MetPA: a web-based metabolomics tool for pathway analysis and visualization

By Jianguo Xia, David S. Wishart. Bioinform., 2010

650 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/aec74017b3adf6e700e29aef9b23e1ed02c5b059

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Citations in the corpus

Histone Methylation Dynamics and Gene Regulation Occur through the Sensing of One-Carbon Metabolism (2015, methodology): “We used a recently developed algorithm for assessing the contribution to different pathways (i.e., pathway impact) defined by the Kyoto Encyclopedia of Genes and Genomes (Xia and Wishart, 2010 ) (Figure 2B).”
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).”
Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data (2012, methodology): “The recently published metabolomics pathways analysis tool MetPA also allows pathways (Xia and Wishart, 2010) and uses several statistical methods to perform pathway enrichment analysis conceptually similar to gene set enrichment analysis methods (Dennis et al. , 2003; Pathways analysis and visualiz”
MetaboAnalyst 3.0—making metabolomics more meaningful (2015, methodology): “After users have uploaded their data, the genes and metabolites are then mapped to KEGG metabolic pathways for over-representation analysis and pathway topology analysis (21).”
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 ”
Pathview: an R/Bioconductor package for pathway-based data integration and visualization (2013, methodology): “It has been successfully and routinely applied to gene expression (both microarray and RNA-Seq) (Luo et al ., 2009), genetic and GWAS (Wang et al ., 2010), proteomic and metabolomics data (Perroud et al ., 2006; Xia and Wishart, 2010).”
Review of recent developments in GC–MS approaches to metabolomics-based research (2018, neutral): “2012), MetPA (Xia and Wishart 2010), Metaboanalyst (Xia et al.”
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 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).”