PDF(Pubmed)
Abstract：
Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intralaboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organization\'s Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionalities, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries in a workflow for extracting, analyzing, and visualizing QC metrics from different sources. Additionally, we show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).
摘要：
质谱是分析复杂生物样品中分子的强大技术。然而,由于各种因素，流产间和流产内的变异性和偏倚会影响数据，包括样品处理和制备，仪器校准和性能，以及数据采集和处理。为了解决这个问题，人类蛋白质组组织蛋白质组学标准计划的质量控制(QC)工作组已经建立了标准的mzQC文件格式,用于报告和交换与数据质量有关的信息.mzQC基于JavaScript对象表示法（JSON）格式，提供了一种轻量级但通用的文件格式，可以在软件中轻松实现。这里,我们提供了开源软件库，以三种编程语言处理mzQC数据：Python，使用pymzqc；R，使用rmzqc;和Java，使用jmzqc。这些库遵循通用数据模型并提供共享功能，包括mzQC文件的（反）序列化和验证。我们演示了在工作流程中使用软件库进行提取，分析,并从不同的来源可视化质量控制指标。此外,我们展示了这些库如何相互集成，使用现有的软件工具，以及质谱数据QC的自动化工作流程。所有软件库都可以在GitHub上的MS-Quality-Hub组织（https://github.com/MS-Quality-Hub）下作为开源提供。
