MetaboLights: a resource evolving in response to the needs of its scientific community.

MetaboLights is a database for metabolomics studies, their raw experimental data and associated metadata. The database is cross-species and cross-technique and it covers metabolite structures and their reference spectra as well as their biological roles and locations. MetaboLights is the recommended metabolomics repository for a number of leading journals and ELIXIR, the European infrastructure for life science information. In this article, we describe the significant updates that we have made over the last two years to the resource to respond to the increasing amount and diversity of data being submitted by the metabolomics community. We refreshed the website and most importantly, our submission process was completely overhauled to enable us to deliver a far more user-friendly submission process and to facilitate the growing demand for reproducibility and integration with other 'omics. Metabolomics resources and data are available under the EMBL-EBI's Terms of Use via the web at https://www.ebi.ac.uk/metabolights and under Apache 2.0 at Github (https://github.com/EBI-Metabolights/).

Automated assembly of species metabolomes through data submission into a public repository.

Following similar global efforts to exchange genomic and other biomedical data, global databases in metabolomics have now been established. MetaboLights, the first general purpose, publically available, cross-species, cross-application database in metabolomics, has become the fastest growing data repository at the European Bioinformatics Institute in terms of data volume. Here we present the automated assembly of species metabolomes in MetaboLights, a crucial reference for chemical biology, which is growing through user submissions.

Building blocks for automated elucidation of metabolites: natural product-likeness for candidate ranking.

BACKGROUND: In metabolomics experiments, spectral fingerprints of metabolites with no known structural identity are detected routinely. Computer-assisted structure elucidation (CASE) has been used to determine the structural identities of unknown compounds. It is generally accepted that a single 1D NMR spectrum or mass spectrum is usually not sufficient to establish the identity of a hitherto unknown compound. When a suite of spectra from 1D and 2D NMR experiments supplemented with a molecular formula are available, the successful elucidation of the chemical structure for candidates with up to 30 heavy atoms has been reported previously by one of the authors. In high-throughput metabolomics, usually 1D NMR or mass spectrometry experiments alone are conducted for rapid analysis of samples. This method subsequently requires that the spectral patterns are analyzed automatically to quickly identify known and unknown structures. In this study, we investigated whether additional existing knowledge, such as the fact that the unknown compound is a natural product, can be used to improve the ranking of the correct structure in the result list after the structure elucidation process. RESULTS: To identify unknowns using as little spectroscopic information as possible, we implemented an evolutionary algorithm-based CASE mechanism to elucidate candidates in a fully automated fashion, with input of the molecular formula and 13C NMR spectrum of the isolated compound. We also tested how filters like natural product-likeness, a measure that calculates the similarity of the compounds to known natural product space, might enhance the performance and quality of the structure elucidation. The evolutionary algorithm is implemented within the SENECA package for CASE reported previously, and is available for free download under artistic license at http://sourceforge.net/projects/seneca/. The natural product-likeness calculator is incorporated as a plugin within SENECA and is available as a GUI client and command-line executable. Significant improvements in candidate ranking were demonstrated for 41 small test molecules when the CASE system was supplemented by a natural product-likeness filter. CONCLUSIONS: In spectroscopically underdetermined structure elucidation problems, natural product-likeness can contribute to a better ranking of the correct structure in the results list.

Natural product-likeness score revisited: an open-source, open-data implementation.

BACKGROUND: Natural product-likeness of a molecule, i.e. similarity of this molecule to the structure space covered by natural products, is a useful criterion in screening compound libraries and in designing new lead compounds. A closed source implementation of a natural product-likeness score, that finds its application in virtual screening, library design and compound selection, has been previously reported by one of us. In this note, we report an open-source and open-data re-implementation of this scoring system, illustrate its efficiency in ranking small molecules for natural product likeness and discuss its potential applications. RESULTS: The Natural-Product-Likeness scoring system is implemented as Taverna 2.2 workflows, and is available under Creative Commons Attribution-Share Alike 3.0 Unported License at http://www.myexperiment.org/packs/183.html. It is also available for download as executable standalone java package from http://sourceforge.net/projects/np-likeness/under Academic Free License. CONCLUSIONS: Our open-source, open-data Natural-Product-Likeness scoring system can be used as a filter for metabolites in Computer Assisted Structure Elucidation or to select natural-product-like molecules from molecular libraries for the use as leads in drug discovery.

New developments on the cheminformatics open workflow environment CDK-Taverna.

BACKGROUND: The computational processing and analysis of small molecules is at heart of cheminformatics and structural bioinformatics and their application in e.g. metabolomics or drug discovery. Pipelining or workflow tools allow for the Lego™-like, graphical assembly of I/O modules and algorithms into a complex workflow which can be easily deployed, modified and tested without the hassle of implementing it into a monolithic application. The CDK-Taverna project aims at building a free open-source cheminformatics pipelining solution through combination of different open-source projects such as Taverna, the Chemistry Development Kit (CDK) or the Waikato Environment for Knowledge Analysis (WEKA). A first integrated version 1.0 of CDK-Taverna was recently released to the public. RESULTS: The CDK-Taverna project was migrated to the most up-to-date versions of its foundational software libraries with a complete re-engineering of its worker's architecture (version 2.0). 64-bit computing and multi-core usage by paralleled threads are now supported to allow for fast in-memory processing and analysis of large sets of molecules. Earlier deficiencies like workarounds for iterative data reading are removed. The combinatorial chemistry related reaction enumeration features are considerably enhanced. Additional functionality for calculating a natural product likeness score for small molecules is implemented to identify possible drug candidates. Finally the data analysis capabilities are extended with new workers that provide access to the open-source WEKA library for clustering and machine learning as well as training and test set partitioning. The new features are outlined with usage scenarios. CONCLUSIONS: CDK-Taverna 2.0 as an open-source cheminformatics workflow solution matured to become a freely available and increasingly powerful tool for the biosciences. The combination of the new CDK-Taverna worker family with the already available workflows developed by a lively Taverna community and published on myexperiment.org enables molecular scientists to quickly calculate, process and analyse molecular data as typically found in e.g. today's systems biology scenarios.