NIBLSE: A network for integrating bioinformatics into life sciences education

Elizabeth Dinsdale, Sarah C.R. Elgin, Neal Grandgenett, William Morgan, Anne Rosenwald, William Tapprich, Eric W. Triplett, Mark A. Pauley

Research output: Contribution to journalLetterpeer-review

20 Citations (Scopus)


Recent advances in high-throughput experimental methods and related computational technologies have provided life scientists with large and complex data sets. These data sets present exciting new opportunities for advancing research into the fundamental processes of living systems. However, this flood of data also presents many challenges, not the least of which is training students to examine and evaluate such data. Despite the increasing importance of bioinformatics and the fact that its interdisciplinary and process-oriented nature aligns directly with the goals of Vision and Change in Under­graduate Education: A Call to Action (American Association for the Advancement of Science [AAAS], 2011), our collective ex-perience is that there is currently a general lack of integration of bioinformatics concepts into undergraduate education in the life sciences. As leaders of an ongoing effort to establish an extended network of educators with a goal to integrate bioinformatics into undergraduate life sciences curricula, we read with interest the recent CBE—Life Sciences Education publication by Magana et al. (2014), which surveyed the liter-ature on stand-alone bioinformatics education efforts. On the basis of their analysis, the authors propose three main steps toward the design of an instructional curriculum in bioinformatics using the “understanding by design” process: identification of desired learning outcomes, development of methods of assessment, and determination of best pedagogical methods.
Original languageEnglish
Article numberle3
Number of pages4
JournalCBE Life Sciences Education
Issue number4
Publication statusPublished - 1 Dec 2015
Externally publishedYes


  • Network for Integrating Bioinformatics into Life Sciences Education (NIBSLE)
  • high-throughput experimental methods
  • computational technologies
  • life scientists
  • data sets


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