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Big Data to the Bench: Transcriptome Analysis for Undergraduates
Next-generation sequencing (NGS)-based methods are revolutionizing biology. Their prevalence requires biologists to be increasingly knowledgeable about computational methods to manage the enormous scale of data. As such, early introduction to NGS analysis and conceptual connection to wet-lab experim...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Cell Biology
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755220/ https://www.ncbi.nlm.nih.gov/pubmed/31074696 http://dx.doi.org/10.1187/cbe.18-08-0161 |
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author | Procko, Carl Morrison, Steven Dunar, Courtney Mills, Sara Maldonado, Brianna Cockrum, Carlee Peters, Nathan Emmanuel Huang, Shao-shan Carol Chory, Joanne |
author_facet | Procko, Carl Morrison, Steven Dunar, Courtney Mills, Sara Maldonado, Brianna Cockrum, Carlee Peters, Nathan Emmanuel Huang, Shao-shan Carol Chory, Joanne |
author_sort | Procko, Carl |
collection | PubMed |
description | Next-generation sequencing (NGS)-based methods are revolutionizing biology. Their prevalence requires biologists to be increasingly knowledgeable about computational methods to manage the enormous scale of data. As such, early introduction to NGS analysis and conceptual connection to wet-lab experiments is crucial for training young scientists. However, significant challenges impede the introduction of these methods into the undergraduate classroom, including the need for specialized computer programs and knowledge of computer coding. Here, we describe a semester-long, course-based undergraduate research experience at a liberal arts college combining RNA-sequencing (RNA-seq) analysis with student-driven, wet-lab experiments to investigate plant responses to light. Students derived hypotheses based on analysis of RNA-seq data and designed follow-up studies of gene expression and plant growth. Our assessments indicate that students acquired knowledge of big data analysis and computer coding; however, earlier exposure to computational methods may be beneficial. Our course requires minimal prior knowledge of plant biology, is easy to replicate, and can be modified to a shorter, directed-inquiry module. This framework promotes exploration of the links between gene expression and phenotype using examples that are clear and tractable and improves computational skills and bioinformatics self-efficacy to prepare students for the “big data” era of modern biology. |
format | Online Article Text |
id | pubmed-6755220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Society for Cell Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-67552202019-10-15 Big Data to the Bench: Transcriptome Analysis for Undergraduates Procko, Carl Morrison, Steven Dunar, Courtney Mills, Sara Maldonado, Brianna Cockrum, Carlee Peters, Nathan Emmanuel Huang, Shao-shan Carol Chory, Joanne CBE Life Sci Educ Article Next-generation sequencing (NGS)-based methods are revolutionizing biology. Their prevalence requires biologists to be increasingly knowledgeable about computational methods to manage the enormous scale of data. As such, early introduction to NGS analysis and conceptual connection to wet-lab experiments is crucial for training young scientists. However, significant challenges impede the introduction of these methods into the undergraduate classroom, including the need for specialized computer programs and knowledge of computer coding. Here, we describe a semester-long, course-based undergraduate research experience at a liberal arts college combining RNA-sequencing (RNA-seq) analysis with student-driven, wet-lab experiments to investigate plant responses to light. Students derived hypotheses based on analysis of RNA-seq data and designed follow-up studies of gene expression and plant growth. Our assessments indicate that students acquired knowledge of big data analysis and computer coding; however, earlier exposure to computational methods may be beneficial. Our course requires minimal prior knowledge of plant biology, is easy to replicate, and can be modified to a shorter, directed-inquiry module. This framework promotes exploration of the links between gene expression and phenotype using examples that are clear and tractable and improves computational skills and bioinformatics self-efficacy to prepare students for the “big data” era of modern biology. American Society for Cell Biology 2019 /pmc/articles/PMC6755220/ /pubmed/31074696 http://dx.doi.org/10.1187/cbe.18-08-0161 Text en © 2019 C. Procko et al. CBE—Life Sciences Education © 2019 The American Society for Cell Biology. “ASCB®” and “The American Society for Cell Biology®” are registered trademarks of The American Society for Cell Biology. http://creativecommons.org/licenses/by-nc-sa/3.0 This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License. |
spellingShingle | Article Procko, Carl Morrison, Steven Dunar, Courtney Mills, Sara Maldonado, Brianna Cockrum, Carlee Peters, Nathan Emmanuel Huang, Shao-shan Carol Chory, Joanne Big Data to the Bench: Transcriptome Analysis for Undergraduates |
title | Big Data to the Bench: Transcriptome Analysis for Undergraduates |
title_full | Big Data to the Bench: Transcriptome Analysis for Undergraduates |
title_fullStr | Big Data to the Bench: Transcriptome Analysis for Undergraduates |
title_full_unstemmed | Big Data to the Bench: Transcriptome Analysis for Undergraduates |
title_short | Big Data to the Bench: Transcriptome Analysis for Undergraduates |
title_sort | big data to the bench: transcriptome analysis for undergraduates |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6755220/ https://www.ncbi.nlm.nih.gov/pubmed/31074696 http://dx.doi.org/10.1187/cbe.18-08-0161 |
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