LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data.
LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data.
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脂质组学：一个Python包，用于全面预测脂质碰撞横截面和保留时间，并分析基于离子迁移-质谱的脂质组学数据。
DOI：
10.1021/acs.analchem.0c02560
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发表时间：
2020-11-17
影响因子：
7.4
通讯作者：
Xu L
中科院分区：
文献类型：
--
作者：
Ross DH;Cho JH;Zhang R;Hines KM;Xu L
生物样品中脂质种类的综合分析或脂质组学是阐明疾病发病机制和鉴定生物标志物的有价值的方法。目前，一个典型的脂质组学实验可以跟踪数百到数千个单独的脂质种类。然而，得出生物学结论需要多个步骤的数据处理，以丰富显着改变的特征和这些特征的可靠识别。针对这些数据分析挑战的现有解决方案（即，多变量统计和脂质鉴定）涉及使用不同的软件应用程序执行各种步骤，这对再现性施加了实际限制和潜在的负面影响。亲水相互作用液相色谱-离子迁移-质谱联用技术（HILIC-IM-MS）在脂质分离中显示出优势。然而，在文献中的脂质类的覆盖范围仍然存在差距。为了实现HILIC-IM-MS脂质组学数据的可重复和有效分析，我们开发了一个开源Python包LiPydomics，它可以执行统计和多变量分析（“统计”模块），生成信息图（“绘图”模块），在不同置信水平下识别脂质种类（“识别”模块），并使用用户友好的基于文本的界面（“交互”模块）执行所有功能。为了支持脂质鉴定，我们组装了45种脂质类别的m/z和CCS的综合实验数据库，其中23种类别含有HILIC保留时间。使用大型实验数据集分别训练22种和23种脂质类的CCS和HILIC保留时间的预测模型，这使得能够生成具有145，388个条目的大型预测脂质数据库。最后，我们使用对各种抗菌剂具有耐药性的金黄色葡萄球菌菌株演示了Python包的实用性。
Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses (“stats” module), generating informative plots (“plotting” module), identifying lipid species at different confidence levels (“identification” module), and carrying out all functions using a user-friendly text-based interface (“interactive” module). To support lipid identification, we assembled a comprehensive experimental database of m/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.
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影响因子：
7.7
作者：
Tumanov S;Kamphorst JJ
通讯作者：
Kamphorst JJ
DOI：
10.1007/s13361-016-1579-6
发表时间：
2017-03
影响因子：
3.2
作者：
Ulmer CZ;Koelmel JP;Ragland JM;Garrett TJ;Bowden JA
通讯作者：
Bowden JA
影响因子：
7.4
作者：
Blazenovic, Ivana;Shen, Tong;Fiehn, Oliver
通讯作者：
Fiehn, Oliver
影响因子：
7.4
作者：
Bijlsma, Lubertus;Bade, Richard;Sancho, Juan V.
通讯作者：
Sancho, Juan V.
离子淌度质谱中代谢物碰撞截面值的大规模预测。
影响因子：
7.4
作者：
Zhou, Zhiwei;Shen, Xiaotao;Zhu, Zheng-Jiang
通讯作者：
Zhu, Zheng-Jiang