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Mathematics and evolutionary biology make bioinformatics education comprehensible
The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes—the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an under...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3771232/ https://www.ncbi.nlm.nih.gov/pubmed/23821621 http://dx.doi.org/10.1093/bib/bbt046 |
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author | Jungck, John R. Weisstein, Anton E. |
author_facet | Jungck, John R. Weisstein, Anton E. |
author_sort | Jungck, John R. |
collection | PubMed |
description | The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes—the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an understanding of the structure, assumptions and limitations of the algorithms used by bioinformatics software—the domain of mathematicians and computer scientists. As a result, bioinformatics often suffers a ‘two-culture’ problem because of the lack of broad overlapping expertise between these two groups. Collaboration among specialists in different fields has greatly mitigated this problem among active bioinformaticians. However, science education researchers report that much of bioinformatics education does little to bridge the cultural divide, the curriculum too focused on solving narrow problems (e.g. interpreting pre-built phylogenetic trees) rather than on exploring broader ones (e.g. exploring alternative phylogenetic strategies for different kinds of data sets). Herein, we present an introduction to the mathematics of tree enumeration, tree construction, split decomposition and sequence alignment. We also introduce off-line downloadable software tools developed by the BioQUEST Curriculum Consortium to help students learn how to interpret and critically evaluate the results of standard bioinformatics analyses. |
format | Online Article Text |
id | pubmed-3771232 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-37712322013-09-12 Mathematics and evolutionary biology make bioinformatics education comprehensible Jungck, John R. Weisstein, Anton E. Brief Bioinform Papers The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes—the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an understanding of the structure, assumptions and limitations of the algorithms used by bioinformatics software—the domain of mathematicians and computer scientists. As a result, bioinformatics often suffers a ‘two-culture’ problem because of the lack of broad overlapping expertise between these two groups. Collaboration among specialists in different fields has greatly mitigated this problem among active bioinformaticians. However, science education researchers report that much of bioinformatics education does little to bridge the cultural divide, the curriculum too focused on solving narrow problems (e.g. interpreting pre-built phylogenetic trees) rather than on exploring broader ones (e.g. exploring alternative phylogenetic strategies for different kinds of data sets). Herein, we present an introduction to the mathematics of tree enumeration, tree construction, split decomposition and sequence alignment. We also introduce off-line downloadable software tools developed by the BioQUEST Curriculum Consortium to help students learn how to interpret and critically evaluate the results of standard bioinformatics analyses. Oxford University Press 2013-09 2013-07-02 /pmc/articles/PMC3771232/ /pubmed/23821621 http://dx.doi.org/10.1093/bib/bbt046 Text en © The Author 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Papers Jungck, John R. Weisstein, Anton E. Mathematics and evolutionary biology make bioinformatics education comprehensible |
title | Mathematics and evolutionary biology make bioinformatics education comprehensible |
title_full | Mathematics and evolutionary biology make bioinformatics education comprehensible |
title_fullStr | Mathematics and evolutionary biology make bioinformatics education comprehensible |
title_full_unstemmed | Mathematics and evolutionary biology make bioinformatics education comprehensible |
title_short | Mathematics and evolutionary biology make bioinformatics education comprehensible |
title_sort | mathematics and evolutionary biology make bioinformatics education comprehensible |
topic | Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3771232/ https://www.ncbi.nlm.nih.gov/pubmed/23821621 http://dx.doi.org/10.1093/bib/bbt046 |
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