Meet-U: Educating through research immersion

A field guide for implementing a flipped classroom

The way flipped classrooms are perceived and even practiced by teachers is sometimes approximate. For instance, while the Covid-19 pandemic has pushed many universities to adopt distance learning, flipped classrooms have often been mentioned as a solution in that context. This inducement maintains a confusion between flipped classrooms and distance learning that might be detrimental for students and teachers. Moreover, embarking on a new pedagogical practice such as flipped classroom could be intimidating and time-consuming for the newcomer teacher. For these reasons, this article aims to share some tips for implementing a flipped classroom, with examples from biology and biochemistry. Based on our experiences but also on the current scientific literature, we structured these advise around three phases: preparation, implementation, and follow-up. In the preparation phase, we advise planning early to invert time in class and outside the classroom, but also to say it explicitly, as well as to identify (or optionally create) resources for students to learn in autonomy. In the implementation phase, we suggest to (i) be explicit in the acquisition of knowledge and foster students' autonomy; (ii) explore active learning in class; (iii) develop cooperation and sharing skills; and (iv) differentiate teaching practices to adapt to student needs. Lastly, in the follow-up phase, we propose to (i) evaluate both student learning and the pedagogical setting; (ii) take care of the logistics and the teacher's posture; (iii) document the flipped classroom, and (iv) share the teaching experience.

KaruBioNet: a network and discussion group for a better collaboration and structuring of bioinformatics in Guadeloupe (French West Indies)

Summary

Sequencing and other biological data are now more frequently available and at a lower price. Mutual tools and strategies are needed to analyze the huge amount of heterogeneous data generated by several research teams and devices. Bioinformatics represents a growing field in the scientific community globally. This multidisciplinary field provides a great amount of tools and methods that can be used to conduct scientific studies in a more strategic way. Coordinated actions and collaborations are needed to find more innovative and accurate methods for a better understanding of real-life data. A wide variety of organizations are contributing to KaruBioNet in Guadeloupe (French West Indies), a Caribbean archipelago. The purpose of this group is to foster collaboration and mutual aid among people from different disciplines using a 'one health' approach, for a better comprehension and surveillance of humans, plants or animals' health and diseases. The KaruBioNet network particularly aims to help researchers in their studies related to 'omics' data, but also more general aspects concerning biological data analysis. This transdisciplinary network is a platform for discussion, sharing, training and support between scientists interested in bioinformatics and related fields. Starting from a little archipelago in the Caribbean, we envision to facilitate exchange between other Caribbean partners in the future, knowing that the Caribbean is a region with non-negligible biodiversity which should be preserved and protected. Joining forces with other Caribbean countries or territories would strengthen scientific collaborative impact in the region. Information related to this network can be found at: http://www.pasteur-guadeloupe.fr/karubionet.html. Furthermore, a dedicated 'Galaxy KaruBioNet' platform is available at: http://calamar.univ-ag.fr/c3i/galaxy_karubionet.html. Availability and implementation Information about KaruBioNet is availabe at: http://www.pasteur-guadeloupe.fr/karubionet.html.

Contact

dcouvin@pasteur-guadeloupe.fr.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Advancing computational biology and bioinformatics research through open innovation competitions

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

Fostering bioinformatics education through skill development of professors: Big Genomic Data Skills Training for Professors

Bioinformatics has become an indispensable part of life science over the past 2 decades. However, bioinformatics education is not well integrated at the undergraduate level, especially in liberal arts colleges and regional universities in the United States. One significant obstacle pointed out by the Network for Integrating Bioinformatics into Life Sciences Education is the lack of faculty in the bioinformatics area. Most current life science professors did not acquire bioinformatics analysis skills during their own training. Consequently, a great number of undergraduate and graduate students do not get the chance to learn bioinformatics or computational biology skills within a structured curriculum during their education. To address this gap, we developed a module-based, week-long short course to train small college and regional university professors with essential bioinformatics skills. The bioinformatics modules were built to be adapted by the professor-trainees afterward and used in their own classes. All the course materials can be accessed at https://github.com/TheJacksonLaboratory/JAXBD2K-ShortCourse.