The metabolome describes the full complement of the tens to hundreds of thousands of low molecular weight metabolites present within a biological system. Identification of the metabolome is critical for discovering the maximum amount of biochemical knowledge from metabolomics datasets. Yet no exhaustive experimental characterisation of any organismal metabolome has been reported to date, dramatically contrasting with the genome sequencing of thousands of plants, animals and microbes. Here we review the status of metabolome annotation and describe advances in the analytical methodologies being applied. In part through new international coordination, we conclude that we are now entering a new era of metabolome annotation.

BackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution.FindingsPhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm.ConclusionsPhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and ‘omics research domains.

We report the first high-resolution spectra for the out-of-plane librational vibration in the water dimer. Three vibrational subbands comprising a total of 188 transitions have been measured by diode laser spectroscopy near 500 cm(-1) and assigned to (H2O)2 libration-rotation-tunneling eigenstates. The band origin for the Ka = 1 subband is ~524 cm(-1). Librational excitation increases the interchange and bifurcation hydrogen bond rearrangement tunneling splittings by factors of 3-5 and 4-40, respectively. Analysis of the rotational constants obtained from a nonlinear least squares fit indicates that additional external perturbations to the energy levels are likely.

Hydrogen bonds in solid and liquid water are formed and broken via librational vibrations, hence characterizing the details of these motions is vital to understanding these important dynamics. Here we report the measurement and assignment of 875 transitions comprising 6 subbands originating from out-of-plane librational transitions of the water pentamer-d near 512 cm. The precisely measured (ca. 1 ppm) transitions reveal bifurcation splittings of ∼1884 MHz, a ∼4000× enhancement over ground state splittings and 100× greater than predicted by theory. The pentamer is thus the third water cluster to display greatly enhanced bifurcation tunneling upon single quantum excitation of librational vibrations. From the intensity pattern of the observed transitions, the mechanism of bifurcation is established by comparison with theoretical predictions.

Although living organisms and the environment may encounter chemical mixtures, the chemical risk assessment has often focused on individual compounds rather than chemical mixtures. However, many scientific findings have reported that toxicological effects can be provoked among substances even at levels below no observed effect concentrations of each toxicant. Conventionally, simple additive models, e.g. concentration addition (CA) and independent action (IA) models, have been frequently employed to estimate the toxicity of chemical mixtures. Since the CA and IA models assumes that mixtures have similar modes of toxic action (MoAs) and dissimilar MoAs, respectively. To develop predictive models explaining both cases which can be substantially occurred in the real world, integrated addition models combining the CA and IA concepts have been developed. The objective of this study was to conduct a comparative validation of simple additive models and integrated addition models via different experimental datasets to evaluate their performance and potential in predicting mixture toxicity.http://dx.