当前位置: X-MOL 学术Gigascience › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Accessible and reproducible mass spectrometry imaging data analysis in Galaxy.
GigaScience ( IF 9.2 ) Pub Date : 2019-12-01 , DOI: 10.1093/gigascience/giz143
Melanie Christine Föll 1, 2 , Lennart Moritz 1 , Thomas Wollmann 3 , Maren Nicole Stillger 1, 2, 4 , Niklas Vockert 3 , Martin Werner 1, 5, 6, 7 , Peter Bronsert 1, 5, 6, 7 , Karl Rohr 3 , Björn Andreas Grüning 8 , Oliver Schilling 1, 5, 7
Affiliation  

BACKGROUND Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. FINDINGS We have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Furthermore, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research. CONCLUSION The Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access, together with high levels of reproducibility and transparency.

中文翻译:

Galaxy中的可访问和可重现的质谱成像数据分析。

背景技术质谱成像越来越多地用于生物学和转化研究中,因为它具有确定样品中数百种分析物的空间分布的能力。由于处于蛋白质组学/代谢组学和成像的界面,所获取的数据集既庞大又复杂,并且经常使用专有软件或内部脚本进行分析,从而阻碍了可重复性。启用可重现数据分析的开源软件解决方案通常需要编程技能,因此许多质谱成像(MSI)研究人员无法使用它们。结果我们将18种专用质谱成像工具集成到了Galaxy框架中,以允许进行可访问,可再现和透明的数据分析。我们的工具基于Cardinal,MALDIquant,和scikit-image,并支持所有主要的MSI分析步骤,例如质量控制,可视化,预处理,统计分析和图像共配准。此外,我们针对蛋白质组学和代谢组学的用例创建了动手培训材料。为了证明我们工具的实用性,我们重新分析了一个可公开获得的N链聚糖成像数据集。通过在线提供整个分析历史记录,我们重点介绍了Galaxy框架如何促进透明和可重复的研究。结论Galaxy框架已成为一种功能强大的分析平台,可轻松使用和访问MSI数据,并具有高度的可重复性和透明度。我们为蛋白质组学和代谢组学的用例创建了动手培训材料。为了证明我们工具的实用性,我们重新分析了一个可公开获得的N链聚糖成像数据集。通过在线提供整个分析历史记录,我们重点介绍了Galaxy框架如何促进透明和可重复的研究。结论Galaxy框架已成为一种功能强大的分析平台,可轻松使用和访问MSI数据,并具有高度的可重复性和透明度。我们为蛋白质组学和代谢组学的用例创建了动手培训材料。为了证明我们工具的实用性,我们重新分析了一个可公开获得的N链聚糖成像数据集。通过在线提供整个分析历史记录,我们重点介绍了Galaxy框架如何促进透明和可重复的研究。结论Galaxy框架已成为一种功能强大的分析平台,可轻松使用和访问MSI数据,并具有高度的可重复性和透明度。
更新日期:2019-12-09
down
wechat
bug