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      Fostering bioinformatics education through skill development of professors: Big Genomic Data Skills Training for Professors

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          Abstract

          Bioinformatics has become an indispensable part of life science over the past 2 decades. However, bioinformatics education is not well integrated at the undergraduate level, especially in liberal arts colleges and regional universities in the United States. One significant obstacle pointed out by the Network for Integrating Bioinformatics into Life Sciences Education is the lack of faculty in the bioinformatics area. Most current life science professors did not acquire bioinformatics analysis skills during their own training. Consequently, a great number of undergraduate and graduate students do not get the chance to learn bioinformatics or computational biology skills within a structured curriculum during their education. To address this gap, we developed a module-based, week-long short course to train small college and regional university professors with essential bioinformatics skills. The bioinformatics modules were built to be adapted by the professor-trainees afterward and used in their own classes. All the course materials can be accessed at https://github.com/TheJacksonLaboratory/JAXBD2K-ShortCourse.

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          Most cited references25

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          The Genomics Education Partnership: Successful Integration of Research into Laboratory Classes at a Diverse Group of Undergraduate Institutions

          Genomics is not only essential for students to understand biology but also provides unprecedented opportunities for undergraduate research. The goal of the Genomics Education Partnership (GEP), a collaboration between a growing number of colleges and universities around the country and the Department of Biology and Genome Center of Washington University in St. Louis, is to provide such research opportunities. Using a versatile curriculum that has been adapted to many different class settings, GEP undergraduates undertake projects to bring draft-quality genomic sequence up to high quality and/or participate in the annotation of these sequences. GEP undergraduates have improved more than 2 million bases of draft genomic sequence from several species of Drosophila and have produced hundreds of gene models using evidence-based manual annotation. Students appreciate their ability to make a contribution to ongoing research, and report increased independence and a more active learning approach after participation in GEP projects. They show knowledge gains on pre- and postcourse quizzes about genes and genomes and in bioinformatic analysis. Participating faculty also report professional gains, increased access to genomics-related technology, and an overall positive experience. We have found that using a genomics research project as the core of a laboratory course is rewarding for both faculty and students.
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            Cloud computing for genomic data analysis and collaboration

            DNA sequencing made huge strides in the last decade. Studies based on large sequencing datasets appear frequently, and public archives for raw sequencing data have been doubling in size every 18 months. Meanwhile, commercial and academic cloud computing have matured, leading to more providers, greater total capacity, and a larger variety of services. Here we describe how cloud computing is used for large-scale genomics collaborations and research and argue how cloud computing will likely be a basic underpinning for future large-scale genomics collaborations and for efforts to re-analyze archived data, including privacy-protected data.
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              Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators

              In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principal investigators (BIO PIs), nearly 90% indicated they are currently or will soon be analyzing large data sets. BIO PIs considered a range of computational needs important to their work, including high performance computing (HPC), bioinformatics support, multistep workflows, updated analysis software, and the ability to store, share, and publish data. Previous studies in the United States and Canada emphasized infrastructure needs. However, BIO PIs said the most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC—acknowledging that data science skills will be required to build a deeper understanding of life. This portends a growing data knowledge gap in biology and challenges institutions and funding agencies to redouble their support for computational training in biology.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                13 June 2019
                June 2019
                : 15
                : 6
                : e1007026
                Affiliations
                [1 ] The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
                [2 ] Genomic Education, The Jackson Laboratory, Bar Harbor, Maine, United States of America
                [3 ] Center for Quantitative Medicine, UConn Health, Farmington, Connecticut, United States of America
                [4 ] Department of Genetics and Genome Sciences, UConn Health, Farmington, Connecticut, United States of America
                University of Toronto, CANADA
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-3548-9227
                http://orcid.org/0000-0003-3281-356X
                http://orcid.org/0000-0003-2431-1076
                http://orcid.org/0000-0002-3298-2358
                Article
                PCOMPBIOL-D-18-01965
                10.1371/journal.pcbi.1007026
                6563947
                31194735
                a5bc8a15-f685-4995-ba58-0891c28da2b8
                © 2019 Zhan et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Page count
                Figures: 2, Tables: 2, Pages: 11
                Funding
                The authors acknowledge the support of National Institutes of Health (NIH) Big Data to Knowledge (BD2K) Initiative (R25 EB022365) ( https://projectreporter.nih.gov/project_description.cfm?projectnumber=1R25EB022365-01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Education
                Research and Analysis Methods
                Database and Informatics Methods
                Bioinformatics
                People and Places
                Population Groupings
                Educational Status
                Undergraduates
                Biology and Life Sciences
                Genetics
                Genomics
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genomic Medicine
                Social Sciences
                Sociology
                Education
                Schools
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                Computer and Information Sciences
                Computing Methods
                Cloud Computing
                Biology and Life Sciences
                Computational Biology

                Quantitative & Systems biology
                Quantitative & Systems biology

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