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Adapting bioinformatics curricula for big data

Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are ch...

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Detalles Bibliográficos
Autores principales: Greene, Anna C., Giffin, Kristine A., Greene, Casey S., Moore, Jason H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4719066/
https://www.ncbi.nlm.nih.gov/pubmed/25829469
http://dx.doi.org/10.1093/bib/bbv018
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author Greene, Anna C.
Giffin, Kristine A.
Greene, Casey S.
Moore, Jason H.
author_facet Greene, Anna C.
Giffin, Kristine A.
Greene, Casey S.
Moore, Jason H.
author_sort Greene, Anna C.
collection PubMed
description Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs.
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spelling pubmed-47190662016-01-21 Adapting bioinformatics curricula for big data Greene, Anna C. Giffin, Kristine A. Greene, Casey S. Moore, Jason H. Brief Bioinform Current Progress in Bioinformatics 2016 Papers Modern technologies are capable of generating enormous amounts of data that measure complex biological systems. Computational biologists and bioinformatics scientists are increasingly being asked to use these data to reveal key systems-level properties. We review the extent to which curricula are changing in the era of big data. We identify key competencies that scientists dealing with big data are expected to possess across fields, and we use this information to propose courses to meet these growing needs. While bioinformatics programs have traditionally trained students in data-intensive science, we identify areas of particular biological, computational and statistical emphasis important for this era that can be incorporated into existing curricula. For each area, we propose a course structured around these topics, which can be adapted in whole or in parts into existing curricula. In summary, specific challenges associated with big data provide an important opportunity to update existing curricula, but we do not foresee a wholesale redesign of bioinformatics training programs. Oxford University Press 2016-01 2015-03-30 /pmc/articles/PMC4719066/ /pubmed/25829469 http://dx.doi.org/10.1093/bib/bbv018 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Current Progress in Bioinformatics 2016 Papers
Greene, Anna C.
Giffin, Kristine A.
Greene, Casey S.
Moore, Jason H.
Adapting bioinformatics curricula for big data
title Adapting bioinformatics curricula for big data
title_full Adapting bioinformatics curricula for big data
title_fullStr Adapting bioinformatics curricula for big data
title_full_unstemmed Adapting bioinformatics curricula for big data
title_short Adapting bioinformatics curricula for big data
title_sort adapting bioinformatics curricula for big data
topic Current Progress in Bioinformatics 2016 Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4719066/
https://www.ncbi.nlm.nih.gov/pubmed/25829469
http://dx.doi.org/10.1093/bib/bbv018
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