Metabolomic- and Molecular Networking-Based Exploration of the Chemical Responses Induced in Leaves Inoculated with .

Citrus canker, caused by the bacterium subsp. (), is a plant disease affecting crops worldwide. However, little is known about defense compounds in .

New insights into the conversion of electropherograms to the effective electrophoretic mobility scale.

CE-MS is increasingly gaining momentum as an analytical tool in metabolomics, due to its ability to obtain information about the most polar elements in biological samples. This has been helped by improvements of robustness in peak identification by means of mobility-scale representations of the electropherograms (mobilograms). As a necessary step toward facilitating the use of CE-MS for untargeted metabolomics data, the authors previously developed and introduced ROMANCE, a software automating mobilogram generation for large untargeted datasets through a simple and self-contained user interface.

Gaining Insights Into Metabolic Networks Using Chemometrics and Bioinformatics: Chronic Kidney Disease as a Clinical Model.

Because of its ability to generate biological hypotheses, metabolomics offers an innovative and promising approach in many fields, including clinical research. However, collecting specimens in this setting can be difficult to standardize, especially when groups of patients with different degrees of disease severity are considered. In addition, despite major technological advances, it remains challenging to measure all the compounds defining the metabolic network of a biological system.

Algorithms to optimize multi-column chromatographic separations of proteins.

The goal of this work was to provide a technical solution for the automated optimization of multi-column systems for protein separation and fractionation. Both algorithm and a software that can be downloaded are provided. In this algorithm, the length and order of the individual column segments can be considered.

Network principal component analysis: a versatile tool for the investigation of multigroup and multiblock datasets.

Motivation: Complex data structures composed of different groups of observations and blocks of variables are increasingly collected in many domains, including metabolomics. Analysing these high-dimensional data constitutes a challenge, and the objective of this article is to present an original multivariate method capable of explicitly taking into account links between data tables when they involve the same observations and/or variables. For that purpose, an extension of standard principal component analysis called NetPCA was developed.