In modern biological research, computing has become an integral component in Biological Big Data (BBD) analysis, yet education in computing has not been fully incorporated into life science education: many biologists are given a diluted treatment of computational genomics that presents the methods central to the field as nothing more than a toolkit. ?The pedagogical challenge facing the development of a computational genomics curriculum is the need to convey the important ideas ?without assuming previous exposure to programming. Biologists would also profit from knowing how to effectively apply various existing genomics software tools and, at the same time, ?understand how these tools work, a condition that is often violated in existing courses. We believe that high-quality online computational genomics education offers a particularly attractive solution to the problem because many universities have failed to address this challenge. It offers a promising pedagogical innovation because it is not ?replacing anything, but rather is fulfilling an important need. It bypasses the need for extensive curricular reform at the level of individual universities and instead adapt to high-quality, open online resources that lower the cost per student. We believe that our proposed online ?Computational Genomics Specialization will contribute to various offline courses (e.g., by enabling a flipped course) that will be developed in response to the same NIH Funding Opportunity Announcement ?Initiative to Maximize Research Education in Genomics.? We have published popular bioinformatics and algorithms textbooks, ?have published papers on various challenges of education in computational biology in reputable journals, delivered a TEDx talk on online education, founded a conference specializing in bioinformatics education (RECOMB-BE), developed multiple successful MOOCs in bioinformatics (including the first bioinformatics MOOC), and advised the development of Rosalind, an open online platform for learning bioinformatics through problem solving that has been used by over 100 professors. Our goals are (1) to develop open, modular, extendable, and adaptable MOOCs covering a broad range of topics in modern computational genomics, (2) use the developed MOOCs to competitively recruit the participants into the proposed offline computational genomics short courses and to bring underrepresented minorities to these events, and (3) establish ?the Computational Genomics Education Alliance, a community of educators who will help develop open, high-quality, modular online content and serve as instructors at our annual courses.
As the cost of DNA sequencing has become increasingly affordable in the past decade, the amount of genomic data procured for a typical biomedical research project has grown rapidly. As a result, it is becoming increasingly essential for the scientists ?conducting the biomedical research to have the core computational skills required to perform the appropriate analyses for their experiments. Our proposed courses will teach these researchers (1) how to use the appropriate workflows for each type of dataset/experiment, (2) how the tools work under the hood, and (3) how to interpret the results of these workflows in a biomedically-meaningful way.