Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses
Abstract Research in evolutionary biology has been progressively influenced by big data such as massive genome and transcriptome sequencing data, scalar measurements of several phenotypes on tens to thousands of individuals, as well as from collecting worldwide environmental data at an increasingly...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-07-01
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| Series: | Evolution: Education and Outreach |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12052-018-0080-z |
| _version_ | 1818585083709227008 |
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| author | Rui Faria Deborah Triant Alvaro Perdomo-Sabogal Bert Overduin Christoph Bleidorn Clara Isabel Bermudez Santana David Langenberger Giovanni Marco Dall’Olio Henrike Indrischek Jan Aerts Jan Engelhardt Johannes Engelken Katja Liebal Mario Fasold Sofia Robb Sonja Grath Sree Rohit Raj Kolora Tiago Carvalho Walter Salzburger Vladimir Jovanovic Katja Nowick |
| author_facet | Rui Faria Deborah Triant Alvaro Perdomo-Sabogal Bert Overduin Christoph Bleidorn Clara Isabel Bermudez Santana David Langenberger Giovanni Marco Dall’Olio Henrike Indrischek Jan Aerts Jan Engelhardt Johannes Engelken Katja Liebal Mario Fasold Sofia Robb Sonja Grath Sree Rohit Raj Kolora Tiago Carvalho Walter Salzburger Vladimir Jovanovic Katja Nowick |
| author_sort | Rui Faria |
| collection | DOAJ |
| description | Abstract Research in evolutionary biology has been progressively influenced by big data such as massive genome and transcriptome sequencing data, scalar measurements of several phenotypes on tens to thousands of individuals, as well as from collecting worldwide environmental data at an increasingly detailed scale. The handling and analysis of such data require computational skills that usually exceed the abilities of most traditionally trained evolutionary biologists. Here we discuss the advantages, challenges and considerations for organizing and running bioinformatics training courses of 2–3 weeks in length to introduce evolutionary biologists to the computational analysis of big data. Extended courses have the advantage of offering trainees the opportunity to learn a more comprehensive set of complementary topics and skills and allowing for more time to practice newly acquired competences. Many organizational aspects are common to any course, as the need to define precise learning objectives and the selection of appropriate and highly motivated instructors and trainees, among others. However, other features assume particular importance in extended bioinformatics training courses. To successfully implement a learning-by-doing philosophy, sufficient and enthusiastic teaching assistants (TAs) are necessary to offer prompt help to trainees. Further, a good balance between theoretical background and practice time needs to be provided and assured that the schedule includes enough flexibility for extra review sessions or further discussions if desired. A final project enables trainees to apply their newly learned skills to real data or case studies of their interest. To promote a friendly atmosphere throughout the course and to build a close-knit community after the course, allow time for some scientific discussions and social activities. In addition, to not exhaust trainees and TAs, some leisure time needs to be organized. Finally, all organization should be done while keeping the budget within fair limits. In order to create a sustainable course that constantly improves and adapts to the trainees’ needs, gathering short- and long-term feedback after the end of the course is important. Based on our experience we have collected a set of recommendations to effectively organize and run extended bioinformatics training courses for evolutionary biologists, which we here want to share with the community. They offer a complementary way for the practical teaching of modern evolutionary biology and reaching out to the biological community. |
| first_indexed | 2024-12-16T08:31:26Z |
| format | Article |
| id | doaj.art-f29f1fa99e81483884059a95e978e6a2 |
| institution | Directory Open Access Journal |
| issn | 1936-6426 1936-6434 |
| language | English |
| last_indexed | 2024-12-16T08:31:26Z |
| publishDate | 2018-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Evolution: Education and Outreach |
| spelling | doaj.art-f29f1fa99e81483884059a95e978e6a22022-12-21T22:37:53ZengBMCEvolution: Education and Outreach1936-64261936-64342018-07-0111111010.1186/s12052-018-0080-zIntroducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training coursesRui Faria0Deborah Triant1Alvaro Perdomo-Sabogal2Bert Overduin3Christoph Bleidorn4Clara Isabel Bermudez Santana5David Langenberger6Giovanni Marco Dall’Olio7Henrike Indrischek8Jan Aerts9Jan Engelhardt10Johannes Engelken11Katja Liebal12Mario Fasold13Sofia Robb14Sonja Grath15Sree Rohit Raj Kolora16Tiago Carvalho17Walter Salzburger18Vladimir Jovanovic19Katja Nowick20CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO, Laboratório Associado, Universidade do PortoDepartment of Biochemistry & Molecular Genetics, University of VirginiaTFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of LeipzigEdinburgh Genomics, The University of Edinburgh, Ashworth LaboratoriesMuseo Nacional de Ciencias Naturales, Spanish Research Council (CSIC)Grupo Rnomica Teórica y Computacional, Departamento de Biologia, Universidad Nacional de ColombiaecSeq Bioinformatics GmbHComputational Biology, Department of Target Sciences, GlaxoSmithKlineComputational EvoDevo Group, Department of Computer Science, University of LeipzigVisual Data Analysis Lab, ESAT/STADIUS, KU LeuvenBioinformatics Group, Department of Computer Science, University of LeipzigIBE, Institute of Evolutionary Biology (CSIC-UPF), Department of Experimental and Health Sciences, Pompeu Fabra UniversityDepartment of Education and Psychology, Comparative Developmental Psychology, Freie Universität BerlinecSeq Bioinformatics GmbHStowers Institute for Medical ResearchDivision of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilians-Universität MünchenGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-LeipzigIBE, Institute of Evolutionary Biology (CSIC-UPF), Department of Experimental and Health Sciences, Pompeu Fabra UniversityZoological Institute, University of BaselFaculty for Biology, Chemistry, and Pharmacy, Freie Universität BerlinTFome Research Group, Bioinformatics Group, Interdisciplinary Center of Bioinformatics, Department of Computer Science, University of LeipzigAbstract Research in evolutionary biology has been progressively influenced by big data such as massive genome and transcriptome sequencing data, scalar measurements of several phenotypes on tens to thousands of individuals, as well as from collecting worldwide environmental data at an increasingly detailed scale. The handling and analysis of such data require computational skills that usually exceed the abilities of most traditionally trained evolutionary biologists. Here we discuss the advantages, challenges and considerations for organizing and running bioinformatics training courses of 2–3 weeks in length to introduce evolutionary biologists to the computational analysis of big data. Extended courses have the advantage of offering trainees the opportunity to learn a more comprehensive set of complementary topics and skills and allowing for more time to practice newly acquired competences. Many organizational aspects are common to any course, as the need to define precise learning objectives and the selection of appropriate and highly motivated instructors and trainees, among others. However, other features assume particular importance in extended bioinformatics training courses. To successfully implement a learning-by-doing philosophy, sufficient and enthusiastic teaching assistants (TAs) are necessary to offer prompt help to trainees. Further, a good balance between theoretical background and practice time needs to be provided and assured that the schedule includes enough flexibility for extra review sessions or further discussions if desired. A final project enables trainees to apply their newly learned skills to real data or case studies of their interest. To promote a friendly atmosphere throughout the course and to build a close-knit community after the course, allow time for some scientific discussions and social activities. In addition, to not exhaust trainees and TAs, some leisure time needs to be organized. Finally, all organization should be done while keeping the budget within fair limits. In order to create a sustainable course that constantly improves and adapts to the trainees’ needs, gathering short- and long-term feedback after the end of the course is important. Based on our experience we have collected a set of recommendations to effectively organize and run extended bioinformatics training courses for evolutionary biologists, which we here want to share with the community. They offer a complementary way for the practical teaching of modern evolutionary biology and reaching out to the biological community.http://link.springer.com/article/10.1186/s12052-018-0080-zActive learningBioinformaticsEvolutionary biologyExtended courseGenomicsHigh-throughput-sequencing |
| spellingShingle | Rui Faria Deborah Triant Alvaro Perdomo-Sabogal Bert Overduin Christoph Bleidorn Clara Isabel Bermudez Santana David Langenberger Giovanni Marco Dall’Olio Henrike Indrischek Jan Aerts Jan Engelhardt Johannes Engelken Katja Liebal Mario Fasold Sofia Robb Sonja Grath Sree Rohit Raj Kolora Tiago Carvalho Walter Salzburger Vladimir Jovanovic Katja Nowick Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses Evolution: Education and Outreach Active learning Bioinformatics Evolutionary biology Extended course Genomics High-throughput-sequencing |
| title | Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses |
| title_full | Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses |
| title_fullStr | Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses |
| title_full_unstemmed | Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses |
| title_short | Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses |
| title_sort | introducing evolutionary biologists to the analysis of big data guidelines to organize extended bioinformatics training courses |
| topic | Active learning Bioinformatics Evolutionary biology Extended course Genomics High-throughput-sequencing |
| url | http://link.springer.com/article/10.1186/s12052-018-0080-z |
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