Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, United States.
Anal Chem. 2023 Apr 18;95(15):6212-6217. doi: 10.1021/acs.analchem.2c05810. Epub 2023 Apr 5.
In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions in relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu) and provides a JSON format for easy data exchange and software interoperability. By generalized preannotation, khipu makes it feasible to connect metabolomics data with common data science tools and supports flexible experimental designs.
在非靶向代谢组学中，通常会为每个原始代谢物测量多个离子，包括同位素形式和源内修饰，如加合物和碎片。由于缺乏对化学身份或公式的先验知识，对这些离子进行计算组织和解释具有挑战性，这也是以前使用网络算法执行该任务的软件工具的不足之处。我们在这里提出了一种广义的树结构，用于注释与原始化合物有关的离子，并推断中性质量。提出了一种算法，可将质量距离网络转换为这种具有高保真度的树结构。该方法既适用于常规的非靶向代谢组学，也适用于稳定同位素示踪实验。它被实现为一个 Python 包（khipu），并提供了一个 JSON 格式，便于数据交换和软件互操作性。通过广义的预注释，khipu 使得将代谢组学数据与常见的数据科学工具连接成为可能，并支持灵活的实验设计。
