [cs]
medicine.disease_cause
Biochemistry
03 medical and health sciences
0302 clinical medicine
Structural Biology
[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]
medicine
Animals
Humans
[INFO]Computer Science [cs]
splice
Biology (General)
Model organism
Molecular Biology
Gene
030304 developmental biology
0303 health sciences
Genome
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
business.industry
ved/biology
Applied Mathematics
Deep learning
fungi
Data quality
Protist
Genome project
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Computer Science Applications
030220 oncology & carcinogenesis
Splice site prediction
RNA splicing
[SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]
Neural Networks, Computer
Artificial intelligence
business
Software
Algorithms
Genome annotation
Subjects
Details
- ISSN :
- 14712105
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- BMC Bioinformatics
- Accession number :
- edsair.doi.dedup.....74fee1e1006bc150643cae02e7e08f2f