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

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Camila L Goclowski, Julia Jakiela, Tyler Collins, Saskia Hiltemann, Morgan Howells, Marisa Loach, Jonathan Manning, Pablo Moreno, Alex Ostrovsky, Helena Rasche, Mehmet Tekman, Graeme Tyson, Pavankumar Videm, Wendi Bacon
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引用次数: 0

Abstract

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.

银河作为生物信息学的门户:多界面银河实践培训套件(MIGHTS)用于scRNA-seq。
背景:生物信息学是生物医学科学的基础,但对生物学家和临床医生来说,掌握它是一个陡峭的学习曲线。在分析数据的同时学习编码是很困难的。通过将这两个方面分开,并为崭露头角的生物信息学家提供中间步骤,曲线可能会变得平坦。生物学家和生物医学科学家对单细胞分析的需求很大,培训活动、材料和人类细胞图谱等全球合作努力的激增证明了这一点。然而,缺乏重新建立的迭代分析,加上不标准化的管道,使得有效的单细胞训练成为一个移动的目标。为了解决这些挑战,我们提出了用于单细胞RNA测序(scRNA-seq)分析的多界面Galaxy动手训练套件(might),它提供了使用图形界面(按钮)或代码的并行分析方法。凭借清晰、可互操作的材料,might促进了环境之间的平稳过渡。为了弥合生物学家和程序员之间的鸿沟,梅茨强调跨学科的交流,以便在各个层次上有效地学习。真实世界的数据分析在might促进批判性思维和最佳实践,而公平数据原则确保结果的验证。MIGHTS是免费提供的,托管在银河训练网络上,并利用银河接口进行两种设置的分析。考虑到基于python (Scanpy)和基于r (Seurat & Monocle)的scRNA-seq分析的持续流行,might可以同时使用这两种分析方法。结论:MIGHTS由11个教程组成,包括录音、幻灯片和交互式可视化,并通过定期更新和社区合作展示了可持续发展的记录。并行路径可能使科学家在任何编程水平的同时培训,解决新手生物信息学家的异质需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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