Big Data to the Bench: Transcriptome Analysis for Undergraduates

Autor/inn/en	Procko, Carl; Morrison, Steven; Dunar, Courtney; Mills, Sara; Maldonado, Brianna; Cockrum, Carlee; Peters, Nathan Emmanuel; Huang, Shao-shan Carol; Chory, Joanne
Titel	Big Data to the Bench: Transcriptome Analysis for Undergraduates
Quelle	In: CBE - Life Sciences Education, 18 (2019) 2, Artikel 19 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1931-7913
DOI	10.1187/cbe.18-08-0161
Schlagwörter	Undergraduate Students; Data Analysis; Biology; Information Science; College Science; Science Instruction; Genetics; Plants (Botany); Laboratory Experiments; Science Laboratories; Coding; Science Activities; Class Activities; Hands on Science + Suchen Sie Ihr Suchwort? Auswertung; Biologie; Informationswissenschaft; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Humangenetik; Pflanze; Laboratory work; Laborarbeit; Codierung; Programmierung
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. (As Provided).
Anmerkungen	American Society for Cell Biology. 8120 Woodmont Avenue Suite 750, Bethesda, MD 20814-2762. Tel: 301-347-9300; Fax: 301-347-9310; e-mail: ascbinfo@ascb.org; Website: http://www.ascb.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01