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Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning

Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavior and regulation using computational models known...

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Detalles Bibliográficos
Autores principales: Louadi, Zakaria, Oubounyt, Mhaned, Tayara, Hilal, Chong, Kil To
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722613/
https://www.ncbi.nlm.nih.gov/pubmed/31374967
http://dx.doi.org/10.3390/genes10080587
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author Louadi, Zakaria
Oubounyt, Mhaned
Tayara, Hilal
Chong, Kil To
author_facet Louadi, Zakaria
Oubounyt, Mhaned
Tayara, Hilal
Chong, Kil To
author_sort Louadi, Zakaria
collection PubMed
description Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavior and regulation using computational models known as “Splicing Codes”. The final goal of these algorithms is to make an in-silico prediction of AS outcome from genomic sequence. Here, we develop a deep learning approach, called Deep Splicing Code (DSC), for categorizing the well-studied classes of AS namely alternatively skipped exons, alternative 5’ss, alternative 3’ss, and constitutively spliced exons based only on the sequence of the exon junctions. The proposed approach significantly improves the prediction and the obtained results reveal that constitutive exons have distinguishable local characteristics from alternatively spliced exons. Using the motif visualization technique, we show that the trained models learned to search for competitive alternative splice sites as well as motifs of important splicing factors with high precision. Thus, the proposed approach greatly expands the opportunities to improve alternative splicing modeling. In addition, a web-server for AS events prediction has been developed based on the proposed method.
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spelling pubmed-67226132019-09-10 Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning Louadi, Zakaria Oubounyt, Mhaned Tayara, Hilal Chong, Kil To Genes (Basel) Article Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavior and regulation using computational models known as “Splicing Codes”. The final goal of these algorithms is to make an in-silico prediction of AS outcome from genomic sequence. Here, we develop a deep learning approach, called Deep Splicing Code (DSC), for categorizing the well-studied classes of AS namely alternatively skipped exons, alternative 5’ss, alternative 3’ss, and constitutively spliced exons based only on the sequence of the exon junctions. The proposed approach significantly improves the prediction and the obtained results reveal that constitutive exons have distinguishable local characteristics from alternatively spliced exons. Using the motif visualization technique, we show that the trained models learned to search for competitive alternative splice sites as well as motifs of important splicing factors with high precision. Thus, the proposed approach greatly expands the opportunities to improve alternative splicing modeling. In addition, a web-server for AS events prediction has been developed based on the proposed method. MDPI 2019-08-01 /pmc/articles/PMC6722613/ /pubmed/31374967 http://dx.doi.org/10.3390/genes10080587 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Louadi, Zakaria
Oubounyt, Mhaned
Tayara, Hilal
Chong, Kil To
Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning
title Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning
title_full Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning
title_fullStr Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning
title_full_unstemmed Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning
title_short Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning
title_sort deep splicing code: classifying alternative splicing events using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722613/
https://www.ncbi.nlm.nih.gov/pubmed/31374967
http://dx.doi.org/10.3390/genes10080587
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