GridMass: a fast two-dimensional feature detection method for LC/MS

Abstract

One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community.

A roadmap for the XCMS family of software solutions in metabolomics

“…It is exciting to consider, for example, that there are hundreds of published algorithms for peak detection and correspondence determination which have not yet been implemented within XCMS for comparative evaluation. [11,16]…”

Joint Bounding of Peaks Across Samples Improves Differential Analysis in Mass Spectrometry-Based Metabolomics

“…We used the density method for peak grouping, the obiwarp method for retention time alignment, and the fillPeaks method to fill in information for peaks missing from certain samples. For MZmine2, we used the GridMass module for peak detection, 36 the join aligner for retention time alignment, and the same-range gap filler module. Details on optimization and parameter settings for XCMS and MZmine2 are provided in the Supporting Information.…”

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

“…Intelligent Metabolomic Quantitation (iMet-Q) [36], a C# software with a GUI whose algorithm includes automatic detection of charge state and isotope ratio of detected peaks and that was developed to minimize the amount of necessary input parameters, significantly facilitates the pipeline for new users. GridMass [37] is a 2D feature detection algorithm implemented in MZmine 2 that is faster than other algorithms and potentially improves detection of low-intensity masses. Metabolomics Spectral Formatting, Alignment, and Conversion Tool (MSFACT) [38] was 1 of the first tools developed for peak alignment.…”