Best practices in bioinformatics training for life scientists

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.

Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses

“…Once you have defined the general and specific aims of your course, you need to communicate them in the advertisement for your course (see below), which helps to address the appropriate target audience (Via et al 2013).…”

“…The steady growth of high-throughput research in evolutionary biology during the last decade has made it necessary for life scientists, and in particular evolutionary biologists, to become acquainted with programming and bioinformatics skills to analyze the large and seemingly unmanageable datasets (Marx 2013;Via et al 2011Via et al , 2013Kwok 2013;Greene et al 2016;Schneider et al 2010). Although we have been observing an increased offer in undergraduate and master courses in bioinformatics, there is still a huge demand for specialized training in bioinformatics and computational biology among evolutionary biologists (or life scientists in general) (Schneider et al 2012;Attwood et al 2015).…”

“…Although we focus on organizational aspects specific to the EBC format, for the sake of completeness we also reiterate some guidelines proposed by others that are key to any course format (e.g. Via et al 2011Via et al , 2013). Here we use examples based on our own experience in organizing a course oriented to evolutionary genomics, but the same principles can be applied to related subdisciplines within the broad field of evolutionary biology.…”

Fostering postgraduate student engagement: online resources supporting self-directed learning in a diverse cohort

“…At the PG level a working knowledge of bioinformatics has become essential for students, as most research projects will generate data that require bioinformatics analysis (Hingamp et al 2008;Via et al 2013). After running a bioinformatics module at PG level for 2 years, differences in the students' prior knowledge became increasingly visible, since the module received students from increasingly diverse undergraduate (UG) backgrounds.…”

Microbial bioinformatics 2020

“…One final challenge for microbial bioinformatics in the run‐up to 2020 is meeting the need for training and the development of a skilled labour force (Via et al ., 2013; Watson‐Haigh et al ., 2013). Cloud computing may play a contribution here, in providing a standardized environment for workshops and hackathons as well as for research groups.…”