Literature DB >> 19562197

Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics.

M Brown1, W B Dunn, P Dobson, Y Patel, C L Winder, S Francis-McIntyre, P Begley, K Carroll, D Broadhurst, A Tseng, N Swainston, I Spasic, R Goodacre, D B Kell.   

Abstract

The chemical identification of mass spectrometric signals in metabolomic applications is important to provide conversion of analytical data to biological knowledge about metabolic pathways. The complexity of electrospray mass spectrometric data acquired from a range of samples (serum, urine, yeast intracellular extracts, yeast metabolic footprints, placental tissue metabolic footprints) has been investigated and has defined the frequency of different ion types routinely detected. Although some ion types were expected (protonated and deprotonated peaks, isotope peaks, multiply charged peaks) others were not expected (sodium formate adduct ions). In parallel, the Manchester Metabolomics Database (MMD) has been constructed with data from genome scale metabolic reconstructions, HMDB, KEGG, Lipid Maps, BioCyc and DrugBank to provide knowledge on 42,687 endogenous and exogenous metabolite species. The combination of accurate mass data for a large collection of metabolites, theoretical isotope abundance data and knowledge of the different ion types detected provided a greater number of electrospray mass spectrometric signals which were putatively identified and with greater confidence in the samples studied. To provide definitive identification metabolite-specific mass spectral libraries for UPLC-MS and GC-MS have been constructed for 1,065 commercially available authentic standards. The MMD data are available at http://dbkgroup.org/MMD/.

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Year:  2009        PMID: 19562197     DOI: 10.1039/b901179j

Source DB:  PubMed          Journal:  Analyst        ISSN: 0003-2654            Impact factor:   4.616


  72 in total

Review 1.  New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells.

Authors:  Sneha P Couvillion; Ying Zhu; Gabe Nagy; Joshua N Adkins; Charles Ansong; Ryan S Renslow; Paul D Piehowski; Yehia M Ibrahim; Ryan T Kelly; Thomas O Metz
Journal:  Analyst       Date:  2019-01-28       Impact factor: 4.616

2.  Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.

Authors:  Warwick B Dunn; David Broadhurst; Paul Begley; Eva Zelena; Sue Francis-McIntyre; Nadine Anderson; Marie Brown; Joshau D Knowles; Antony Halsall; John N Haselden; Andrew W Nicholls; Ian D Wilson; Douglas B Kell; Royston Goodacre
Journal:  Nat Protoc       Date:  2011-06-30       Impact factor: 13.491

3.  biochem4j: Integrated and extensible biochemical knowledge through graph databases.

Authors:  Neil Swainston; Riza Batista-Navarro; Pablo Carbonell; Paul D Dobson; Mark Dunstan; Adrian J Jervis; Maria Vinaixa; Alan R Williams; Sophia Ananiadou; Jean-Loup Faulon; Pedro Mendes; Douglas B Kell; Nigel S Scrutton; Rainer Breitling
Journal:  PLoS One       Date:  2017-07-14       Impact factor: 3.240

4.  Many InChIs and quite some feat.

Authors:  Wendy A Warr
Journal:  J Comput Aided Mol Des       Date:  2015-06-17       Impact factor: 3.686

Review 5.  Challenges in Identifying the Dark Molecules of Life.

Authors:  María Eugenia Monge; James N Dodds; Erin S Baker; Arthur S Edison; Facundo M Fernández
Journal:  Annu Rev Anal Chem (Palo Alto Calif)       Date:  2019-03-18       Impact factor: 10.745

Review 6.  Bioanalysis of eukaryotic organelles.

Authors:  Chad P Satori; Michelle M Henderson; Elyse A Krautkramer; Vratislav Kostal; Mark D Distefano; Mark M Distefano; Edgar A Arriaga
Journal:  Chem Rev       Date:  2013-04-10       Impact factor: 60.622

7.  xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.

Authors:  Karan Uppal; Douglas I Walker; Dean P Jones
Journal:  Anal Chem       Date:  2017-01-04       Impact factor: 6.986

Review 8.  Introduction to metabolomics and its applications in ophthalmology.

Authors:  S Z Tan; P Begley; G Mullard; K A Hollywood; P N Bishop
Journal:  Eye (Lond)       Date:  2016-03-18       Impact factor: 3.775

9.  Accurate prediction of retention in hydrophilic interaction chromatography by back calculation of high pressure liquid chromatography gradient profiles.

Authors:  Nu Wang; Paul G Boswell
Journal:  J Chromatogr A       Date:  2017-08-26       Impact factor: 4.759

10.  Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles.

Authors:  Masahiro Sugimoto; David T Wong; Akiyoshi Hirayama; Tomoyoshi Soga; Masaru Tomita
Journal:  Metabolomics       Date:  2009-09-10       Impact factor: 4.290

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