Dataset Information

Testing the "Grandma Hypothesis": Characterizing Skin Microbiome Diversity as a Project-Based Learning Approach to Genomics.

ABSTRACT: The constantly evolving nature of genomics provides new challenges for students in Public Health as they try to understand how genomic information relates to health and disease. As Public Health curricula attempt to keep pace with the most recent advances in genomics, students should gain experience with analyzing genomic data and applying genomic tools to the study of health-related issues. To advance undergraduate and graduate student education and provide a more comprehensive view of genomics, we developed an educational project including both pedagogic and research components to characterize skin microbial communities (microbiomes) using targeted amplicon sequencing of their genomes (metataxonomy). All students completed the lab procedures, analyzed 16S rRNA genomic data (formative assessments), and wrote a five-page scientific report summarizing and discussing their results (summative assessment). Student grades for the summative assessment ranged from 31.5 to 40 (out of 40) points. They also successfully completed two practicums (problem sets) focused on microbiome sequence data and responded to 12 minute-papers related to genomic topics covered in class. In all these exercises the 2019 students outperformed 2018 students, who did not participate in this educational lab project. By fulfilling all the requirements of this project-based learning experience, students better understood the complexity of genomics and acquired a valuable set of marketable experience and skills in molecular technologies, bioinformatics and statistics (quantitative skills). Additionally, students were able to generate new valuable microbial 16S rRNA genomic data and test hypotheses about the composition and diversity of the microbes living on our skin (microbiota).

SUBMITTER: Perez-Losada M

PROVIDER: S-EPMC7048396 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Testing the "Grandma Hypothesis": Characterizing Skin Microbiome Diversity as a Project-Based Learning Approach to Genomics.

Pérez-Losada Marcos M Crandall Kelly M KM Crandall Keith A KA

Journal of microbiology & biology education 20200228 1

The constantly evolving nature of genomics provides new challenges for students in Public Health as they try to understand how genomic information relates to health and disease. As Public Health curricula attempt to keep pace with the most recent advances in genomics, students should gain experience with analyzing genomic data and applying genomic tools to the study of health-related issues. To advance undergraduate and graduate student education and provide a more comprehensive view of genomics ...[more]

PMID: 32148604

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Project description:BACKGROUND: Functional genomics technologies that measure genome expression at a global scale are accelerating biological knowledge discovery. Generating these high throughput datasets is relatively easy compared to the downstream functional modelling necessary for elucidating the molecular mechanisms that govern the biology under investigation. A number of publicly available 'discovery-based' computational tools use the computationally amenable Gene Ontology (GO) for hypothesis generation. However, there are few tools that support hypothesis-based testing using the GO and none that support testing with user defined hypothesis terms.Here, we present GOModeler, a tool that enables researchers to conduct hypothesis-based testing of high throughput datasets using the GO. GOModeler summarizes the overall effect of a user defined gene/protein differential expression dataset on specific GO hypothesis terms selected by the user to describe a biological experiment. The design of the tool allows the user to complement the functional information in the GO with his/her domain specific expertise for comprehensive hypothesis testing. RESULTS: GOModeler tests the relevance of the hypothesis terms chosen by the user for the input gene dataset by providing the individual effects of the genes on the hypothesis terms and the overall effect of the entire dataset on each of the hypothesis terms. It matches the GO identifiers (ids) of the genes with the GO ids of the hypothesis terms and parses the names of those ids that match to assign effects. We demonstrate the capabilities of GOModeler with a dataset of nine differentially expressed cytokine genes and compare the results to those obtained through manual analysis of the dataset by an immunologist. The direction of overall effects on all hypothesis terms except one was consistent with the results obtained by manual analysis. The tool's editing capability enables the user to augment the information extracted. GOModeler is available as a part of the AgBase tool suite (http://www.agbase.msstate.edu). CONCLUSIONS: GOModeler allows hypothesis driven analysis of high throughput datasets using the GO. Using this tool, researchers can quickly evaluate the overall effect of quantitative expression changes of gene set on specific biological processes of interest. The results are provided in both tabular and graphical formats.

Dataset Information

Testing the "Grandma Hypothesis": Characterizing Skin Microbiome Diversity as a Project-Based Learning Approach to Genomics.

Publications

Testing the "Grandma Hypothesis": Characterizing Skin Microbiome Diversity as a Project-Based Learning Approach to Genomics.

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