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AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data
Multiple sequence alignment (MSA) is essential for understanding genetic variations controlling phenotypic traits in all living organisms. The post-analysis of MSA results is a difficult step for researchers who do not have programming skills. Especially those working with large scale data and looki...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511070/ https://www.ncbi.nlm.nih.gov/pubmed/37729135 http://dx.doi.org/10.1371/journal.pone.0291204 |
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author | Alsamman, Alsamman M. El Allali, Achraf Mokhtar, Morad M. Al-Sham’aa, Khaled Nassar, Ahmed E. Mousa, Khaled H. Kehel, Zakaria |
author_facet | Alsamman, Alsamman M. El Allali, Achraf Mokhtar, Morad M. Al-Sham’aa, Khaled Nassar, Ahmed E. Mousa, Khaled H. Kehel, Zakaria |
author_sort | Alsamman, Alsamman M. |
collection | PubMed |
description | Multiple sequence alignment (MSA) is essential for understanding genetic variations controlling phenotypic traits in all living organisms. The post-analysis of MSA results is a difficult step for researchers who do not have programming skills. Especially those working with large scale data and looking for potential variations or variable sample groups. Generating bi-allelic data and the comparison of wild and alternative gene forms are important steps in population genetics. Customising MSA visualisation for a single page view is difficult, making viewing potential indels and variations challenging. There are currently no bioinformatics tools that permit post-MSA analysis, in which data on gene and single nucleotide scales could be combined with gene annotations and used for cluster analysis. We introduce “AlignStatPlot,” a new R package and online tool that is well-documented and easy-to use for MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analyses on sequencing data and generates new visualisation methods for MSA results. When compared to currently available tools, AlignStatPlot provides a robust ability to handle and visualise diversity data, while the online version will save time and encourage researchers to focus on explaining their findings. It is a simple tool that can be used in conjunction with population genetics software. |
format | Online Article Text |
id | pubmed-10511070 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-105110702023-09-21 AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data Alsamman, Alsamman M. El Allali, Achraf Mokhtar, Morad M. Al-Sham’aa, Khaled Nassar, Ahmed E. Mousa, Khaled H. Kehel, Zakaria PLoS One Research Article Multiple sequence alignment (MSA) is essential for understanding genetic variations controlling phenotypic traits in all living organisms. The post-analysis of MSA results is a difficult step for researchers who do not have programming skills. Especially those working with large scale data and looking for potential variations or variable sample groups. Generating bi-allelic data and the comparison of wild and alternative gene forms are important steps in population genetics. Customising MSA visualisation for a single page view is difficult, making viewing potential indels and variations challenging. There are currently no bioinformatics tools that permit post-MSA analysis, in which data on gene and single nucleotide scales could be combined with gene annotations and used for cluster analysis. We introduce “AlignStatPlot,” a new R package and online tool that is well-documented and easy-to use for MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analyses on sequencing data and generates new visualisation methods for MSA results. When compared to currently available tools, AlignStatPlot provides a robust ability to handle and visualise diversity data, while the online version will save time and encourage researchers to focus on explaining their findings. It is a simple tool that can be used in conjunction with population genetics software. Public Library of Science 2023-09-20 /pmc/articles/PMC10511070/ /pubmed/37729135 http://dx.doi.org/10.1371/journal.pone.0291204 Text en © 2023 Alsamman et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Alsamman, Alsamman M. El Allali, Achraf Mokhtar, Morad M. Al-Sham’aa, Khaled Nassar, Ahmed E. Mousa, Khaled H. Kehel, Zakaria AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data |
title | AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data |
title_full | AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data |
title_fullStr | AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data |
title_full_unstemmed | AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data |
title_short | AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data |
title_sort | alignstatplot: an r package and online tool for robust sequence alignment statistics and innovative visualization of big data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511070/ https://www.ncbi.nlm.nih.gov/pubmed/37729135 http://dx.doi.org/10.1371/journal.pone.0291204 |
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