Summary: The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. Availability and implementation: http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB). Contact:contact@workflow4metabolomics.org

Serotonin (5-HT) controls a wide range of biological functions. In the brain, its implication as a neurotransmitter and in the control of behavioral traits has been largely documented. At the periphery, its modulatory role in physiological processes, such as the cardiovascular function, is still poorly understood. The rate-limiting enzyme of 5-HT synthesis, tryptophan hydroxylase (TPH), is encoded by two genes, the well characterizedtph1gene and a recently identifiedtph2gene. In this article, based on the study of a mutant mouse in which thetph1gene has been inactivated by replacement with the β-galactosidase gene, we establish that the neuronaltph2is expressed in neurons of the raphe nuclei and of the myenteric plexus, whereas the nonneuronaltph1, as detected by β-galactosidase expression, is in the pineal gland and the enterochromaffin cells. Anatomic examination of the mutant mice revealed larger heart sizes than in wild-type mice. Histological investigation indicates that the primary structure of the heart muscle is not affected. Hemodynamic analyses demonstrate abnormal cardiac activity, which ultimately leads to heart failure of the mutant animals. This report links loss oftph1gene expression, and thus of peripheral 5-HT, to a cardiac dysfunction phenotype. Thetph1^-/-mutant may be valuable for investigating cardiovascular dysfunction observed in heart failure in humans.

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

High-throughput technologies such as transcriptomics, proteomics and metabolomics show great promise for the discovery of biomarkers for diagnosis and prognosis. Selection of the most promising candidates between the initial untargeted step and the subsequent validation phases is critical within the pipeline leading to clinical tests. Several statistical and data mining methods have been described for feature selection: in particular, wrapper approaches iteratively assess the performance of the classifier on distinct subsets of variables. Current wrappers, however, do not estimate the significance of the selected features. We therefore developed a new methodology to find the smallest feature subset which significantly contributes to the model performance, by using a combination of resampling, ranking of variable importance, significance assessment by permutation of the feature values in the test subsets, and half-interval search. We wrapped our \emph{biosigner} algorithm around three reference binary classifiers (Partial Least Squares - Discriminant Analysis, Random Forest, and Support Vector Machines) which have been shown to achieve specific performances depending on the structure of the dataset. By using three real biological and clinical metabolomics and transcriptomics datasets (containing up to 7,000 features), complementary signatures were obtained in a few minutes, generally providing higher prediction accuracies than the initial full model. Comparison with alternative feature selection approaches further indicated that our method provides signatures of restricted size and high stability. Finally, by using our methodology to seek metabolites discriminating type 1 from type 2 diabetic patients, several features were selected, including a fragment from the taurochenodeoxycholic bile acid. Our methodology, implemented in the \emph{biosigner} R/Bioconductor package and Galaxy/Workflow4metabolomics module, should be of interest for both experimenters and statisticians to identify robust molecular signatures from large omics datasets in the process of developing new diagnostics.