The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America.
Institute of Parasitology, McGill University, Montreal, Quebec, Canada.
PLoS Comput Biol. 2024 Jun 6;20(6):e1011912. doi: 10.1371/journal.pcbi.1011912. eCollection 2024 Jun.
To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.
为了规范代谢组学数据分析并促进未来的计算发展，拥有一套定义良好的通用数据结构模板是至关重要的。在这里，我们描述了一组涉及代谢组学数据处理的数据结构，并说明了它们如何在一个功能齐全的以 Python 为中心的管道中使用。我们展示了该管道的性能，以及使用大规模 LC-MS 代谢组学和脂质组学数据和 LC-MS/MS 数据进行注释和质量控制的详细信息。还重新分析了多个以前发表的数据集，以展示其在生物数据分析中的实用性。该管道允许用户以高效和透明的方式简化数据处理、质量控制、注释和标准化。这项工作填补了 Python 生态系统中计算代谢组学的一个主要空白。
