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mtDNAcombine: tools to combine sequences from multiple studies

BACKGROUND: Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide ran...

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Autores principales: Miller, Eleanor F., Manica, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7945669/
https://www.ncbi.nlm.nih.gov/pubmed/33750296
http://dx.doi.org/10.1186/s12859-021-04048-0
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author Miller, Eleanor F.
Manica, Andrea
author_facet Miller, Eleanor F.
Manica, Andrea
author_sort Miller, Eleanor F.
collection PubMed
description BACKGROUND: Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. RESULTS: Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. CONCLUSIONS: There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank.
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spelling pubmed-79456692021-03-11 mtDNAcombine: tools to combine sequences from multiple studies Miller, Eleanor F. Manica, Andrea BMC Bioinformatics Software BACKGROUND: Today an unprecedented amount of genetic sequence data is stored in publicly available repositories. For decades now, mitochondrial DNA (mtDNA) has been the workhorse of genetic studies, and as a result, there is a large volume of mtDNA data available in these repositories for a wide range of species. Indeed, whilst whole genome sequencing is an exciting prospect for the future, for most non-model organisms’ classical markers such as mtDNA remain widely used. By compiling existing data from multiple original studies, it is possible to build powerful new datasets capable of exploring many questions in ecology, evolution and conservation biology. One key question that these data can help inform is what happened in a species’ demographic past. However, compiling data in this manner is not trivial, there are many complexities associated with data extraction, data quality and data handling. RESULTS: Here we present the mtDNAcombine package, a collection of tools developed to manage some of the major decisions associated with handling multi-study sequence data with a particular focus on preparing sequence data for Bayesian skyline plot demographic reconstructions. CONCLUSIONS: There is now more genetic information available than ever before and large meta-data sets offer great opportunities to explore new and exciting avenues of research. However, compiling multi-study datasets still remains a technically challenging prospect. The mtDNAcombine package provides a pipeline to streamline the process of downloading, curating, and analysing sequence data, guiding the process of compiling data sets from the online database GenBank. BioMed Central 2021-03-09 /pmc/articles/PMC7945669/ /pubmed/33750296 http://dx.doi.org/10.1186/s12859-021-04048-0 Text en © The Author(s) 2021 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/. The Creative Commons Public Domain Dedication waiver (http://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
Miller, Eleanor F.
Manica, Andrea
mtDNAcombine: tools to combine sequences from multiple studies
title mtDNAcombine: tools to combine sequences from multiple studies
title_full mtDNAcombine: tools to combine sequences from multiple studies
title_fullStr mtDNAcombine: tools to combine sequences from multiple studies
title_full_unstemmed mtDNAcombine: tools to combine sequences from multiple studies
title_short mtDNAcombine: tools to combine sequences from multiple studies
title_sort mtdnacombine: tools to combine sequences from multiple studies
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7945669/
https://www.ncbi.nlm.nih.gov/pubmed/33750296
http://dx.doi.org/10.1186/s12859-021-04048-0
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