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Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data
Transposable elements (TEs) are classified into two classes according to their mobilization mechanism. Compared to DNA transposons that move by the “cut and paste” mechanism, retrotransposons mobilize via the “copy and paste” method. They have been an essential research topic because some of the act...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605557/ https://www.ncbi.nlm.nih.gov/pubmed/36295018 http://dx.doi.org/10.3390/life12101583 |
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author | Lee, Haeun Min, Jun Won Mun, Seyoung Han, Kyudong |
author_facet | Lee, Haeun Min, Jun Won Mun, Seyoung Han, Kyudong |
author_sort | Lee, Haeun |
collection | PubMed |
description | Transposable elements (TEs) are classified into two classes according to their mobilization mechanism. Compared to DNA transposons that move by the “cut and paste” mechanism, retrotransposons mobilize via the “copy and paste” method. They have been an essential research topic because some of the active elements, such as Long interspersed element 1 (LINE-1), Alu, and SVA elements, have contributed to the genetic diversity of primates beyond humans. In addition, they can cause genetic disorders by altering gene expression and generating structural variations (SVs). The development and rapid technological advances in next-generation sequencing (NGS) have led to new perspectives on detecting retrotransposon-mediated SVs, especially insertions. Moreover, various computational methods have been developed based on NGS data to precisely detect the insertions and deletions in the human genome. Therefore, this review discusses details about the recently studied and utilized NGS technologies and the effective computational approaches for discovering retrotransposons through it. The final part covers a diverse range of computational methods for detecting retrotransposon insertions with human NGS data. This review will give researchers insights into understanding the TEs and how to investigate them and find connections with research interests. |
format | Online Article Text |
id | pubmed-9605557 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96055572022-10-27 Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data Lee, Haeun Min, Jun Won Mun, Seyoung Han, Kyudong Life (Basel) Review Transposable elements (TEs) are classified into two classes according to their mobilization mechanism. Compared to DNA transposons that move by the “cut and paste” mechanism, retrotransposons mobilize via the “copy and paste” method. They have been an essential research topic because some of the active elements, such as Long interspersed element 1 (LINE-1), Alu, and SVA elements, have contributed to the genetic diversity of primates beyond humans. In addition, they can cause genetic disorders by altering gene expression and generating structural variations (SVs). The development and rapid technological advances in next-generation sequencing (NGS) have led to new perspectives on detecting retrotransposon-mediated SVs, especially insertions. Moreover, various computational methods have been developed based on NGS data to precisely detect the insertions and deletions in the human genome. Therefore, this review discusses details about the recently studied and utilized NGS technologies and the effective computational approaches for discovering retrotransposons through it. The final part covers a diverse range of computational methods for detecting retrotransposon insertions with human NGS data. This review will give researchers insights into understanding the TEs and how to investigate them and find connections with research interests. MDPI 2022-10-12 /pmc/articles/PMC9605557/ /pubmed/36295018 http://dx.doi.org/10.3390/life12101583 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Lee, Haeun Min, Jun Won Mun, Seyoung Han, Kyudong Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data |
title | Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data |
title_full | Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data |
title_fullStr | Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data |
title_full_unstemmed | Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data |
title_short | Human Retrotransposons and Effective Computational Detection Methods for Next-Generation Sequencing Data |
title_sort | human retrotransposons and effective computational detection methods for next-generation sequencing data |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9605557/ https://www.ncbi.nlm.nih.gov/pubmed/36295018 http://dx.doi.org/10.3390/life12101583 |
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