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Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning
Transposable elements (TEs) are genomic units able to move within the genome of virtually all organisms. Due to their natural repetitive numbers and their high structural diversity, the identification and classification of TEs remain a challenge in sequenced genomes. Although TEs were initially rega...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696364/ https://www.ncbi.nlm.nih.gov/pubmed/31390781 http://dx.doi.org/10.3390/ijms20153837 |
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author | Orozco-Arias, Simon Isaza, Gustavo Guyot, Romain |
author_facet | Orozco-Arias, Simon Isaza, Gustavo Guyot, Romain |
author_sort | Orozco-Arias, Simon |
collection | PubMed |
description | Transposable elements (TEs) are genomic units able to move within the genome of virtually all organisms. Due to their natural repetitive numbers and their high structural diversity, the identification and classification of TEs remain a challenge in sequenced genomes. Although TEs were initially regarded as “junk DNA”, it has been demonstrated that they play key roles in chromosome structures, gene expression, and regulation, as well as adaptation and evolution. A highly reliable annotation of these elements is, therefore, crucial to better understand genome functions and their evolution. To date, much bioinformatics software has been developed to address TE detection and classification processes, but many problematic aspects remain, such as the reliability, precision, and speed of the analyses. Machine learning and deep learning are algorithms that can make automatic predictions and decisions in a wide variety of scientific applications. They have been tested in bioinformatics and, more specifically for TEs, classification with encouraging results. In this review, we will discuss important aspects of TEs, such as their structure, importance in the evolution and architecture of the host, and their current classifications and nomenclatures. We will also address current methods and their limitations in identifying and classifying TEs. |
format | Online Article Text |
id | pubmed-6696364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66963642019-09-05 Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning Orozco-Arias, Simon Isaza, Gustavo Guyot, Romain Int J Mol Sci Review Transposable elements (TEs) are genomic units able to move within the genome of virtually all organisms. Due to their natural repetitive numbers and their high structural diversity, the identification and classification of TEs remain a challenge in sequenced genomes. Although TEs were initially regarded as “junk DNA”, it has been demonstrated that they play key roles in chromosome structures, gene expression, and regulation, as well as adaptation and evolution. A highly reliable annotation of these elements is, therefore, crucial to better understand genome functions and their evolution. To date, much bioinformatics software has been developed to address TE detection and classification processes, but many problematic aspects remain, such as the reliability, precision, and speed of the analyses. Machine learning and deep learning are algorithms that can make automatic predictions and decisions in a wide variety of scientific applications. They have been tested in bioinformatics and, more specifically for TEs, classification with encouraging results. In this review, we will discuss important aspects of TEs, such as their structure, importance in the evolution and architecture of the host, and their current classifications and nomenclatures. We will also address current methods and their limitations in identifying and classifying TEs. MDPI 2019-08-06 /pmc/articles/PMC6696364/ /pubmed/31390781 http://dx.doi.org/10.3390/ijms20153837 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 | Review Orozco-Arias, Simon Isaza, Gustavo Guyot, Romain Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning |
title | Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning |
title_full | Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning |
title_fullStr | Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning |
title_full_unstemmed | Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning |
title_short | Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning |
title_sort | retrotransposons in plant genomes: structure, identification, and classification through bioinformatics and machine learning |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696364/ https://www.ncbi.nlm.nih.gov/pubmed/31390781 http://dx.doi.org/10.3390/ijms20153837 |
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