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QPMASS: A parallel peak alignment and quantification software for the analysis of large-scale gas chromatography-mass spectrometry (GC-MS)-based metabolomics datasets.
Journal of Chromatography A ( IF 3.8 ) Pub Date : 2020-02-26 , DOI: 10.1016/j.chroma.2020.460999
Lixin Duan 1 , Aimin Ma 2 , Xianbin Meng 3 , Guo-An Shen 4 , Xiaoquan Qi 5
Affiliation  

Gas chromatography-mass spectrometry (GC-MS) is a robust analytical platform for analysis of small molecules. Recently, it is widely used for large-scale metabolomics studies, in which hundreds or even thousands of samples are analyzed simultaneously, producing a very large and complex GC-MS datasets. A number of software are currently available for processing GC-MS data, but it is too compute-intensive for them to efficiently and accurately align chromatographic peaks from thousands of samples. Here, we report a newly developed software, QPMASS, for analysis of large-scale GC-MS data. The parallel computing with an advanced dynamic programming approach is implemented in QPMASS to align peaks from multiple samples based on retention time and mass spectra, enabling fast processing large-scale datasets. Furthermore, the missing value filtering and backfilling are introduced into the program, greatly reducing false positive and false negative errors to be less than 5%. We demonstrated that it took only 8 h to align and quantify a GC-TOF-MS dataset from 300 rice leaves samples, and 17 h to process a GC-qMS dataset from 1000 rice seed samples by using a personal computer (3.70 GHz CPU, 16 GB of memory and > 100 GB hard disk). QPMASS is written in C++ programming language, and is able to run under Windows operation system with a user-friendly interface.

中文翻译:

QPMASS:一种平行峰比对和定量软件,用于分析基于大规模气相色谱-质谱(GC-MS)的代谢组学数据集。

气相色谱-质谱(GC-MS)是用于分析小分子的强大分析平台。最近,它被广泛用于大规模代谢组学研究,其中同时分析了数百甚至数千个样品,从而生成了非常庞大而复杂的GC-MS数据集。当前有许多软件可用于处理GC-MS数据,但对于它们而言,计算量太大,因此无法高效,准确地对齐数千个样品中的色谱峰。在这里,我们报告了一个新开发的软件QPMASS,用于分析大规模GC-MS数据。在QPMASS中实现了具有高级动态编程方法的并行计算,以根据保留时间和质谱图对来自多个样品的峰进行对齐,从而可以快速处理大规模数据集。此外,该程序引入了缺失值过滤和回填,大大减少了误报和误报错误,将其减少到5%以下。我们证明,使用个人计算机(3.70 GHz CPU,仅用8小时即可对300个水稻叶片样品中的GC-TOF-MS数据集进行对齐和定量,而用时17小时即可处理1000种水稻种子样品中的GC-qMS数据集。 16 GB内存和> 100 GB硬盘)。QPMASS用C ++编程语言编写,并且能够在Windows操作系统下以友好的用户界面运行。17小时后,使用个人计算机(3.70 GHz CPU,16 GB内存和> 100 GB硬盘)处理来自1000个水稻种子样品的GC-qMS数据集。QPMASS用C ++编程语言编写,并且能够在Windows操作系统下以友好的用户界面运行。17小时后,使用个人计算机(3.70 GHz CPU,16 GB内存和> 100 GB硬盘)处理来自1000个水稻种子样品的GC-qMS数据集。QPMASS用C ++编程语言编写,并且能够在Windows操作系统下以友好的用户界面运行。
更新日期:2020-02-26
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