Mass-spectra-based peak alignment for automatic nontargeted metabolic profiling analysis for biomarker screening in plant samples

Abstract

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.

Keywords: Automatic data mining; Biomarker; Complicated sample analysis; Nontargeted metabolic profiling; Plant sample.