MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data

By Jianguo Xia, David S. Wishart. Nucleic Acids Res., 2010

728 citations recorded by the source corpus.

Gene set enrichment analysis (GSEA) is a widely used technique in transcriptomic data analysis that uses a database of predefined gene sets to rank lists of genes from microarray studies to identify significant and coordinated changes in gene expression data. While GSEA has been playing a significant role in understanding transcriptomic data, no similar tools are currently available for understanding metabolomic data. Here, we introduce a web-based server, called Metabolite Set Enrichment Analysis (MSEA), to help researchers identify and interpret patterns of human or mammalian metabolite concentration changes in a biologically meaningful context. Key to the development of MSEA has been the creation of a library of ∼1000 predefined metabolite sets covering various metabolic pathways, disease states, biofluids, and tissue locations. MSEA also supports user-defined or custom metabolite sets for more specialized analysis. MSEA offers three different enrichment analyses for metabolomic studies including overrepresentation analysis (ORA), single sample profiling (SSP) and quantitative enrichment analysis (QEA). ORA requires only a list of compound names, while SSP and QEA require both compound names and compound concentrations. MSEA generates easily understood graphs or tables embedded with hyperlinks to relevant pathway images and disease descriptors. For non-mammalian or more specialized metabolomic studies, MSEA allows users to provide their own metabolite sets for enrichment analysis. The MSEA server also supports conversion between metabolite common names, synonyms, and major database identifiers. MSEA has the potential to help users identify obvious as well as ‘subtle but coordinated’ changes among a group of related metabolites that may go undetected with conventional approaches. MSEA is freely available at http://www.msea.ca.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/139868478c4557d8bd1b03cd5f76aa85817c8e0d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Spectroscopy

Citations in the corpus

The ConsensusPathDB interaction database: 2013 update (2012, methodology): “Although several tools for pathway-based evaluation of metabolite lists are already available (32–34), ConsensusPathDB has the advantage of possessing a rich pathway repertoire collected from 12 resources for biochemical pathways.”
Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis (2016, methodology): “Indeed, key concepts such as interactive Web-based data processing, data pipelining, name normalization, data normalization, SAM, volcano plots, heatmaps with hierarchical clustering, pathway topology analysis (appearing as MetPA), and gene-set enrichment analysis technology (appearing as MSEA) are ”
Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst (2011, methodology): “One of the most recent enhancements has been the incorporation of metabolite set enrichment analysis (MSEA”
MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis (2018, methodology): “0, which was released in 2012 (2), incorporated three new modules for metabolite set enrichment analysis (MSEA) (3), metabolic pathway analysis (MetPA) (4), as well as advanced twofactor and time-series analyses (MetATT) (5).”
Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis (2019, methodology): “The second version of the server, MetaboAnalyst 2.0 (Xia, Mandal, Sinelnikov, Broadhurst, & Wishart, 2012), incorporated additional components for functional analyses such as metabolic pathway analysis (MetPA; Xia & Wishart, 2010) and metabolite set enrichment analysis (MSEA; Xia & Wishart, 2010).”
MetaboAnalyst 3.0—making metabolomics more meaningful (2015, methodology): “There are two main approaches in functional analysis in MetaboAnalyst–metabolite set enrichment analysis (20) and metabolic pathway analysis (21).”
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources (2020, background): “modalities include Gene Set Enrichment Analysis (GSEA) [213], Metabolite Set Enrichment Analysis (MSEA) [214], and the Lipid Ontology web-based interface (LION/Web) [215].”
MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis (2012, background): “These two general approaches, along with their four speciﬁc functions have been discussed in more detail in previous papers by our group (21,22).”
SMPDB 2.0: Big Improvements to the Small Molecule Pathway Database (2013, background): “It has also been linked into several online metabolomic data analysis resources including MetaboAnalyst (14) and MSEA (15).”
Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data (2022, methodology): “Brie ﬂ y, three levels ( − 1, 0 and 1) of all parameters are used to construct 44 combinations.”