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      The why, when, and how of computing in biology classrooms

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          Abstract

          Many biologists are interested in teaching computing skills or using computing in the classroom, despite not being formally trained in these skills themselves. Thus biologists may find themselves researching how to teach these skills, and therefore many individuals are individually attempting to discover resources and methods to do so. Recent years have seen an expansion of new technologies to assist in delivering course content interactively. Educational research provides insights into how learners absorb and process information during interactive learning. In this review, we discuss the value of teaching foundational computing skills to biologists, and strategies and tools to do so. Additionally, we review the literature on teaching practices to support the development of these skills. We pay special attention to meeting the needs of diverse learners, and consider how different ways of delivering course content can be leveraged to provide a more inclusive classroom experience. Our goal is to enable biologists to teach computational skills and use computing in the classroom successfully.

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          Most cited references30

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          ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data

          The Environment for Tree Exploration (ETE) is a computational framework that simplifies the reconstruction, analysis, and visualization of phylogenetic trees and multiple sequence alignments. Here, we present ETE v3, featuring numerous improvements in the underlying library of methods, and providing a novel set of standalone tools to perform common tasks in comparative genomics and phylogenetics. The new features include (i) building gene-based and supermatrix-based phylogenies using a single command, (ii) testing and visualizing evolutionary models, (iii) calculating distances between trees of different size or including duplications, and (iv) providing seamless integration with the NCBI taxonomy database. ETE is freely available at http://etetoolkit.org
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            The iPlant Collaborative: Cyberinfrastructure for Plant Biology

            The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.
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              The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences

              The iPlant Collaborative provides life science research communities access to comprehensive, scalable, and cohesive computational infrastructure for data management; identity management; collaboration tools; and cloud, high-performance, high-throughput computing. iPlant provides training, learning material, and best practice resources to help all researchers make the best use of their data, expand their computational skill set, and effectively manage their data and computation when working as distributed teams. iPlant’s platform permits researchers to easily deposit and share their data and deploy new computational tools and analysis workflows, allowing the broader community to easily use and reuse those data and computational analyses.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project AdministrationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000 Research Limited (London, UK )
                2046-1402
                5 November 2019
                2019
                : 8
                : 1854
                Affiliations
                [1 ]Department of Biological Sciences, Southeastern Louisiana University, Hammond, LA, 70403, USA
                [2 ]Department of Biological Sciences, University of Rhode Island, Kingston, RI, 02881, USA
                [3 ]Department of Biological Sciences, Auburn University, Auburn, AL, 36849, USA
                [4 ]Biology Program, University of Louisiana Monroe, Monroe, LA, 71209, USA
                [5 ]School of Biological Sciences, University of Canterbury, Christchurch, 8042, New Zealand
                [1 ]European XFEL, Schenefeld, Germany
                [1 ]University of British Columbia, Vancouver, BC, Canada
                Author notes

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0003-4692-3225
                https://orcid.org/0000-0001-7414-9893
                https://orcid.org/0000-0002-2226-4213
                Article
                10.12688/f1000research.20873.1
                6971840
                e1c54cf8-17cd-433b-82f0-67e5f3dec33b
                Copyright: © 2019 Wright AM et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 October 2019
                Funding
                Funded by: University of Canterbury
                Funded by: National Institute of Food and Agriculture
                Award ID: 1017848
                Funded by: National Institute of General Medical Sciences
                Award ID: P20GM103424-17
                Funded by: National Science Foundation
                Award ID: DEB1656004
                AMW was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health [P20 GM103424-17]. This work was supported by the USDA National Institute of Food and Agriculture, Hatch project accession no. 1017848 to the University of Rhode Island. JRO was supported by funding from the National Science Foundation [DEB 1656004]. SPF was supported by a Teaching Development Grant from the University of Canterbury.
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Opinion Article
                Articles

                computation,biology,education,undergraduate
                computation, biology, education, undergraduate

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