QPMASS: A parallel peak alignment and quantification software for the analysis of large-scale gas chromatography-mass spectrometry (GC-MS)-based metabolomics datasets

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.