The Mastery Rubric for Bioinformatics: A tool to support design and evaluation of career-spanning education and training

Biological databases in the age of generative artificial intelligence

Summary

Modern biological research critically depends on public databases. The introduction and propagation of errors within and across databases can lead to wasted resources as scientists are led astray by bad data or have to conduct expensive validation experiments. The emergence of generative artificial intelligence systems threatens to compound this problem owing to the ease with which massive volumes of synthetic data can be generated. We provide an overview of several key issues that occur within the biological data ecosystem and make several recommendations aimed at reducing data errors and their propagation. We specifically highlight the critical importance of improved educational programs aimed at biologists and life scientists that emphasize best practices in data engineering. We also argue for increased theoretical and empirical research on data provenance, error propagation, and on understanding the impact of errors on analytic pipelines. Furthermore, we recommend enhanced funding for the stewardship and maintenance of public biological databases.

Availability and implementation

Not applicable.

A genomics learning framework for undergraduates

Genomics is an increasingly important part of biology research. However, educating undergraduates in genomics is not yet a standard part of life sciences curricula. We believe this is, in part, due to a lack of standard concepts for the teaching of genomics. To address this deficit, the members of the Genomics Education Alliance created a set of genomics concepts that was then further refined by input from a community of undergraduate educators who engage in genomics instruction. The final genomics concepts list was compared to existing learning frameworks, including the Vision and Change initiative (V&C), as well as ones for genetics, biochemistry and molecular biology, and bioinformatics. Our results demonstrate that the new genomics framework fills a niche not addressed by previous inventories. This new framework should be useful to educators seeking to design stand-alone courses in genomics as well as those seeking to incorporate genomics into existing coursework. Future work will involve designing curriculum and assessments to go along with this genomics learning framework.

Galaxy as a gateway to bioinformatics: Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for scRNA-seq

BACKGROUND

Bioinformatics is fundamental to biomedical sciences, but its mastery presents a steep learning curve for bench biologists and clinicians. Learning to code while analyzing data is difficult. The curve may be flattened by separating these two aspects and providing intermediate steps for budding bioinformaticians. Single-cell analysis is in great demand from biologists and biomedical scientists, as evidenced by the proliferation of training events, materials, and collaborative global efforts like the Human Cell Atlas. However, iterative analyses lacking reinstantiation, coupled with unstandardized pipelines, have made effective single-cell training a moving target.

FINDINGS

To address these challenges, we present a Multi-Interface Galaxy Hands-on Training Suite (MIGHTS) for single-cell RNA sequencing (scRNA-seq) analysis, which offers parallel analytical methods using a graphical interface (buttons) or code. With clear, interoperable materials, MIGHTS facilitates smooth transitions between environments. Bridging the biologist-programmer gap, MIGHTS emphasizes interdisciplinary communication for effective learning at all levels. Real-world data analysis in MIGHTS promotes critical thinking and best practices, while FAIR data principles ensure validation of results. MIGHTS is freely available, hosted on the Galaxy Training Network, and leverages Galaxy interfaces for analyses in both settings. Given the ongoing popularity of Python-based (Scanpy) and R-based (Seurat & Monocle) scRNA-seq analyses, MIGHTS enables analyses using both.

CONCLUSIONS

MIGHTS consists of 11 tutorials, including recordings, slide decks, and interactive visualizations, and a demonstrated track record of sustainability via regular updates and community collaborations. Parallel pathways in MIGHTS enable concurrent training of scientists at any programming level, addressing the heterogeneous needs of novice bioinformaticians.

The ISCB competency framework v. 3: a revised and extended standard for bioinformatics education and training

Motivation

Developing competency in the broad area of bioinformatics is challenging globally, owing to the breadth of the field and the diversity of its audiences for education and training. Course design can be facilitated by the use of a competency framework-a set of competency requirements that define the knowledge, skills and attitudes needed by individuals in (or aspiring to be in) a particular profession or role. These competency requirements can help to define curricula as they can inform both the content and level to which competency needs to be developed. The International Society for Computational Biology (ISCB) developed a list of bioinformatics competencies in 2014, and these have undergone several rounds of improvement. In consultation with a broad bioinformatics training community, these have now been further refined and extended to include knowledge skills and attitudes, and mappings to previous and other existing competency frameworks.

Results

Here, we present version 3 of the ISCB competency framework. We describe how it was developed and how to access it, as well as providing some examples of how it has been used.

Availability and implementation

The framework is openly accessible at https://competency.ebi.ac.uk/framework/iscb/3.0/competencies.

An international consensus on effective, inclusive, and career-spanning short-format training in the life sciences and beyond

Science, technology, engineering, mathematics, and medicine (STEMM) fields change rapidly and are increasingly interdisciplinary. Commonly, STEMM practitioners use short-format training (SFT) such as workshops and short courses for upskilling and reskilling, but unaddressed challenges limit SFT's effectiveness and inclusiveness. Education researchers, students in SFT courses, and organizations have called for research and strategies that can strengthen SFT in terms of effectiveness, inclusiveness, and accessibility across multiple dimensions. This paper describes the project that resulted in a consensus set of 14 actionable recommendations to systematically strengthen SFT. A diverse international group of 30 experts in education, accessibility, and life sciences came together from 10 countries to develop recommendations that can help strengthen SFT globally. Participants, including representation from some of the largest life science training programs globally, assembled findings in the educational sciences and encompassed the experiences of several of the largest life science SFT programs. The 14 recommendations were derived through a Delphi method, where consensus was achieved in real time as the group completed a series of meetings and tasks designed to elicit specific recommendations. Recommendations cover the breadth of SFT contexts and stakeholder groups and include actions for instructors (e.g., make equity and inclusion an ethical obligation), programs (e.g., centralize infrastructure for assessment and evaluation), as well as organizations and funders (e.g., professionalize training SFT instructors; deploy SFT to counter inequity). Recommendations are aligned with a purpose-built framework-"The Bicycle Principles"-that prioritizes evidenced-based teaching, inclusiveness, and equity, as well as the ability to scale, share, and sustain SFT. We also describe how the Bicycle Principles and recommendations are consistent with educational change theories and can overcome systemic barriers to delivering consistently effective, inclusive, and career-spanning SFT.

Nine quick tips for efficient bioinformatics curriculum development and training

Biomedical research is becoming increasingly data driven. New technologies that generate large-scale, complex data are continually emerging and evolving. As a result, there is a concurrent need for training researchers to use and understand new computational tools. Here we describe an efficient and effective approach to developing curriculum materials that can be deployed in a research environment to meet this need.