"Sacurine-comprehensive" history (W4M00002)

Workflow4Metabolomics Object Identifier: W4M00002

Digital Object Identifier: 10.15454/1.481114233733302E12

Creator of the history: Etienne Thévenot

Maintainer: Etienne Thévenot (etienne.thevenot at cea.fr)

Creation|Updating date: 2015-06-19

Format: Workflow4Metabolomics Galaxy histories

W4M History: https://workflow4metabolomics.usegalaxy.fr/histories/list_published

Size:  18 Go

Keywords: age, bmi, gender, homosapiens, urine, lcms, preprocessing, statistics, annotation


Study: Characterization of the physiological variations of the metabolome in biofluids is critical to understand human physiology and to avoid confounding effects in cohort studies aiming at biomarker discovery. In this study conducted by the MetaboHUB French Infrastructure for Metabolomics, urine samples from 184 volunteers were analyzed by reversed-phase (C18) ultrahigh performance liquid chromatography (UPLC) coupled to high-resolution mass spectrometry (LTQ-Orbitrap).
Dataset: The dataset contains 234 mzML files from the negative ionization mode, corresponding to two batches (ne1 = 131 files and ne2 = 127 files), with a total of 24 blanks + 26 pools + 184 samples. The raw files (in both profile Thermo format and centroid mzML format) are available on the MetaboLights repository (MTBLS404).
Workflow: The workflow consists of the following steps: preprocessing with XCMS, pre-annotation with CAMERA, variable filtering (sample mean over blank mean ratio), correction of signal drift (loess model built on QC pools) and batch effects, variable filtering (QC coefficent of variation < 30%), normalization by the sample osmolality, log10 transformation, sample filtering (Hotelling, decile and missing pvalues > 0.001), univariate hypothesis testing of significant variations with age, BMI, or between genders (FDR < 0.05), OPLS(-DA) modeling of age, BMI and gender, and mass annotation by query of the KEGG and HMDB databases.

Comments: Compared with the companion ‘W4M00001_Sacurine-statistics’ reference history, this workflow starts from the preprocessing of the raw files and includes all detected features in the subsequent steps (without restricting to identified metabolites only).

 Rights:  Creative Commons


  • Thevenot E.A., Roux A., Xu Y., Ezan E. and Junot C. (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research, 14:3322-3335. DOI:10.1021/acs.jproteome.5b00354

Raw data repository


 Please find more referenced W4M histories here.