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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...

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Autores principales: Procko, Carl, Morrison, Steven, Dunar, Courtney, Mills, Sara, Maldonado, Brianna, Cockrum, Carlee, Peters, Nathan Emmanuel, Huang, Shao-shan Carol, Chory, Joanne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Cell Biology 2019
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.
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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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