Warpgroup: Increased precision of metabolomic data processing by consensus integration bound analysis

Bioinformatics

Volumn 32, Issue 2, 2016, Pages 268-275

  Mahieu, Nathaniel G ^a,b   Spalding, Jonathan L ^a,b   Patti, Gary J ^a,b  

^a WASHINGTON UNIVERSITY   (United States)

^b WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; CHEMICAL DATABASE; CONSENSUS; HUMAN; INFORMATION PROCESSING; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; METABOLOMICS; PROCEDURES; STATISTICAL ANALYSIS;

ALGORITHMS; AUTOMATIC DATA PROCESSING; CHROMATOGRAPHY, LIQUID; CONSENSUS; DATA INTERPRETATION, STATISTICAL; DATABASES, CHEMICAL; HUMANS; MASS SPECTROMETRY; METABOLOMICS;

EID: 84949454389     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv564     Document Type: Article

References (28)

1. Abate-Pella, D. et al. (2015) Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods. J. Chromatogr. A, 1412, 43-51.
2. Aberg, K. M. et al. (2009) The correspondence problem for metabonomics datasets. Anal. Bioanal. Chem., 394, 151-162.
3. Buszewski, B. And Noga, S. (2012) Hydrophilic interaction liquid chromatography (HILIC)-a powerful separation technique. Anal. Bioanal. Chem., 402, 231-247.
4. Cappadona, S. et al. (2012) Current challenges in software solutions for mass spectrometry-based quantitative proteomics. Amino Acids, 43, 1087-1108.
5. Crutchfield, C. A. et al. (2010) Mass spectrometry-based metabolomics of yeast. Methods Enzymol., 470, 393-426.
6. Csardi, G. And Nepusz, T. (2006) The igraph software package for complex network research. InterJournal Complex Sy, 1695.
7. Fuhrer, T. And Zamboni, N. (2015) High-throughput discovery metabolomics. Curr. Opin. Biotechnol., 31, 73-78.
8. Giorgino, T. (2009) Computing and visualizing dynamic time warping alignments in R: The dtw package. J. Stat. Softw., 31, 1-24.
9. Goldberger, A. L. et al. (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101, e215-e220.
10. Ivanisevic, J. et al. (2013) Toward 'omic scale metabolite profiling: A dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal. Chem., 85, 6876-6884.
11. Käll, L. And Vitek, O. (2011) Computational mass spectrometry-based proteomics. PLoS Comput. Biol., 7, e1002277.
12. Kele, M. And Guiochon, G. (2000) Repeatability and reproducibility of retention data and band profiles on reversed-phase liquid chromatography columns. J. Chromatogr. A, 869, 181-209.
13. Mahieu, N. G. et al. (2014) Credentialing features: A platform to benchmark and optimize untargeted metabolomic methods. Anal. Chem., 86, 9583-9589.
14. Nikolskiy, I. et al. (2013) An untargeted metabolomic workflow to improve structural characterization of metabolites. Anal. Chem., 85, 7713-7719.
15. Patti, G. J. et al. (2012) Innovation: Metabolomics: The apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol., 13, 263-269.
16. Penzel, T. et al. (2000) The apnea-ECG database. In: Computers in Cardiology 2000. Vol. 27 (Cat. 00CH37163). IEEE, pp. 255-258.
17. Podwojski, K. et al. (2009) Retention time alignment algorithms for LC/MS data must consider non-linear shifts. Bioinformatics, 25, 758-764.
18. Pons, P. And Latapy, M. (2005) Computing communities in large networks using randomwalks (long version). Lect. Notes Comput. Sci., 3733, 284-293.
19. Prince, J. T. And Marcotte, E. M. (2006) Chromatographic alignment of ESI-LCMS proteomics data sets by ordered bijective interpolated warping. Anal. Chem., 78, 6140-6152.
20. Quarry, M. A. et al. (1984) Measurement and use of retention data from highperformance gradient elution. J. Chromatogr. A, 285, 19-51.
21. R Core Team (2014) R: A Language and Environment for Statistical Computing.
22. Rabiner, L. R. (1978) Considerations in dynamic time warping algorithms for discrete word recognition. J. Acoust. Soc. Am., 63, S79.
23. Smith, C. A. et al. (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem., 78, 779-787.
24. Smith, R. et al. (2015) LC-MS alignment in theory and practice: A comprehensive algorithmic review. Brief. Bioinform., 16, 104-117.
25. Tautenhahn, R. et al. (2008) Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics, 9, 504.
26. Tautenhahn, R. et al. (2012) XCMS Online: A web-based platform to process untargeted metabolomic data. Anal. Chem., 84, 5035-5039.
27. Vandenbogaert, M. et al. (2008) Alignment of LC-MS images, with applications to biomarker discovery and protein identification. Proteomics, 8, 650-672.
28. Wehrens, R. et al. (2015) Fast parametric time warping of peak lists. Bioinformatics, 31, 3063-3065.