As the ability to generate large datasets becomes more accessible in many areas of biology, proficiency in computational tools that can manage these data becomes increasingly important. Computational biology and large dataset management are universally applicable skills in many subfields of the life sciences including medicine, molecular and cell biology, environmental, and geological sciences. While biology students may not develop expertise in informatics over the course of a semester, relevant exposure to these tools can help them develop confidence to try these analyses and build self-efficacy in the discipline for future work. Here we present an open-sourced laboratory module that can be used to identify certain types of RNA transcripts from a larger pool of high-throughput RNAseq data. This experiment uses publicly available gene expression data from environmental response in agriculturally relevant crop species. The core of this analysis pipeline utilizes EvolincI, which employs algorithms that capitalize on canonical protein-coding features, such as start codons, to identify transcripts that are expressed but likely not translated. Additionally, this module can be combined with other activities including reading related journal articles to help students understand how these large data sets are generated, exercises in initial transcriptome read mapping, and follow-up analyses to track differential gene expression. This module has been piloted as part of a Molecular Genetics course at Centenary College of Louisiana, and in addition to more traditional assessment, students were asked their confidence level with general biological concepts, genomics, computers, and large data sets both before and after the module. Data from this preliminary pilot are positive, indicating that deployment of this module show promising increases in confidence surrounding computer-based analysis and general biology (IRB Approval number 19-002).
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