Chapter 11 Exposome

According to CDC, The exposome can be defined as the measure of all the exposures of an individual in a lifetime and how those exposures relate to health. Exposomics is the study of the exposome and relies on the application of internal and external exposure assessment methods.

  • Internal exposure relies on fields of study such as genomics, metabolomics, lipidomics, transcriptomics and proteomics.

  • External exposure assessment relies on measuring environmental stressors.

11.1 Internal exposure

  • HMDB is a freely available electronic database containing detailed information about small molecule metabolites found in the human body.

  • Lipid Maps

  • GMDB a multistage tandem mass spectral database using a variety of structurally defined glycans.

  • KEGG is a collection of small molecules, biopolymers, and other chemical substances that are relevant to biological systems.

  • Virtual Metabolic Human Database integrating human and gut microbiome metabolism with nutrition and disease.

11.2 External exposure

11.2.1 Environmental fate of compounds QSPR

  • Chemicalize is a powerful online platform for chemical calculations, search, and text processing.

  • QSPR molecular descriptor generate tools list

  • Spark uses computational algorithms based on fundamental chemical structure theory to estimate a wide variety of reactivity parameters strictly from molecular structure.

LogP is important for analytical chemistry. Mannhold (Mannhold et al. 2009) report a comprehensive comparison of logP algorithms. Later, Rajarshi Guha make a comparison with logP algorithms with CDK based on logPstar dataset. Commercial software such as Spark, ACS Labs and ChemAxon might always claim a better performance on in-house dataset compared with public software like KowWIN within EPI Suite. However, we should be careful to evaluate the influnce of logP accuracy on the metabolites or unknown compounds. Fate

  • Wania Group developed software tools to address various aspects of organic contaminant fate and behaviour.

  • Trent University release models to predict environmental fate for pollutions such as Level 3.

  • EAWAG-BBD could provide information on microbial enzyme-catalyzed reactions that are important for biotechnology.

11.2.2 Exposure study database

  • The information system PANGAEA is operated as an Open Access library aimed at archiving, publishing and distributing georeferenced data from earth system research.

  • Environmental Health Criteria (EHC) Monographs

  • CTD is a robust, publicly available database that aims to advance understanding about how environmental exposures affect human health.

  • ODMOA facilitates and coordinates the collection, access to, and use of public health data in order to monitor and improve population health. This data is better for general public health research for Massachusetts.

  • The Surveillance, Epidemiology, and End Results (SEER) Program provides information on cancer statistics in an effort to reduce the cancer burden among the U.S. population.

  • CompTox compounds, exposure and toxicity database. Here is related data.

  • T3DB is a unique bioinformatics resource that combines detailed toxin data with comprehensive toxin target information.

  • FooDB is the world’s largest and most comprehensive resource on food constituents, chemistry and biology.

  • Phenol explorer is the first comprehensive database on polyphenol content in foods.

  • Drugbank is a unique bioinformatics and cheminformatics resource that combines detailed drug data with comprehensive drug target information.

  • LMDB is a freely available electronic database containing detailed information about small molecule metabolites found in different livestock species.

Aguilar-Mogas, Antoni, Marta Sales-Pardo, Miriam Navarro, Roger Guimerà, and Oscar Yanes. 2017. “IMet: A Network-Based Computational Tool To Assist in the Annotation of Metabolites from Tandem Mass Spectra.” Anal. Chem. 89 (6): 3474–82. doi:10.1021/acs.analchem.6b04512.

Allen, Felicity, Allison Pon, Michael Wilson, Russ Greiner, and David Wishart. 2014. “CFM-ID: A Web Server for Annotation, Spectrum Prediction and Metabolite Identification from Tandem Mass Spectra.” Nucleic Acids Res 42 (W1): W94–W99. doi:10.1093/nar/gku436.

Alonso, Arnald, Sara Marsal, and Antonio Julià. 2015. “Analytical Methods in Untargeted Metabolomics: State of the Art in 2015.” Front Bioeng Biotechnol 3 (March). doi:10.3389/fbioe.2015.00023.

Andersson, Martin. 2009. “A Comparison of Nine PLS1 Algorithms.” J. Chemom. 23 (10): 518–29. doi:10.1002/cem.1248.

Baran, Richard, and Trent R. Northen. 2013. “Robust Automated Mass Spectra Interpretation and Chemical Formula Calculation Using Mixed Integer Linear Programming.” Anal. Chem. 85 (20): 9777–84. doi:10.1021/ac402180c.

Barnes, Stephen, H. Paul Benton, Krista Casazza, Sara J. Cooper, Xiangqin Cui, Xiuxia Du, Jeffrey Engler, et al. 2016a. “Training in Metabolomics Research. I. Designing the Experiment, Collecting and Extracting Samples and Generating Metabolomics Data.” J. Mass Spectrom. 51 (7): 461–75. doi:10.1002/jms.3782.

———. 2016b. “Training in Metabolomics Research. II. Processing and Statistical Analysis of Metabolomics Data, Metabolite Identification, Pathway Analysis, Applications of Metabolomics and Its Future.” J. Mass Spectrom. 51 (8): 535–48. doi:10.1002/jms.3780.

Basu, Sumanta, William Duren, Charles R. Evans, Charles F. Burant, George Michailidis, and Alla Karnovsky. 2017. “Sparse Network Modeling and Metscape-Based Visualization Methods for the Analysis of Large-Scale Metabolomics Data.” Bioinformatics 33 (10): 1545–53. doi:10.1093/bioinformatics/btx012.

Bennett, Bryson D., Elizabeth H. Kimball, Melissa Gao, Robin Osterhout, Stephen J. Van Dien, and Joshua D. Rabinowitz. 2009. “Absolute Metabolite Concentrations and Implied Enzyme Active Site Occupancy in Escherichia Coli.” Nat Chem Biol 5 (8): 593–99. doi:10.1038/nchembio.186.

Blaise, Benjamin J., Gonçalo Correia, Adrienne Tin, J. Hunter Young, Anne-Claire Vergnaud, Matthew Lewis, Jake T. M. Pearce, et al. 2016. “Power Analysis and Sample Size Determination in Metabolic Phenotyping.” Anal. Chem. 88 (10): 5179–88. doi:10.1021/acs.analchem.6b00188.

Brereton, Richard G., and Gavin R. Lloyd. 2018. “Partial Least Squares Discriminant Analysis for Chemometrics and Metabolomics: How Scores, Loadings, and Weights Differ According to Two Common Algorithms.” J. Chemom. 32 (4): e3028. doi:10.1002/cem.3028.

Broeckling, C. D., F. A. Afsar, S. Neumann, A. Ben-Hur, and J. E. Prenni. 2014. “RAMClust: A Novel Feature Clustering Method Enables Spectral-Matching-Based Annotation for Metabolomics Data.” Anal. Chem. 86 (14): 6812–7. doi:10.1021/ac501530d.

Cai, Qingpo, Jessica A. Alvarez, Jian Kang, and Tianwei Yu. 2017. “Network Marker Selection for Untargeted LCMS Metabolomics Data.” J. Proteome Res. 16 (3): 1261–9. doi:10.1021/acs.jproteome.6b00861.

Cajka, Tomas, and Oliver Fiehn. 2016. “Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics.” Anal. Chem. 88 (1): 524–45. doi:10.1021/acs.analchem.5b04491.

De Livera, Alysha M., Daniel A. Dias, David De Souza, Thusitha Rupasinghe, James Pyke, Dedreia Tull, Ute Roessner, Malcolm McConville, and Terence P. Speed. 2012. “Normalizing and Integrating Metabolomics Data.” Anal. Chem. 84 (24): 10768–76. doi:10.1021/ac302748b.

Domingo-Almenara, Xavier, Jesus Brezmes, Maria Vinaixa, Sara Samino, Noelia Ramirez, Marta Ramon-Krauel, Carles Lerin, et al. 2016. “ERah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics.” Anal. Chem. 88 (19): 9821–9. doi:10.1021/acs.analchem.6b02927.

Domingo-Almenara, Xavier, J. Rafael Montenegro-Burke, H. Paul Benton, and Gary Siuzdak. 2018. “Annotation: A Computational Solution for Streamlining Metabolomics Analysis.” Anal. Chem. 90 (1): 480–89. doi:10.1021/acs.analchem.7b03929.

Du, Xiuxia, and Steven H Zeisel. 2013. “SPECTRAL DECONVOLUTION FOR GAS CHROMATOGRAPHY MASS SPECTROMETRY-BASED METABOLOMICS: CURRENT STATUS AND FUTURE PERSPECTIVES.” Computational and Structural Biotechnology Journal 4 (5): 1–10. doi:10.5936/csbj.201301013.

Dührkop, Kai, Huibin Shen, Marvin Meusel, Juho Rousu, and Sebastian Böcker. 2015. “Searching Molecular Structure Databases with Tandem Mass Spectra Using CSI:FingerID.” PNAS 112 (41): 12580–5. doi:10.1073/pnas.1509788112.

Fernández-Albert, Francesc, Rafael Llorach, Cristina Andrés-Lacueva, and Alexandre Perera. 2014. “An R Package to Analyse LC/MS Metabolomic Data: MAIT (Metabolite Automatic Identification Toolkit).” Bioinformatics 30 (13): 1937–9. doi:10.1093/bioinformatics/btu136.

Franceschi, Pietro, Domenico Masuero, Urska Vrhovsek, Fulvio Mattivi, and Ron Wehrens. 2012. “A Benchmark Spike-in Data Set for Biomarker Identification in Metabolomics.” J. Chemometrics 26 (1-2): 16–24. doi:10.1002/cem.1420.

Fu, Hai-Yan, Ou Hu, Yue-Ming Zhang, Li Zhang, Jing-Jing Song, Peang Lu, Qing-Xia Zheng, et al. 2017. “Mass-Spectra-Based Peak Alignment for Automatic Nontargeted Metabolic Profiling Analysis for Biomarker Screening in Plant Samples.” Journal of Chromatography A 1513 (Supplement C): 201–9. doi:10.1016/j.chroma.2017.07.044.

Gerlich, Michael, and Steffen Neumann. 2013. “MetFusion: Integration of Compound Identification Strategies.” J. Mass Spectrom. 48 (3): 291–98. doi:10.1002/jms.3123.

Guijas, Carlos, J. Rafael Montenegro-Burke, Xavier Domingo-Almenara, Amelia Palermo, Benedikt Warth, Gerrit Hermann, Gunda Koellensperger, et al. 2018. “METLIN: A Technology Platform for Identifying Knowns and Unknowns.” Anal. Chem. 90 (5): 3156–64. doi:10.1021/acs.analchem.7b04424.

Guitton, Yann, Marie Tremblay-Franco, Gildas Le Corguillé, Jean-François Martin, Mélanie Pétéra, Pierrick Roger-Mele, Alexis Delabrière, et al. 2017. “Create, Run, Share, Publish, and Reference Your LCMS, FIAMS, GCMS, and NMR Data Analysis Workflows with the Workflow4Metabolomics 3.0 Galaxy Online Infrastructure for Metabolomics.” The International Journal of Biochemistry & Cell Biology 93 (Supplement C): 89–101. doi:10.1016/j.biocel.2017.07.002.

Haug, Kenneth, Reza M Salek, and Christoph Steinbeck. 2017. “Global Open Data Management in Metabolomics.” Current Opinion in Chemical Biology, Omics, 36 (February): 58–63. doi:10.1016/j.cbpa.2016.12.024.

Hufsky, Franziska, Kerstin Scheubert, and Sebastian Böcker. 2014. “Computational Mass Spectrometry for Small-Molecule Fragmentation.” TrAC Trends in Analytical Chemistry 53 (January): 41–48. doi:10.1016/j.trac.2013.09.008.

Jorge, Tiago F., Ana T. Mata, and Carla António. 2016. “Mass Spectrometry as a Quantitative Tool in Plant Metabolomics.” Phil. Trans. R. Soc. A 374 (2079): 20150370. doi:10.1098/rsta.2015.0370.

Jr, Stephen Salerno, Mahya Mehrmohamadi, Maria V. Liberti, Muting Wan, Martin T. Wells, James G. Booth, and Jason W. Locasale. 2017. “RRmix: A Method for Simultaneous Batch Effect Correction and Analysis of Metabolomics Data in the Absence of Internal Standards.” PLOS ONE 12 (6): e0179530. doi:10.1371/journal.pone.0179530.

Kapoore, Rahul Vijay, and Seetharaman Vaidyanathan. 2016. “Towards Quantitative Mass Spectrometry-Based Metabolomics in Microbial and Mammalian Systems.” Phil. Trans. R. Soc. A 374 (2079): 20150363. doi:10.1098/rsta.2015.0363.

Karpievitch, Yuliya V., Sonja B. Nikolic, Richard Wilson, James E. Sharman, and Lindsay M. Edwards. 2014. “Metabolomics Data Normalization with EigenMS.” PLOS ONE 9 (12): e116221. doi:10.1371/journal.pone.0116221.

Koelmel, Jeremy P., Nicholas M. Kroeger, Candice Z. Ulmer, John A. Bowden, Rainey E. Patterson, Jason A. Cochran, Christopher W. W. Beecher, Timothy J. Garrett, and Richard A. Yost. 2017. “LipidMatch: An Automated Workflow for Rule-Based Lipid Identification Using Untargeted High-Resolution Tandem Mass Spectrometry Data.” BMC Bioinformatics 18 (July): 331. doi:10.1186/s12859-017-1744-3.

Kuhl, Carsten, Ralf Tautenhahn, Christoph Böttcher, Tony R. Larson, and Steffen Neumann. 2012. “CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets.” Anal. Chem. 84 (1): 283–89. doi:10.1021/ac202450g.

Kuligowski, Julia, Ángel Sánchez-Illana, Daniel Sanjuán-Herráez, Máximo Vento, and Guillermo Quintás. 2015. “Intra-Batch Effect Correction in Liquid Chromatography-Mass Spectrometry Using Quality Control Samples and Support Vector Regression (QC-SVRC).” Analyst 140 (22): 7810–7. doi:10.1039/C5AN01638J.

Kusonmano, Kanthida, Wanwipa Vongsangnak, and Pramote Chumnanpuen. 2016. “Informatics for Metabolomics.” In Translational Biomedical Informatics, 91–115. Advances in Experimental Medicine and Biology. Springer, Singapore. doi:10.1007/978-981-10-1503-8_5.

Leek, Jeffrey T., and John D. Storey. 2007. “Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis.” PLOS Genet 3 (9): e161. doi:10.1371/journal.pgen.0030161.

———. 2008. “A General Framework for Multiple Testing Dependence.” PNAS 105 (48): 18718–23. doi:10.1073/pnas.0808709105.

Leek, Jeffrey T., W. Evan Johnson, Hilary S. Parker, Andrew E. Jaffe, and John D. Storey. 2012. “The Sva Package for Removing Batch Effects and Other Unwanted Variation in High-Throughput Experiments.” Bioinformatics 28 (6): 882–83. doi:10.1093/bioinformatics/bts034.

Lê Cao, Kim-Anh, Simon Boitard, and Philippe Besse. 2011. “Sparse PLS Discriminant Analysis: Biologically Relevant Feature Selection and Graphical Displays for Multiclass Problems.” BMC Bioinformatics 12 (June): 253. doi:10.1186/1471-2105-12-253.

Li, Bo, Jing Tang, Qingxia Yang, Shuang Li, Xuejiao Cui, Yinghong Li, Yuzong Chen, Weiwei Xue, Xiaofeng Li, and Feng Zhu. 2017. “NOREVA: Normalization and Evaluation of MS-Based Metabolomics Data.” Nucleic Acids Res 45 (W1): W162–W170. doi:10.1093/nar/gkx449.

Li, Liang, Ronghong Li, Jianjun Zhou, Azeret Zuniga, Avalyn E. Stanislaus, Yiman Wu, Tao Huan, et al. 2013. “MyCompoundID: Using an Evidence-Based Metabolome Library for Metabolite Identification.” Anal. Chem. 85 (6): 3401–8. doi:10.1021/ac400099b.

Lisec, Jan, Friederike Hoffmann, Clemens Schmitt, and Carsten Jaeger. 2016. “Extending the Dynamic Range in Metabolomics Experiments by Automatic Correction of Peaks Exceeding the Detection Limit.” Anal. Chem. 88 (15): 7487–92. doi:10.1021/acs.analchem.6b02515.

Livera, Alysha M. De, Marko Sysi-Aho, Laurent Jacob, Johann A. Gagnon-Bartsch, Sandra Castillo, Julie A. Simpson, and Terence P. Speed. 2015. “Statistical Methods for Handling Unwanted Variation in Metabolomics Data.” Anal. Chem. 87 (7): 3606–15. doi:10.1021/ac502439y.

Lu, Wenyun, Xiaoyang Su, Matthias S. Klein, Ian A. Lewis, Oliver Fiehn, and Joshua D. Rabinowitz. 2017. “Metabolite Measurement: Pitfalls to Avoid and Practices to Follow.” Annu. Rev. Biochem. 86 (1): 277–304. doi:10.1146/annurev-biochem-061516-044952.

Lu, Xin, and Guowang Xu. 2008. “LC-MS Metabonomics Methodology in Biomarker Discovery.” In Biomarker Methods in Drug Discovery and Development, edited by Feng Wang, 291–315. Methods in Pharmacology and Toxicology. Humana Press. doi:10.1007/978-1-59745-463-6_14.

Luo, Xian, and Liang Li. 2017. “Metabolomics of Small Numbers of Cells: Metabolomic Profiling of 100, 1000, and 10000 Human Breast Cancer Cells.” Anal. Chem. 89 (21): 11664–71. doi:10.1021/acs.analchem.7b03100.

Mahieu, Nathaniel G., Jonathan L. Spalding, Susan J. Gelman, and Gary J. Patti. 2016. “Defining and Detecting Complex Peak Relationships in Mass Spectral Data: The Mz.Unity Algorithm.” Anal. Chem. 88 (18): 9037–46. doi:10.1021/acs.analchem.6b01702.

Mannhold, Raimund, Gennadiy I. Poda, Claude Ostermann, and Igor V. Tetko. 2009. “Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on More Than 96,000 Compounds.” Journal of Pharmaceutical Sciences 98 (3): 861–93. doi:10.1002/jps.21494.

Matsuo, Teruko, Hiroshi Tsugawa, Hiromi Miyagawa, and Eiichiro Fukusaki. 2017. “Integrated Strategy for Unknown EIMS Identification Using Quality Control Calibration Curve, Multivariate Analysis, EIMS Spectral Database, and Retention Index Prediction.” Anal. Chem. 89 (12): 6766–73. doi:10.1021/acs.analchem.7b01010.

Menikarachchi, Lochana C., Shannon Cawley, Dennis W. Hill, L. Mark Hall, Lowell Hall, Steven Lai, Janine Wilder, and David F. Grant. 2012. “MolFind: A Software Package Enabling HPLC/MS-Based Identification of Unknown Chemical Structures.” Anal. Chem. 84 (21): 9388–94. doi:10.1021/ac302048x.

Misra, Biswapriya B., and prefix=van der family=Hooft given=Justin J. J. 2016. “Updates in Metabolomics Tools and Resources: 20142015.” ELECTROPHORESIS 37 (1): 86–110. doi:10.1002/elps.201500417.

Najdekr, Lukáš, David Friedecký, Ralf Tautenhahn, Tomáš Pluskal, Junhua Wang, Yingying Huang, and Tomáš Adam. 2016. “Influence of Mass Resolving Power in Orbital Ion-Trap Mass Spectrometry-Based Metabolomics.” Anal. Chem. 88 (23): 11429–35. doi:10.1021/acs.analchem.6b02319.

Ni, Yan, Mingming Su, Yunping Qiu, Wei Jia, and Xiuxia Du. 2016. “ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-Eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies.” Anal. Chem. 88 (17): 8802–11. doi:10.1021/acs.analchem.6b02222.

Ortmayr, Karin, Verena Charwat, Cornelia Kasper, Stephan Hann, and Gunda Koellensperger. 2016. “Uncertainty Budgeting in Fold Change Determination and Implications for Non-Targeted Metabolomics Studies in Model Systems” 142 (1): 80–90. doi:10.1039/C6AN01342B.

Qiu, Feng, Dennis D. Fine, Daniel J. Wherritt, Zhentian Lei, and Lloyd W. Sumner. 2016. “PlantMAT: A Metabolomics Tool for Predicting the Specialized Metabolic Potential of a System and for Large-Scale Metabolite Identifications.” Anal. Chem. 88 (23): 11373–83. doi:10.1021/acs.analchem.6b00906.

Robbat Jr., Albert, Nicole Kfoury, Eugene Baydakov, and Yuriy Gankin. 2017. “Optimizing Targeted/Untargeted Metabolomics by Automating Gas Chromatography/Mass Spectrometry Workflows.” Journal of Chromatography A 1505 (July): 96–105. doi:10.1016/j.chroma.2017.05.017.

Ruttkies, Christoph, Emma L. Schymanski, Sebastian Wolf, Juliane Hollender, and Steffen Neumann. 2016. “MetFrag Relaunched: Incorporating Strategies Beyond in Silico Fragmentation.” Journal of Cheminformatics 8 (January): 3. doi:10.1186/s13321-016-0115-9.

Schrimpe-Rutledge, Alexandra C., Simona G. Codreanu, Stacy D. Sherrod, and John A. McLean. 2016. “Untargeted Metabolomics StrategiesChallenges and Emerging Directions.” J. Am. Soc. Mass Spectrom. 27 (12): 1897–1905. doi:10.1007/s13361-016-1469-y.

Silva, Ricardo R., Fabien Jourdan, Diego M. Salvanha, Fabien Letisse, Emilien L. Jamin, Simone Guidetti-Gonzalez, Carlos A. Labate, and Ricardo Z. N. Vêncio. 2014. “ProbMetab: An R Package for Bayesian Probabilistic Annotation of LCMS-Based Metabolomics.” Bioinformatics 30 (9): 1336–7. doi:10.1093/bioinformatics/btu019.

Sitnikov, Dmitri G., Cian S. Monnin, and Dajana Vuckovic. 2016. “Systematic Assessment of Seven Solvent and Solid-Phase Extraction Methods for Metabolomics Analysis of Human Plasma by LC-MS.” Sci Rep 6 (December). doi:10.1038/srep38885.

Smith, Colin A., Elizabeth J. Want, Grace O’Maille, Ruben Abagyan, and Gary Siuzdak. 2006. “XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification.” Anal. Chem. 78 (3): 779–87. doi:10.1021/ac051437y.

Sumner, Lloyd W., Alexander Amberg, Dave Barrett, Michael H. Beale, Richard Beger, Clare A. Daykin, Teresa W.-M. Fan, et al. 2007. “Proposed Minimum Reporting Standards for Chemical Analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI).” Metabolomics 3 (3): 211–21. doi:10.1007/s11306-007-0082-2.

Tautenhahn, Ralf, Christoph Böttcher, and Steffen Neumann. 2008. “Highly Sensitive Feature Detection for High Resolution LC/MS.” BMC Bioinformatics 9: 504. doi:10.1186/1471-2105-9-504.

Thonusin, Chanisa, Heidi B. IglayReger, Tanu Soni, Amy E. Rothberg, Charles F. Burant, and Charles R. Evans. 2017. “Evaluation of Intensity Drift Correction Strategies Using MetaboDrift, a Normalization Tool for Multi-Batch Metabolomics Data.” Journal of Chromatography A, Pushing the boundaries of chromatography and electrophoresis, 1523 (Supplement C): 265–74. doi:10.1016/j.chroma.2017.09.023.

Tian, Tze-Feng, San-Yuan Wang, Tien-Chueh Kuo, Cheng-En Tan, Guan-Yuan Chen, Ching-Hua Kuo, Chi-Hsin Sally Chen, Chang-Chuan Chan, Olivia A. Lin, and Y. Jane Tseng. 2016. “Web Server for Peak Detection, Baseline Correction, and Alignment in Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics Data.” Anal. Chem. 88 (21): 10395–10403. doi:10.1021/acs.analchem.6b00755.

Townsend, Mary K., Hugues Aschard, Immaculata De Vivo, Karin B. Michels, and Peter Kraft. 2016. “Genomics, Telomere Length, Epigenetics, and Metabolomics in the Nurses’ Health Studies.” Am J Public Health 106 (9): 1663–8. doi:10.2105/AJPH.2016.303344.

Treutler, Hendrik, Hiroshi Tsugawa, Andrea Porzel, Karin Gorzolka, Alain Tissier, Steffen Neumann, and Gerd Ulrich Balcke. 2016. “Discovering Regulated Metabolite Families in Untargeted Metabolomics Studies.” Anal. Chem. 88 (16): 8082–90. doi:10.1021/acs.analchem.6b01569.

Tsugawa, Hiroshi, Tobias Kind, Ryo Nakabayashi, Daichi Yukihira, Wataru Tanaka, Tomas Cajka, Kazuki Saito, Oliver Fiehn, and Masanori Arita. 2016. “Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software.” Anal. Chem. 88 (16): 7946–58. doi:10.1021/acs.analchem.6b00770.

Uppal, Karan, Douglas I. Walker, and Dean P. Jones. 2017. “XMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data.” Anal. Chem. 89 (2): 1063–7. doi:10.1021/acs.analchem.6b01214.

Viant, Mark R, Irwin J Kurland, Martin R Jones, and Warwick B Dunn. 2017. “How Close Are We to Complete Annotation of Metabolomes?” Current Opinion in Chemical Biology, Omics, 36 (February): 64–69. doi:10.1016/j.cbpa.2017.01.001.

Wang, Mingxun, Jeremy J. Carver, Vanessa V. Phelan, Laura M. Sanchez, Neha Garg, Yao Peng, Don Duy Nguyen, et al. 2016. “Sharing and Community Curation of Mass Spectrometry Data with Global Natural Products Social Molecular Networking.” Nat. Biotechnol. 34 (8): 828–37. doi:10.1038/nbt.3597.

Wang, San-Yuan, Ching-Hua Kuo, and Yufeng J. Tseng. 2013. “Batch Normalizer: A Fast Total Abundance Regression Calibration Method to Simultaneously Adjust Batch and Injection Order Effects in Liquid Chromatography/Time-of-Flight Mass Spectrometry-Based Metabolomics Data and Comparison with Current Calibration Methods.” Anal. Chem. 85 (2): 1037–46. doi:10.1021/ac302877x.

Watrous, Jeramie D., Mir Henglin, Brian Claggett, Kim A. Lehmann, Martin G. Larson, Susan Cheng, and Mohit Jain. 2017. “Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data.” Anal. Chem. 89 (3): 1399–1404. doi:10.1021/acs.analchem.6b04337.

Weber, Ralf J. M., and Mark R. Viant. 2010. “MI-Pack: Increased Confidence of Metabolite Identification in Mass Spectra by Integrating Accurate Masses and Metabolic Pathways.” Chemometrics and Intelligent Laboratory Systems, OMICS, 104 (1): 75–82. doi:10.1016/j.chemolab.2010.04.010.

Wiklund, Susanne, Erik Johansson, Lina Sjöström, Ewa J. Mellerowicz, Ulf Edlund, John P. Shockcor, Johan Gottfries, Thomas Moritz, and Johan Trygg. 2008. “Visualization of GC/TOF-MS-Based Metabolomics Data for Identification of Biochemically Interesting Compounds Using OPLS Class Models.” Anal. Chem. 80 (1): 115–22. doi:10.1021/ac0713510.

Witting, Michael, Christoph Ruttkies, Steffen Neumann, and Philippe Schmitt-Kopplin. 2017. “LipidFrag: Improving Reliability of in Silico Fragmentation of Lipids and Application to the Caenorhabditis Elegans Lipidome.” PLOS ONE 12 (3): e0172311. doi:10.1371/journal.pone.0172311.

Wu, Yiman, and Liang Li. 2016. “Sample Normalization Methods in Quantitative Metabolomics.” Journal of Chromatography A, Editors’ choice x, 1430 (January): 80–95. doi:10.1016/j.chroma.2015.12.007.

Yamamoto, Hiroyuki, Tamaki Fujimori, Hajime Sato, Gen Ishikawa, Kenjiro Kami, and Yoshiaki Ohashi. 2014. “Statistical Hypothesis Testing of Factor Loading in Principal Component Analysis and Its Application to Metabolite Set Enrichment Analysis.” BMC Bioinformatics 15 (February): 51. doi:10.1186/1471-2105-15-51.

Yang, Qin, Shan-Shan Lin, Jiang-Tao Yang, Li-Juan Tang, and Ru-Qin Yu. 2017. “Detection of Inborn Errors of Metabolism Utilizing GC-MS Urinary Metabolomics Coupled with a Modified Orthogonal Partial Least Squares Discriminant Analysis.” Talanta 165 (April): 545–52. doi:10.1016/j.talanta.2017.01.018.

Zampieri, Mattia, Karthik Sekar, Nicola Zamboni, and Uwe Sauer. 2017. “Frontiers of High-Throughput Metabolomics.” Current Opinion in Chemical Biology, Omics, 36 (February): 15–23. doi:10.1016/j.cbpa.2016.12.006.


Mannhold, Raimund, Gennadiy I. Poda, Claude Ostermann, and Igor V. Tetko. 2009. “Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on More Than 96,000 Compounds.” Journal of Pharmaceutical Sciences 98 (3): 861–93. doi:10.1002/jps.21494.