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Integration of Bioinformatics into Life Science Curricula: Community Development, Dissemination, and Assessment of a NIBLSE Learning Resource

Author(s): Adam Kleinschmit1, Rachel Cook2, Barbara Murdoch3, Elizabeth F Ryder4, William Tapprich5

1. University of Dubuque 2. Fairmont State University 3. Eastern Connecticut State University 4. Worcester Polytechnic Institute 5. University of Nebraska-Omaha

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Big data and computational tools have transformed the way we address biological questions. To prepare undergraduates for tomorrow’s challenges, life science curricula should integrate the understanding and use of these tools at all levels.

Licensed under CC Attribution-NonCommercial-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 23 Jul 2020 doi:10.25334/F138-SS53 - cite this


 Introductory bioinformatics exercises typically walk students through the use of computational tools, but often provide little understanding of what a tool does "under the hood." A solid understanding of how computational algorithms function, including their limitations, is essential for interpreting the output in a biologically relevant context. Here we describe the development, assessment, and dissemination of an introductory learning resource that focuses on the core concept of sequence similarity and its biological applications, using the NIBLSE framework.

This is a part of the Genomics Education Alliance Posters & Beyond materials for the BIOME Institute.

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    Marie C. Montes-Matias @ on

    I also work at a community college and would like to learn more about this approach and the student experience.  Very interesting!

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    Anne Rosenwald @ on

    Happy to address questions about this for NIBLSE - Anne

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