MI-Pack: Increased confidence of metabolite identification in mass spectra by integrating accurate masses and metabolic pathways

Chemometrics and Intelligent Laboratory Systems

Volumn 104, Issue 1, 2010, Pages 75-82

  Weber, Ralf J M ^a   Viant, Mark R ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Data mining; Mass error; Mass spectrometry; Metabolite identification; Metabolomics

Indexed keywords

ACCURACY; ALGORITHM; ANALYTICAL ERROR; ARTICLE; AUTOMATION; DATA BASE; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; HUMAN; HUMAN CELL; KEGG DATABASE; MASS SPECTROMETRY; MATHEMATICAL COMPUTING; METABOLISM; METABOLITE; PRIORITY JOURNAL; SIMULATION;

EID: 77958506191     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2010.04.010     Document Type: Article

References (27)

1. Gipson G., Tatsuoka K., Sokhansanj B., Ball R., Connor S. Assignment of MS-based metabolomic datasets via compound interaction pair mapping. Metabolomics 2008, 4(1):94-103.
2. Rogers S., Scheltema R.A., Girolami M., Breitling R. Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Bioinformatics 2009, 25(4):512-518.
3. Dunn W.B. Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes. Phys. Biol. 2008, 5(1):11001.
4. Giavalisco P., Hummel J., Lisec J., Inostroza A.C., Catchpole G., Willmitzer L. High-resolution direct infusion-based mass spectrometry in combination with whole (13)C metabolome isotope labeling allows unambiguous assignment of chemical sum formulas. Anal. Chem. 2008, 80(24):9417-9425.
5. Han J., Danell R., Patel J., Gumerov D., Scarlett C., Speir J., Parker C., Rusyn I., Zeisel S., Borchers C. Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry. Metabolomics 2008, 4(2):128-140.
6. Taylor N., Weber R., Southam A., Payne T., Hrydziuszko O., Arvanitis T., Viant M. A new approach to toxicity testing in Daphnia magna: application of high throughput FT-ICR mass spectrometry metabolomics. Metabolomics 2009, 5(1):44-58.
7. Zhang N.R., Yu S., Tiller P., Yeh S., Mahan E., Emary W.B. Quantitation of small molecules using high-resolution accurate mass spectrometers - a different approach for analysis of biological samples. Rapid Commun. Mass Spectrom. 2009, 23(7):1085-1094.
8. Breitling R., Vitkup D., Barrett M.P. New surveyor tools for charting microbial metabolic maps. Nat. Rev., Microbiol. 2008, 6(2):156-161.
9. Sumner L., Amberg A., Barrett D., Beale M., Beger R., Daykin C., Fan T., Fiehn O., Goodacre R., Griffin J., Hankemeier T., Hardy N., Harnly J., Higashi R., Kopka J., Lane A., Lindon J., Marriott P., Nicholls A., Reily M., Thaden J., Viant M. Proposed minimum reporting standards for chemical analysis. Metabolomics 2007, 3(3):211-221.
10. Smith C.A., Want E.J., O'Maille G., Abagyan R., Siuzdak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 2006, 78(3):779-787.
11. Kind T., Fiehn O. Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1ppm. BMC Bioinformatics 2006, 7:234.
12. Brown M., Dunn W.B., Dobson P., Patel Y., Winder C.L., Francis-McIntyre S., Begley P., Carroll K., Broadhurst D., Tseng A., Swainston N., Spasic I., Goodacre R., Kell D.B. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 2009, 134(7):1322-1332.
13. Draper J., Enot D., Parker D., Beckmann M., Snowdon S., Lin W., Zubair H. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'. BMC Bioinformatics 2009, 10(1):227.
14. Shinbo Y., Nakamura Y., Altaf-Ul-Amin M., Asahi H., Kurokawa K., Arita M., Saito K., Ohta D., Shibata D., Kanaya S. KNApSAcK: a comprehensive species-metabolite relationship database. Biotechnol. Agric. For. 2006, 57:166-181.
15. Wishart D.S., Knox C., Guo A.C., Eisner R., Young N., Gautam B., Hau D.D., Psychogios N., Dong E., Bouatra S., Mandal R., Sinelnikov I., Xia J., Jia L., Cruz J.A., Lim E., Sobsey C.A., Shrivastava S., Huang P., Liu P., Fang L., Peng J., Fradette R., Cheng D., Tzur D., Clements M., Lewis A., Souza A.D., Zuniga A., Dawe M., Xiong Y., Clive D., Greiner R., Nazyrova A., Shaykhutdinov R., Li L., Vogel H.J., Forsythe I. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 2009, 37:D603-D610.
16. Kind T., Fiehn O. Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 2007, 8:105.
17. Breitling R., Ritchie S., Goodenowe D., Stewart M., Barrett M. Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data. Metabolomics 2006, 2(3):155-164.
18. Breitling R., Pitt A.R., Barrett M.P. Precision mapping of the metabolome. Trends Biotechnol. 2006, 24(12):543-548.
19. Jourdan F., Breitling R., Barrett M.P., Gilbert D. Metanetter: inference and visualization of high-resolution metabolomic networks. Bioinformatics 2008, 24(1):143-145.
20. Herrgård Markus J., Swainston Neil, Dobson Paul, Dunn Warwick B., Yalçin Arga K., Arvas Mikko, Blüthgen Nils, Borger Simon, Costenoble Roeland, Heinemann Matthias, Hucka Michael, Novère Nicolas Le., Li Peter, Liebermeister Wolfram, Mo Monica L., Oliveira Ana Paula, Petranovic Dina, Pettifer Stephen, Simeonidis Evangelos, Smallbone Kieran, Spasić Irena, Weichart Dieter, Brent Roger, Broomhead David S., Westerhoff Hans V., Kirdar Betül, Penttilä Merja, Klipp Edda, Palsson Bernhard Ø., Sauer Uwe, Oliver Stephen G., Mendes Pedro, Nielsen Jens, Kell Douglas B. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat. Biotechnol. 2008, 26(10):1155-1160.
21. Southam A.D., Payne T.G., Cooper H.J., Arvanitis T.N., Viant M.R. Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method. Anal. Chem. 2007, 79(12):4595-4602.
22. Kanehisa M., Araki M., Goto S., Hattori M., Hirakawa M., Itoh M., Katayama T., Kawashima S., Okuda S., Tokimatsu T., Yamanishi Y. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008, 36:D480-D484. Database issue.
23. Tiziani S., Lodi A., Khanim F.L., Viant M.R., Bunce C.M., Gunther U.L. Metabolomic profiling of drug responses in acute myeloid leukaemia cell lines. PLoS One 2009, 4(1):e4251.
24. Payne T.G., Southam A.D., Arvanitis T.N., Viant M.R. A signal filtering method for improved quantification and noise discrimination in Fourier transform ion cyclotron resonance mass spectrometry-based metabolomics data. J. Am. Soc. Mass Spectrom. 2009, 20(6):1087-1095.
25. Wieser M.E., Berglund M. Atomic weights of the elements 2007. Pure Appl. Chem. 2009, 81(12):2131-2156.
26. Southam A.D., Easton J.M., Stentiford G.D., Ludwig C., Arvanitis T.N., Viant M.R. Metabolic changes in flatfish hepatic tumours revealed by NMR-based metabolomics and metabolic correlation networks. J. Proteome Res. 2008, 7(12):5277-5285.
27. Nookaew I., Jewett M.C., Meechai A., Thammarongtham C., Laoteng K., Cheevadhanarak S., Nielsen J., Bhumiratana S. The genome-scale metabolic model iIN800 of Saccharomyces cerevisiae and its validation: a scaffold to query lipid metabolism. BMC Syst. Biol. 2008, 2:71.