References
1. Emwas, A.-H.; Roy, R.; McKay, R.T.; Tenori, L.; Saccenti, E.; Gowda, G.A.N.; Raftery, D.; Alahmari, F.; Jaremko, L.; Jaremko, M. et al. NMR Spectroscopy for Metabolomics Research. Metabolites 2019, 9, doi:10.3390/metabo9070123.
2. Metabolomics in Practice: Successful Strategies to Generate and Analyze Metabolic Data; Lämmerhofer, M., Weckwerth, W., Eds.; Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2013; ISBN 9783527330898.
3. Villas-Boas, S.G.; Nielsen, J.; Smedsgaard, J.; Hansen, M.A.E.; Roessner-Tunali, U. Metabolome Analysis: An Introduction; 1st ed.; Wiley, John & Sons, Incorporated, 2007; p. 319; ISBN 978-0-471-74344-6.
4. Metabolomics: Practical Guide to Design and Analysis; Wehrens, R., Salek, R., Eds.; Chapman & hall/CRC mathematical and computational biology; Chapman; Hall/CRC, 2019; ISBN 1498725260.
5. International Metabolomics Society Free Tools & Learning Resources - Metabolomics Society Wiki.
6. Salek, R.; Emery, L.; Beisken, S. Metabolomics: An Introduction EMBL-EBI Train Online.
7. R Core Development Team R: A Language and Environment for Statistical Computing 2018.
8. Spicer, R. GitHub - RASpicer/MetabolomicsTools 2018.
9. Spicer, R.; Salek, R.M.; Moreno, P.; Cañueto, D.; Steinbeck, C. Navigating Freely-Available Software Tools for Metabolomics Analysis. Metabolomics : Official journal of the Metabolomic Society 2017, 13, 106, doi:10.1007/s11306-017-1242-7.
10. Misra, B.B.; Hooft, J.J.J. van der Updates in Metabolomics Tools and Resources: 2014-2015. Electrophoresis 2016, 37, 86–110, doi:10.1002/elps.201500417.
11. Misra, B.B.; Fahrmann, J.F.; Grapov, D. Review of Emerging Metabolomic Tools and Resources: 2015-2016. Electrophoresis 2017, 38, 2257–2274, doi:10.1002/elps.201700110.
12. Misra, B.B. New Tools and Resources in Metabolomics: 2016-2017. Electrophoresis 2018, 39, 909–923, doi:10.1002/elps.201700441.
13. Misra, B. GitHub - Biswapriyamisra/Metabolomics: Tools Databases Resources in Metabolomics & Integrated Omics in 2015-2016 2017.
14. Kannan, L.; Ramos, M.; Re, A.; El-Hachem, N.; Safikhani, Z.; Gendoo, D.M.A.; Davis, S.; Gomez-Cabrero, D.; Castelo, R.; Hansen, K.D. et al. Public Data and Open Source Tools for Multi-Assay Genomic Investigation of Disease. Briefings in Bioinformatics 2016, 17, 603–615, doi:10.1093/bib/bbv080.
15. Blaženović, I.; Kind, T.; Ji, J.; Fiehn, O. Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics. Metabolites 2018, 8, doi:10.3390/metabo8020031.
16. Mullen, K. CRAN Task View: Chemometrics and Computational Physics 2019.
17. Gentleman, R.C.; Carey, V.J.; Bates, D.M.; Bolstad, B.; Dettling, M.; Dudoit, S.; Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J. et al. Bioconductor: Open Software Development for Computational Biology and Bioinformatics. Genome Biology 2004, 5, R80, doi:10.1186/gb-2004-5-10-r80.
18. Bioconductor Bioconductor - BiocViews.
19. The Comprehensive R Archive Network CRAN Repository Policy.
20. Bioconductor Bioconductor - Developers.
21. Theußl, S.; Zeileis, A. Collaborative Software Development Using R-Forge. The R journal 2009, 1, 9, doi:10.32614/{RJ}-2009-007.
22. Boettiger, C.; Chamberlain, S.; Hart, E.; Ram, K. Building Software, Building Community: Lessons from the rOpenSci Project. Journal of open research software 2015, 3, doi:10.5334/jors.bu.
23. Vries, A. de; Rickert, J. The Network Structure of R Packages on CRAN & BioConductor 2015.
24. Vries, A. de Differences in the Network Structure of CRAN and BioConductor (Revolutions) 2015.
25. Vries, A. de GitHub - Andrie/Cran-Network-Structure: Scripts Used for My UseR!2015 Presentation on the Network Structure of CRAN 2015.
26. Neumann, S. GitHub - Sneumann/metaRbolomics: Metabolomics in R and Bioconductor 2019.
27. Chambers, M.C.; Maclean, B.; Burke, R.; Amodei, D.; Ruderman, D.L.; Neumann, S.; Gatto, L.; Fischer, B.; Pratt, B.; Egertson, J. et al. A Cross-Platform Toolkit for Mass Spectrometry and Proteomics. Nature Biotechnology 2012, 30, 918–920, doi:10.1038/nbt.2377.
28. Kessner, D.; Chambers, M.; Burke, R.; Agus, D.; Mallick, P. ProteoWizard: Open Source Software for Rapid Proteomics Tools Development. Bioinformatics 2008, 24, 2534–2536, doi:10.1093/bioinformatics/btn323.
29. Fuhrer, T.; Heer, D.; Begemann, B.; Zamboni, N. High-Throughput, Accurate Mass Metabolome Profiling of Cellular Extracts by Flow Injection-Time-of-Flight Mass Spectrometry. Analytical Chemistry 2011, 83, 7074–7080, doi:10.1021/ac201267k.
30. Mahieu, N.G.; Genenbacher, J.L.; Patti, G.J. A Roadmap for the XCMS Family of Software Solutions in Metabolomics. Current Opinion in Chemical Biology 2016, 30, 87–93, doi:10.1016/j.cbpa.2015.11.009.
31. 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. Analytical Chemistry 2006, 78, 779–787, doi:10.1021/ac051437y.
32. Tautenhahn, R.; Böttcher, C.; Neumann, S. Highly Sensitive Feature Detection for High Resolution LC/MS. BMC Bioinformatics 2008, 9, 504, doi:10.1186/1471-2105-9-504.
33. Conley, C.J.; Smith, R.; Torgrip, R.J.O.; Taylor, R.M.; Tautenhahn, R.; Prince, J.T. Massifquant: Open-Source Kalman Filter-Based XC-MS Isotope Trace Feature Detection. Bioinformatics 2014, 30, 2636–2643, doi:10.1093/bioinformatics/btu359.
34. Huber, W.; Carey, V.J.; Gentleman, R.; Anders, S.; Carlson, M.; Carvalho, B.S.; Bravo, H.C.; Davis, S.; Gatto, L.; Girke, T. et al. Orchestrating High-Throughput Genomic Analysis with Bioconductor. Nature Methods 2015, 12, 115–121, doi:10.1038/nmeth.3252.
35. Martin Morgan, V.O. SummarizedExperiment. Bioconductor 2017, doi:10.18129/b9.bioc.summarizedexperiment.
36. Zhu, C. Zhuchcn/Metabase: A R Package to Store, Manipulate, Analyze, and Visualize Metabolomics Data 2019.
37. Hoffmann, N.; Rein, J.; Sachsenberg, T.; Hartler, J.; Haug, K.; Mayer, G.; Alka, O.; Dayalan, S.; Pearce, J.T.M.; Rocca-Serra, P. et al. mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics. Analytical Chemistry 2019, 91, 3302–3310, doi:10.1021/acs.analchem.8b04310.
38. Scheltema, R.A.; Jankevics, A.; Jansen, R.C.; Swertz, M.A.; Breitling, R. PeakML/mzMatch: A File Format, Java Library, R Library, and Tool-Chain for Mass Spectrometry Data Analysis. Analytical Chemistry 2011, 83, 2786–2793, doi:10.1021/ac2000994.
39. Shahaf, N.; Rogachev, I.; Heinig, U.; Meir, S.; Malitsky, S.; Battat, M.; Wyner, H.; Zheng, S.; Wehrens, R.; Aharoni, A. The WEIZMA Spectral Library for High-Confidence Metabolite Identification. Nature Communications 2016, 7, 12423, doi:10.1038/ncomms12423.
40. Witting, M. GitHub - Michaelwitting/Ms2dbworkflow.
41. Horai, H.; Arita, M.; Kanaya, S.; Nihei, Y.; Ikeda, T.; Suwa, K.; Ojima, Y.; Tanaka, K.; Tanaka, S.; Aoshima, K. et al. MassBank: A Public Repository for Sharing Mass Spectral Data for Life Sciences. Journal of Mass Spectrometry 2010, 45, 703–714, doi:10.1002/jms.1777.
42. Mylonas, R.; Mauron, Y.; Masselot, A.; Binz, P.-A.; Budin, N.; Fathi, M.; Viette, V.; Hochstrasser, D.F.; Lisacek, F. X-Rank: A Robust Algorithm for Small Molecule Identification Using Tandem Mass Spectrometry. Analytical Chemistry 2009, 81, 7604–7610, doi:10.1021/ac900954d.
43. Collins, J.R.; Edwards, B.R.; Fredricks, H.F.; Van Mooy, B.A.S. LOBSTAHS: An Adduct-Based Lipidomics Strategy for Discovery and Identification of Oxidative Stress Biomarkers. Analytical Chemistry 2016, 88, 7154–7162, doi:10.1021/acs.analchem.6b01260.
44. Koelmel, J.P.; Kroeger, N.M.; Ulmer, C.Z.; Bowden, J.A.; Patterson, R.E.; Cochran, J.A.; Beecher, C.W.W.; Garrett, T.J.; Yost, R.A. LipidMatch: An Automated Workflow for Rule-Based Lipid Identification Using Untargeted High-Resolution Tandem Mass Spectrometry Data. BMC Bioinformatics 2017, 18, 331, doi:10.1186/s12859-017-1744-3.
45. Alcoriza-Balaguer, M.I.; García-Cañaveras, J.C.; Lopez, A.; Conde, I.; Juan, O.; Carretero, J.; Lahoz, A. LipidMS: An R Package for Lipid Annotation in Untargeted Liquid Chromatography-Data Independent Acquisition-Mass Spectrometry Lipidomics. Analytical Chemistry 2018, 91, 836–845, doi:10.1021/acs.analchem.8b03409.
46. Standards, T.N.I. of; Technology Library Conversion Tool 2012.
47. Tsugawa, H.; Cajka, T.; Kind, T.; Ma, Y.; Higgins, B.; Ikeda, K.; Kanazawa, M.; VanderGheynst, J.; Fiehn, O.; Arita, M. MS-DIAL: Data-Independent MS/MS Deconvolution for Comprehensive Metabolome Analysis. Nature Methods 2015, 12, 523–526, doi:10.1038/nmeth.3393.
48. MassBank of North America MoNA Downloads.
49. Wang, M.; Carver, J.J.; Phelan, V.V.; Sanchez, L.M.; Garg, N.; Peng, Y.; Nguyen, D.D.; Watrous, J.; Kapono, C.A.; Luzzatto-Knaan, T. et al. Sharing and Community Curation of Mass Spectrometry Data with Global Natural Products Social Molecular Networking. Nature Biotechnology 2016, 34, 828–837, doi:10.1038/nbt.3597.
50. Jacob, D.; Deborde, C.; Lefebvre, M.; Maucourt, M.; Moing, A. NMRProcFlow: A Graphical and Interactive Tool Dedicated to 1D Spectra Processing for NMR-Based Metabolomics. Metabolomics : Official journal of the Metabolomic Society 2017, 13, 36, doi:10.1007/s11306-017-1178-y.
51. Dieterle, F.; Ross, A.; Schlotterbeck, G.; Senn, H. Probabilistic Quotient Normalization as Robust Method to Account for Dilution of Complex Biological Mixtures. Application in 1H NMR Metabonomics. Analytical Chemistry 2006, 78, 4281–4290, doi:10.1021/ac051632c.
52. Wishart, D.S.; Knox, C.; Guo, A.C.; Eisner, R.; Young, N.; Gautam, B.; Hau, D.D.; Psychogios, N.; Dong, E.; Bouatra, S. et al. HMDB: A Knowledgebase for the Human Metabolome. Nucleic Acids Research 2009, 37, D603–10, doi:10.1093/nar/gkn810.
53. Wishart, D.S.; Jewison, T.; Guo, A.C.; Wilson, M.; Knox, C.; Liu, Y.; Djoumbou, Y.; Mandal, R.; Aziat, F.; Dong, E. et al. HMDB 3.0–the Human Metabolome Database in 2013. Nucleic Acids Research 2013, 41, D801–7, doi:10.1093/nar/gks1065.
54. Wishart, D.S.; Tzur, D.; Knox, C.; Eisner, R.; Guo, A.C.; Young, N.; Cheng, D.; Jewell, K.; Arndt, D.; Sawhney, S. et al. HMDB: The Human Metabolome Database. Nucleic Acids Research 2007, 35, D521–6, doi:10.1093/nar/gkl923.
55. Wishart, D.S.; Feunang, Y.D.; Marcu, A.; Guo, A.C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Li, C.; Karu, N. et al. HMDB 4.0: The Human Metabolome Database for 2018. Nucleic Acids Research 2018, 46, D608–D617, doi:10.1093/nar/gkx1089.
56. Beckonert, O.; Keun, H.C.; Ebbels, T.M.D.; Bundy, J.; Holmes, E.; Lindon, J.C.; Nicholson, J.K. Metabolic Profiling, Metabolomic and Metabonomic Procedures for NMR Spectroscopy of Urine, Plasma, Serum and Tissue Extracts. Nature Protocols 2007, 2, 2692–2703, doi:10.1038/nprot.2007.376.
57. Pudakalakatti, S.M.; Dubey, A.; Jaipuria, G.; Shubhashree, U.; Adiga, S.K.; Moskau, D.; Atreya, H.S. A Fast NMR Method for Resonance Assignments: Application to Metabolomics. Journal of Biomolecular NMR 2014, 58, 165–173, doi:10.1007/s10858-014-9814-6.
58. Ludwig, C.; Viant, M.R. Two-Dimensional J-Resolved NMR Spectroscopy: Review of a Key Methodology in the Metabolomics Toolbox. Phytochemical Analysis 2010, 21, 22–32, doi:10.1002/pca.1186.
59. Gómez, J.; Brezmes, J.; Mallol, R.; Rodríguez, M.A.; Vinaixa, M.; Salek, R.M.; Correig, X.; Cañellas, N. Dolphin: A Tool for Automatic Targeted Metabolite Profiling Using 1D and 2D (1)H-NMR Data. Analytical and Bioanalytical Chemistry 2014, 406, 7967–7976, doi:10.1007/s00216-014-8225-6.
60. Shinzawa, H.; Nishida, M.; Kanematsu, W.; Tanaka, T.; Suzuki, K.; Noda, I. Parallel Factor (PARAFAC) Kernel Analysis of Temperature- and Composition-Dependent NMR Spectra of Poly(lactic Acid) Nanocomposites. The Analyst 2012, 137, 1913–1921, doi:10.1039/c2an16019f.
61. Chen, K.; Park, J.; Li, F.; Patil, S.M.; Keire, D.A. Chemometric Methods to Quantify 1D and 2D NMR Spectral Differences Among Similar Protein Therapeutics. AAPS PharmSciTech 2018, 19, 1011–1019, doi:10.1208/s12249-017-0911-1.
62. Pedersen, H.T.; Dyrby, M.; Engelsen, S.; Bro, R. Application of multi-way analysis to 2D NMR data. In; Annual reports on NMR spectroscopy; Elsevier, 2006; Vol. 59, pp. 207–233 ISBN 9780125054591.
63. Hao, J.; Astle, W.; De Iorio, M.; Ebbels, T.M.D. BATMAN–an R Package for the Automated Quantification of Metabolites from Nuclear Magnetic Resonance Spectra Using a Bayesian Model. Bioinformatics 2012, 28, 2088–2090, doi:10.1093/bioinformatics/bts308.
64. Bioconductor Bioconductor - BiocViews: Packages Found Under StatisticalMethod.
65. Groemping, U. CRAN Task View: Design of Experiments (DoE) & Analysis of Experimental Data 2019.
66. Leisch, F.; Gruen, B. CRAN Task View: Cluster Analysis & Finite Mixture Models 2019.
67. Hewson, P. CRAN Task View: Multivariate Statistics 2018.
68. Hothorn, T. CRAN Task View: Machine Learning & Statistical Learning 2019.
69. The Comprehensive R Archive Network CRAN Task Views.
70. Bishop, C.M. Pattern Recognition and Machine Learning (Information Science and Statistics); Springer: New York, 2006; p. 738; ISBN 978-0387310732.
71. Brusco, M.J.; Shireman, E.; Steinley, D. A Comparison of Latent Class, K-Means, and K-Median Methods for Clustering Dichotomous Data. Psychological methods 2017, 22, 563–580, doi:10.1037/met0000095.
72. Felici, G. Mathematical Methods for Knowledge Discovery and Data Mining; Idea Group Reference: Hershey, 2007; p. 371; ISBN 978-1599045283.
73. Introduction to Multivariate Analysis; Routledge, 2018; ISBN 9780203749999.
74. Manly, B.F. Multivariate Statistical Methods; 4th ed.; Routledge: Boca Raton, 2017; p. 270; ISBN 9781498728966.
75. Müllner, D. Modern Hierarchical, Agglomerative Clustering Algorithms. arXiv 2011, abs/1109.2378.
76. Murtagh, F.; Contreras, P. Algorithms for Hierarchical Clustering: An Overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2012, 2, 86–97, doi:10.1002/widm.53.
77. Shaw, P.J.A. Multivariate Statistics for the Environmental Sciences (Mathematics); 1st ed.; Hodder Education Publishers: London, 2003; p. 233; ISBN 0-340-80763-6.
78. Zaslavsky, L.; Ciufo, S.; Fedorov, B.; Tatusova, T. Clustering Analysis of Proteins from Microbial Genomes at Multiple Levels of Resolution. BMC Bioinformatics 2016, 17 Suppl 8, 276, doi:10.1186/s12859-016-1112-8.
79. Hall; Robert, D. Annual Plant Reviews, Biology of Plant Metabolomics; 1st ed.; Wiley, John & Sons, Incorporated, 2011; p. 448; ISBN 978-1-4051-9954-4.
80. Cai, Q.; Alvarez, J.A.; Kang, J.; Yu, T. Network Marker Selection for Untargeted LC-MS Metabolomics Data. Journal of Proteome Research 2017, 16, 1261–1269, doi:10.1021/acs.jproteome.6b00861.
81. Christin, C.; Hoefsloot, H.C.J.; Smilde, A.K.; Hoekman, B.; Suits, F.; Bischoff, R.; Horvatovich, P. A Critical Assessment of Feature Selection Methods for Biomarker Discovery in Clinical Proteomics. Molecular & Cellular Proteomics 2013, 12, 263–276, doi:10.1074/mcp.M112.022566.
82. Rohart, F.; Gautier, B.; Singh, A.; Lê Cao, K.-A. mixOmics: An R Package for ’Omics Feature Selection and Multiple Data Integration. PLoS Computational Biology 2017, 13, e1005752, doi:10.1371/journal.pcbi.1005752.
83. Wen, B.; Mei, Z.; Zeng, C.; Liu, S. metaX: A Flexible and Comprehensive Software for Processing Metabolomics Data. BMC Bioinformatics 2017, 18, 183, doi:10.1186/s12859-017-1579-y.
84. Peters, K.; Worrich, A.; Weinhold, A.; Alka, O.; Balcke, G.; Birkemeyer, C.; Bruelheide, H.; Calf, O.W.; Dietz, S.; Dührkop, K. et al. Current Challenges in Plant Eco-Metabolomics. International Journal of Molecular Sciences 2018, 19, doi:10.3390/ijms19051385.
85. Gromski, P.S.; Xu, Y.; Correa, E.; Ellis, D.I.; Turner, M.L.; Goodacre, R. A Comparative Investigation of Modern Feature Selection and Classification Approaches for the Analysis of Mass Spectrometry Data. Analytica Chimica Acta 2014, 829, 1–8, doi:10.1016/j.aca.2014.03.039.
86. Legendre, P.; Legendre, L.F.J. Numerical Ecology, Volume 24 (Developments in Environmental Modelling); 3rd ed.; Elsevier, 2012; p. 1006; ISBN 978-0-444-53868-0.
87. Clarke, B.; Fokoue, E.; Zhang, H.H. Principles and Theory for Data Mining and Machine Learning; Springer series in statistics; Springer New York: New York, NY, 2009; ISBN 978-0-387-98134-5.
88. Feng, J.; Li, B.; Jiang, X.; Yang, Y.; Wells, G.F.; Zhang, T.; Li, X. Antibiotic Resistome in a Large-Scale Healthy Human Gut Microbiota Deciphered by Metagenomic and Network Analyses. Environmental Microbiology 2018, 20, 355–368, doi:10.1111/1462-2920.14009.
89. Fukushima, A.; Kusano, M.; Redestig, H.; Arita, M.; Saito, K. Integrated Omics Approaches in Plant Systems Biology. Current Opinion in Chemical Biology 2009, 13, 532–538, doi:10.1016/j.cbpa.2009.09.022.
90. Vaughan, A.A.; Dunn, W.B.; Allwood, J.W.; Wedge, D.C.; Blackhall, F.H.; Whetton, A.D.; Dive, C.; Goodacre, R. Liquid Chromatography-Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion. Analytical Chemistry 2012, 84, 9848–9857, doi:10.1021/ac302227c.
91. Wei, R.; Wang, J.; Su, M.; Jia, E.; Chen, S.; Chen, T.; Ni, Y. Missing Value Imputation Approach for Mass Spectrometry-Based Metabolomics Data. Scientific reports 2018, 8, 663, doi:10.1038/s41598-017-19120-0.
92. Degenhardt, F.; Seifert, S.; Szymczak, S. Evaluation of Variable Selection Methods for Random Forests and Omics Data Sets. Briefings in Bioinformatics 2019, 20, 492–503, doi:10.1093/bib/bbx124.
93. Saeys, Y.; Inza, I.; Larrañaga, P. A Review of Feature Selection Techniques in Bioinformatics. Bioinformatics 2007, 23, 2507–2517, doi:10.1093/bioinformatics/btm344.
94. Determan Jr, C.E. Optimal Algorithm for Metabolomics Classification and Feature Selection Varies by Dataset. International journal of biology 2014, 7, doi:10.5539/ijb.v7n1p100.
95. Rinaudo, P.; Boudah, S.; Junot, C.; Thévenot, E.A. Biosigner: A New Method for the Discovery of Significant Molecular Signatures from Omics Data. Frontiers in molecular biosciences 2016, 3, 26, doi:10.3389/fmolb.2016.00026.
96. Shi, L.; Westerhuis, J.A.; Rosén, J.; Landberg, R.; Brunius, C. Variable Selection and Validation in Multivariate Modelling. Bioinformatics 2019, 35, 972–980, doi:10.1093/bioinformatics/bty710.
97. Wehrens, R.; Franceschi, P. Meta-Statistics for Variable Selection: TheR PackageBioMark. Journal of statistical software 2012, 51, doi:10.18637/jss.v051.i10.
98. Li, S.; Park, Y.; Duraisingham, S.; Strobel, F.H.; Khan, N.; Soltow, Q.A.; Jones, D.P.; Pulendran, B. Predicting Network Activity from High Throughput Metabolomics. PLoS Computational Biology 2013, 9, e1003123, doi:10.1371/journal.pcbi.1003123.
99. Willighagen, E.L.; Mayfield, J.W.; Alvarsson, J.; Berg, A.; Carlsson, L.; Jeliazkova, N.; Kuhn, S.; Pluskal, T.; Rojas-Chertó, M.; Spjuth, O. et al. The Chemistry Development Kit (CDK) V2.0: Atom Typing, Depiction, Molecular Formulas, and Substructure Searching. Journal of cheminformatics 2017, 9, 33, doi:10.1186/s13321-017-0220-4.
100. Heller, S.R.; McNaught, A.; Pletnev, I.; Stein, S.; Tchekhovskoi, D. Inchi, the IUPAC International Chemical Identifier. Journal of cheminformatics 2015, 7, 23, doi:10.1186/s13321-015-0068-4.
101. Backman, T.W.H.; Cao, Y.; Girke, T. ChemMine Tools: An Online Service for Analyzing and Clustering Small Molecules. Nucleic Acids Research 2011, 39, W486–91, doi:10.1093/nar/gkr320.
102. O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An Open Chemical Toolbox. Journal of cheminformatics 2011, 3, 33, doi:10.1186/1758-2946-3-33.
103. Landrum, G. RDKit: Open-Source Cheminformatics Software. 2016.
104. Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A. et al. PubChem Substance and Compound Databases. Nucleic Acids Research 2016, 44, D1202–13, doi:10.1093/nar/gkv951.
105. Wang, Y.; Xiao, J.; Suzek, T.O.; Zhang, J.; Wang, J.; Bryant, S.H. PubChem: A Public Information System for Analyzing Bioactivities of Small Molecules. Nucleic Acids Research 2009, 37, W623–33, doi:10.1093/nar/gkp456.
106. Pence, H.E.; Williams, A. Chemspider: An Online Chemical Information Resource. Journal of chemical education 2010, 87, 1123–1124, doi:10.1021/ed100697w.
107. Erxleben, F.; Günther, M.; Krötzsch, M.; Mendez, J.; Vrandečić, D. Introducing wikidata to the linked data web. In The semantic web – ISWC 2014; Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C., Vrandečić, D., Groth, P., Noy, N., Janowicz, K., Goble, C., Eds.; Lecture notes in computer science; Springer International Publishing: Cham, 2014; Vol. 8796, pp. 50–65 ISBN 978-3-319-11963-2.
108. Wohlgemuth, G.; Haldiya, P.K.; Willighagen, E.; Kind, T.; Fiehn, O. The Chemical Translation Service–a Web-Based Tool to Improve Standardization of Metabolomic Reports. Bioinformatics 2010, 26, 2647–2648, doi:10.1093/bioinformatics/btq476.
109. SRC, Inc. Scientific Databases.
110. Group NCI/CADD Chemical Identifier Resolver.
111. Kopczynski, D.; Hoffmann, N.; Peng, B.; Ahrends, R. Goslin: A Grammar of Succinct Lipid Nomenclature. Analytical Chemistry 2020, 92, 10957–10960, doi:10.1021/acs.analchem.0c01690.
112. Djoumbou Feunang, Y.; Eisner, R.; Knox, C.; Chepelev, L.; Hastings, J.; Owen, G.; Fahy, E.; Steinbeck, C.; Subramanian, S.; Bolton, E. et al. ClassyFire: Automated Chemical Classification with a Comprehensive, Computable Taxonomy. Journal of cheminformatics 2016, 8, 61, doi:10.1186/s13321-016-0174-y.
113. Watrous, J.; Roach, P.; Alexandrov, T.; Heath, B.S.; Yang, J.Y.; Kersten, R.D.; Voort, M. van der; Pogliano, K.; Gross, H.; Raaijmakers, J.M. et al. Mass Spectral Molecular Networking of Living Microbial Colonies. Proceedings of the National Academy of Sciences of the United States of America 2012, 109, E1743–52, doi:10.1073/pnas.1203689109.
114. Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New Perspectives on Genomes, Pathways, Diseases and Drugs. Nucleic Acids Research 2017, 45, D353–D361, doi:10.1093/nar/gkw1092.
115. Kanehisa, M.; Sato, Y.; Furumichi, M.; Morishima, K.; Tanabe, M. New Approach for Understanding Genome Variations in KEGG. Nucleic Acids Research 2019, 47, D590–D595, doi:10.1093/nar/gky962.
116. Kanehisa, M.; Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 2000, 28, 27–30, doi:10.1093/nar/28.1.27.
117. Lim, E.; Pon, A.; Djoumbou, Y.; Knox, C.; Shrivastava, S.; Guo, A.C.; Neveu, V.; Wishart, D.S. T3DB: A Comprehensively Annotated Database of Common Toxins and Their Targets. Nucleic Acids Research 2010, 38, D781–6, doi:10.1093/nar/gkp934.
118. Wishart, D.; Arndt, D.; Pon, A.; Sajed, T.; Guo, A.C.; Djoumbou, Y.; Knox, C.; Wilson, M.; Liang, Y.; Grant, J. et al. T3DB: The Toxic Exposome Database. Nucleic Acids Research 2015, 43, D928–34, doi:10.1093/nar/gku1004.
119. Fahy, E.; Sud, M.; Cotter, D.; Subramaniam, S. LIPID MAPS Online Tools for Lipid Research. Nucleic Acids Research 2007, 35, W606–12, doi:10.1093/nar/gkm324.
120. Shen, X.; Wang, R.; Xiong, X.; Yin, Y.; Cai, Y.; Ma, Z.; Liu, N.; Zhu, Z.-J. Metabolic Reaction Network-Based Recursive Metabolite Annotation for Untargeted Metabolomics. Nature Communications 2019, 10, 1516, doi:10.1038/s41467-019-09550-x.
121. Schaefer, C.F.; Anthony, K.; Krupa, S.; Buchoff, J.; Day, M.; Hannay, T.; Buetow, K.H. PID: The Pathway Interaction Database. Nucleic Acids Research 2009, 37, D674–9, doi:10.1093/nar/gkn653.
122. Nishimura, D. BioCarta. Biotech Software & Internet Report 2001, 2, 117–120, doi:10.1089/152791601750294344.
123. Fabregat, A.; Jupe, S.; Matthews, L.; Sidiropoulos, K.; Gillespie, M.; Garapati, P.; Haw, R.; Jassal, B.; Korninger, F.; May, B. et al. The Reactome Pathway Knowledgebase. Nucleic Acids Research 2018, 46, D649–D655, doi:10.1093/nar/gkx1132.
124. Kramer, F.; Bayerlová, M.; Beißbarth, T. R-based software for the integration of pathway data into bioinformatic algorithms. Biology 2014, 3, 85–100, doi:10.3390/biology3010085.
125. Tenenbaum, D. Bioconductor - KEGGREST 2019.
126. Chang, W.; Cheng, J.; Allaire, J.; Xie, Y.; McPherson, J. Shiny: Web Application Framework for R 2012.
127. Rocca-Serra, P.; Brandizi, M.; Maguire, E.; Sklyar, N.; Taylor, C.; Begley, K.; Field, D.; Harris, S.; Hide, W.; Hofmann, O. et al. ISA Software Suite: Supporting Standards-Compliant Experimental Annotation and Enabling Curation at the Community Level. Bioinformatics 2010, 26, 2354–2356, doi:10.1093/bioinformatics/btq415.
128. Sansone, S.-A.; Rocca-Serra, P.; Field, D.; Maguire, E.; Taylor, C.; Hofmann, O.; Fang, H.; Neumann, S.; Tong, W.; Amaral-Zettler, L. et al. Toward Interoperable Bioscience Data. Nature Genetics 2012, 44, 121–126, doi:10.1038/ng.1054.
129. Plotly Technologies Inc Collaborative Data Science 2015.
130. Wickham, H. Ggplot2: Elegant Graphics for Data Analysis; Springer International Publishing : 2016; ISBN 978-3-319-24277-4.
131. Jupyter, P.; Bussonnier, M.; Forde, J.; Freeman, J.; Granger, B.; Head, T.; Holdgraf, C.; Kelley, K.; Nalvarte, G.; Osheroff, A. et al. Binder 2.0 - Reproducible, Interactive, Sharable Environments for Science at Scale. In Proceedings of the Proceedings of the 17th python in science conference; SciPy, 2018; pp. 113–120.
132. Ram, K. Configure Your R Project for Binderhub • Hole Punch.
133. Liggi, S.; Hinz, C.; Hall, Z.; Santoru, M.L.; Poddighe, S.; Fjeldsted, J.; Atzori, L.; Griffin, J.L. KniMet: A Pipeline for the Processing of Chromatography-Mass Spectrometry Metabolomics Data. Metabolomics : Official journal of the Metabolomic Society 2018, 14, 52, doi:10.1007/s11306-018-1349-5.
134. Verhoeven, A.; Giera, M.; Mayboroda, O.A. KIMBLE: A Versatile Visual NMR Metabolomics Workbench in KNIME. Analytica Chimica Acta 2018, 1044, 66–76, doi:10.1016/j.aca.2018.07.070.
135. Davidson, R.L.; Weber, R.J.M.; Liu, H.; Sharma-Oates, A.; Viant, M.R. Galaxy-M: A Galaxy Workflow for Processing and Analyzing Direct Infusion and Liquid Chromatography Mass Spectrometry-Based Metabolomics Data. GigaScience 2016, 5, 10, doi:10.1186/s13742-016-0115-8.
136. Giacomoni, F.; Le Corguillé, G.; Monsoor, M.; Landi, M.; Pericard, P.; Pétéra, M.; Duperier, C.; Tremblay-Franco, M.; Martin, J.-F.; Jacob, D. et al. Workflow4Metabolomics: A Collaborative Research Infrastructure for Computational Metabolomics. Bioinformatics 2015, 31, 1493–1495, doi:10.1093/bioinformatics/btu813.
137. Goecks, J.; Nekrutenko, A.; Taylor, J.; Team, G. Galaxy: A Comprehensive Approach for Supporting Accessible, Reproducible, and Transparent Computational Research in the Life Sciences. Genome Biology 2010, 11, R86, doi:10.1186/gb-2010-11-8-r86.
138. Metabohub National Infrastructure in Metabolomics and Fluxomics 2019.
139. Guitton, Y.; Tremblay-Franco, M.; Le Corguillé, G.; Martin, J.-F.; Pétéra, M.; Roger-Mele, P.; Delabrière, A.; Goulitquer, S.; Monsoor, M.; Duperier, C. et al. Create, Run, Share, Publish, and Reference Your LC-MS, FIA-MS, GC-MS, and NMR Data Analysis Workflows with the Workflow4Metabolomics 3.0 Galaxy Online Infrastructure for Metabolomics. The International Journal of Biochemistry & Cell Biology 2017, 93, 89–101, doi:10.1016/j.biocel.2017.07.002.
140. Workflow4metabolomics Referenced W4M Histories Workflow4metabolomics.org.
141. Goble, C.; Cohen-Boulakia, S.; Soiland-Reyes, S.; Garijo, D.; Gil, Y.; Crusoe, M.R.; Peters, K.; Schober, D. FAIR Computational Workflows. Data Intelligence 2020, 2, 108–121, doi:10.1162/dint_a_00033.
142. Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.-W.; Silva Santos, L.B. da; Bourne, P.E. et al. The FAIR Guiding Principles for Scientific Data Management and Stewardship. Scientific data 2016, 3, 160018, doi:10.1038/sdata.2016.18.
143. Haug, K.; Salek, R.M.; Conesa, P.; Hastings, J.; Matos, P. de; Rijnbeek, M.; Mahendraker, T.; Williams, M.; Neumann, S.; Rocca-Serra, P. et al. MetaboLights–an Open-Access General-Purpose Repository for Metabolomics Studies and Associated Meta-Data. Nucleic Acids Research 2013, 41, D781–6, doi:10.1093/nar/gks1004.
144. Sud, M.; Fahy, E.; Cotter, D.; Azam, K.; Vadivelu, I.; Burant, C.; Edison, A.; Fiehn, O.; Higashi, R.; Nair, K.S. et al. Metabolomics Workbench: An International Repository for Metabolomics Data and Metadata, Metabolite Standards, Protocols, Tutorials and Training, and Analysis Tools. Nucleic Acids Research 2016, 44, D463–70, doi:10.1093/nar/gkv1042.
145. Gray, A.J.G.; Goble, C.A.; Jimenez, R. Bioschemas: From Potato Salad to Protein Annotation. In Proceedings of the Proceedings of the ISWC 2017 posters & demonstrations and industry tracks co-located with 16th international semantic web conference (ISWC 2017), vienna, austria, october 23rd - to - 25th, 2017.; Nikitina, N., Song, D., Fokoue, A., Haase, P., Eds.; CEUR-WS.org, 2017; Vol. 1963.
146. Collberg, C.; Proebsting, T.A. Repeatability in Computer Systems Research. Communications of the ACM 2016, 59, 62–69, doi:10.1145/2812803.
147. Taschuk, M.; Wilson, G. Ten Simple Rules for Making Research Software More Robust. PLoS Computational Biology 2017, 13, e1005412, doi:10.1371/journal.pcbi.1005412.
148. Stanstrup, J.; Broeckling, C.D.; Helmus, R.; Hoffmann, N.; Mathé, E.; Naake, T.; Nicolotti, L.; Peters, K.; Rainer, J.; Salek, R. et al. The MetaRbolomics Book.