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dc.creatorFaria, Rui
dc.creatorTriant, Deborah
dc.creatorPerdomo-Sabogal, Alvaro
dc.creatorOverduin, Bert
dc.creatorBleidorn, Christoph
dc.creatorSantana, Clara Isabel Bermudez
dc.creatorLangenberger, David
dc.creatorDall’Olio, Giovanni Marco
dc.creatorIndrischek, Henrike
dc.creatorAerts, Jan
dc.creatorEngelhardt, Jan
dc.creatorEngelken, Johannes
dc.creatorLiebal, Katja
dc.creatorFasold, Mario
dc.creatorRobb, Sofia
dc.creatorGrath, Sonja
dc.creatorKolora, Sree Rohit Raj
dc.creatorCarvalho, Tiago
dc.creatorSalzburger, Walter
dc.creatorJovanović, Vladimir
dc.creatorNowick, Katja
dc.date.accessioned2019-04-25T09:36:29Z
dc.date.available2019-04-25T09:36:29Z
dc.date.issued2018
dc.identifier.urihttps://evolution-outreach.biomedcentral.com/articles/10.1186/s12052-018-0080-z
dc.identifier.urihttps://radar.ibiss.bg.ac.rs/handle/123456789/3327
dc.description.abstractResearch 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.en
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceEvolution: Education and Outreach
dc.subjectActive learning
dc.subjectEvolutionary Biology
dc.subjectBioinformatics
dc.subjectExtended course
dc.subjectGenomics
dc.subjectHigh-throughput-sequencing
dc.subjectProgramming
dc.subjectSustainable course
dc.titleIntroducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training coursesen
dc.typearticleen
dc.rights.licenseBY
dcterms.abstractЛангенбергер, Давид; Далл’Олио, Гиованни Марцо; Салзбургер, Wалтер; Царвалхо, Тиаго; Колора, Срее Рохит Рај; Гратх, Соња; Робб, Софиа; Фасолд, Марио; Енгелкен, Јоханнес; Индрисцхек, Хенрике; Aертс, Јан; Енгелхардт, Јан; Лиебал, Катја; Јовановић, Владимир; Ноwицк, Катја; Фариа, Руи; Триант, Деборах; Пердомо-Сабогал, Aлваро; Овердуин, Берт; Блеидорн, Цхристопх; Сантана, Цлара Исабел Бермудез;
dc.rights.holder© The Author(s) 2018
dc.citation.issue1
dc.citation.volume11
dc.identifier.doi10.1186/s12052-018-0080-z
dc.identifier.scopus2-s2.0-85064079418
dc.citation.apaFaria, R., Triant, D., Perdomo-Sabogal, A., Overduin, B., Bleidorn, C., Santana, C. I. B., … Nowick, K. (2018). Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses. Evolution: Education and Outreach, 11(1), 8.
dc.citation.vancouverFaria R, Triant D, Perdomo-Sabogal A, Overduin B, Bleidorn C, Santana CIB, Langenberger D, Dall’Olio GM, Indrischek H, Aerts J, Engelhardt J, Engelken J, Liebal K, Fasold M, Robb S, Grath S, Kolora SRR, Carvalho T, Salzburger W, Jovanovic V, Nowick K. Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses. Evol Educ Outreach. 2018;11(1):8.
dc.citation.spage8
dc.type.versionpublishedVersionen
dc.identifier.fulltexthttps://radar.ibiss.bg.ac.rs//bitstream/id/4966/EvolEducOutreach_2018_11_1_8.pdf


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