Big Data to the Bench: Transcriptome Analysis for Undergraduates

Carl Procko, Steven Morrison, Courtney Dunar, Sara Mills, Brianna Maldonado, Carlee Cockrum, Nathan Emmanuel Peters, Shao-Shan Huang, Joanne Chory

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)ar19
JournalCBE life sciences education
Volume18
Issue number2
DOIs
StatePublished - Jun 1 2019

Fingerprint

Gene Expression Profiling
Students
biology
RNA
Computational methods
Gene expression
coding
student
RNA Sequence Analysis
Gene Expression
experiment
Self Efficacy
data processing program
Bioinformatics
Art
Computational Biology
semester
self-efficacy
Computer program listings
data analysis

ASJC Scopus subject areas

  • Education
  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Procko, C., Morrison, S., Dunar, C., Mills, S., Maldonado, B., Cockrum, C., ... Chory, J. (2019). Big Data to the Bench: Transcriptome Analysis for Undergraduates. CBE life sciences education, 18(2), ar19. https://doi.org/10.1187/cbe.18-08-0161

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; Chory, Joanne.

In: CBE life sciences education, Vol. 18, No. 2, 01.06.2019, p. ar19.

Research output: Contribution to journalArticle

Procko, C, Morrison, S, Dunar, C, Mills, S, Maldonado, B, Cockrum, C, Peters, NE, Huang, S-S & Chory, J 2019, 'Big Data to the Bench: Transcriptome Analysis for Undergraduates', CBE life sciences education, vol. 18, no. 2, pp. ar19. https://doi.org/10.1187/cbe.18-08-0161
Procko C, Morrison S, Dunar C, Mills S, Maldonado B, Cockrum C et al. Big Data to the Bench: Transcriptome Analysis for Undergraduates. CBE life sciences education. 2019 Jun 1;18(2):ar19. https://doi.org/10.1187/cbe.18-08-0161
Procko, Carl ; Morrison, Steven ; Dunar, Courtney ; Mills, Sara ; Maldonado, Brianna ; Cockrum, Carlee ; Peters, Nathan Emmanuel ; Huang, Shao-Shan ; Chory, Joanne. / Big Data to the Bench : Transcriptome Analysis for Undergraduates. In: CBE life sciences education. 2019 ; Vol. 18, No. 2. pp. ar19.
@article{77e9af8915214b76934ad0c3d5ed1819,
title = "Big Data to the Bench: Transcriptome Analysis for Undergraduates",
abstract = "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.",
author = "Carl Procko and Steven Morrison and Courtney Dunar and Sara Mills and Brianna Maldonado and Carlee Cockrum and Peters, {Nathan Emmanuel} and Shao-Shan Huang and Joanne Chory",
year = "2019",
month = "6",
day = "1",
doi = "10.1187/cbe.18-08-0161",
language = "English (US)",
volume = "18",
pages = "ar19",
journal = "CBE Life Sciences Education",
issn = "1931-7913",
publisher = "American Society for Cell Biology",
number = "2",

}

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

AU - Chory, Joanne

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.

UR - http://www.scopus.com/inward/record.url?scp=85065881561&partnerID=8YFLogxK

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

VL - 18

SP - ar19

JO - CBE Life Sciences Education

JF - CBE Life Sciences Education

SN - 1931-7913

IS - 2

ER -