MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.
TL;DR: 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.
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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TL;DR: This work introduces MetaboAnalystR 2.0, a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment and integrates XCMS and CAMERA to support raw spectral processing and peak annotation.
Abstract: Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.
234 citations
TL;DR: It is shown that 4 weeks of strict alternate day fasting (ADF) improved markers of general health in healthy, middle-aged humans while causing a 37% calorie reduction on average, and supports its safety.
227 citations
TL;DR: In this paper , the authors discuss the recent progress of metabolomics in the early diagnosis, disease prognosis, and pathogenesis of diabetic kidney disease at the level of small molecule metabolites in vivo.
Abstract: Abstract Metabolomics is a field of systems biology that draws on the scientific methods of other groups to qualitatively or quantitatively characterize small molecule metabolites in organisms, revealing their interconnections with the state of the organism at an overall relative macroscopic level. Diabetic kidney disease (DKD) is well known as a chronic metabolic disease, and metabolomics provides an excellent platform for its clinical study. A growing number of metabolomic analyses have revealed that individuals with DKD have metabolic disturbances of multiple substances in their bodies. With the continuous development and improvement of metabolomic analysis technology, the application of metabolomics in the clinical research of DKD is also expanding. This review discusses the recent progress of metabolomics in the early diagnosis, disease prognosis, and pathogenesis of DKD at the level of small molecule metabolites in vivo.
219 citations
TL;DR: The recent progress of metabolomics in the early diagnosis, disease prognosis, and pathogenesis of DKD at the level of small molecule metabolites in vivo is discussed.
Abstract: Metabolomics is a field of systems biology that draws on the scientific methods of other groups to qualitatively or quantitatively characterize small molecule metabolites in organisms, revealing their interconnections with the state of the organism at an overall relative macroscopic level. Diabetic kidney disease (DKD) is well known as a chronic metabolic disease, and metabolomics provides an excellent platform for its clinical study. A growing number of metabolomic analyses have revealed that individuals with DKD have metabolic disturbances of multiple substances in their bodies. With the continuous development and improvement of metabolomic analysis technology, the application of metabolomics in the clinical research of DKD is also expanding. This review discusses the recent progress of metabolomics in the early diagnosis, disease prognosis, and pathogenesis of DKD at the level of small molecule metabolites in vivo.
218 citations
TL;DR: The parameters involved in metabolite reporting are reviewed and a workflow to estimate the level of confidence in reported metabolite annotation is provided, to enable data to be shared, re-analyzed and re-annotated in an automated fashion.
206 citations
References
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TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
TL;DR: The content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases, and the newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined.
Abstract: KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.
5,741 citations
"MetaboAnalyst 4.0: towards more tra..." refers background in this paper
...For metabolites, MetaboAnalyst 4.0 currently accepts compound names, HMDB IDs or KEGG compound IDs as metabolite identifiers....
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...The knowledgebase for this module consists of five genome-scale metabolic models obtained from the original Python implementation which have either been manually curated or downloaded from BioCyc, as well as an expanded library of 21 organisms derived from the KEGG metabolic pathways....
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...For genes, Entrez IDs, ENSEMBL IDs, official gene symbols or KEGG orthologs are currently supported....
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...On the network visualization page, users can use their mouse or touchpad to zoom in and out, highlight, drag and drop nodes (except the KEGG global metabolic network), or click on a node/edge for further details....
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...The aim of this module is to provide users with an easy-to-use interface that permits the mapping of their metabolites and/or genes (including KEGG orthologs or KOs) onto different types of molecular interaction networks....
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TL;DR: An overview of the PubChem Substance and Compound databases is provided, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access.
Abstract: PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.
3,328 citations
"MetaboAnalyst 4.0: towards more tra..." refers background in this paper
...MetaboAnalyst performs in-house mapping of common compound names to a wide variety of database identifiers including KEGG (20), HMDB (21), ChEBI (22), METLIN (23) and PubChem (24) prior to performing any functional analysis....
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TL;DR: A new generation of a popular open-source data processing toolbox, MZmine 2 is introduced, suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
Abstract: Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/
. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
2,884 citations
"MetaboAnalyst 4.0: towards more tra..." refers methods in this paper
...A number of excellent methods have been developed to deal with the first two tasks (26,27), which typically yield a list of ‘clean’ MS peaks....
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TL;DR: This year's update to the HMDB, HMDB 4.0, represents the most significant upgrade to the database in its history and should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.
Abstract: The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.
2,608 citations
"MetaboAnalyst 4.0: towards more tra..." refers background in this paper
...For metabolites, MetaboAnalyst 4.0 currently accepts compound names, HMDB IDs or KEGG compound IDs as metabolite identifiers....
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...The last four networks are created based on information gathered primarily from HMDB and STITCH databases (43), and are applicable to human studies only....
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...This knowledgebase has been updated with HMDB Version 4.0, including updates of HMDB identifiers and links to other databases....
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...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)....
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...MetaboAnalyst performs in-house mapping of common compound names to a wide variety of database identifiers including KEGG (20), HMDB (21), ChEBI (22), METLIN (23) and PubChem (24) prior to performing any functional analysis....
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