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TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches
Transposable Element MOnitoring with LOng-reads (TrEMOLO) is a new software that combines assembly- and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high- or low-quality genome assemblies, TrEMOLO can detect most TE insertions and deletions a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069131/ https://www.ncbi.nlm.nih.gov/pubmed/37013657 http://dx.doi.org/10.1186/s13059-023-02911-2 |
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author | Mohamed, Mourdas Sabot, François Varoqui, Marion Mugat, Bruno Audouin, Katell Pélisson, Alain Fiston-Lavier, Anna-Sophie Chambeyron, Séverine |
author_facet | Mohamed, Mourdas Sabot, François Varoqui, Marion Mugat, Bruno Audouin, Katell Pélisson, Alain Fiston-Lavier, Anna-Sophie Chambeyron, Séverine |
author_sort | Mohamed, Mourdas |
collection | PubMed |
description | Transposable Element MOnitoring with LOng-reads (TrEMOLO) is a new software that combines assembly- and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high- or low-quality genome assemblies, TrEMOLO can detect most TE insertions and deletions and estimate their allele frequency in populations. Benchmarking with simulated data revealed that TrEMOLO outperforms other state-of-the-art computational tools. TE detection and frequency estimation by TrEMOLO were validated using simulated and experimental datasets. Therefore, TrEMOLO is a comprehensive and suitable tool to accurately study TE dynamics. TrEMOLO is available under GNU GPL3.0 at https://github.com/DrosophilaGenomeEvolution/TrEMOLO. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02911-2. |
format | Online Article Text |
id | pubmed-10069131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100691312023-04-04 TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches Mohamed, Mourdas Sabot, François Varoqui, Marion Mugat, Bruno Audouin, Katell Pélisson, Alain Fiston-Lavier, Anna-Sophie Chambeyron, Séverine Genome Biol Software Transposable Element MOnitoring with LOng-reads (TrEMOLO) is a new software that combines assembly- and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high- or low-quality genome assemblies, TrEMOLO can detect most TE insertions and deletions and estimate their allele frequency in populations. Benchmarking with simulated data revealed that TrEMOLO outperforms other state-of-the-art computational tools. TE detection and frequency estimation by TrEMOLO were validated using simulated and experimental datasets. Therefore, TrEMOLO is a comprehensive and suitable tool to accurately study TE dynamics. TrEMOLO is available under GNU GPL3.0 at https://github.com/DrosophilaGenomeEvolution/TrEMOLO. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02911-2. BioMed Central 2023-04-03 /pmc/articles/PMC10069131/ /pubmed/37013657 http://dx.doi.org/10.1186/s13059-023-02911-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Mohamed, Mourdas Sabot, François Varoqui, Marion Mugat, Bruno Audouin, Katell Pélisson, Alain Fiston-Lavier, Anna-Sophie Chambeyron, Séverine TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches |
title | TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches |
title_full | TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches |
title_fullStr | TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches |
title_full_unstemmed | TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches |
title_short | TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches |
title_sort | tremolo: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10069131/ https://www.ncbi.nlm.nih.gov/pubmed/37013657 http://dx.doi.org/10.1186/s13059-023-02911-2 |
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