Navigating freely-available software tools for metabolomics analysis

Metabolomics

Volumn 13, Issue 9, 2017, Pages

  Spicer, Rachel ^a   Salek, Reza M ^a   Moreno, Pablo ^a   Cañueto, Daniel ^c   Steinbeck, Christoph ^a,b  

^a EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^b FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

^c INSTITUT D INVESTIGACIÓ SANITÀRIA PERE VIRGILI IISPV   (Spain)

Author keywords

Bioinformatics; Data analysis; Freely available; Metabolomics; Software

Indexed keywords

BIOINFORMATICS; CONTROLLED VOCABULARY; DATA ANALYSIS; HUMAN; HUMAN EXPERIMENT; METABOLOMICS; NUCLEAR MAGNETIC RESONANCE; SOFTWARE; STATISTICAL ANALYSIS; WEB OF SCIENCE; WORKFLOW;

EID: 85027372274     PISSN: 15733882     EISSN: 15733890     Source Type: Journal    
DOI: 10.1007/s11306-017-1242-7     Document Type: Review

References (157)

1. Afgan, E., Baker, D., van den Beek, M., Blankenberg, D., Bouvier, D., Čech, M., et al. (2016). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Research, 44(W1), W3–W10.
2. Allen, F., Pon, A., Wilson, M., Greiner, R., & Wishart, D. (2014). CFM-ID: A web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra. Nucleic Acids Research, 42(W1), 94–99.
3. Alonso, A., Marsal, S., & Julià, A. (2015). Analytical methods in untargeted metabolomics: State of the art in 2015. Frontiers in Bioengineering and Biotechnology, 3, 23.
4. Alonso, A., Rodríguez, M. a., Vinaixa, M., Tortosa, R., Correig, X., Julià, A., & Marsal, S. (2014). Focus: A robust workflow for one-dimensional NMR spectral analysis. Analytical Chemistry, 86, 1160–1169.
5. Armitage, E. G., Godzien, J., Alonso-Herranz, V., López-Gonzálvez, Á., & Barbas, C. (2015). Missing value imputation strategies for metabolomics data. Electrophoresis, 36(24), 3050–3060.
6. Bertsch, A., Gröpl, C., Reinert, K., & Kohlbacher, O. (2010). OpenMS and TOPP: Open source software for LC-MS data analysis. In M. Hamacher, M. Eisenacher & C. Stephan (Eds.), Data mining in proteomics (pp. 353–367). New York: Humana Press.
7. Bioconductor - Rdisop. (2016). Accessed August 18, 2016 from http://bioconductor.org/packages/release/bioc/html/Rdisop.html.
8. Bharti, S. K., & Roy, R. (2012). Quantitative 1H NMR spectroscopy. Trends in Analytical Chemistry, 35, 5–26.
9. Bocker, S., Letzel, M. C., Liptak, Z., & Pervukhin, A. (2009). SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics, 25(2), 218–224.
10. Booth, S. C., Weljie, A. M., & Turner, R. J. (2013). Computational tools for the secondary analysis of metabolomics experiments. Computational and Structural Biotechnology Journal, 4, e201301003.
11. Broeckling, C. D., Reddy, I. R., Duran, A. L., Zhao, X., Sumner, L. W., Division, P. B., et al. (2006). MET-IDEA: Data extraction tool for mass spectrometry-based metabolomics. Analytical Chemistry, 78(13), 4334–4341.
12. Brouard, C., Shen, H., Dührkop, K., d’Alché-Buc, F., Böcker, S., & Rousu, J. (2016). Fast metabolite identification with input output kernel regression. Bioinformatics, 32(12), i28–i36.
13. Brown, M., Wedge, D. C., Goodacre, R., Kell, D. B., Baker, P. N., Kenny, L. C., et al. (2011). Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics, 27(8), 1108–1112.
14. Brunius, C., Shi, L., & Landberg, R. (2016). Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction. Metabolomics, 12(11), 173.
15. Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., et al. (2016). The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Research, 44(D1), D471–D480.
16. Chambers, M. C., Maclean, B., Burke, R., Amodei, D., Ruderman, D. L., Neumann, S., et al. (2012). A cross-platform toolkit for mass spectrometry and proteomics. Nature Biotechnology, 30(10), 918–920.
17. Chen, Y. T., Zhang, J., Zhang, X., & Kim, S. (2011). Statistical Analysis of Gas Chromatography Retention Index Database. In 2011 5th International Conference on Bioinformatics and Biomedical Engineering (pp. 1–4).
18. Chikayama, E., Sekiyama, Y., Okamoto, M., Nakanishi, Y., Tsuboi, Y., Akiyama, K., et al. (2010). Statistical indices for simultaneous large-scale metabolite detections for a single NMR spectrum. Analytical Chemistry, 82(5), 1653–1658.
19. Chokkathukalam, A., Jankevics, A., Creek, D. J., Achcar, F., Barrett, M. P., & Breitling, R. (2013). mzMatch–ISO: an R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data. Bioinformatics, 29(2), 281–283.
20. Cloarec, O., Dumas, M.-E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., et al. (2005). Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1 H NMR data sets. Analytical Chemistry, 77(5), 1282–1289.
21. Coble, J. B., & Fraga, C. G. (2014). Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery. Journal of Chromatography A, 1358, 155–164.
22. Conley, C. J., Smith, R., Torgrip, R. J. O., Taylor, R. M., Tautenhahn, R., & Prince, J. T. (2014). Massifquant: open-source Kalman filter-based XC-MS isotope trace feature detection. Bioinformatics, 1–8.
23. Craig, A., Cloarec, O., Holmes, E., Nicholson, J. K., & Lindon, J. C. (2006). Scaling and normalization effects in NMR spectroscopic metabonomic data sets. Analytical Chemistry, 78(7), 2262–2267.
24. Creek, D. J., Jankevics, A., Burgess, K. E. V., Breitling, R., & Barrett, M. P. (2012). IDEOM: an Excel interface for analysis of LC-MS-based metabolomics data. Bioinformatics, 28(7), 1048–1049.
25. Cui, Q., Lewis, I. A., Hegeman, A. D., Anderson, M. E., Li, J., Schulte, C. F., et al. (2008). Metabolite identification via the Madison Metabolomics Consortium Database. Nature Biotechnology, 26(2), 162–164.
26. Daly, R., Rogers, S., Wandy, J., Jankevics, A., Burgess, K. E. V., & Breitling, R. (2014). MetAssign: Probabilistic annotation of metabolites from LC–MS data using a Bayesian clustering approach. Bioinformatics, 30(19), 2764–2771.
27. da Leprevost, V., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., et al. (2017). BioContainers: An open-source and community-driven framework for software standardization. Bioinformatics. doi:10.1093/bioinformatics/btx192.
28. Davidson, R. L., Weber, R. J. M., Liu, H., Sharma-Oates, A., & Viant, M. R. (2016). Galaxy-M: A Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data. GigaScience, 5, 10.
29. De Livera, A. M., Dias, D. A., De Souza, D., Rupasinghe, T., Pyke, J., Tull, D., et al (2012). Normalizing and integrating metabolomics data. Analytical Chemistry, 84(24), 10768–10776.
30. Di Guida, R., Engel, J., Allwood, J. W., Weber, R. J. M., Jones, M. R., Sommer, U., et al. (2016). Non-targeted UHPLC-MS metabolomic data processing methods: A comparative investigation of normalisation, missing value imputation, transformation and scaling. Metabolomics, 12, 93.
31. Docker (2017). Accessed 6 July 2017 from https://www.docker.com/.
32. Domingo-Almenara, X., Brezmes, J., Vinaixa, M., Samino, S., Ramirez, N., Ramon-Krauel, M., et al. (2016). eRah: A computational tool integrating spectral deconvolution and alignment with quantification and identification of metabolites in GC/MS-based metabolomics. Analytical Chemistry, 88(19), 9821–9829.
33. Dona, A. C., Kyriakides, M., Scott, F., Shephard, E. A., Varshavi, D., Veselkov, K., & Everett, J. R. (2016). A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Computational and Structural Biotechnology Journal, 14, 135–153.
34. Döös, K., Kjellsson, J., & Jönsson, B. (2013). TRACMASS—A Lagrangian trajectory model. In T. Soomere & E. Quak (Eds.), Preventive methods for coastal protection (pp. 225–249). New York: Springer.
35. Draper, J., Enot, D. P., Parker, D., Beckmann, M., Snowdon, S., Lin, W., & Zubair, H. (2009). Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour “rules.” BMC Bioinformatics, 10, 227.
36. Du, P., Kibbe, W. a., & Lin, S. M. (2006). Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics, 22(17), 2059–2065.
37. Earll, M. (2012). Open source software for mass spectrometry and metabolomics. In Open source software in life science research: Practical solutions to common challenges in the pharmaceutical industry and beyond (pp. 89–129).
38. Ellinger, J. J., Chylla, R. A., Ulrich, E. L., & Markley, J. L. (2013). Databases and software for NMR-based metabolomics. Current Metabolomics, 1(1), 1–22.
39. Emwas, A.-H. M. (2015). The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods in Molecular Biology, 1277, 161–193.
40. Ernest, B., Gooding, J. R., Campagna, S. R., Saxton, A. M., & Voy, B. H. (2012). MetabR: An R script for linear model analysis of quantitative metabolomic data. BMC Research Notes, 5(1), 596.
41. Everett, J. R. (2015). A new paradigm for known metabolite identification in metabonomics/metabolomics: Metabolite identification efficiency. Computational and Structural Biotechnology Journal, 13, 131–144.
42. Fernández-Albert, F., Llorach, R., Andrés-Lacueva, C., & Perera, A. (2014). An R package to analyse LC/MS metabolomic data: MAIT (Metabolite Automatic Identification Toolkit). Bioinformatics, 30(13), 1937–1939.
43. Fiehn, O. (2002). Metabolomics-the link between genotypes and phenotypes. Plant Molecular Biology. http://link.springer.com/article/10.1023/A:1013713905833.
44. Gaude, E., Chignola, F., Spiliotopoulos, D., Spitaleri, A., Ghitti, M., Garcia-Manteiga, J. M., et al. (2013). muma, An R package for metabolomics univariate and multivariate statistical analysis. Current Metabolomics, 1(2), 180–189.
45. Giacomoni, F., Corguillé, G. L., Monsoor, M., Landi, M., Pericard, P., Pétéra, M., et al. (2014). Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics. Bioinformatics 31, 1–2.
46. Gómez, J., Brezmes, J., Mallol, R., Rodríguez, M. A., Vinaixa, M., Salek, R. M., et al. (2014). Dolphin: A tool for automatic targeted metabolite profiling using 1D and 2D 1 H-NMR data. Analytical and Bioanalytical Chemistry, 406(30), 7967–7976.
47. Gromski, P. S., Muhamadali, H., Ellis, D. I., Xu, Y., Correa, E., Turner, M. L., & Goodacre, R. (2015). A tutorial review: Metabolomics and partial least squares-discriminant analysis—a marriage of convenience or a shotgun wedding. Analytica Chimica Acta, 879, 10–23.
48. Gromski, P. S., Xu, Y., Hollywood, K. A., Turner, M. L., & Goodacre, R. (2014). The influence of scaling metabolomics data on model classification accuracy. Metabolomics, 11(3), 684–695.
49. Gromski, P. S., Xu, Y., Kotze, H. L., Correa, E., Ellis, D. I., Armitage, E. G., et al. (2014). Influence of missing values substitutes on multivariate analysis of metabolomics data. Metabolites, 4(2), 433–452.
50. Hao, J., Astle, W., De iorio, M., & Ebbels, T. M. D. (2012). Batman: An R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a bayesian model. Bioinformatics, 28(15), 2088–2090.
51. Haug, K., Salek, R. M., Conesa, P., Hastings, J., de Matos, P., Rijnbeek, M., et al. (2013). MetaboLights–an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Research, 41(D1), D781–D786.
52. Hedjazi, L., Gauguier, D., Zalloua, P. A., Nicholson, J. K., Dumas, M.-E., & Cazier, J.-B. (2015). mQTL.NMR: An integrated suite for genetic mapping of quantitative variations of (1)H NMR-based metabolic profiles. Analytical Chemistry, 87(8), 4377–4384.
53. Heinonen, M., Shen, H., Zamboni, N., & Rousu, J. (2012). Metabolite identification and molecular fingerprint prediction through machine learning. Bioinformatics, 28(18), 2333–2341.
54. Henry, V. J., Bandrowski, A. E., Pepin, A.-S., Gonzalez, B. J., & Desfeux, A. (2014). OMICtools: An informative directory for multi-omic data analysis. Database: The Journal of Biological Databases and Curation. doi:10.1093/database/bau069.
55. Hiller, K., Hangebrauk, J., Jäger, C., Spura, J., Schreiber, K., & Schomburg, D. (2009). Metabolite detector: Comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. Analytical Chemistry, 81(9), 3429–3439.
56. Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., et al. (2010). MassBank: A public repository for sharing mass spectral data for life sciences. Journal of Mass Spectrometry, 45(7), 703–714.
57. Huan, T., Tang, C., Li, R., Shi, Y., Lin, G., & Li, L. (2015). MyCompoundID MS/MS Search: Metabolite identification using a library of predicted fragment-ion-spectra of 383,830 possible human metabolites. Analytical Chemistry, 87(20), 10619–10626.
58. Huan, T., Wu, Y., Tang, C., Lin, G., & Li, L. (2015). DnsID in MyCompoundID for rapid identification of dansylated amine- and phenol-containing metabolites in LC–MS-based metabolomics. Analytical Chemistry, 87(19), 9838–9845.
59. Hughes, G., Cruickshank-Quinn, C., Reisdorph, R., Lutz, S., Petrache, I., Reisdorph, N., et al. (2014). MSPrep–summarization, normalization and diagnostics for processing of mass spectrometry-based metabolomic data. Bioinformatics, 30(1), 133–134.
60. Jauhiainen, A., Madhu, B., Narita, M., Narita, M., Griffiths, J., & Tavaré, S. (2014). Normalization of metabolomics data with applications to correlation maps. Bioinformatics, 30(15), 2155–2161.
61. Jensen, K. L., Styczynski, M. P., Rigoutsos, I., & Stephanopoulos, G. N. (2006). A generic motif discovery algorithm for sequential data. Bioinformatics, 22(1), 21–28.
62. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., & Hattori, M. (2004). The KEGG resource for deciphering the genome. Nucleic Acids Research, 32(suppl 1), D277–D280.
63. Karpievitch, Y. V., Nikolic, S. B., Wilson, R., Sharman, J. E., & Edwards, L. M. (2014). Metabolomics data normalization with EigenMS. PLoS One, 9(12), 1–10.
64. Kell, D. B., & Oliver, S. G. (2016). The metabolome 18 years on: A concept comes of age. Metabolomics, 12(9), 148.
65. Keller, B. O., Sui, J., Young, A. B., & Whittal, R. M. (2008). Interferences and contaminants encountered in modern mass spectrometry. Analytica Chimica Acta, 627(1), 71–81.
66. Kenar, E., Franken, H., Forcisi, S., Wormann, K., Haring, H. U., Lehmann, R., et al. (2014). Metabolites from liquid chromatography–mass spectrometry data. Molecular & Cellular Proteomics, 13(1), 348–359.
67. Kessler, N., Neuweger, H., Bonte, A., Langenkämper, G., Niehaus, K., Nattkemper, T. W., & Goesmann, A. (2013). MeltDB 2.0-advances of the metabolomics software system. Bioinformatics, 29(19), 2452–2459.
68. Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., et al. (2016). PubChem substance and compound databases. Nucleic Acids Research, 44(D1), D1202–D1213.
69. Kokubun, T., & D’Costa, L. (2013). Direct and unbiased information recovery from liquid chromatography-mass spectrometry raw data for phenotype-differentiating metabolites based on screening window coefficient of ion currents. Analytical Chemistry, 85(18), 8684–8691.
70. Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R., & Neumann, S. (2012). CAMERA: An integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Analytical Chemistry, 84(1), 283–289.
71. Metabolomics - Fiehn Lab. (2016). Accessed September 28, 2016 from http://fiehnlab.ucdavis.edu/staff/kind/Metabolomics.
72. Lange, E., Tautenhahn, R., Neumann, S., & Gröpl, C. (2008). Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinformatics, 9, 375.
73. Lewis, I. a., Schommer, S. C. & Markley, J. L. (2009). rNMR: Open source software for identifying and quantifying metabolites in NMR spectra. Magnetic Resonance in Chemistry. doi:10.1002/mrc.2526.
74. Li, L., Li, R., Zhou, J., Zuniga, A., Stanislaus, A. E., Wu, Y., et al. (2013). MyCompoundID: Using an evidence-based metabolome library for metabolite identification. Analytical Chemistry, 85(6), 3401–3408.
75. Livera, A. M. D., Sysi-Aho, M., Jacob, L., Gagnon-Bartsch, J. A., Castillo, S., Simpson, J. A., & Speed, T. P. (2015). Statistical methods for handling unwanted variation in metabolomics data. Analytical Chemistry, 87(7), 3606–3615.
76. Lommen, A., & Kools, H. J. (2012). MetAlign 3.0: Performance enhancement by efficient use of advances in computer hardware. Metabolomics, 8(4), 719–726.
77. Ludwig, C., Easton, J. M., Lodi, A., Tiziani, S., Manzoor, S. E., Southam, A. D., et al. (2012). Birmingham metabolite library: A publicly accessible database of 1-D 1 H and 2-D 1 H J-resolved NMR spectra of authentic metabolite standards (BML-NMR). Metabolomics, 8(1), 8–18.
78. Mahieu, N. G., Huang, X., Chen, Y.-J., & Patti, G. J. (2014). Credentialing features: a platform to benchmark and optimize untargeted metabolomic methods. Analytical Chemistry, 86(19), 9583–9589.
79. Mak, T. D., Laiakis, E. C., Goudarzi, M., & Fornace, A. J. (2014). MetaboLyzer: A novel statistical workflow for analyzing postprocessed LC-MS metabolomics data. Analytical Chemistry, 86(1), 506–513.
80. Martens, L., Chambers, M., Sturm, M., Kessner, D., Levander, F., Shofstahl, J., et al. (2011). mzML—a community standard for mass spectrometry data. Molecular & Cellular Proteomics, 10(1), R110–R000133.
81. MATLAB - MathWorks. (2016). Accessed September 14, 2016 from http://www.mathworks.com/products/matlab/.
82. Melamud, E., Vastag, L., & Rabinowitz, J. D. (2010). Metabolomic analysis and visualization engine for LC-MS data. Analytical Chemistry, 82(23), 9818–9826.
83. Mercier, P., Lewis, M. J., Chang, D., Baker, D., & Wishart, D. S. (2011). Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra. Journal of Biomolecular NMR, 49(3–4), 307–323.
84. Metabolomics Society: Metabolomics Software and Servers. (2016). http://metabolomicssociety.org/resources/metabolomics-software. Accessed September 5, 2016.
85. Meyer, M. R., Peters, F. T., & Maurer, H. H. (2010). Automated mass spectral deconvolution and identification system for GC-MS screening for drugs, poisons, and metabolites in urine. Clinical Chemistry, 56(4), 575–584.
86. Misra, B. B., & van der Hooft, J. J. J. (2016). Updates in metabolomics tools and resources: 2014–2015. Electrophoresis, 37(1), 86–110.
87. Morris, G. A. (2017). NMR data processing. In Encyclopedia of Spectroscopy and Spectrometry (pp. 125–133). New York: Elsevier.
88. mfitzp/icoshift - GitHub. (2017). Accessed January 31, 2017 from https://github.com/mfitzp/icoshift.
89. ms-utils.org - Software List. (2016). Accessed November 23, 2016 from http://www.ms-utils.org/.
90. mzCloud - advanced mass spectral database. (2016). Accessed September 15, 2016 from https://www.mzcloud.org/.
91. Neumann, S., Thum, A., & Böttcher, C. (2012). Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data. Metabolomics, 84–91.
92. Nicolè, F., Guitton, Y., Courtois, E. A., Moja, S., Legendre, L., & Hossaert-Mckey, M. (2012). MSeasy: Unsupervised and untargeted GC-MS data processing. Bioinformatics, 28(17), 2278–2280.
93. Niu, W., Knight, E., Xia, Q., & McGarvey, B. D. (2014). Comparative evaluation of eight software programs for alignment of gas chromatography–mass spectrometry chromatograms in metabolomics experiments. Journal of Chromatography A, 1374, 199–206.
94. NIST Standard Reference Database 1 A v14. (2016). Accessed August 20, 2016 from http://www.nist.gov/srd/nist1a.cfm.
95. Nodzenski, M., Muehlbauer, M. J., Bain, J. R., Reisetter, a. C., Lowe, W. L., & Scholtens, D. M. (2014). Metabomxtr: An R package for mixture-model analysis of non-targeted metabolomics data. Bioinformatics, 30(22), 1–2.
96. Nyamundanda, G., Brennan, L., & Gormley, I. C. (2010). Probabilistic principal component analysis for metabolomic data. BMC Bioinformatics, 11(1), 571.
97. Nyamundanda, G., Gormley, I. C., Fan, Y., Gallagher, W. M., & Brennan, L. (2013). MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach. BMC Bioinformatics, 14, 338.
98. GNU Octave. (2017). Accessed March 28, 2017 from https://www.gnu.org/software/octave/.
99. Pedrioli, P. G. A., Eng, J. K., Hubley, R., Vogelzang, M., Deutsch, E. W., Raught, B., et al. (2004). A common open representation of mass spectrometry data and its application to proteomics research. Nature Biotechnology, 22(11), 1459–1466.
100. PhenoMeNal. (2017). Accessed June 2, 2017 from http://phenomenal-h2020.eu.
101. Pluskal, T., Castillo, S., Villar-Briones, A., & Oresic, M. (2010). MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics, 11, 395.
102. Prince, J. T., & Marcotte, E. M. (2006). Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. Analytical Chemistry, 78(17), 6140–6152.
103. Rafiei, A., & Sleno, L. (2015). Comparison of peak-picking workflows for untargeted liquid chromatography/high-resolution mass spectrometry metabolomics data analysis. Rapid Communications in Mass Spectrometry, 29(1), 119–127.
104. Ravanbakhsh, S., Liu, P., Bjordahl, T. C., Mandal, R., Grant, J. R., Wilson, M., et al. (2015). Accurate, fully-automated NMR spectral profiling for metabolomics. PLoS ONE, 10(5), e0124219.
105. Redestig, H., Fukushima, A., Stenlund, H., Moritz, T., Arita, M., Saito, K., & Kusano, M. (2009). Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data. Analytical Chemistry, 81(19), 7974–7980.
106. Ren, S., Hinzman, A. A., Kang, E. L., Szczesniak, R. D., & Lu, L. J. (2015). Computational and statistical analysis of metabolomics data. Metabolomics, 11(6), 1492–1513.
107. Rew, R., & Davis, G. (1990). NetCDF: An interface for scientific data access. IEEE Computer Graphics and Applications, 10(4), 76–82.
108. Ridder, L., van der Hooft, J. J. J., Verhoeven, S., de Vos, R. C. H., Bino, R. J., & Vervoort, J. (2013). Automatic chemical structure annotation of an LC–MSn based metabolic profile from green tea. Analytical Chemistry, 85(12), 6033–6040.
109. Rocca-Serra, P., Brandizi, M., Maguire, E., Sklyar, N., Taylor, C., Begley, K., et al. (2010). ISA software suite: Supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics, 26(18), 2354–2356.
110. Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J., & Neumann, S. (2016). MetFrag relaunched: Incorporating strategies beyond in silico fragmentation. Journal of Cheminformatics, 8, 3.
111. Salek, R. M., Steinbeck, C., Viant, M. R., Goodacre, R., & Dunn, W. B. (2013). The role of reporting standards for metabolite annotation and identification in metabolomic studies. GigaScience, 2(1), 13.
112. Scheltema, R. A., Jankevics, A., Jansen, R C., Swertz, Ma. & Breitling, R. (2011). PeakML/mzMatch: A file format, Java library, R library, and tool-chain for mass spectrometry data analysis. Analytical Chemistry, 83(7), 2786–2793.
113. Schymanski, E., & Neumann, S. (2016). Critical assessment of small molecule identification. Metabolites, 3, 517–538. Accessed September 28, 2016 from http://casmi-contest.org/2016/index.shtml.
114. Schymanski, E. L., Jeon, J., Gulde, R., Fenner, K., Ruff, M., Singer, H. P., & Hollender, J. (2014). Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environmental Science & Technology, 48(4), 2097–2098.
115. Shah, J. S., Brock, G. N., & Rai, S. N. (2015). Metabolomics data analysis and missing value issues with application to infarcted mouse hearts. BMC Bioinformatics, 16(15), P16.
116. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504.
117. Silva, R. R., Jourdan, F., Salvanha, D. M., Letisse, F., Jamin, E. L., Guidetti-Gonzalez, S., et al. (2014). ProbMetab: an R package for Bayesian probabilistic annotation of LC–MS-based metabolomics. Bioinformatics, 30(9), 1336–1337.
118. Smith, C. A., O’Maille, G., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., et al. (2005). METLIN: a metabolite mass spectral database. Therapeutic Drug Monitoring, 27(6), 747–751.
119. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. ACS Publications, 78(3), 779–787.
120. Smolinska, A., Blanchet, L., Buydens, L. M. C., & Wijmenga, S. S. (2012). NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review. Analytica Chimica Acta, 750, 82–97.
121. Stravs, M. A., Schymanski, E L., Singer, H. P., & Hollender, J. (2013). Automatic recalibration and processing of tandem mass spectra using formula annotation. Journal of Mass Spectrometry, 48(1), 89–99.
122. Styczynski, M. P., Moxley, J. F., Tong, L. V., Walther, J. L., Jensen, K. L., & Stephanopoulos, G. N. (2007). Systematic identification of conserved metabolites in GC/MS data for metabolomics and biomarker discovery. Analytical Chemistry, 79(3), 966–973.
123. Sud, M., Fahy, E., Cotter, D., Azam, K., Vadivelu, I., Burant, C., et al. (2016). Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Research, 44(D1), D463–D470.
124. Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E. A., Glass, C. K., et al. (2007). LMSD: LIPID MAPS structure database. Nucleic Acids Research, 35(suppl_1), D527–D532.
125. Sugimoto, M., Kawakami, M., Robert, M., Soga, T., & Tomita, M. (2012). Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Current Bioinformatics, 7, 96–108.
126. Szymańska, E., Saccenti, E., Smilde, A. K., & Westerhuis, J. A. (2012). Double-check: Validation of diagnostic statistics for PLS-DA models in metabolomics studies. Metabolomics, 8(Suppl 1), 3–16.
127. Tang, Y., Li, R., Lin, G., & Li, L. (2014). PEP search in MyCompoundID: detection and identification of dipeptides and tripeptides using dimethyl labeling and hydrophilic interaction liquid chromatography tandem mass spectrometry. Analytical Chemistry, 86(7), 3568–3574.
128. Tautenhahn, R., Böttcher, C., & Neumann, S. (2008). Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics, 9, 504.
129. Tautenhahn, R., Patti, G. J., Rinehart, D., & Siuzdak, G. (2012). XCMS Online: A web-based platform to process untargeted metabolomic data. Analytical Chemistry, 84(11), 5035–5039.
130. Thévenot, E. A., Roux, A., Xu, Y., Ezan, E., & Junot, C. (2015). Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research, 14(8), 3322–3335.
131. Tomasi, G., Savorani, F., & Engelsen, S. B. (2011). Icoshift: An effective tool for the alignment of chromatographic data. Journal of Chromatography A, 1218(43), 7832–7840.
132. Treutler, H., & Neumann, S. (2016). Prediction, detection, and validation of isotope clusters in mass spectrometry data. Metabolites, 6(4), 37.
133. Treviño, V., Yañez-Garza, I.-L., Rodriguez-López, C. E., Urrea-López, R., Garza-Rodriguez, M.-L., Tamez-Peña, J. G., et al. (2015). GridMass: A fast two-dimensional feature detection method for LC/MS. Journal of Mass Spectrometry, 50, 165–174.
134. Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., et al. (2015). MS-DIAL: Data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nature Methods. doi:10.1038/nmeth.3393.
135. Tsugawa, H., Kind, T., Nakabayashi, R., Yukihira, D., Tanaka, W., Cajka, T., et al. (2016). Hydrogen rearrangement rules: Computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Analytical Chemistry, 88(16), 7946–7958.
136. Turewicz, M., & Deutsch, E. W. (2010). Spectra, chromatograms, metadata: mzML-the standard data format for mass spectrometer output. In M. Hamacher, M. Eisenacher, & C. Stephan (Eds.), Data mining in proteomics (pp. 179–203). New York: Humana Press.
137. Ulrich, E. L., Akutsu, H., Doreleijers, J. F., Harano, Y., Ioannidis, Y. E., Lin, J., et al. (2008). BioMagResBank. Nucleic Acids Research, 36(SUPPL. 1), 402–408.
138. van Beek, J. D. (2007). matNMR: A flexible toolbox for processing, analyzing and visualizing magnetic resonance data in Matlab. Journal of Magnetic Resonance, 187(1), 19–26.
139. van den Berg, R. A., Hoefsloot, H. C. J., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genomics, 7, 142.
140. Vaniya, A., & Fiehn, O. (2015). Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics. Trends in analytical chemistry: TRAC, 69, 52–61.
141. Vettukattil, R. (2015). Preprocessing of raw metabonomic data. Methods in Molecular Biology, 1277, 123–136.
142. Want, E., & Masson, P. (2011). Processing and analysis of GC/LC-MS-based metabolomics data. Methods in Molecular Biology, 708, 277–298.
143. Weber, R. J. M., Lawson, T. N., Salek, R. M., Ebbels, T. M. D., Glen, R. C., Goodacre, R., et al. (2016). Computational tools and workflows in metabolomics: An international survey highlights the opportunity for harmonisation through Galaxy. Metabolomics, 13(2), 12.
144. Weber, R. J. M., & Viant, M. R. (2010). MI-Pack: Increased confidence of metabolite identification in mass spectra by integrating accurate masses and metabolic pathways. Chemometrics and Intelligent Laboratory Systems, 104(1), 75–82.
145. Wehrens, R., Weingart, G., & Mattivi, F. (2014). metaMS: An open-source pipeline for GC–MS-based untargeted metabolomics. Journal of Chromatography B, 966, 109–116.
146. Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., Vis, D. J., Smilde, A. K., van Velzen, E. J. J., et al. (2008). Assessment of PLSDA cross validation. Metabolomics, 4(1), 81–89.
147. Winnike, J. H., Wei, X., Knagge, K. J., Colman, S. D., Gregory, S. G., & Zhang, X. (2015). Comparison of GC-MS and GC × GC-MS in the analysis of human serum samples for biomarker discovery. Journal of Proteome Research, 14(4), 1810–1817.
148. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2013). HMDB 3.0-The human metabolome database in 2013. Nucleic Acids Research, 41(D1), 801–807.
149. Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., et al. (2006). DrugBank: A comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 34(suppl_1), D668–D672.
150. Wolfram Mathematica: Modern technical computing. (2016). Accessed September 14, 2016 from https://www.wolfram.com/mathematica/.
151. Wu, Y., & Li, L. (2016). Sample normalization methods in quantitative metabolomics. Journal of Chromatography A, 1430, 80–95.
152. Xia, J., Bjorndahl, T. C., Tang, P., & Wishart, D. S. (2008). MetaboMiner—semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics, 9, 507.
153. Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015). MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Research, 43(W1), W251–W257.
154. Zhan, X., Patterson, A. D., & Ghosh, D. (2015). Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data. BMC Bioinformatics, 16(1), 77.
155. Zhang, F., Robinette, S. L., Bruschweiler-Li, L., & Brüschweiler, R. (2009). Web server suite for complex mixture analysis by covariance NMR. Magnetic Resonance in Chemistry, 47(SUPPL. 1), 1–10.
156. Zhao, Q., Stoyanova, R., Du, S., Sajda, P., & Brown, T. R. (2006). HiRes—a tool for comprehensive assessment and interpretation of metabolomic data. Bioinformatics, 22(20), 2562–2564.
157. Zhou, B., Xiao, J. F., Tuli, L., & Ressom, H. W. (2012). LC-MS-based metabolomics. Molecular Biosystems, 8(2), 470–481.