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Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Untargeted metabolic profiling analysis is employed to screen metabolites for specific purposes, such as geographical origin discrimination. However, the data analysis remains a challenging task. In this work, a new automatic untargeted metabolic profiling analysis coupled with a chemometric strategy was developed to improve the metabolite identification results and to enhance the geographical origin discrimination capability. Automatic untargeted metabolic profiling analysis with chemometrics (AuMPAC) was used to screen the total ion chromatographic (TIC) peaks that showed significant differences among the various geographical regions. Then, a chemometric peak resolution strategy is employed for the screened TIC peaks. The retrieved components were further analyzed using ANOVA, and those that showed significant differences were used to build a geographical origin discrimination model by using two-way encoding partial least squares. To demonstrate its performance, a geographical origin discrimination of flaxseed samples from six geographical regions in China was conducted, and 18 TIC peaks were screened. A total of 19 significant different metabolites were obtained after the peak resolution. The accuracy of the geographical origin discrimination was up to 98%. A comparison of the AuMPAC, AMDIS, and XCMS indicated that AuMPACobtained the best geographical origin discrimination results. In conclusion, AuMPAC provided another method for data analysis., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Nontargeted metabolic profiling analysis is a difficult task in a routine investigation because hundreds of chromatographic peaks are eluted within a short time, and the time shift problem is severe across samples. To address these problems, the present work developed an automatic nontargeted metabolic profiling analysis (anTMPA) method. First, peaks from the total ion chromatogram were extracted using modified multiscale Gaussian smoothing method. Then, a novel peak alignment strategy was employed based on the mass spectra and retention times of the peaks in which the maximum mass spectral correlation coefficient path was extracted using a modified dynamic programming method. Moreover, an automatic landmark peak-searching strategy was employed for self-adapting time shift modification. Missing peaks across samples were grouped and registered into the aligned peak list table for final refinement. Finally, the aligned peaks across samples were analyzed using statistical methods to identify potential biomarkers. Mass spectral information on the screened biomarkers could be directly imported into the National Institute of Standards and Technology library to select the candidate compounds. The performance of the anTMPA method was evaluated using a complicated plant gas chromatography-mass spectrometry dataset with the aim of identifying biomarkers between the growth and maturation stages of the tested plant., (Copyright © 2017 Elsevier B.V. All rights reserved.)