Best practices in bioinformatics training for life scientists

Allegra Via; Thomas Blicher; Erik Bongcam-Rudloff; Michelle D Brazas; Cath Brooksbank; Aidan Budd; Javier De Las Rivas; Jacqueline Dreyer; Pedro L Fernandes; Celia van Gelder; Joachim Jacob; Rafael C Jimenez; Jane Loveland; Federico Moran; Nicola Mulder; Tommi Nyrönen; Kristian Rother; Maria Victoria Schneider; Teresa K Attwood

doi:10.1093/bib/bbt043

Best practices in bioinformatics training for life scientists

Allegra Via, Thomas Blicher, Erik Bongcam-Rudloff, Michelle D Brazas, Cath Brooksbank, Aidan Budd, Javier De Las Rivas, Jacqueline Dreyer, Pedro L Fernandes, Celia van Gelder, Joachim Jacob, Rafael C Jimenez, Jane Loveland, Federico Moran, Nicola Mulder, Tommi Nyrönen, Kristian Rother, Maria Victoria Schneider, Teresa K Attwood

36 Citations (Scopus)

Abstract

The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists.

Original language	English
Journal	Briefings in Bioinformatics
Volume	14
Issue number	5
Pages (from-to)	528-37
Number of pages	10
ISSN	1467-5463
DOIs	https://doi.org/10.1093/bib/bbt043
Publication status	Published - Sept 2013

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Via, A., Blicher, T., Bongcam-Rudloff, E., Brazas, M. D., Brooksbank, C., Budd, A., De Las Rivas, J., Dreyer, J., Fernandes, P. L., van Gelder, C., Jacob, J., Jimenez, R. C., Loveland, J., Moran, F., Mulder, N., Nyrönen, T., Rother, K., Schneider, M. V., & Attwood, T. K. (2013). Best practices in bioinformatics training for life scientists. Briefings in Bioinformatics, 14(5), 528-37. https://doi.org/10.1093/bib/bbt043

Via, A, Blicher, T, Bongcam-Rudloff, E, Brazas, MD, Brooksbank, C, Budd, A, De Las Rivas, J, Dreyer, J, Fernandes, PL, van Gelder, C, Jacob, J, Jimenez, RC, Loveland, J, Moran, F, Mulder, N, Nyrönen, T, Rother, K, Schneider, MV & Attwood, TK 2013, 'Best practices in bioinformatics training for life scientists', Briefings in Bioinformatics, vol. 14, no. 5, pp. 528-37. https://doi.org/10.1093/bib/bbt043

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abstract = "The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists.",

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AU - De Las Rivas, Javier

AU - Dreyer, Jacqueline

AU - Fernandes, Pedro L

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AU - Jacob, Joachim

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AB - The mountains of data thrusting from the new landscape of modern high-throughput biology are irrevocably changing biomedical research and creating a near-insatiable demand for training in data management and manipulation and data mining and analysis. Among life scientists, from clinicians to environmental researchers, a common theme is the need not just to use, and gain familiarity with, bioinformatics tools and resources but also to understand their underlying fundamental theoretical and practical concepts. Providing bioinformatics training to empower life scientists to handle and analyse their data efficiently, and progress their research, is a challenge across the globe. Delivering good training goes beyond traditional lectures and resource-centric demos, using interactivity, problem-solving exercises and cooperative learning to substantially enhance training quality and learning outcomes. In this context, this article discusses various pragmatic criteria for identifying training needs and learning objectives, for selecting suitable trainees and trainers, for developing and maintaining training skills and evaluating training quality. Adherence to these criteria may help not only to guide course organizers and trainers on the path towards bioinformatics training excellence but, importantly, also to improve the training experience for life scientists.

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Best practices in bioinformatics training for life scientists

Abstract

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