xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data

Abstract

BackgroundDetection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation.ResultsxMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites.ConclusionsxMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

Metabolomics of fescue toxicosis in grazing beef steers

“…Data were stored as raw spectral files and converted to computable document format files using Xcalibur file converter software (Thermo Fisher). Peak detection, noise filtering, m/z and retention time alignment, feature quantification, and quality filter were performed using xMSanalyzer v2.0.7 (Uppal et al, 2013) with apLCMS v6.1.3 (Yu et al, 2009). Data were extracted as m/z features, defined by m/z, retention time, and integrated ion intensities.…”

“…Samples are analyzed in triplicate to enhance the reliability of detection of low abundance chemicals, and data extraction methods utilize adaptive processing LCMS (apLCMS; Yu et al, 2009) and automated re-extraction, statistical filtering and data merger to enhance data quality (Uppal et al, 2013). To overcome a bottleneck metabolite identification in this information-rich data, statistical tests and bioinformatics methods are used to select mass spectral features associated with endpoints of interest (e.g., consequences of EA exposure), and computer algorithms are then used to test for pathway enrichment (Dunn et al, 2012;Li et al, 2013).…”

Metabolome-Wide Association Study of Primary Open Angle Glaucoma

“…24 We used xMSanalyzer (in the public domain at http://sourceforge.net/projects/xmsanalyzer/) to enhance the feature detection process by performing systematic data re-extraction, statistical filtering, and data merger to enhance quality of data extraction. 25 To ensure quality control, each sample was analyzed in triplicate, and a coefficient of variation (CV) was calculated for each m/z feature in each sample. Median CV, percent missing values, and a combined quality score were determined for each m/z.…”

Putrescine as indicator of manganese neurotoxicity: Dose-response study in human SH-SY5Y cells

“…To help overcome the false discovery due to multiple comparisons, the experiments were designed with nine independent biological replicates for each of the six experimental conditions of Mn concentration. Metabolic extracts were prepared as described previously (Go et al, 2014, 2015a; Soltow et al, 2013; Uppal et al, 2013, 2016, 2017). Briefly, after the 5-h treatment, cells were washed with phosphate-buffered saline and 200 μL of 1:2 HPLC grade water: acetonitrile solution containing a mixture of stable isotopic standards (Go et al, 2015a; Soltow et al, 2013) was added to each plate to precipitate proteins and extract metabolites (Go et al, 2014).…”