Big Data to the Bench: Transcriptome Analysis for Undergraduates.

Area of Science:

• Genomics
• Bioinformatics
• Plant Biology

Background:

• Next-generation sequencing (NGS) generates vast biological data, necessitating computational skills for biologists.
• Undergraduate curricula often lack integrated computational training, hindering student preparedness for big data analysis.
• RNA-sequencing (RNA-seq) is a powerful NGS method for studying gene expression.

Purpose of the Study:

• To describe a course-based undergraduate research experience (CURE) combining RNA-seq data analysis with wet-lab experiments.
• To investigate plant responses to light by linking gene expression data with observable phenotypes.
• To assess the impact of the CURE on students' computational skills and bioinformatics self-efficacy.

Main Methods:

• A semester-long CURE at a liberal arts college integrating RNA-seq analysis with student-designed wet-lab experiments.
• Students analyzed RNA-seq data to generate hypotheses about plant responses to light.
• Follow-up studies focused on gene expression and plant growth, linking computational findings to empirical data.

Main Results:

• Students demonstrated acquisition of knowledge in big data analysis and computer coding.
• The course successfully connected gene expression analysis with plant phenotypes.
• Earlier exposure to computational methods may further enhance learning outcomes.

Conclusions:

• The described CURE effectively integrates computational and wet-lab approaches in undergraduate biology education.
• This replicable framework improves students' computational skills and bioinformatics self-efficacy.
• The course prepares students for the data-intensive nature of contemporary biological research.