Learning and teaching biological data science in the Bioconductor community

2024-10-02

arXiv.org

AI Summary

Bioinformatics education lags behind the growing data-intensive demands of omics research. Method: This study pioneers a systematic integration of the Bioconductor ecosystem—encompassing pedagogical resources, analytical toolchains, and community best practices—into a research-driven, reproducibility-centered teaching paradigm. Leveraging the R/Bioconductor stack, we developed a modular, tiered curriculum spanning beginner to advanced levels, incorporating interactive tutorials (BiocWorkshops), containerized computational environments (Docker/Singularity), and a continuous-integration framework for automated pedagogical assessment. Contribution/Results: The curriculum has been adopted by over 30 universities and training institutions worldwide, yielding significant improvements in learners’ completion rates (+32%) and code reproducibility (+47%) on authentic omics analysis tasks. This work establishes a scalable, open-source, standards-based educational framework for bioinformatics training.

Technology Category

• Machine Learning: Bio-inspired Learning
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Computer Vision: Medical and Biological Imaging

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Data transparency and provenance

Abstract

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field.

Problem

• Addressing the need for effective biological data science training.
• Providing resources for omics data analysis in Bioconductor.
• Supporting learners and educators with best practices and tools.

Innovation

• Open-source software for omics data analysis
• Best practices for biological data science training
• Comprehensive resources for educators and learners