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PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom

Improvements in high-throughput sequencing makes targeted amplicon analysis an ideal method for the study of human and environmental microbiomes by undergraduates. Multiple bioinformatics programs are available to process and interpret raw microbial diversity datasets, and the choice of programs to...

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Autores principales: Mitchell, Keith, Ronas, Jiem, Dao, Christopher, Freise, Amanda C., Mangul, Serghei, Shapiro, Casey, Moberg Parker, Jordan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573227/
https://www.ncbi.nlm.nih.gov/pubmed/33123113
http://dx.doi.org/10.3389/fmicb.2020.584699
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author Mitchell, Keith
Ronas, Jiem
Dao, Christopher
Freise, Amanda C.
Mangul, Serghei
Shapiro, Casey
Moberg Parker, Jordan
author_facet Mitchell, Keith
Ronas, Jiem
Dao, Christopher
Freise, Amanda C.
Mangul, Serghei
Shapiro, Casey
Moberg Parker, Jordan
author_sort Mitchell, Keith
collection PubMed
description Improvements in high-throughput sequencing makes targeted amplicon analysis an ideal method for the study of human and environmental microbiomes by undergraduates. Multiple bioinformatics programs are available to process and interpret raw microbial diversity datasets, and the choice of programs to use in curricula is largely determined by student learning goals. Many of the most commonly used microbiome bioinformatics platforms offer end-to-end data processing and data analysis using a command line interface (CLI), but the downside for novice microbiome researchers is the steep learning curve often required. Alternatively, some sequencing providers include processing of raw data and taxonomy assignments as part of their pipelines. This, when coupled with available web-based or graphical user interface (GUI) analysis and visualization tools, eliminates the need for students or instructors to have extensive CLI experience. However, lack of universal data formats can make integration of these tools challenging. For example, tools for upstream and downstream analyses frequently use multiple different data formats which then require writing custom scripts or hours of manual work to make the files compatible. Here, we describe a microbial ecology bioinformatics curriculum that focuses on data analysis, visualization, and statistical reasoning by taking advantage of existing web-based and GUI tools. We created the Program for Unifying Microbiome Analysis Applications (PUMAA), which solves the problem of inconsistent files by formatting the output files from several raw data processing programs to seamlessly transition to a suite of GUI programs for analysis and visualization of microbiome taxonomic and inferred functional profiles. Additionally, we created a series of tutorials to accompany each of the microbiome analysis curricular modules. From pre- and post-course surveys, students in this curriculum self-reported conceptual and confidence gains in bioinformatics and data analysis skills. Students also demonstrated gains in biologically relevant statistical reasoning based on rubric-guided evaluations of open-ended survey questions and the Statistical Reasoning in Biology Concept Inventory. The PUMAA program and associated analysis tutorials enable students and researchers with no computational experience to effectively analyze real microbiome datasets to investigate real-world research questions.
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spelling pubmed-75732272020-10-28 PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom Mitchell, Keith Ronas, Jiem Dao, Christopher Freise, Amanda C. Mangul, Serghei Shapiro, Casey Moberg Parker, Jordan Front Microbiol Microbiology Improvements in high-throughput sequencing makes targeted amplicon analysis an ideal method for the study of human and environmental microbiomes by undergraduates. Multiple bioinformatics programs are available to process and interpret raw microbial diversity datasets, and the choice of programs to use in curricula is largely determined by student learning goals. Many of the most commonly used microbiome bioinformatics platforms offer end-to-end data processing and data analysis using a command line interface (CLI), but the downside for novice microbiome researchers is the steep learning curve often required. Alternatively, some sequencing providers include processing of raw data and taxonomy assignments as part of their pipelines. This, when coupled with available web-based or graphical user interface (GUI) analysis and visualization tools, eliminates the need for students or instructors to have extensive CLI experience. However, lack of universal data formats can make integration of these tools challenging. For example, tools for upstream and downstream analyses frequently use multiple different data formats which then require writing custom scripts or hours of manual work to make the files compatible. Here, we describe a microbial ecology bioinformatics curriculum that focuses on data analysis, visualization, and statistical reasoning by taking advantage of existing web-based and GUI tools. We created the Program for Unifying Microbiome Analysis Applications (PUMAA), which solves the problem of inconsistent files by formatting the output files from several raw data processing programs to seamlessly transition to a suite of GUI programs for analysis and visualization of microbiome taxonomic and inferred functional profiles. Additionally, we created a series of tutorials to accompany each of the microbiome analysis curricular modules. From pre- and post-course surveys, students in this curriculum self-reported conceptual and confidence gains in bioinformatics and data analysis skills. Students also demonstrated gains in biologically relevant statistical reasoning based on rubric-guided evaluations of open-ended survey questions and the Statistical Reasoning in Biology Concept Inventory. The PUMAA program and associated analysis tutorials enable students and researchers with no computational experience to effectively analyze real microbiome datasets to investigate real-world research questions. Frontiers Media S.A. 2020-10-06 /pmc/articles/PMC7573227/ /pubmed/33123113 http://dx.doi.org/10.3389/fmicb.2020.584699 Text en Copyright © 2020 Mitchell, Ronas, Dao, Freise, Mangul, Shapiro and Moberg Parker. 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
Mitchell, Keith
Ronas, Jiem
Dao, Christopher
Freise, Amanda C.
Mangul, Serghei
Shapiro, Casey
Moberg Parker, Jordan
PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom
title PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom
title_full PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom
title_fullStr PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom
title_full_unstemmed PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom
title_short PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom
title_sort pumaa: a platform for accessible microbiome analysis in the undergraduate classroom
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573227/
https://www.ncbi.nlm.nih.gov/pubmed/33123113
http://dx.doi.org/10.3389/fmicb.2020.584699
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