Sueur Maxime, Maillard Julien F, Lacroix-Andrivet Oscar, Rüger Christopher P, Giusti Pierre, Lavanant Hélène, Afonso Carlos
Normandie Univ, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000 Rouen, France.
International Joint Laboratory - iC2MC: Complex Matrices Molecular Characterization, TRTG, BP 27, 76700 Harfleur, France.
J Am Soc Mass Spectrom. 2023 Apr 5;34(4):617-626. doi: 10.1021/jasms.2c00323. Epub 2023 Feb 22.
Complex molecular mixtures are encountered in almost all research disciplines, such as biomedical 'omics, petroleomics, and environmental sciences. State-of-the-art characterization of sample materials related to these fields, deploying high-end instrumentation, allows for gathering large quantities of molecular composition data. One established technological platform is ultrahigh-resolution mass spectrometry, e.g., Fourier-transform mass spectrometry (FT-MS). However, the huge amounts of data acquired in FT-MS often result in tedious data treatment and visualization. FT-MS analysis of complex matrices can easily lead to single mass spectra with more than 10,000 attributed unique molecular formulas. Sophisticated software solutions to conduct these treatment and visualization attempts from commercial and noncommercial origins exist. However, existing applications have distinct drawbacks, such as focusing on only one type of graphic representation, being unable to handle large data sets, or not being publicly available. In this respect, we developed a software, within the international complex matrices molecular characterization joint lab (IC2MC), named "python tools for complex matrices molecular characterization" (PyC2MC). This piece of software will be open-source and free to use. PyC2MC is written under python 3.9.7 and relies on well-known libraries such as pandas, NumPy, or SciPy. It is provided with a graphical user interface developed under PyQt5. The two options for execution, (1) a user-friendly route with a prepacked executable file or (2) running the main python script through a Python interpreter, ensure a high applicability but also an open characteristic for further development by the community. Both are available on the GitHub platform (https://github.com/iC2MC/PyC2MC_viewer).
几乎在所有研究领域都会遇到复杂的分子混合物,例如生物医学“组学”、石油组学和环境科学。利用高端仪器对与这些领域相关的样品材料进行的先进表征,能够收集大量分子组成数据。一个成熟的技术平台是超高分辨率质谱,例如傅里叶变换质谱(FT-MS)。然而,FT-MS获取的大量数据往往导致繁琐的数据处理和可视化。对复杂基质进行FT-MS分析很容易得到具有10000多个归属独特分子式的单张质谱图。存在来自商业和非商业来源的用于进行这些处理和可视化尝试的复杂软件解决方案。然而,现有应用程序存在明显的缺点,例如仅专注于一种图形表示类型、无法处理大数据集或不公开可用。在这方面,我们在国际复杂基质分子表征联合实验室(IC2MC)内开发了一款名为“用于复杂基质分子表征的Python工具”(PyC2MC)的软件。该软件将是开源且免费使用的。PyC2MC是在Python 3.9.7下编写的,依赖于pandas、NumPy或SciPy等知名库。它配备了在PyQt5下开发的图形用户界面。两种执行选项,(1)通过预打包的可执行文件的用户友好路线,或(2)通过Python解释器运行主Python脚本,确保了高适用性以及社区进一步开发的开放性。两者均可在GitHub平台(https://github.com/iC2MC/PyC2MC_viewer)上获取。