MWASTools: an R/bioconductor package for metabolome-wide association studies

Summary MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large‐scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome‐wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome‐wide association studies results. Availability and implementation The MWASTools R package is implemented in R (version > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. Contact m.dumas@imperial.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.

[1]  D. Gauguier,et al.  Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. , 2005, Analytical chemistry.

[2]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[3]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[4]  P. Elliott,et al.  Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. , 2006, Analytical chemistry.

[5]  Joram M. Posma,et al.  MetaboSignal: a network-based approach for topological analysis of metabotype regulation via metabolic and signaling pathways , 2016, Bioinform..

[6]  Joram M. Posma,et al.  MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG , 2013, Bioinform..

[7]  Dimitrios Spiliotopoulos,et al.  muma, An R Package for Metabolomics Univariate and Multivariate Statistical Analysis , 2013 .

[8]  E. Thévenot,et al.  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. , 2015, Journal of proteome research.

[9]  J. Lindon,et al.  Metabonomics: a platform for studying drug toxicity and gene function , 2002, Nature Reviews Drug Discovery.

[10]  D. Gauguier,et al.  J-Resolved 1H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis. , 2017, Analytical chemistry.

[11]  Ian J. Brown,et al.  Human metabolic phenotype diversity and its association with diet and blood pressure , 2008, Nature.

[12]  P. Elliott,et al.  Urinary metabolic signatures of human adiposity , 2015, Science Translational Medicine.

[13]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .