Supporting analysis, visualisation and biological interpretation of metabolomics datasets

Over the past decades, the emerging omics technologies have enabled scientists to take a step further in the investigation of biological systems. From food safety to stratified medicine, omics technologies are now an essential and powerful means to study biological processes. Omics technologies are however at different stages of maturity, and the most recent field of the omics family, metabolomics, is still in its infancy. Metabolomics attempts to catalogue, characterise and quantify all small molecules constitutive of a biological system. Liquid Chromatography - Mass Spectrometry (LCMS) is now the most commonly used technique to generate metabolomics data. The method allows the detection of hundreds of metabolites from a single sample and can provide a rapid assignment of formulae to detected masses using high accuracy mass spectrometers. While analytical methods are well developed, support for linking metabolites to detected features and interpreting the results of a data analysis in a biological context is still poorly developed. Significant challenges also arise from the additional steps required to export the data to third party environments to create a biological context. The study of integrated omics datasets as a single system has also shown to provide greater inferences than the study of each omics separately. Methods to integrate the different omics layers of biological systems are, however, at an early stage of development and no standard approach currently exists to provide a holistic view of organisms systems organisation. The objective of this thesis is to formalise, standardise and unify the data analysis of the metabolomics field, by providing to biologists the tools to support them from planning to analysis to biological impact reporting. The work presented here focuses particularly on untargeted LC-MS metabolomics approaches and attempts to assist non-expert users in performing their own analysis of metabolomics datasets. The project also aims to enable systematic biological interpretation of metabolomics datasets. The first part of the thesis focuses on creating the foundation of a unified environment for LC-MS metabolomics data analysis. Subsequently, the created environment will be expanded to integrate and support the latest technological advances in the field and provide better support for both designing studies and interpreting analysis results in a biological context. Finally, the last part of this thesis concentrates on integrating metabolomics data with other omics datasets in an attempt to provide a holistic view of a biological system

MetExploreViz: web component for interactive metabolic network visualization

Summary: MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context. Availability and implementation: Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc

PiMP my metabolome:An integrated, web-based tool for LC-MS metabolomics data

Summary: The Polyomics integrated Metabolomics Pipeline (PiMP) fulfils an unmet need in metabolomics data analysis. PiMP offers automated and user-friendly analysis from mass spectrometry data acquisition to biological interpretation. Our key innovations are the Summary Page, which provides a simple overview of the experiment in the format of a scientific paper, containing the key findings of the experiment along with associated metadata; and the Metabolite Page, which provides a list of each metabolite accompanied by ‘evidence cards’, which provide a variety of criteria behind metabolite annotation including peak shapes, intensities in different sample groups and database information. Availability: PiMP is available at http://polyomics.mvls.gla.ac.uk, and access is freely available on request. 50 GB of space is allocated for data storage, with unrestricted number of samples and analyses per user. Source code is available at https://github.com/RonanDaly/pimp and licensed under the GPL

A computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities

Transcriptomic and metabolic responses of Calotropis procera to salt and drought stress

Background: Calotropis procera is a wild plant species in the family Apocynaceae that is able to grow in harsh, arid and heat stressed conditions. Understanding how this highly adapted plant persists in harsh environments should inform future efforts to improve the hardiness of crop and forage plant species. To study the plant response to droμght and osmotic stress, we treated plants with polyethylene glycol and NaCl and carried out transcriptomic and metabolomics measurements across a time-course of five days. Results: We identified a highly dynamic transcriptional response across the time-course including dramatic changes in inositol signaling, stress response genes and cytokinins. The resulting metabolome changes also involved sharp increases of myo-inositol, a key signaling molecule and elevated amino acid metabolites at later times. Conclusions: The data generated here provide a first glimpse at the expressed genome of C. procera, a plant that is exceptionally well adapted to arid environments. We demonstrate, through transcriptome and metabolome analysis that myo-inositol signaling is strongly induced in response to drought and salt stress and that there is elevation of amino acid concentrations after prolonged osmotic stress. This work should lay the foundations of future studies in adaptation to arid environments

PiMP: An integrated, web-enabled tool for LC– MS data analysis and visualization

<p>Rapid advances in metabolomics technology create enormous challenges for the development of data processing and visualization techniques that enable biological scientists to gain insight from complex data sets. Although programming libraries are available for informaticians, there remains a need for powerful, user-friendly software to support biological specialists in analysing metabolomics data. Glasgow Polyomics is developing PiMP (Polyomics Metabolomics Pipeline), a Web-based application to support interactive data analysis for liquid chromatography – mass spectrometry (LC– MS) platforms. From raw data to the biological context of samples, our pipeline guides users through each core step in LC– MS data analysis. World-wide collaboration between groups is enabled by sharing both experimental designs and results online, overcoming limitations in big-data transfer. A robust and systematic statistical analysis is built into our pipeline, enabling us to differentiate and report both identified and annotated metabolites according to the Metabolomics Standard Initiative (MSI). State-of-the-art, multi-scale, multi-viewpoint visualization allows scientists to navigate and extract relevant information from sample data and to examine this information in a biological context. The ultimate goal of our software is to standardize and automate metabolomics analysis by integrating all steps of a study -- from planning to analysis to reporting -- into one comprehensive tool spanning raw data to biological insight</p

Machine learning-based classification to improve Gas Chromatography-Mass spectrometry data processing.

Methodological & Technological developmentsIntroductionLack of reliable peak detection impedes automated analysis of large-scale gas chromatography-mass spectrometry (GCMS) metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters, as well as data acquisition method. Therefore, comparing and contrasting between algorithms is difficult.Technological and methodological innovationWe present part of the work published in [1] and implemented in our workflow for improved peak picking (WiPP),focusing on the use of machine learning-based classification to optimize and improve different steps of the common GC-MS metabolomics data processing workflow. Our approach evaluates the quality of detected peaks using a machine learning based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high-quality peak set for immediate down-stream analysis.Results and impactWe benchmarked our workflow to standard compound mixes and a complex biological dataset, demonstrating that peak detection is improved. Furthermore, the approach can provide an impartial performance comparison of different peak picking algorithms. We also discuss the applicability of the approach to liquid chromatography-mass spectrometry data.References[1] Gloaguen, Y.; Borgsmüller, N. et al. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-MassSpectrometry (GC-MS) Data. Metabolites 2019, 9, 171

MetExplore: collaborative edition and exploration of metabolic networks

Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data

L'Auto-vélo : automobilisme, cyclisme, athlétisme, yachting, aérostation, escrime, hippisme / dir. Henri Desgranges

17 juillet 19171917/07/17 (A18,N6021)