Goclowski Camila L, Jakiela Julia, Collins Tyler, Hiltemann Saskia, Howells Morgan, Loach Marisa, Manning Jonathan, Moreno Pablo, Ostrovsky Alex, Rasche Helena, Tekman Mehmet, Tyson Graeme, Videm Pavankumar, Bacon Wendi
Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT, 84112, USA.
School of Chemistry, University of Edinburgh, Edinburgh, EH9 3FJ, UK.
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giae107.
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.
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.
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.
生物信息学是生物医学科学的基础，但对于实验生物学家和临床医生而言，掌握它存在陡峭的学习曲线。在分析数据的同时学习编码很困难。通过将这两个方面分开，并为初露头角的生物信息学家提供中间步骤，或许可以平缓这条曲线。单细胞分析受到生物学家和生物医学科学家的广泛需求，培训活动、材料的激增以及像人类细胞图谱这样的全球合作努力就是明证。然而，缺乏重新实例化的迭代分析，再加上未标准化的流程，使得有效的单细胞培训成为一个难以捉摸的目标。
为应对这些挑战，我们推出了用于单细胞RNA测序（scRNA-seq）分析的多界面银河实践培训套件（MIGHTS），它提供了使用图形界面（按钮）或代码的并行分析方法。借助清晰、可互操作的材料，MIGHTS便于在不同环境之间顺利过渡。MIGHTS弥合了生物学家与程序员之间的差距，强调跨学科交流以促进各级的有效学习。MIGHTS中的实际数据分析促进批判性思维和最佳实践，而FAIR数据原则确保结果的验证。MIGHTS可免费获取，托管在银河培训网络上，并利用银河界面在两种环境中进行分析。鉴于基于Python（Scanpy）和基于R（Seurat & Monocle）的scRNA-seq分析持续流行，MIGHTS支持使用这两种方法进行分析。
MIGHTS由11个教程组成，包括录制内容、幻灯片和交互式可视化，并且通过定期更新和社区合作展示了可持续性的记录。MIGHTS中的并行路径能够对任何编程水平的科学家进行同步培训，满足新手生物信息学家的不同需求。
