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Plaisier SB, Alarid DO, Denning JA, Brownell SE, Buetow KH, Cooper KM, Wilson MA. Design and implementation of an asynchronous online course-based undergraduate research experience (CURE) in computational genomics. PLoS Comput Biol 2024; 20:e1012384. [PMID: 39264874 DOI: 10.1371/journal.pcbi.1012384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2024] Open
Abstract
As genomics technologies advance, there is a growing demand for computational biologists trained for genomics analysis but instructors face significant hurdles in providing formal training in computer programming, statistics, and genomics to biology students. Fully online learners represent a significant and growing community that can contribute to meet this need, but they are frequently excluded from valuable research opportunities which mostly do not offer the flexibility they need. To address these opportunity gaps, we developed an asynchronous course-based undergraduate research experience (CURE) for computational genomics specifically for fully online biology students. We generated custom learning materials and leveraged remotely accessible computational tools to address 2 novel research questions over 2 iterations of the genomics CURE, one testing bioinformatics approaches and one mining cancer genomics data. Here, we present how the instructional team distributed analysis needed to address these questions between students over a 7.5-week CURE and provided concurrent training in biology and statistics, computer programming, and professional development. Scores from identical learning assessments administered before and after completion of each CURE showed significant learning gains across biology and coding course objectives. Open-response progress reports were submitted weekly and identified self-reported adaptive coping strategies for challenges encountered throughout the course. Progress reports identified problems that could be resolved through collaboration with instructors and peers via messaging platforms and virtual meetings. We implemented asynchronous communication using the Slack messaging platform and an asynchronous journal club where students discussed relevant publications using the Perusall social annotation platform. The online genomics CURE resulted in unanticipated positive outcomes, including students voluntarily discussing plans to continue research after the course. These outcomes underscore the effectiveness of this genomics CURE for scientific training, recruitment and student-mentor relationships, and student successes. Asynchronous genomics CUREs can contribute to a more skilled, diverse, and inclusive workforce for the advancement of biomedical science.
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Affiliation(s)
- Seema B Plaisier
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, United States of America
| | - Danielle O Alarid
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, United States of America
| | - Joelle A Denning
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, United States of America
| | - Sara E Brownell
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
- Research for Inclusive STEM Education Center, Arizona State University, Tempe, Arizona, United States of America
| | - Kenneth H Buetow
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, United States of America
| | - Katelyn M Cooper
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
- Research for Inclusive STEM Education Center, Arizona State University, Tempe, Arizona, United States of America
| | - Melissa A Wilson
- School of Life Sciences, Arizona State University, Tempe, Arizona, United States of America
- Center for Evolution and Medicine, Arizona State University, Tempe, Arizona, United States of America
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2
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Wilkins OM, Campbell R, Yosufzai Z, Doe V, Soucy SM. Cloud-based introduction to BASH programming for biologists. Brief Bioinform 2024; 25:bbae244. [PMID: 39041911 PMCID: PMC11264290 DOI: 10.1093/bib/bbae244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/13/2024] [Indexed: 07/24/2024] Open
Abstract
This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning', https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement. This module delivers learning materials introducing the utility of the BASH (Bourne Again Shell) programming language for genomic data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. The next-generation sequencing revolution has generated massive amounts of novel biological data from a multitude of platforms that survey an ever-growing list of genomic modalities. These data require significant downstream computational and statistical analyses to glean meaningful biological insights. However, the skill sets required to generate these data are vastly different from the skills required to analyze these data. Bench scientists that generate next-generation data often lack the training required to perform analysis of these datasets and require support from bioinformatics specialists. Dedicated computational training is required to empower biologists in the area of genomic data analysis, however, learning to efficiently leverage a command line interface is a significant barrier in learning how to leverage common analytical tools. Cloud platforms have the potential to democratize access to the technical tools and computational resources necessary to work with modern sequencing data, providing an effective framework for bioinformatics education. This module aims to provide an interactive platform that slowly builds technical skills and knowledge needed to interact with genomics data on the command line in the Cloud. The sandbox format of this module enables users to move through the material at their own pace and test their grasp of the material with knowledge self-checks before building on that material in the next sub-module. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.
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Affiliation(s)
- Owen M Wilkins
- Genomic Data Science Core, Center for Quantitative Biology (COBRE), Dartmouth College, 1 Medical Center Drive, Lebanon, NH 03766, United States
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, 1 Medical Center Drive, Lebanon, NH 03766, United States
- Dartmouth Cancer Center, Geisel School of Medicine, Dartmouth Health, 1 Medical Center Drive, Lebanon, NH 03766, United States
| | - Ross Campbell
- National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, United States
| | - Zelaikha Yosufzai
- Health Data and AI, Deloitte Consulting LLP, 1919 N Lynn St, Suite 1500, Arlington, VA 22209, United States
| | - Valena Doe
- Google Cloud, 1900 Reston Metro Plaza, Reston, VA 20190, United States
| | - Shannon M Soucy
- Genomic Data Science Core, Center for Quantitative Biology (COBRE), Dartmouth College, 1 Medical Center Drive, Lebanon, NH 03766, United States
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, 1 Medical Center Drive, Lebanon, NH 03766, United States
- Dartmouth Cancer Center, Geisel School of Medicine, Dartmouth Health, 1 Medical Center Drive, Lebanon, NH 03766, United States
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Lowndes JS, Holder AM, Markowitz EH, Clatterbuck C, Bradford AL, Doering K, Stevens MH, Butland S, Burke D, Kross S, Hollister JW, Stawitz C, Siple MC, Rios A, Welch JN, Li B, Nojavan F, Davis A, Steiner E, London JM, Fenwick I, Hunzinger A, Verstaen J, Holmes E, Virdi M, Barrett AP, Robinson E. Shifting institutional culture to develop climate solutions with Open Science. Ecol Evol 2024; 14:e11341. [PMID: 38826171 PMCID: PMC11143379 DOI: 10.1002/ece3.11341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/24/2024] [Accepted: 04/01/2024] [Indexed: 06/04/2024] Open
Abstract
To address our climate emergency, "we must rapidly, radically reshape society"-Johnson & Wilkinson, All We Can Save. In science, reshaping requires formidable technical (cloud, coding, reproducibility) and cultural shifts (mindsets, hybrid collaboration, inclusion). We are a group of cross-government and academic scientists that are exploring better ways of working and not being too entrenched in our bureaucracies to do better science, support colleagues, and change the culture at our organizations. We share much-needed success stories and action for what we can all do to reshape science as part of the Open Science movement and 2023 Year of Open Science.
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Affiliation(s)
| | - Anna M. Holder
- California Environmental Protection AgencySacramentoCaliforniaUSA
| | | | | | - Amanda L. Bradford
- NOAA Fisheries Pacific Islands Fisheries Science CenterHonoluluHawaiiUSA
| | - Kathryn Doering
- ECS Federal LLC in support of NOAA Fisheries Office of Science and TechnologySeattleWashingtonUSA
| | - Molly H. Stevens
- NOAA Fisheries Southeast Fisheries Science CenterMiamiFloridaUSA
| | | | - Devan Burke
- California Environmental Protection AgencySacramentoCaliforniaUSA
| | - Sean Kross
- Fred Hutch Cancer CenterSeattleWashingtonUSA
| | | | - Christine Stawitz
- ECS Federal LLC in support of NOAA Fisheries Office of Science and TechnologySeattleWashingtonUSA
| | | | - Adyan Rios
- NOAA Fisheries Southeast Fisheries Science CenterMiamiFloridaUSA
| | | | - Bai Li
- ECS Federal LLC in support of NOAA Fisheries Office of Science and TechnologySeattleWashingtonUSA
| | - Farnaz Nojavan
- United States Environmental Protection AgencyWashingtonDCUSA
| | - Alexandra Davis
- University of California, Los AngelesLos AngelesCaliforniaUSA
| | - Erin Steiner
- NOAA Fisheries Northwest Fisheries Science CenterSeattleWashingtonUSA
| | - Josh M. London
- NOAA Fisheries Alaska Fisheries Science CenterSeattleWashingtonUSA
| | - Ileana Fenwick
- The University of North Carolina at Chapel HillChapel HillNorth CarolinUSA
| | - Alexis Hunzinger
- Adnet Systems, Inc. / NASA Goddard Earth Sciences Data and Information Services CenterGreenbeltMDUSA
| | - Juliette Verstaen
- NOAA Fisheries Pacific Islands Fisheries Science CenterHonoluluHawaiiUSA
- Cooperative Institute for Marine and Atmospheric Research, University of HawaiiHonoluluHawaiiUSA
| | - Elizabeth Holmes
- NOAA Fisheries Northwest Fisheries Science CenterSeattleWashingtonUSA
| | - Makhan Virdi
- NASA Atmospheric Science Data CenterWashingtonDCUSA
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Yang MA, Korsnack K. Pairing a bioinformatics-focused course-based undergraduate research experience with specifications grading in an introductory biology classroom. Biol Methods Protoc 2024; 9:bpae013. [PMID: 38463936 PMCID: PMC10924719 DOI: 10.1093/biomethods/bpae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/27/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open
Abstract
Introducing bioinformatics-focused concepts and skills in a biology classroom is difficult, especially in introductory biology classrooms. Course-based Undergraduate Research Experiences (CUREs) facilitate this process, introducing genomics and bioinformatics through authentic research experiences, but the many learning objectives needed in scientific research and communication, foundational biology concepts, and bioinformatics-focused concepts and skills can make the process challenging. Here, the pairing of specifications grading with a bioinformatics-focused CURE developed by the Genomics Education Partnership is described. The study examines how the course structure with specifications grading facilitated scaffolding of writing assignments, group work, and metacognitive activities; and describes the synergies between CUREs and specifications grading. CUREs require mastery of related concepts and skills for working through the research process, utilize common research practices of revision and iteration, and encourage a growth mindset to learning-all of which are heavily incentivized in assessment practices focused on specifications grading.
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Affiliation(s)
- Melinda A Yang
- Department of Biology, University of Richmond, Richmond, VA 23173, United States
| | - Kylie Korsnack
- Teaching and Scholarship Hub, University of Richmond, Richmond, VA 23173, United States
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Froney MM, Jarstfer MB, Pattenden SG, Solem AC, Aina OO, Eslinger MR, Thomas A, Alexander CM. Behind the screen: drug discovery using the big data of phenotypic analysis. FRONTIERS IN EDUCATION 2024; 9:1342378. [PMID: 39239383 PMCID: PMC11376653 DOI: 10.3389/feduc.2024.1342378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today's Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed "Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis" to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.
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Affiliation(s)
- Merrill M Froney
- Department of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Michael B Jarstfer
- Department of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Samantha G Pattenden
- Department of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Amanda C Solem
- Department of Biology, Hastings College, Hastings, NE, United States
| | - Olubunmi O Aina
- Department of Biology, Allen University, Columbia, SC, United States
| | - Melissa R Eslinger
- Department of Chemistry and Life Science, United States Military Academy, West Point, NY, United States
| | - Aeisha Thomas
- Department of Biological and Health Sciences, Crown College, St. Bonifacius, MN, United States
| | - Courtney M Alexander
- Department of Biology, University of North Carolina at Pembroke, Pembroke, NC, United States
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Fredston AL, Lowndes JSS. Welcoming More Participation in Open Data Science for the Oceans. ANNUAL REVIEW OF MARINE SCIENCE 2024; 16:537-549. [PMID: 37418835 DOI: 10.1146/annurev-marine-041723-094741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2023]
Abstract
Open science is a global movement happening across all research fields. Enabled by technology and the open web, it builds on years of efforts by individuals, grassroots organizations, institutions, and agencies. The goal is to share knowledge and broaden participation in science, from early ideation to making research outputs openly accessible to all (open access). With an emphasis on transparency and collaboration, the open science movement dovetails with efforts to increase diversity, equity, inclusion, and belonging in science and society. The US Biden-Harris Administration and many other US government agencies have declared 2023 the Year of Open Science, providing a great opportunity to boost participation in open science for the oceans. For researchers day-to-day, open science is a critical piece of modern analytical workflows with increasing amounts of data. Therefore, we focus this article on open data science-the tooling and people enabling reproducible, transparent, inclusive practices for data-intensive research-and its intersection with the marine sciences. We discuss the state of various dimensions of open science and argue that technical advancements have outpaced our field's culture change to incorporate them. Increasing inclusivity and technical skill building are interlinked and must be prioritized within the marine science community to find collaborative solutions for responding to climate change and other threats to marine biodiversity and society.
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Affiliation(s)
- Alexa L Fredston
- Department of Ocean Sciences, University of California, Santa Cruz, California, USA;
| | - Julia S Stewart Lowndes
- National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, California, USA
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7
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Alomair L, Abolfotouh MA. Awareness and Predictors of the Use of Bioinformatics in Genome Research in Saudi Arabia. Int J Gen Med 2023; 16:3413-3425. [PMID: 37587979 PMCID: PMC10426440 DOI: 10.2147/ijgm.s421815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
Background With the advances in genomics research, many countries still need more bioinformatics skills. This study aimed to assess the levels of awareness of bioinformatics and predictors of its use in genomics research among scientists in Saudi Arabia. Methods In a cross-sectional survey, 309 scientists of different biological and biomedical specialties were subjected to a previously validated e-questionnaire to collect data on (1) Knowledge about bioinformatics programming languages and tools, (2) Attitude toward acceptance of bioinformatics resources in genome-related research, and (3) The pattern of information-seeking to online bioinformatics resources. Logistic regression analysis was applied to identify the predictors of using bioinformatics in research. Significance was set at p<0.05. Results More than one-half (248, 56.4%) of all scientists reported a lack of bioinformatics knowledge. Most participants had a neutral attitude toward bioinformatics (295, 95.4%). The barriers facing acceptance of bioinformatics tools reported were; lack of training (210, 67.9%), insufficient support (180, 58.2%), and complexity of software (138, 44.6%). The limited experience was reported in; having one or more bioinformatics tools (98, 31.7%), using a supercomputer in their research inside (44, 14.2%) and outside Saudi Arabia (55, 17.8%), the need for developing a program to solve a biological problem (129, 41.7%), working in one or more fields of bioinformatics (93, 30.1%), using web applications (112, 36.2%), and using programming languages (102, 33.0%). Significant predictors of conducting genomics research were; younger scientists (p=0.039), Ph.D. education (p=0.003), more than five years of experience (p<0.05), previous training (p<0.001), and higher bioinformatics knowledge scores (p<0.001). Conclusion The study revealed a short knowledge, a neutral attitude, a lack of resources, and limited use of bioinformatics resources in genomics research. Education and training during each education level and during the job is recommended. Cloud-based resources may help scientists do research using publicly available Omics data. Further studies are necessary to evaluate collaboration among bioinformatics software developers and biologists.
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Affiliation(s)
- Lamya Alomair
- AI and Bioinformatics Department, King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
- King Saud Bin-Abdulaziz University for Health Sciences (KSAU-HS), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
| | - Mostafa A Abolfotouh
- King Saud Bin-Abdulaziz University for Health Sciences (KSAU-HS), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
- Research Training and Development Section, King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
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8
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Işık EB, Brazas MD, Schwartz R, Gaeta B, Palagi PM, van Gelder CWG, Suravajhala P, Singh H, Morgan SL, Zahroh H, Ling M, Satagopam VP, McGrath A, Nakai K, Tan TW, Gao G, Mulder N, Schönbach C, Zheng Y, De Las Rivas J, Khan AM. Grand challenges in bioinformatics education and training. Nat Biotechnol 2023; 41:1171-1174. [PMID: 37568018 DOI: 10.1038/s41587-023-01891-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2023]
Affiliation(s)
- Esra Büşra Işık
- Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Istanbul, Turkey
- APBioNET.org, Singapore, Singapore
| | - Michelle D Brazas
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Bioinformatics.ca, Toronto, Ontario, Canada
| | | | - Bruno Gaeta
- School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia
| | | | | | - Prashanth Suravajhala
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Clappana, India
- Bioclues.org, Hyderabad, India
| | - Harpreet Singh
- APBioNET.org, Singapore, Singapore
- Bioclues.org, Hyderabad, India
- Department of Bioinformatics, Hans Raj Mahila Maha Vidyalaya, Jalandhar, India
| | - Sarah L Morgan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Hilyatuz Zahroh
- APBioNET.org, Singapore, Singapore
- Genetics Research Centre, Universitas YARSI, Jakarta, Indonesia
| | - Maurice Ling
- APBioNET.org, Singapore, Singapore
- School of Applied Science, Temasek Polytechnic, Singapore, Singapore
| | - Venkata P Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
- International Society for Computational Biology, Leesburg, VA, USA
| | | | - Kenta Nakai
- Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Tin Wee Tan
- Department of Biochemistry, YLL School of Medicine, National University of Singapore, Singapore, Singapore
- National Supercomputing Centre, Singapore, Singapore
| | - Ge Gao
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Biomedical Pioneering Innovative Center and Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, Peking University, Beijing, China
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Christian Schönbach
- Department of Biology, School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
| | - Yun Zheng
- School of Landscape and Horticulture, Yunnan Agricultural University, Kunming, China
| | - Javier De Las Rivas
- Cancer Research Center, Spanish National Research Council, University of Salamanca & Institute for Biomedical Research of Salamanca, Salamanca, Spain
| | - Asif M Khan
- Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Istanbul, Turkey.
- APBioNET.org, Singapore, Singapore.
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Kuala Lumpur, Malaysia.
- College of Computing and Information Technology, University of Doha for Science and Technology, Doha, Qatar.
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Gaudier-Diaz MM, Parekh SV, Penton RE, Robertson SD, Thomas A. Sleepy Mice Case Study: Implementation and Assessment. JOURNAL OF UNDERGRADUATE NEUROSCIENCE EDUCATION : JUNE : A PUBLICATION OF FUN, FACULTY FOR UNDERGRADUATE NEUROSCIENCE 2023; 21:A108-A116. [PMID: 37588653 PMCID: PMC10426825 DOI: 10.59390/rhsn3470] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/10/2022] [Accepted: 10/14/2023] [Indexed: 08/18/2023]
Abstract
Case studies are a valuable teaching tool to engage students in course content using real-world scenarios. As part of the High-throughput Discovery Science & Inquiry-based Case Studies for Today's Students (HITS) Research Coordination Network (RCN), our team has created the Sleepy Mice Case Study for students to engage with RStudio and the Allen Institute for Brain Science's open access high-throughput sleep dataset on mice. Sleep is important for health, a familiar concern to college students, and was a basis for this case study. In this case, students completed an initial homework assignment, in-class work, and a final take-home application assignment. The case study was implemented in synchronous and asynchronous Introductory Neuroscience courses, a Biopsychology course, and a Human Anatomy and Physiology course, reflecting its versatility. The case can be used to teach course-specific learning objectives such as sleep-related content and/or science data processing skills. The case study was successful as shown by gains in student scores and confidence in achieving learning objectives. Most students reported enjoying learning about sleep deprivation course content using the case study. Best practices based on instructor experiences in implementation are also included to facilitate future use so that the Sleepy Mice Case Study can be used to teach content and/or research-related skills in various courses and modalities.
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Affiliation(s)
- Monica M. Gaudier-Diaz
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 25799
| | - Shveta V. Parekh
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 25799
| | - Rachel E. Penton
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 25799
| | - Sabrina D. Robertson
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC 25799
| | - Aeisha Thomas
- Department of Biological and Health Sciences, Crown College, St. Bonifacius, MN 55375
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10
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Sano T, Sampad MJN, Gonzalez-Ferrer J, Hernandez S, Vera-Choqqueccota S, Vargas PA, Urcuyo R, Duran NM, Teodorescu M, Haussler D, Schmidt H, Mostajo-Radji MA. Open-loop lab-on-a-chip technology enables remote computer science training in Latinx life sciences students. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.28.538776. [PMID: 37205466 PMCID: PMC10187215 DOI: 10.1101/2023.04.28.538776] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Despite many interventions, science education remains highly inequitable throughout the world. Among all life sciences fields, Bioinformatics and Computational Biology suffer from the strongest underrepresentation of racial and gender minorities. Internet-enabled project-based learning (PBL) has the potential to reach underserved communities and increase the diversity of the scientific workforce. Here, we demonstrate the use of lab-on-a-chip (LoC) technologies to train Latinx life science undergraduate students in concepts of computer programming by taking advantage of open-loop cloud-integrated LoCs. We developed a context-aware curriculum to train students at over 8,000 km from the experimental site. We showed that this approach was sufficient to develop programming skills and increase the interest of students in continuing careers in Bioinformatics. Altogether, we conclude that LoC-based Internet-enabled PBL can become a powerful tool to train Latinx students and increase the diversity in STEM.
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Affiliation(s)
- Tyler Sano
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064
| | | | - Jesus Gonzalez-Ferrer
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060
| | - Sebastian Hernandez
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060
- Centro de Electroquímica y Energía Química (CELEQ), Universidad de Costa Rica, San José, 11501 2060, Costa Rica
| | - Samira Vera-Choqqueccota
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060
| | - Paola A Vargas
- Biotechnology, Universidad Católica Boliviana San Pablo, Santa Cruz de la Sierra, Bolivia
| | - Roberto Urcuyo
- Centro de Electroquímica y Energía Química (CELEQ), Universidad de Costa Rica, San José, 11501 2060, Costa Rica
| | | | - Mircea Teodorescu
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060
| | - David Haussler
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, 95060
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060
| | - Holger Schmidt
- Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, 95064
| | - Mohammed A Mostajo-Radji
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, 95060
- Live Cell Biotechnology Discovery Lab, University of California Santa Cruz, Santa Cruz, CA, 95060
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11
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The Riffomonas YouTube Channel: An Educational Resource To Foster Reproducible Research Practices. Microbiol Resour Announc 2023; 12:e0131022. [PMID: 36651754 PMCID: PMC9933679 DOI: 10.1128/mra.01310-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Methods for analyzing data in a reproducible manner are often viewed as impenetrable to scientists more familiar with laboratory research. The Riffomonas YouTube channel is committed to teaching these scientists and others how to engage in reproducible research using modern data science tools.
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12
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Diversifying the genomic data science research community. Genome Res 2022; 32:1231-1241. [PMID: 35858750 PMCID: PMC9341509 DOI: 10.1101/gr.276496.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/02/2022] [Indexed: 11/25/2022]
Abstract
Over the past 20 years, the explosion of genomic data collection and the cloud computing revolution have made computational and data science research accessible to anyone with a web browser and an internet connection. However, students at institutions with limited resources have received relatively little exposure to curricula or professional development opportunities that lead to careers in genomic data science. To broaden participation in genomics research, the scientific community needs to support these programs in local education and research at underserved institutions (UIs). These include community colleges, historically Black colleges and universities, Hispanic-serving institutions, and tribal colleges and universities that support ethnically, racially, and socioeconomically underrepresented students in the United States. We have formed the Genomic Data Science Community Network to support students, faculty, and their networks to identify opportunities and broaden access to genomic data science. These opportunities include expanding access to infrastructure and data, providing UI faculty development opportunities, strengthening collaborations among faculty, recognizing UI teaching and research excellence, fostering student awareness, developing modular and open-source resources, expanding course-based undergraduate research experiences (CUREs), building curriculum, supporting student professional development and research, and removing financial barriers through funding programs and collaborator support.
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13
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Parks ST, Taylor C. Development of a Remote, Course-Based Undergraduate Experience to Facilitate In Silico Study of Microbial Metabolic Pathways. JOURNAL OF MICROBIOLOGY & BIOLOGY EDUCATION 2022; 23:jmbe00318-21. [PMID: 35340445 PMCID: PMC8941885 DOI: 10.1128/jmbe.00318-21] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/19/2022] [Indexed: 05/09/2023]
Abstract
Course-based undergraduate research experiences (CUREs) often occur in a physical lab space, but they can also be offered remotely while maintaining course expectations and providing opportunity for authentic student engagement in research. Using a novel framework, remote Microbial Ecology CURE students used microbes isolated via antimicrobial-challenged Winogradsky columns to investigate phylogeny and metabolism through a hypothesis-driven meta-analysis (MA). Students used 16S rRNA and key metabolic enzymes to compare phylogeny; enzymes were modeled and evaluated for putative conserved domains, culminating in primer design and analysis. Using in silico tools facilitated student development of bioinformatics skills. The MA was subdivided into discrete sections in order to (i) provide a timeline for students to remain on schedule throughout a remote-learning lab experience, (ii) encourage feedback throughout the project, and (iii) facilitate student understanding of the experimental design. MA deliverables were designed to be specific figures with individual titles, legends, and analyses to enable their feedback for subsequent presentations. The six key formative deliverables included a word cloud (used to develop the works cited list and hypothesis), a 16S rRNA phylogenetic tree, an annotated metabolic pathway and three-dimensional model of the key metabolic enzyme, a phylogenetic tree based on the key metabolic enzyme, design and analysis of a primer set for the key metabolic enzyme, and a summative poster and graphical abstract. The MA project yielded poster presentations at virtual conferences, lab presentations, and written reports. Using the hypothesis-based MA model encouraged an authentic research experience, enabling students to develop, discuss, and progress in meaningful experiments.
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14
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Bain SA, Plaisier H, Anderson F, Cook N, Crouch K, Meagher TR, Ritchie MG, Wallace EWJ, Barker D. Bringing bioinformatics to schools with the 4273pi project. PLoS Comput Biol 2022; 18:e1009705. [PMID: 35051174 PMCID: PMC8775354 DOI: 10.1371/journal.pcbi.1009705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Over the last few decades, the nature of life sciences research has changed enormously, generating a need for a workforce with a variety of computational skills such as those required to store, manage, and analyse the large biological datasets produced by next-generation sequencing. Those with such expertise are increasingly in demand for employment in both research and industry. Despite this, bioinformatics education has failed to keep pace with advances in research. At secondary school level, computing is often taught in isolation from other sciences, and its importance in biological research is not fully realised, leaving pupils unprepared for the computational component of Higher Education and, subsequently, research in the life sciences. The 4273pi Bioinformatics at School project (https://4273pi.org) aims to address this issue by designing and delivering curriculum-linked, hands-on bioinformatics workshops for secondary school biology pupils, with an emphasis on equitable access. So far, we have reached over 180 schools across Scotland through visits or teacher events, and our open education resources are used internationally. Here, we describe our project, our aims and motivations, and the practical lessons we have learned from implementing a successful bioinformatics education project over the last 5 years.
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Affiliation(s)
- Stevie A. Bain
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
- * E-mail: (SAB); (HP); (DB)
| | - Heleen Plaisier
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
- * E-mail: (SAB); (HP); (DB)
| | - Felicity Anderson
- Institute for Cell Biology and SynthSys, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Nicola Cook
- Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, United Kingdom
| | - Kathryn Crouch
- Wellcome Centre for Integrative Parasitology, University of Glasgow, Glasgow, United Kingdom
| | - Thomas R. Meagher
- Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, United Kingdom
| | - Michael G. Ritchie
- Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, United Kingdom
| | - Edward W. J. Wallace
- Institute for Cell Biology and SynthSys, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Daniel Barker
- Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
- * E-mail: (SAB); (HP); (DB)
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15
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Emery NC, Crispo E, Supp SR, Farrell KJ, Kerkhoff AJ, Bledsoe EK, O'Donnell KL, McCall AC, Aiello-Lammens ME. Data Science in Undergraduate Life Science Education: A Need for Instructor Skills Training. Bioscience 2021; 71:1274-1287. [PMID: 34867087 PMCID: PMC8634500 DOI: 10.1093/biosci/biab107] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
There is a clear demand for quantitative literacy in the life sciences, necessitating competent instructors in higher education. However, not all instructors are versed in data science skills or research-based teaching practices. We surveyed biological and environmental science instructors (n = 106) about the teaching of data science in higher education, identifying instructor needs and illuminating barriers to instruction. Our results indicate that instructors use, teach, and view data management, analysis, and visualization as important data science skills. Coding, modeling, and reproducibility were less valued by the instructors, although this differed according to institution type and career stage. The greatest barriers were instructor and student background and space in the curriculum. The instructors were most interested in training on how to teach coding and data analysis. Our study provides an important window into how data science is taught in higher education biology programs and how we can best move forward to empower instructors across disciplines.
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Affiliation(s)
- Nathan C Emery
- Michigan State University, East Lansing, Michigan, United States
| | - Erika Crispo
- Pace University, New York City, New York, United States
| | | | | | | | - Ellen K Bledsoe
- University of Regina with CIEE's Living Data Project, Regina, Saskatchewan, Canada
| | | | | | - Matthew E Aiello-Lammens
- Environmental Studies and Science Department and director of the Environmental Science Graduate Program at Pace University, New York City, New York, United States
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16
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Drew JC, Grandgenett N, Dinsdale EA, Vázquez Quiñones LE, Galindo S, Morgan WR, Pauley M, Rosenwald A, Triplett EW, Tapprich W, Kleinschmit AJ. There Is More than Multiple Choice: Crowd-Sourced Assessment Tips for Online, Hybrid, and Face-to-Face Environments. JOURNAL OF MICROBIOLOGY & BIOLOGY EDUCATION 2021; 22:e00205-21. [PMID: 34970386 PMCID: PMC8673258 DOI: 10.1128/jmbe.00205-21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 08/12/2021] [Indexed: 06/14/2023]
Abstract
Developing effective assessments of student learning is a challenging task for faculty and even more difficult for those in emerging disciplines that lack readily available resources and standards. With the power of technology-enhanced education and accessible digital learning platforms, instructors are also looking for assessments that work in an online format. This article will be useful for all teachers, but especially for entry-level instructors, in addition to more mature instructors who are looking to become more well versed in assessment, who seek a succinct summary of assessment types to springboard the integration of new forms of assessment of student learning into their courses. In this paper, ten assessment types, all appropriate for face-to-face, blended, and online modalities, are discussed. The assessments are mapped to a set of bioinformatics core competencies with examples of how they have been used to assess student learning. Although bioinformatics is used as the focus of the assessment types, the question types are relevant to many disciplines.
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Affiliation(s)
- Jennifer C. Drew
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, Florida, USA
| | - Neal Grandgenett
- Department of Teacher Education, University of Nebraska at Omaha, Omaha, Nebraska, USA
| | - Elizabeth A. Dinsdale
- College of Science and Engineering, Flinders University, Bedford Park, South Australia, Australia
| | - Luis E. Vázquez Quiñones
- Division of Science and Technology, Universidad Ana G. Méndez–Cupey Campus, San Juan, Puerto Rico
| | - Sebastian Galindo
- Department of Agricultural Education and Communication, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, Florida, USA
| | | | - Mark Pauley
- Division of Undergraduate Education, National Science Foundation, Alexandria, Virginia, USA
| | - Anne Rosenwald
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Eric W. Triplett
- Department of Microbiology and Cell Science, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, Florida, USA
| | - William Tapprich
- Department of Biology, University of Nebraska at Omaha, Omaha, Nebraska, USA
| | - Adam J. Kleinschmit
- Department of Natural and Applied Sciences, University of Dubuque, Dubuque, Iowa, USA
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17
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Yang L, Zheng S, Xu X, Sun Y, Wang X, Li J. Medical Data Mining Course Development in Postgraduate Medical Education: Web-Based Survey and Case Study. JMIR MEDICAL EDUCATION 2021; 7:e24027. [PMID: 34596575 PMCID: PMC8520135 DOI: 10.2196/24027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/16/2020] [Accepted: 08/08/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Medical postgraduates' demand for data capabilities is growing, as biomedical research becomes more data driven, integrative, and computational. In the context of the application of big data in health and medicine, the integration of data mining skills into postgraduate medical education becomes important. OBJECTIVE This study aimed to demonstrate the design and implementation of a medical data mining course for medical postgraduates with diverse backgrounds in a medical school. METHODS We developed a medical data mining course called "Practical Techniques of Medical Data Mining" for postgraduate medical education and taught the course online at Peking Union Medical College (PUMC). To identify the background knowledge, programming skills, and expectations of targeted learners, we conducted a web-based questionnaire survey. After determining the instructional methods to be used in the course, three technical platforms-Rain Classroom, Tencent Meeting, and WeChat-were chosen for online teaching. A medical data mining platform called Medical Data Mining - R Programming Hub (MedHub) was developed for self-learning, which could support the development and comprehensive testing of data mining algorithms. Finally, we carried out a postcourse survey and a case study to demonstrate that our online course could accommodate a diverse group of medical students with a wide range of academic backgrounds and programming experience. RESULTS In total, 200 postgraduates from 30 disciplines participated in the precourse survey. Based on the analysis of students' characteristics and expectations, we designed an optimized course structured into nine logical teaching units (one 4-hour unit per week for 9 weeks). The course covered basic knowledge of R programming, machine learning models, clinical data mining, and omics data mining, among other topics, as well as diversified health care analysis scenarios. Finally, this 9-week course was successfully implemented in an online format from May to July in the spring semester of 2020 at PUMC. A total of 6 faculty members and 317 students participated in the course. Postcourse survey data showed that our course was considered to be very practical (83/83, 100% indicated "very positive" or "positive"), and MedHub received the best feedback, both in function (80/83, 96% chose "satisfied") and teaching effect (80/83, 96% chose "satisfied"). The case study showed that our course was able to fill the gap between student expectations and learning outcomes. CONCLUSIONS We developed content for a data mining course, with online instructional methods to accommodate the diversified characteristics of students. Our optimized course could improve the data mining skills of medical students with a wide range of academic backgrounds and programming experience.
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Affiliation(s)
- Lin Yang
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Si Zheng
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaowei Xu
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yueping Sun
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuwen Wang
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiao Li
- Institute of Medical Information and Library, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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18
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Community development, implementation, and assessment of a NIBLSE bioinformatics sequence similarity learning resource. PLoS One 2021; 16:e0257404. [PMID: 34506617 PMCID: PMC8432852 DOI: 10.1371/journal.pone.0257404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 08/31/2021] [Indexed: 11/19/2022] Open
Abstract
As powerful computational tools and 'big data' transform the biological sciences, bioinformatics training is becoming necessary to prepare the next generation of life scientists. Furthermore, because the tools and resources employed in bioinformatics are constantly evolving, bioinformatics learning materials must be continuously improved. In addition, these learning materials need to move beyond today's typical step-by-step guides to promote deeper conceptual understanding by students. One of the goals of the Network for Integrating Bioinformatics into Life Sciences Education (NIBSLE) is to create, curate, disseminate, and assess appropriate open-access bioinformatics learning resources. Here we describe the evolution, integration, and assessment of a learning resource that explores essential concepts of biological sequence similarity. Pre/post student assessment data from diverse life science courses show significant learning gains. These results indicate that the learning resource is a beneficial educational product for the integration of bioinformatics across curricula.
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19
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Melendrez MC, Shaw S, Brown CT, Goodner BW, Kvaal C. Editorial: Curriculum Applications in Microbiology: Bioinformatics in the Classroom. Front Microbiol 2021; 12:705233. [PMID: 34276638 PMCID: PMC8281245 DOI: 10.3389/fmicb.2021.705233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 06/07/2021] [Indexed: 11/18/2022] Open
Affiliation(s)
| | - Sophie Shaw
- Centre for Genome Enabled Biology and Medicine, University of Aberdeen, Aberdeen, United Kingdom
| | - C Titus Brown
- Department of Population Health and Reproduction, University of California, Davis, Davis, CA, United States
| | | | - Christopher Kvaal
- Department of Biology, St. Cloud State University, St. Cloud, MN, United States
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20
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Goller CC, Srougi MC, Chen SH, Schenkman LR, Kelly RM. Integrating Bioinformatics Tools Into Inquiry-Based Molecular Biology Laboratory Education Modules. FRONTIERS IN EDUCATION 2021; 6:711403. [PMID: 35036827 PMCID: PMC8758113 DOI: 10.3389/feduc.2021.711403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The accelerating expansion of online bioinformatics tools has profoundly impacted molecular biology, with such tools becoming integral to the modern life sciences. As a result, molecular biology laboratory education must train students to leverage bioinformatics in meaningful ways to be prepared for a spectrum of careers. Institutions of higher learning can benefit from a flexible and dynamic instructional paradigm that blends up-to-date bioinformatics training with best practices in molecular biology laboratory pedagogy. At North Carolina State University, the campus-wide interdisciplinary Biotechnology (BIT) Program has developed cutting-edge, flexible, inquiry-based Molecular Biology Laboratory Education Modules (MBLEMs). MBLEMs incorporate relevant online bioinformatics tools using evidenced-based pedagogical practices and in alignment with national learning frameworks. Students in MBLEMs engage in the most recent experimental developments in modern biology (e.g., CRISPR, metagenomics) through the strategic use of bioinformatics, in combination with wet-lab experiments, to address research questions. MBLEMs are flexible educational units that provide a menu of inquiry-based laboratory exercises that can be used as complete courses or as parts of existing courses. As such, MBLEMs are designed to serve as resources for institutions ranging from community colleges to research-intensive universities, involving a diverse range of learners. Herein, we describe this new paradigm for biology laboratory education that embraces bioinformatics as a critical component of inquiry-based learning for undergraduate and graduate students representing the life sciences, the physical sciences, and engineering.
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Affiliation(s)
- Carlos C. Goller
- Biotechnology (BIT) Program, North Carolina State University, Raleigh, NC, United States
- Department of Biological Sciences, College of Sciences, North Carolina State University, Raleigh, NC, United States
| | - Melissa C. Srougi
- Biotechnology (BIT) Program, North Carolina State University, Raleigh, NC, United States
- Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, United States
| | - Stefanie H. Chen
- Biotechnology (BIT) Program, North Carolina State University, Raleigh, NC, United States
- Department of Biological Sciences, College of Sciences, North Carolina State University, Raleigh, NC, United States
| | - Laura R. Schenkman
- Biotechnology (BIT) Program, North Carolina State University, Raleigh, NC, United States
| | - Robert M. Kelly
- Biotechnology (BIT) Program, North Carolina State University, Raleigh, NC, United States
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, United States
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21
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Niepielko MG, Shumskaya M. Early Requirement for Bioinformatics in Undergraduate Biology Curricula. FRONTIERS IN BIOINFORMATICS 2021; 1:656531. [PMID: 36303737 PMCID: PMC9581004 DOI: 10.3389/fbinf.2021.656531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Matthew G. Niepielko
- New Jersey Center for Science, Technology, and Mathematics, Kean University, Union, NJ, United States
| | - Maria Shumskaya
- School of Natural Sciences, Biology, Kean University, Union, NJ, United States
- *Correspondence: Maria Shumskaya,
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22
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Abstract
Cell imaging has entered the 'Big Data' era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the 'omics' fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools - democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.
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Affiliation(s)
- Meghan K Driscoll
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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23
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Petrie KL, Xie R. Resequencing of Microbial Isolates: A Lab Module to Introduce Novices to Command-Line Bioinformatics. Front Microbiol 2021; 12:578859. [PMID: 33796082 PMCID: PMC8008064 DOI: 10.3389/fmicb.2021.578859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 02/16/2021] [Indexed: 11/23/2022] Open
Abstract
Familiarity with genome-scale data and the bioinformatic skills to analyze it have become essential for understanding and advancing modern biology and human health, yet many undergraduate biology majors are never exposed to hands-on bioinformatics. This paper presents a module that introduces students to applied bioinformatic analysis within the context of a research-based microbiology lab course. One of the most commonly used genomic analyses in biology is resequencing: determining the sequence of DNA bases in a derived strain of some organism, and comparing it to the known ancestral genome of that organism to better understand the phenotypic differences between them. Many existing CUREs - Course Based Undergraduate Research Experiences - evolve or select new strains of bacteria and compare them phenotypically to ancestral strains. This paper covers standardized strategies and procedures, accessible to undergraduates, for preparing and analyzing microbial whole-genome resequencing data to examine the genotypic differences between such strains. Wet-lab protocols and computational tutorials are provided, along with additional guidelines for educators, providing instructors without a next-generation sequencing or bioinformatics background the necessary information to incorporate whole-genome sequencing and command-line analysis into their class. This module introduces novice students to running software at the command-line, giving them exposure and familiarity with the types of tools that make up the vast majority of open-source scientific software used in contemporary biology. Completion of the module improves student attitudes toward computing, which may make them more likely to pursue further bioinformatics study.
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Affiliation(s)
- Katherine Lynn Petrie
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, United States
- Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan
| | - Rujia Xie
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA, United States
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24
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Tapprich WE, Reichart L, Simon DM, Duncan G, McClung W, Grandgenett N, Pauley MA. An instructional definition and assessment rubric for bioinformatics instruction. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2021; 49:38-45. [PMID: 32744803 DOI: 10.1002/bmb.21361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 02/06/2020] [Accepted: 04/20/2020] [Indexed: 06/11/2023]
Abstract
The lack of an instructional definition of bioinformatics delays its effective integration into biology coursework. Using an iterative process, our team of biologists, a mathematician/computer scientist, and a bioinformatician together with an educational evaluation and assessment specialist, developed an instructional definition of the discipline: Bioinformatics is "an interdisciplinary field that is concerned with the development and application of algorithms that analyze biological data to investigate the structure and function of biological polymers and their relationships to living systems." The field is defined in terms of its two primary foundational disciplines, biology and computer science, and its interdisciplinary nature. At the same time, we also created a rubric for assessing open-ended responses to a prompt about what bioinformatics is and how it is used. The rubric has been shown to be reliable in successive rounds of testing using both common percent agreement (89.7%) and intraclass correlation coefficient (0.620) calculations. We offer the definition and rubric to life sciences instructors to help further integrate bioinformatics into biology instruction, as well as for fostering further educational research projects.
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Affiliation(s)
- William E Tapprich
- Department of Biology, University of Nebraska at Omaha, Omaha, Nebraska, USA
| | - Letitia Reichart
- Department of Biology, University of Nebraska at Kearney, Kearney, Nebraska, USA
| | - Dawn M Simon
- Department of Biology, University of Nebraska at Kearney, Kearney, Nebraska, USA
| | - Garry Duncan
- Biology Department, Nebraska Wesleyan University, Lincoln, Nebraska, USA
| | - William McClung
- Mathematics and Computer Science Department, Nebraska Wesleyan University, Lincoln, Nebraska, USA
| | - Neal Grandgenett
- Department of Teacher Education, University of Nebraska at Omaha, Omaha, Nebraska, USA
| | - Mark A Pauley
- School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, Nebraska, USA
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25
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Abstract
Microbiome research projects are often interdisciplinary, involving fields such as microbiology, genetics, ecology, evolution, bioinformatics, and statistics. These research projects can be an excellent fit for undergraduate courses ranging from introductory biology labs to upper-level capstone courses. Microbiome research projects can attract the interest of students majoring in health and medical sciences, environmental sciences, and agriculture, and there are meaningful ties to real-world issues relating to human health, climate change, and environmental sustainability and resilience in pristine, fragile ecosystems to bustling urban centers. In this review, we will discuss the potential of microbiome research integrated into classes using a number of different modalities. Our experience scaling-up and implementing microbiome projects at a range of institutions across the US has provided us with insight and strategies for what works well and how to diminish common hurdles that are encountered when implementing undergraduate microbiome research projects. We will discuss how course-based microbiome research can be leveraged to help faculty make advances in their own research and professional development and the resources that are available to support faculty interested in integrating microbiome research into their courses.
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Affiliation(s)
- Theodore R Muth
- Department of Biology, Brooklyn College of The City University of New York, Brooklyn, NY, United States.,Molecular, Cellular, and Developmental Biology Department at The Graduate Center of The City University of New York, New York, NY, United States
| | - Avrom J Caplan
- Department of Biology, Dyson College of Arts and Sciences, Pace University, New York, NY, United States
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26
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Jungck JR, Robeva R, Gross LJ. Mathematical Biology Education: Changes, Communities, Connections, and Challenges. Bull Math Biol 2020; 82:117. [PMID: 32888094 DOI: 10.1007/s11538-020-00793-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ryder EF, Morgan WR, Sierk M, Donovan SS, Robertson SD, Orndorf HC, Rosenwald AG, Triplett EW, Dinsdale E, Pauley MA, Tapprich WE. Incubators: Building community networks and developing open educational resources to integrate bioinformatics into life science education. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2020; 48:381-390. [PMID: 32585745 PMCID: PMC7496352 DOI: 10.1002/bmb.21387] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 05/02/2020] [Accepted: 05/18/2020] [Indexed: 06/11/2023]
Abstract
While it is essential for life science students to be trained in modern techniques and approaches, rapidly developing, interdisciplinary fields such as bioinformatics present distinct challenges to undergraduate educators. In particular, many educators lack training in new fields, and high-quality teaching and learning materials may be sparse. To address this challenge with respect to bioinformatics, the Network for the Integration of Bioinformatics into Life Science Education (NIBLSE), in partnership with Quantitative Undergraduate Biology Education and Synthesis (QUBES), developed incubators, a novel collaborative process for the development of open educational resources (OER). Incubators are short-term, online communities that refine unpublished teaching lessons into more polished and widely usable learning resources. The resulting products are published and made freely available in the NIBLSE Resource Collection, providing recognition of scholarly work by incubator participants. In addition to producing accessible, high-quality resources, incubators also provide opportunities for faculty development. Because participants are intentionally chosen to represent a range of expertise in bioinformatics and pedagogy, incubators also build professional connections among educators with diverse backgrounds and perspectives and promote the discussion of practical issues involved in deploying a resource in the classroom. Here we describe the incubator process and provide examples of beneficial outcomes. Our experience indicates that incubators are a low cost, short-term, flexible method for the development of OERs and professional community that could be adapted to a variety of disciplinary and pedagogical contexts.
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Affiliation(s)
- Elizabeth F. Ryder
- Department of Biology and BiotechnologyWorcester Polytechnic InstituteWorcesterMassachusettsUSA
| | | | - Michael Sierk
- Interdisciplinary Science DepartmentSaint Vincent CollegeLatrobePennsylvaniaUSA
| | - Samuel S. Donovan
- Department of Biological SciencesUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Sabrina D. Robertson
- Department of Psychology and NeuroscienceUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Hayley C. Orndorf
- Department of Biological SciencesUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Anne G. Rosenwald
- Department of BiologyGeorgetown UniversityWashingtonDistrict of ColumbiaUSA
| | - Eric W. Triplett
- Microbiology and Cell Science DepartmentUniversity of FloridaGainesvilleFloridaUSA
| | | | - Mark A. Pauley
- Division of Undergraduate Education, Directorate for Education and Human ResourcesNational Science FoundationAlexandriaVirginiaUSA
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