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Rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge
BACKGROUND: Mitochondrial DNA is an ideal source of information to conduct evolutionary and phylogenetic studies due to its extraordinary properties and abundance. Many insights can be gained from these, including but not limited to screening genetic variation to identify potentially deleterious mut...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3123235/ https://www.ncbi.nlm.nih.gov/pubmed/21595926 http://dx.doi.org/10.1186/1471-2105-12-174 |
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author | Blanco, Roberto Mayordomo, Elvira Montoya, Julio Ruiz-Pesini, Eduardo |
author_facet | Blanco, Roberto Mayordomo, Elvira Montoya, Julio Ruiz-Pesini, Eduardo |
author_sort | Blanco, Roberto |
collection | PubMed |
description | BACKGROUND: Mitochondrial DNA is an ideal source of information to conduct evolutionary and phylogenetic studies due to its extraordinary properties and abundance. Many insights can be gained from these, including but not limited to screening genetic variation to identify potentially deleterious mutations. However, such advances require efficient solutions to very difficult computational problems, a need that is hampered by the very plenty of data that confers strength to the analysis. RESULTS: We develop a systematic, automated methodology to overcome these difficulties, building from readily available, public sequence databases to high-quality alignments and phylogenetic trees. Within each stage in an autonomous workflow, outputs are carefully evaluated and outlier detection rules defined to integrate expert knowledge and automated curation, hence avoiding the manual bottleneck found in past approaches to the problem. Using these techniques, we have performed exhaustive updates to the human mitochondrial phylogeny, illustrating the power and computational scalability of our approach, and we have conducted some initial analyses on the resulting phylogenies. CONCLUSIONS: The problem at hand demands careful definition of inputs and adequate algorithmic treatment for its solutions to be realistic and useful. It is possible to define formal rules to address the former requirement by refining inputs directly and through their combination as outputs, and the latter are also of help to ascertain the performance of chosen algorithms. Rules can exploit known or inferred properties of datasets to simplify inputs through partitioning, therefore cutting computational costs and affording work on rapidly growing, otherwise intractable datasets. Although expert guidance may be necessary to assist the learning process, low-risk results can be fully automated and have proved themselves convenient and valuable. |
format | Online Article Text |
id | pubmed-3123235 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31232352011-06-25 Rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge Blanco, Roberto Mayordomo, Elvira Montoya, Julio Ruiz-Pesini, Eduardo BMC Bioinformatics Research Article BACKGROUND: Mitochondrial DNA is an ideal source of information to conduct evolutionary and phylogenetic studies due to its extraordinary properties and abundance. Many insights can be gained from these, including but not limited to screening genetic variation to identify potentially deleterious mutations. However, such advances require efficient solutions to very difficult computational problems, a need that is hampered by the very plenty of data that confers strength to the analysis. RESULTS: We develop a systematic, automated methodology to overcome these difficulties, building from readily available, public sequence databases to high-quality alignments and phylogenetic trees. Within each stage in an autonomous workflow, outputs are carefully evaluated and outlier detection rules defined to integrate expert knowledge and automated curation, hence avoiding the manual bottleneck found in past approaches to the problem. Using these techniques, we have performed exhaustive updates to the human mitochondrial phylogeny, illustrating the power and computational scalability of our approach, and we have conducted some initial analyses on the resulting phylogenies. CONCLUSIONS: The problem at hand demands careful definition of inputs and adequate algorithmic treatment for its solutions to be realistic and useful. It is possible to define formal rules to address the former requirement by refining inputs directly and through their combination as outputs, and the latter are also of help to ascertain the performance of chosen algorithms. Rules can exploit known or inferred properties of datasets to simplify inputs through partitioning, therefore cutting computational costs and affording work on rapidly growing, otherwise intractable datasets. Although expert guidance may be necessary to assist the learning process, low-risk results can be fully automated and have proved themselves convenient and valuable. BioMed Central 2011-05-19 /pmc/articles/PMC3123235/ /pubmed/21595926 http://dx.doi.org/10.1186/1471-2105-12-174 Text en Copyright ©2011 Blanco et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Blanco, Roberto Mayordomo, Elvira Montoya, Julio Ruiz-Pesini, Eduardo Rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge |
title | Rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge |
title_full | Rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge |
title_fullStr | Rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge |
title_full_unstemmed | Rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge |
title_short | Rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge |
title_sort | rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3123235/ https://www.ncbi.nlm.nih.gov/pubmed/21595926 http://dx.doi.org/10.1186/1471-2105-12-174 |
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