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A Curricular Bioinformatics Approach to Teaching Undergraduates to Analyze Metagenomic Datasets Using R
Biologists with bioinformatic skills will be better prepared for the job market, but relatively few biology programs require bioinformatics courses. Inclusion in the curriculum may be hindered by several barriers, including lack of faculty expertise, student resistance to computational work, and few...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7511545/ https://www.ncbi.nlm.nih.gov/pubmed/33013816 http://dx.doi.org/10.3389/fmicb.2020.578600 |
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author | Kruchten, Anne E. |
author_facet | Kruchten, Anne E. |
author_sort | Kruchten, Anne E. |
collection | PubMed |
description | Biologists with bioinformatic skills will be better prepared for the job market, but relatively few biology programs require bioinformatics courses. Inclusion in the curriculum may be hindered by several barriers, including lack of faculty expertise, student resistance to computational work, and few examples in the pedagogical literature. An 8-week wet-lab and in silico research experience for undergraduates was implemented. Students performed DNA purification and metagenomics analysis to compare the diversity and abundance of microbes in two samples. Students sampled snow from sites in northern Minnesota and purified genomic DNA from the microbes, followed by metagenomic analysis. Students used an existing metagenomic dataset to practice analysis skills, including comparing the use of Excel versus R for analysis and visualization of a large dataset. Upon receipt of the snow data, students applied their recently acquired skills to their new dataset and reported their results via a poster. Several outcomes were achieved as a result of this module. First, YouTube videos demonstrating hands-on metagenomics and R techniques were used as professional development for faculty, leading to broadened research capabilities and comfort with bioinformatics. Second, students were introduced to computational skills in a manner that was intentional, with time for both introduction and reinforcement of skills. Finally, the module was effectively included in a biology curriculum because it could function as either a stand-alone course or a module within another course such as microbiology. This module, developed with Course-based Undergraduate Research Experience guidelines in mind, introduces students and faculty to bioinformatics in biology research. |
format | Online Article Text |
id | pubmed-7511545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75115452020-10-02 A Curricular Bioinformatics Approach to Teaching Undergraduates to Analyze Metagenomic Datasets Using R Kruchten, Anne E. Front Microbiol Microbiology Biologists with bioinformatic skills will be better prepared for the job market, but relatively few biology programs require bioinformatics courses. Inclusion in the curriculum may be hindered by several barriers, including lack of faculty expertise, student resistance to computational work, and few examples in the pedagogical literature. An 8-week wet-lab and in silico research experience for undergraduates was implemented. Students performed DNA purification and metagenomics analysis to compare the diversity and abundance of microbes in two samples. Students sampled snow from sites in northern Minnesota and purified genomic DNA from the microbes, followed by metagenomic analysis. Students used an existing metagenomic dataset to practice analysis skills, including comparing the use of Excel versus R for analysis and visualization of a large dataset. Upon receipt of the snow data, students applied their recently acquired skills to their new dataset and reported their results via a poster. Several outcomes were achieved as a result of this module. First, YouTube videos demonstrating hands-on metagenomics and R techniques were used as professional development for faculty, leading to broadened research capabilities and comfort with bioinformatics. Second, students were introduced to computational skills in a manner that was intentional, with time for both introduction and reinforcement of skills. Finally, the module was effectively included in a biology curriculum because it could function as either a stand-alone course or a module within another course such as microbiology. This module, developed with Course-based Undergraduate Research Experience guidelines in mind, introduces students and faculty to bioinformatics in biology research. Frontiers Media S.A. 2020-09-10 /pmc/articles/PMC7511545/ /pubmed/33013816 http://dx.doi.org/10.3389/fmicb.2020.578600 Text en Copyright © 2020 Kruchten. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Kruchten, Anne E. A Curricular Bioinformatics Approach to Teaching Undergraduates to Analyze Metagenomic Datasets Using R |
title | A Curricular Bioinformatics Approach to Teaching Undergraduates to Analyze Metagenomic Datasets Using R |
title_full | A Curricular Bioinformatics Approach to Teaching Undergraduates to Analyze Metagenomic Datasets Using R |
title_fullStr | A Curricular Bioinformatics Approach to Teaching Undergraduates to Analyze Metagenomic Datasets Using R |
title_full_unstemmed | A Curricular Bioinformatics Approach to Teaching Undergraduates to Analyze Metagenomic Datasets Using R |
title_short | A Curricular Bioinformatics Approach to Teaching Undergraduates to Analyze Metagenomic Datasets Using R |
title_sort | curricular bioinformatics approach to teaching undergraduates to analyze metagenomic datasets using r |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7511545/ https://www.ncbi.nlm.nih.gov/pubmed/33013816 http://dx.doi.org/10.3389/fmicb.2020.578600 |
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