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      MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

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          Abstract

          Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC–MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

          Graphical Abstract

          Graphical Abstract

          From raw data to statistical and functional insights using MetaboAnalyst 5.0.

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          Most cited references67

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          KEGG: integrating viruses and cellular organisms

          Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.
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            MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

            Abstract We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.
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              PubChem 2019 update: improved access to chemical data

              Abstract PubChem (https://pubchem.ncbi.nlm.nih.gov) is a key chemical information resource for the biomedical research community. Substantial improvements were made in the past few years. New data content was added, including spectral information, scientific articles mentioning chemicals, and information for food and agricultural chemicals. PubChem released new web interfaces, such as PubChem Target View page, Sources page, Bioactivity dyad pages and Patent View page. PubChem also released a major update to PubChem Widgets and introduced a new programmatic access interface, called PUG-View. This paper describes these new developments in PubChem.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                02 July 2021
                21 May 2021
                21 May 2021
                : 49
                : W1
                : W388-W396
                Affiliations
                Institute of Parasitology, McGill University , Montreal, Quebec, Canada
                Institute of Parasitology, McGill University , Montreal, Quebec, Canada
                Institute of Parasitology, McGill University , Montreal, Quebec, Canada
                Centre de Calcul Scientifique, Université de Sherbrooke , Sherbrooke, Quebec, Canada
                Department of Human Genetics, McGill University , Montreal, Quebec, Canada
                Centre de Calcul Scientifique, Université de Sherbrooke , Sherbrooke, Quebec, Canada
                Centre de Calcul Scientifique, Université de Sherbrooke , Sherbrooke, Quebec, Canada
                Centre de Calcul Scientifique, Université de Sherbrooke , Sherbrooke, Quebec, Canada
                Département de Biologie, Université de Sherbrooke , Sherbrooke, Quebec, Canada
                The Jackson Laboratory for Genomic Medicine , Farmington, Connecticut, USA
                Institute of Parasitology, McGill University , Montreal, Quebec, Canada
                Department of Human Genetics, McGill University , Montreal, Quebec, Canada
                Department of Animal Science, McGill University , Montreal, Quebec, Canada
                Author notes
                To whom correspondence should be addressed. Tel: +1 514 398 8668; Email: jeff.xia@ 123456mcgill.ca
                Author information
                https://orcid.org/0000-0002-0966-7923
                https://orcid.org/0000-0002-3961-294X
                https://orcid.org/0000-0003-2040-2624
                Article
                gkab382
                10.1093/nar/gkab382
                8265181
                34019663
                ac34c3d6-e259-48bc-8eba-1b772bfebad0
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 April 2021
                : 17 April 2021
                : 09 March 2021
                Page count
                Pages: 9
                Funding
                Funded by: Genome Canada, DOI 10.13039/100008762;
                Funded by: Génome Québec, DOI 10.13039/100013062;
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: U01 CA235493
                Funded by: Natural Sciences and Engineering Research Council of Canada, DOI 10.13039/501100000038;
                Funded by: Canada Research Chairs, DOI 10.13039/501100001804;
                Funded by: Calcul Québec;
                Funded by: Compute Canada, DOI 10.13039/100013020;
                Funded by: Fonds de Recherche du Québec - Santé, DOI 10.13039/501100000156;
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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