MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data.
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 approximately 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.
Xia
Jianguo
J
Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
Wishart
David S
DS
D000818:Animals; D016208:Databases, Factual; D004014:Dictionaries as Topic; D006801:Humans; D020407:Internet; D008322:Mammals / Q000378:metabolism; D055432:Metabolomics / Q000379:methods*; D012984:Software; D014584:User-Computer Interface
BMC Bioinformatics. 2007 Jul 05;8:242
17612399
Bioinformatics. 2005 Sep 15;21(18):3587-95
15994189
Nucleic Acids Res. 2007 Jan;35(Database issue):D521-6
17202168
Nucleic Acids Res. 2005 Oct 24;33(19):6083-9
16246909
Bioinformatics. 2005 May 1;21(9):1943-9
15647293
Nat Genet. 2003 Jul;34(3):267-73
12808457
Anal Chem. 2009 May 1;81(9):3623-9
19320491
Bioinformatics. 2004 Jan 1;20(1):93-9
14693814
BMC Bioinformatics. 2005 Nov 09;6:269
16280084
BMC Bioinformatics. 2008 Nov 27;9:502
19038052
Proteomics. 2006 Sep;6(17):4716-23
16888765
Nucleic Acids Res. 2009 Jul;37(Web Server issue):W652-60
19429898
Nat Rev Microbiol. 2009 Feb;7(2):129-43
19116616
BMC Bioinformatics. 2005 Jun 08;6:144
15941488
Bioinformatics. 2008 Dec 1;24(23):2726-32
18765459
Nucleic Acids Res. 2010 Jan;38(Database issue):D480-7
19948758
Nat Biotechnol. 2007 Aug;25(8):846-8
17687353
Brief Bioinform. 2008 May;9(3):189-97
18202032
Science. 2004 Nov 12;306(5699):1138-9
15542455
Ther Drug Monit. 2005 Dec;27(6):747-51
16404815
Genome Biol. 2003;4(5):P3
12734009
BMC Proc. 2009 Jul 16;3 Suppl 4:S10
19615110
PLoS Genet. 2008 Nov;4(11):e1000282
19043545
Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
16199517
Proc Natl Acad Sci U S A. 2005 Jun 21;102(25):8961-5
15951424
BMC Bioinformatics. 2007 Nov 07;8:431
17988400
Nucleic Acids Res. 2004 Jan 1;32(Database issue):D277-80
14681412
Bioinformatics. 2007 Dec 1;23(23):3251-3
17644558
Nucleic Acids Res. 2008 Jan;36(Database issue):D344-50
17932057
Nucleic Acids Res. 2009 Jan;37(Database issue):D619-22
18981052
Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W358-63
18487275
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W186-92
17526521