New algorithms demonstrate untargeted detection of chemically meaningful changing units and formula assignment for HRMS data of polymeric mixtures in the open-source constellation web application.
New algorithms demonstrate untargeted detection of chemically meaningful changing units and formula assignment for HRMS data of polymeric mixtures in the open-source constellation web application.
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新算法在开源星座网络应用程序中展示了对化学意义变化单位的无目标检测，以及聚合物混合物 HRMS 数据的配方分配。
DOI：
10.1186/s13321-023-00680-5
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发表时间：
2023-01-18
影响因子：
8.6
通讯作者：
中科院分区：
文献类型：
--
作者：
高分辨率质谱（HRMS）和附属连字符技术领域构成了一个迅速扩展和发展的领域。随着HRMS仪器的普及，同时也需要工具和解决方案来简化和自动化这些分析产生的大型复杂数据集的处理。Constellation就是其中一种工具，由我们的团队在过去两年中开发，用于对复杂混合物（如天然有机物、油或木质素）的HRMS数据中的重复聚合物单元进行无监督趋势检测。在这项工作中，我们开发了两种新的无监督算法，用于在HRMS数据中寻找有化学意义的变化单元，并结合了来自开源CoreMS软件包的分子式查找算法，这两种算法都在Constellation软件环境中进行了演示。这些算法在包含聚合物分析物的开源HRMS数据集（PEG 400和NIST标准参考物质1950，人类血浆中的代谢物，以及含有聚合物的拭子提取物）上进行评估，并能够成功识别数据中所有已知的变化单元，包括分配正确的公式。通过这些新的发展，我们很高兴能够加入到一个不断增长的开源软件中，这些软件专门用于从复杂的数据集中提取有用的信息，而不需要高成本、技术知识和处理器需求。
The field of high-resolution mass spectrometry (HRMS) and ancillary hyphenated techniques comprise a rapidly expanding and evolving area. As popularity of HRMS instruments grows, there is a concurrent need for tools and solutions to simplify and automate the processing of the large and complex datasets that result from these analyses. Constellation is one such of these tools, developed by our group over the last two years to perform unsupervised trend detection for repeating, polymeric units in HRMS data of complex mixtures such as natural organic matter, oil, or lignin. In this work, we develop two new unsupervised algorithms for finding chemically-meaningful changing units in HRMS data, and incorporate a molecular-formula-finding algorithm from the open-source CoreMS software package, both demonstrated here in the Constellation software environment. These algorithms are evaluated on a collection of open-source HRMS datasets containing polymeric analytes (PEG 400 and NIST standard reference material 1950, both metabolites in human plasma, as well as a swab extract containing polymers), and are able to successfully identify all known changing units in the data, including assigning the correct formulas. Through these new developments, we are excited to add to a growing body of open-source software specialized in extracting useful information from complex datasets without the high costs, technical knowledge, and processor-demand typically associated with such tools.
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