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Informatics for Nutritional Genetics and Genomics

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Translational Informatics in Smart Healthcare

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1005))

Abstract

While traditional nutrition science is focusing on nourishing population, modern nutrition is aiming at benefiting individual people. The goal of modern nutritional research is to promote health, prevent diseases, and improve performance. With the development of modern technologies like bioinformatics, metabolomics, and molecular genetics, this goal is becoming more attainable. In this chapter, we will discuss the new concepts and technologies especially in informatics and molecular genetics and genomics, and how they have been implemented to change the nutrition science and lead to the emergence of new branches like nutrigenomics, nutrigenetics, and nutritional metabolomics.

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References

  1. Kwan D, Bartle WR, Walker SE. Abnormal serum transaminases following therapeutic doses of acetaminophen in the absence of known risk factors. Dig Dis Sci. 1995;40(9):1951–5.

    Article  CAS  PubMed  Google Scholar 

  2. German JB, et al. Metabolomics in the opening decade of the 21st century: building the roads to individualized health. J Nutr. 2004;134(10):2729–32.

    CAS  PubMed  Google Scholar 

  3. German JB, Roberts MA, Watkins SM. Genomics and metabolomics as markers for the interaction of diet and health: lessons from lipids. J Nutr. 2003;133(6 Suppl 1):2078S–83S.

    CAS  PubMed  Google Scholar 

  4. German JB, Roberts MA, Watkins SM. Personal metabolomics as a next generation nutritional assessment. J Nutr. 2003;133(12):4260–6.

    CAS  PubMed  Google Scholar 

  5. Gibney MJ, et al. Metabolomics in human nutrition: opportunities and challenges. Am J Clin Nutr. 2005;82(3):497–503.

    CAS  PubMed  Google Scholar 

  6. Scalbert A, et al. Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics. 2009;5(4):435–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Whitfield PD, German AJ, Noble PJ. Metabolomics: an emerging post-genomic tool for nutrition. Br J Nutr. 2004;92(4):549–55.

    Article  CAS  PubMed  Google Scholar 

  8. Zeisel SH, et al. The nutritional phenotype in the age of metabolomics. J Nutr. 2005;135(7):1613–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Wild CP. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomark Prev. 2005;14(8):1847–50.

    Article  CAS  Google Scholar 

  10. Markley JL, et al. New bioinformatics resources for metabolomics. Pac Symp Biocomput. 2007:157–68.

    Google Scholar 

  11. Draper J, et al. Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour ‘rules’. BMC Bioinformatics. 2009;10:227.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kussmann M, Van Bladeren PJ. The extended nutrigenomics – understanding the interplay between the genomes of food, gut microbes, and human host. Front Genet. 2011;2:21.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Corthesy-Theulaz I, et al. Nutrigenomics: the impact of biomics technology on nutrition research. Ann Nutr Metab. 2005;49(6):355–65.

    Article  CAS  PubMed  Google Scholar 

  14. Trujillo E, Davis C, Milner J. Nutrigenomics, proteomics, metabolomics, and the practice of dietetics. J Am Diet Assoc. 2006;106(3):403–13.

    Article  CAS  PubMed  Google Scholar 

  15. van Ommen B, Stierum R. Nutrigenomics: exploiting systems biology in the nutrition and health arena. Curr Opin Biotechnol. 2002;13(5):517–21.

    Article  PubMed  Google Scholar 

  16. Jaenisch R, Bird A. Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet. 2003;33(Suppl):245–54.

    Article  CAS  PubMed  Google Scholar 

  17. Ordovas JM, Corella D. Nutritional genomics. Annu Rev Genomics Hum Genet. 2004;5:71–118.

    Article  CAS  PubMed  Google Scholar 

  18. Sebat J, et al. Large-scale copy number polymorphism in the human genome. Science. 2004;305(5683):525–8.

    Article  CAS  PubMed  Google Scholar 

  19. Wieczorek SJ, Tsongalis GJ. Pharmacogenomics: will it change the field of medicine? Clin Chim Acta. 2001;308(1–2):1–8.

    Article  CAS  PubMed  Google Scholar 

  20. Human Microbiome Project Consortium. A framework for human microbiome research. Nature. 2012;486(7402):215–21.

    Article  CAS  Google Scholar 

  21. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207–14.

    Article  CAS  Google Scholar 

  22. Viladomiu M, et al. Nutritional protective mechanisms against gut inflammation. J Nutr Biochem. 2013;24(6):929–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Carbo A, et al. Predictive computational modeling of the mucosal immune responses during helicobacter pylori infection. PLoS One. 2013;8(9):e73365.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Carbo A, et al. Systems modeling of molecular mechanisms controlling cytokine-driven CD4+ T cell differentiation and phenotype plasticity. PLoS Comput Biol. 2013;9(4):e1003027.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Carbo A, et al. Systems modeling of the role of interleukin-21 in the maintenance of effector CD4+ T cell responses during chronic helicobacter pylori infection. MBio. 2014;5(4):e01243–14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Mei Y, et al. Multiscale modeling of mucosal immune responses. BMC Bioinformatics. 2015;16(Suppl 12):S2.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Leber A, et al. Systems modeling of interactions between mucosal immunity and the gut microbiome during clostridium difficile infection. PLoS One. 2015;10(7):e0134849.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Georgiou NA, Garssen J, Witkamp RF. Pharma-nutrition interface: the gap is narrowing. Eur J Pharmacol. 2011;651(1–3):1–8.

    Article  CAS  PubMed  Google Scholar 

  29. Kaput J, Rodriguez RL. Nutritional genomics: the next frontier in the postgenomic era. Physiol Genomics. 2004;16(2):166–77.

    Article  CAS  PubMed  Google Scholar 

  30. Fenech M, et al. Nutrigenetics and nutrigenomics: viewpoints on the current status and applications in nutrition research and practice. J Nutrigenet Nutrigenomics. 2011;4(2):69–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. van Ommen B, et al. The micronutrient genomics project: a community-driven knowledge base for micronutrient research. Genes Nutr. 2010;5(4):285–96.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Ferguson JF, et al. Nutrigenomics, the microbiome, and gene-environment interactions: new directions in cardiovascular disease research, prevention, and treatment: a scientific statement from the American Heart Association. Circ Cardiovasc Genet. 2016;9(3):291–313.

    Article  CAS  PubMed  Google Scholar 

  33. Kussmann M, et al. Perspective: a systems approach to diabetes research. Front Genet. 2013;4:205.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. McVean G, Spencer CC, Chaix R. Perspectives on human genetic variation from the HapMap project. PLoS Genet. 2005;1(4):e54.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Hinds DA, et al. Whole-genome patterns of common DNA variation in three human populations. Science. 2005;307(5712):1072–9.

    Article  CAS  PubMed  Google Scholar 

  36. Mutch DM, Wahli W, Williamson G. Nutrigenomics and nutrigenetics: the emerging faces of nutrition. FASEB J. 2005;19(12):1602–16.

    Article  CAS  PubMed  Google Scholar 

  37. Frayling TM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Stratakis CA, et al. Anisomastia associated with interstitial duplication of chromosome 16, mental retardation, obesity, dysmorphic facies, and digital anomalies: molecular mapping of a new syndrome by fluorescent in situ hybridization and microsatellites to 16q13 (D16S419-D16S503). J Clin Endocrinol Metab. 2000;85(9):3396–401.

    CAS  PubMed  Google Scholar 

  39. Minihane AM, et al. ApoE polymorphism and fish oil supplementation in subjects with an atherogenic lipoprotein phenotype. Arterioscler Thromb Vasc Biol. 2000;20(8):1990–7.

    Article  CAS  PubMed  Google Scholar 

  40. Olefsky JM, Saltiel AR. PPAR gamma and the treatment of insulin resistance. Trends Endocrinol Metab. 2000;11(9):362–8.

    Article  CAS  PubMed  Google Scholar 

  41. Robitaille J, et al. The PPAR-gamma P12A polymorphism modulates the relationship between dietary fat intake and components of the metabolic syndrome: results from the Quebec family study. Clin Genet. 2003;63(2):109–16.

    Article  CAS  PubMed  Google Scholar 

  42. Sarkkinen E, et al. Effect of apolipoprotein E polymorphism on serum lipid response to the separate modification of dietary fat and dietary cholesterol. Am J Clin Nutr. 1998;68(6):1215–22.

    CAS  PubMed  Google Scholar 

  43. Juran BD, Lazaridis KN. Genomics in the post-GWAS era. Semin Liver Dis. 2011;31(2):215–22.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Crott JW, et al. Effects of dietary folate and aging on gene expression in the colonic mucosa of rats: implications for carcinogenesis. Carcinogenesis. 2004;25(1):69–76.

    Article  CAS  PubMed  Google Scholar 

  45. Choi SW, Friso S. Epigenetics: a new bridge between nutrition and health. Adv Nutr. 2010;1(1):8–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cheng X, Blumenthal RM. Coordinated chromatin control: structural and functional linkage of DNA and histone methylation. Biochemistry. 2010;49(14):2999–3008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kim KC, Friso S, Choi SW. DNA methylation, an epigenetic mechanism connecting folate to healthy embryonic development and aging. J Nutr Biochem. 2009;20(12):917–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Jang H, Mason JB, Choi SW. Genetic and epigenetic interactions between folate and aging in carcinogenesis. J Nutr. 2005;135(12 Suppl):2967S–71S.

    CAS  PubMed  Google Scholar 

  49. Cannon G, Leitzmann C. The new nutrition science project. Public Health Nutr. 2005;8(6A):673–94.

    PubMed  Google Scholar 

  50. McCluskey S, et al. Reductions in cardiovascular risk in association with population screening: a 10-year longitudinal study. J Public Health (Oxf). 2007;29(4):379–87.

    Article  CAS  Google Scholar 

  51. Jablonka E, Lamb MJ. The inheritance of acquired epigenetic variations. J Theor Biol. 1989;139(1):69–83.

    Article  CAS  PubMed  Google Scholar 

  52. Jablonka E, Raz G. Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution. Q Rev Biol. 2009;84(2):131–76.

    Article  PubMed  Google Scholar 

  53. Burdge GC, et al. Epigenetic regulation of transcription: a mechanism for inducing variations in phenotype (fetal programming) by differences in nutrition during early life? Br J Nutr. 2007;97(6):1036–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Heerwagen MJ, et al. Maternal obesity and fetal metabolic programming: a fertile epigenetic soil. Am J Physiol Regul Integr Comp Physiol. 2010;299(3):R711–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Finkel T, Serrano M, Blasco MA. The common biology of cancer and ageing. Nature. 2007;448(7155):767–74.

    Article  CAS  PubMed  Google Scholar 

  56. Serrano M, Blasco MA. Cancer and ageing: convergent and divergent mechanisms. Nat Rev Mol Cell Biol. 2007;8(9):715–22.

    Article  CAS  PubMed  Google Scholar 

  57. Derhovanessian E, et al. Immunity, ageing and cancer. Immun Ageing. 2008;5:11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Kushi LH, et al. American Cancer Society guidelines on nutrition and physical activity for cancer prevention: reducing the risk of cancer with healthy food choices and physical activity. CA Cancer J Clin. 2012;62(1):30–67.

    Article  PubMed  Google Scholar 

  59. Wicki A, Hagmann J. Diet and cancer. Swiss Med Wkly. 2011;141:w13250.

    CAS  PubMed  Google Scholar 

  60. Cappellani A, et al. Diet, obesity and breast cancer: an update. Front Biosci (Schol Ed). 2012;4:90–108.

    Article  Google Scholar 

  61. Key TJ. Fruit and vegetables and cancer risk. Br J Cancer. 2011;104(1):6–11.

    Article  CAS  PubMed  Google Scholar 

  62. Wang X, et al. Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ. 2014;349:g4490.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Larsson SC, Wolk A. Coffee consumption and risk of liver cancer: a meta-analysis. Gastroenterology. 2007;132(5):1740–5.

    Article  PubMed  Google Scholar 

  64. Ferguson LR. Meat and cancer. Meat Sci. 2010;84(2):308–13.

    Article  CAS  PubMed  Google Scholar 

  65. Zheng W, Lee SA. Well-done meat intake, heterocyclic amine exposure, and cancer risk. Nutr Cancer. 2009;61(4):437–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ross R. The pathogenesis of atherosclerosis: a perspective for the 1990s. Nature. 1993;362(6423):801–9.

    Article  CAS  PubMed  Google Scholar 

  67. Taubes G. Nutrition. The soft science of dietary fat. Science. 2001;291(5513):2536–45.

    Article  CAS  PubMed  Google Scholar 

  68. Song JH, Fujimoto K, Miyazawa T. Polyunsaturated (n-3) fatty acids susceptible to peroxidation are increased in plasma and tissue lipids of rats fed docosahexaenoic acid-containing oils. J Nutr. 2000;130(12):3028–33.

    CAS  PubMed  Google Scholar 

  69. Yap SC, et al. Oxidative susceptibility of low density lipoprotein from rabbits fed atherogenic diets containing coconut, palm, or soybean oils. Lipids. 1995;30(12):1145–50.

    Article  CAS  PubMed  Google Scholar 

  70. Greco AV, Mingrone G. Serum and biliary lipid pattern in rabbits feeding a diet enriched with unsaturated fatty acids. Exp Pathol. 1990;40(1):19–33.

    Article  CAS  PubMed  Google Scholar 

  71. Mattes RD. Fat taste and lipid metabolism in humans. Physiol Behav. 2005;86(5):691–7.

    Article  CAS  PubMed  Google Scholar 

  72. Dobarganes C, Marquez-Ruiz G. Oxidized fats in foods. Curr Opin Clin Nutr Metab Care. 2003;6(2):157–63.

    Article  CAS  PubMed  Google Scholar 

  73. Hu N, et al. Nutrition and the risk of Alzheimer’s disease. Biomed Res Int. 2013;2013:524820.

    PubMed  PubMed Central  Google Scholar 

  74. Solfrizzi V, et al. Diet and Alzheimer’s disease risk factors or prevention: the current evidence. Expert Rev Neurother. 2011;11(5):677–708.

    Article  CAS  PubMed  Google Scholar 

  75. Kanoski SE, Davidson TL. Western diet consumption and cognitive impairment: links to hippocampal dysfunction and obesity. Physiol Behav. 2011;103(1):59–68.

    Article  CAS  PubMed  Google Scholar 

  76. Solfrizzi V, et al. Lifestyle-related factors in predementia and dementia syndromes. Expert Rev Neurother. 2008;8(1):133–58.

    Article  PubMed  Google Scholar 

  77. Panza F, et al. Alcohol drinking, cognitive functions in older age, predementia, and dementia syndromes. J Alzheimers Dis. 2009;17(1):7–31.

    Article  PubMed  Google Scholar 

  78. Santos C, et al. Caffeine intake and dementia: systematic review and meta-analysis. J Alzheimers Dis. 2010;20(Suppl 1):S187–204.

    Article  PubMed  Google Scholar 

  79. Stoclet JC, Schini-Kerth V. Dietary flavonoids and human health. Ann Pharm Fr. 2011;69(2):78–90.

    Article  CAS  PubMed  Google Scholar 

  80. Lerner AJ, et al. Retinoids for treatment of Alzheimer’s disease. Biofactors. 2012;38(2):84–9.

    Article  CAS  PubMed  Google Scholar 

  81. Ono K, Yamada M. Vitamin a and Alzheimer’s disease. Geriatr Gerontol Int. 2012;12(2):180–8.

    Article  PubMed  Google Scholar 

  82. Boothby LA, Doering PL. Vitamin C and vitamin E for Alzheimer’s disease. Ann Pharmacother. 2005;39(12):2073–80.

    Article  CAS  PubMed  Google Scholar 

  83. Heo JH, Hyon L, Lee KM. The possible role of antioxidant vitamin C in Alzheimer’s disease treatment and prevention. Am J Alzheimers Dis Other Demen. 2013;28(2):120–5.

    Article  PubMed  Google Scholar 

  84. Loef M, Schrauzer GN, Walach H. Selenium and Alzheimer’s disease: a systematic review. J Alzheimers Dis. 2011;26(1):81–104.

    CAS  PubMed  Google Scholar 

  85. Avan A, Hoogenraad TU. Zinc and copper in Alzheimer’s disease. J Alzheimers Dis. 2015;46(1):89–92.

    Article  CAS  PubMed  Google Scholar 

  86. Loef M, von Stillfried N, Walach H. Zinc diet and Alzheimer’s disease: a systematic review. Nutr Neurosci. 2012;15(5):2–12.

    Article  CAS  PubMed  Google Scholar 

  87. Malouf R, Grimley Evans J. Folic acid with or without vitamin B12 for the prevention and treatment of healthy elderly and demented people. Cochrane Database Syst Rev. 2008;4:CD004514.

    Google Scholar 

  88. Wald DS, Kasturiratne A, Simmonds M. Effect of folic acid, with or without other B vitamins, on cognitive decline: meta-analysis of randomized trials. Am J Med. 2010;123(6):522–7. e2

    Article  CAS  PubMed  Google Scholar 

  89. Burckhardt M, et al. Omega-3 fatty acids for the treatment of dementia. Cochrane Database Syst Rev. 2016;4:CD009002.

    PubMed  Google Scholar 

  90. Cunnane SC, et al. Docosahexaenoic acid homeostasis, brain aging and Alzheimer’s disease: can we reconcile the evidence? Prostaglandins Leukot Essent Fatty Acids. 2013;88(1):61–70.

    Article  CAS  PubMed  Google Scholar 

  91. Hamaguchi T, Ono K, Yamada M. REVIEW: curcumin and Alzheimer’s disease. CNS Neurosci Ther. 2010;16(5):285–97.

    Article  CAS  PubMed  Google Scholar 

  92. Birks J, Grimley Evans J. Ginkgo biloba for cognitive impairment and dementia. Cochrane Database Syst Rev. 2009;1:CD003120.

    Google Scholar 

  93. Krishnan S, Cairns R, Howard R. Cannabinoids for the treatment of dementia. Cochrane Database Syst Rev. 2009;2:CD007204.

    Google Scholar 

  94. Bilkei-Gorzo A. The endocannabinoid system in normal and pathological brain ageing. Philos Trans R Soc Lond Ser B Biol Sci. 2012;367(1607):3326–41.

    Article  CAS  Google Scholar 

  95. Vijan S. In the clinic. Type 2 diabetes. Ann Intern Med. 2010;152(5):ITC31–15. quiz ITC316

    Article  PubMed  Google Scholar 

  96. Zanuso S, et al. Exercise for the management of type 2 diabetes: a review of the evidence. Acta Diabetol. 2010;47(1):15–22.

    Article  PubMed  Google Scholar 

  97. Davis N, Forbes B, Wylie-Rosett J. Nutritional strategies in type 2 diabetes mellitus. Mt Sinai J Med. 2009;76(3):257–68.

    Article  PubMed  Google Scholar 

  98. Feinman RD, et al. Dietary carbohydrate restriction as the first approach in diabetes management: critical review and evidence base. Nutrition. 2015;31(1):1–13.

    Article  CAS  PubMed  Google Scholar 

  99. Thomas D, Elliott EJ. Low glycaemic index, or low glycaemic load, diets for diabetes mellitus. Cochrane Database Syst Rev. 2009;1:CD006296.

    Google Scholar 

  100. Hawthorne K, et al. Culturally appropriate health education for type 2 diabetes mellitus in ethnic minority groups. Cochrane Database Syst Rev. 2008;3:CD006424.

    Google Scholar 

  101. Schellenberg ES, et al. Lifestyle interventions for patients with and at risk for type 2 diabetes: a systematic review and meta-analysis. Ann Intern Med. 2013;159(8):543–51.

    Article  PubMed  Google Scholar 

  102. Glick-Bauer M, Yeh MC. The health advantage of a vegan diet: exploring the gut microbiota connection. Forum Nutr. 2014;6(11):4822–38.

    Google Scholar 

  103. Leach MJ, Kumar S. Cinnamon for diabetes mellitus. Cochrane Database Syst Rev. 2012;9:CD007170.

    Google Scholar 

  104. Mehta T, Allison DB. From measurement to analysis reporting: grand challenges in nutritional methodology. Front Nutr. 2014;1(6):00006.

    PubMed  Google Scholar 

  105. Einav L, Levin J. Economics in the age of big data. Science. 2014;346(6210):1243089.

    Article  PubMed  Google Scholar 

  106. Wallace PJ, et al. Optum labs: building a novel node in the learning health care system. Health Aff (Millwood). 2014;33(7):1187–94.

    Article  Google Scholar 

  107. Schneeweiss S, et al. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20(4):512–22.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Wiener N. Cybernetics. Sci Am. 1948;179(5):14–8.

    Article  CAS  PubMed  Google Scholar 

  109. Westerhoff HV, et al. Systems biochemistry in practice: experimenting with modelling and understanding, with regulation and control. Biochem Soc Trans. 2010;38(5):1189–96.

    Article  CAS  PubMed  Google Scholar 

  110. Bordbar A, Palsson BO. Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Intern Med. 2012;271(2):131–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Loor JJ, Bionaz M, Drackley JK. Systems physiology in dairy cattle: nutritional genomics and beyond. Annu Rev Anim Biosci. 2013;1:365–92.

    Article  PubMed  CAS  Google Scholar 

  112. Woelders H, et al. Systems biology in animal sciences. Animal. 2011;5(7):1036–47.

    Article  CAS  PubMed  Google Scholar 

  113. Ideker T, Galitski T, Hood L. A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet. 2001;2:343–72.

    Article  CAS  PubMed  Google Scholar 

  114. Hood L. A personal view of molecular technology and how it has changed biology. J Proteome Res. 2002;1(5):399–409.

    Article  CAS  PubMed  Google Scholar 

  115. Winter G, Kromer JO. Fluxomics – connecting ‘omics analysis and phenotypes. Environ Microbiol. 2013;15(7):1901–16.

    Article  CAS  PubMed  Google Scholar 

  116. Wenk MR. The emerging field of lipidomics. Nat Rev Drug Discov. 2005;4(7):594–610.

    Article  CAS  PubMed  Google Scholar 

  117. Shriver Z, Raguram S, Sasisekharan R. Glycomics: a pathway to a class of new and improved therapeutics. Nat Rev Drug Discov. 2004;3(10):863–73.

    Article  CAS  PubMed  Google Scholar 

  118. Tempelman RJ. Assessing statistical precision, power, and robustness of alternative experimental designs for two color microarray platforms based on mixed effects models. Vet Immunol Immunopathol. 2005;105(3–4):175–86.

    Article  CAS  PubMed  Google Scholar 

  119. Reeb PD, Steibel JP. Evaluating statistical analysis models for RNA sequencing experiments. Front Genet. 2013;4:178.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Shi L, et al. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies. BMC Bioinformatics. 2008;9(Suppl 9):S10.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  121. Voelkerding KV, Dames SA, Durtschi JD. Next-generation sequencing: from basic research to diagnostics. Clin Chem. 2009;55(4):641–58.

    Article  CAS  PubMed  Google Scholar 

  122. Humphrey SJ, et al. Dynamic adipocyte phosphoproteome reveals that Akt directly regulates mTORC2. Cell Metab. 2013;17(6):1009–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. May C, et al. Instruments and methods in proteomics. Methods Mol Biol. 2011;696:3–26.

    Article  CAS  PubMed  Google Scholar 

  124. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature. 2003;422(6928):198–207.

    Article  CAS  PubMed  Google Scholar 

  125. Rivers J, et al. Absolute multiplexed quantitative analysis of protein expression during muscle development using QconCAT. Mol Cell Proteomics. 2007;6(8):1416–27.

    Article  CAS  PubMed  Google Scholar 

  126. Zhang A, et al. Modern analytical techniques in metabolomics analysis. Analyst. 2012;137(2):293–300.

    Article  CAS  PubMed  Google Scholar 

  127. Schellenberger J, et al. BiGG: a biochemical genetic and genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics. 2010;11:213.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  128. Shahzad K, Loor JJ. Application of top-down and bottom-up systems approaches in ruminant physiology and metabolism. Curr Genomics. 2012;13(5):379–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Price ND, Reed JL, Palsson BO. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat Rev Microbiol. 2004;2(11):886–97.

    Article  CAS  PubMed  Google Scholar 

  130. Loor JJ, Moyes KM, Bionaz M. Functional adaptations of the transcriptome to mastitis-causing pathogens: the mammary gland and beyond. J Mammary Gland Biol Neoplasia. 2011;16(4):305–22.

    Article  PubMed  Google Scholar 

  131. Haider S, Pal R. Integrated analysis of transcriptomic and proteomic data. Curr Genomics. 2013;14(2):91–110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Gonzalez I, et al. Visualising associations between paired ‘omics’ data sets. BioData Min. 2012;5(1):19.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgment

This work was supported by the National Natural Science Foundation of China grants (31400712) and Technology R&D Program of Suzhou (SYN201409).

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, Medical College of Soochow University, No.1 Kerui Road, Suzhou, 215011, Jiangsu, China

    Yuan Gao

  2. School of Chemistry, Biology and Materials Engineering, Suzhou University of Science and Technology, No.1 Kerui road, Suzhou, Jiangsu, 215011, China

    Jiajia Chen

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Correspondence to Jiajia Chen .

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  1. Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China

    Bairong Shen

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Gao, Y., Chen, J. (2017). Informatics for Nutritional Genetics and Genomics. In: Shen, B. (eds) Translational Informatics in Smart Healthcare. Advances in Experimental Medicine and Biology, vol 1005. Springer, Singapore. https://doi.org/10.1007/978-981-10-5717-5_7

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