TY - JOUR
T1 - Big data to the bench
T2 - Transcriptome analysis for undergraduates
AU - Procko, Carl
AU - Morrison, Steven
AU - Dunar, Courtney
AU - Mills, Sara
AU - Maldonado, Brianna
AU - Cockrum, Carlee
AU - Peters, Nathan Emmanuel
AU - Huang, Shao Shan Carol
AU - Chory, Joanne
N1 - Publisher Copyright:
© 2019 C. Procko et al. CBE-Life Sciences Education.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=85065881561&partnerID=8YFLogxK
U2 - 10.1187/cbe.18-08-0161
DO - 10.1187/cbe.18-08-0161
M3 - Article
C2 - 31074696
AN - SCOPUS:85065881561
SN - 1931-7913
VL - 18
JO - CBE life sciences education
JF - CBE life sciences education
IS - 2
M1 - ar19
ER -