MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments

Multiple sequence alignments are usually phylogenetically driven. They are studied in the framework of evolution. But sometimes, it is interesting to study residue conservation at positions unconstrained by evolutionary rules. We present a supervised method to access a layer of information difficult...

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Detalles Bibliográficos
Autores principales: Mier, Pablo, Andrade-Navarro, Miguel A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Methods and Protocols
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218316/
https://www.ncbi.nlm.nih.gov/pubmed/32425492
http://dx.doi.org/10.1177/1176934320916199
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author Mier, Pablo
Andrade-Navarro, Miguel A
author_facet Mier, Pablo
Andrade-Navarro, Miguel A
author_sort Mier, Pablo
collection PubMed
description Multiple sequence alignments are usually phylogenetically driven. They are studied in the framework of evolution. But sometimes, it is interesting to study residue conservation at positions unconstrained by evolutionary rules. We present a supervised method to access a layer of information difficult to appreciate visually when many protein sequences are aligned. This new tool (MAGA; http://cbdm-01.zdv.uni-mainz.de/~munoz/maga/) locates positions in multiple sequence alignments differentially conserved in manually defined groups of sequences.
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spelling pubmed-72183162020-05-18 MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments Mier, Pablo Andrade-Navarro, Miguel A Evol Bioinform Online Methods and Protocols Multiple sequence alignments are usually phylogenetically driven. They are studied in the framework of evolution. But sometimes, it is interesting to study residue conservation at positions unconstrained by evolutionary rules. We present a supervised method to access a layer of information difficult to appreciate visually when many protein sequences are aligned. This new tool (MAGA; http://cbdm-01.zdv.uni-mainz.de/~munoz/maga/) locates positions in multiple sequence alignments differentially conserved in manually defined groups of sequences. SAGE Publications 2020-04-29 /pmc/articles/PMC7218316/ /pubmed/32425492 http://dx.doi.org/10.1177/1176934320916199 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Methods and Protocols
Mier, Pablo
Andrade-Navarro, Miguel A
MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments
title MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments
title_full MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments
title_fullStr MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments
title_full_unstemmed MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments
title_short MAGA: A Supervised Method to Detect Motifs From Annotated Groups in Alignments
title_sort maga: a supervised method to detect motifs from annotated groups in alignments
topic Methods and Protocols
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218316/
https://www.ncbi.nlm.nih.gov/pubmed/32425492
http://dx.doi.org/10.1177/1176934320916199
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