From big data analysis to personalized medicine for all: Challenges and opportunities

BMC Medical Genomics

Volumn 8, Issue 1, 2015, Pages

  Alyass, Akram ^a   Turcotte, Michelle ^a   Meyre, David ^a  

^a MCMASTER UNIVERSITY   (Canada)

Author keywords

Big data; Cloud computing; High dimensionality; High throughput technologies; Integrative methods; Omics; Personalized medicine

Indexed keywords

ACCESS TO INFORMATION; BIOINFORMATICS; COMPUTER PROGRAM; DATA ANALYSIS; GENOMICS; HEALTH CARE COST; HUMAN; INFORMATION PROCESSING; INTERDISCIPLINARY RESEARCH; MEDICAL EDUCATION; MORTALITY; NOISE; PERSONALIZED MEDICINE; PRIORITY JOURNAL; PROTEOMICS; REVIEW; TRANSCRIPTOMICS; ECONOMICS; HIGH THROUGHPUT SEQUENCING; INFORMATION RETRIEVAL; MEDICAL INFORMATICS; PROCEDURES; SOCIOECONOMICS; SYSTEMS BIOLOGY;

HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; INFORMATION STORAGE AND RETRIEVAL; MEDICAL INFORMATICS; PRECISION MEDICINE; SOCIOECONOMIC FACTORS; SYSTEMS BIOLOGY;

EID: 84933530616     PISSN: None     EISSN: 17558794     Source Type: Journal    
DOI: 10.1186/s12920-015-0108-y     Document Type: Review

References (119)

1. Hood L, Flores M. A personal view on systems medicine and the emergence of proactive P4 medicine: predictive, preventive, personalized and participatory. New Biotechnol. 2012;29(6):613-24.
2. Khoury MJ, Gwinn ML, Glasgow RE, Kramer BS. A population approach to precision medicine. Am J Prev Med. 2012;42(6):639-45.
3. Taubes G. Epidemiology faces its limits. Science. 1995;269(5221):164-9.
4. Loos RJ, Schadt EE. This I believe: gaining new insights through integrating "old" data. Front Genet. 2012;3:137.
5. Schadt EE, Bjorkegren JL. NEW: network-enabled wisdom in biology, medicine, and health care. Sci Transl Med. 2012;4(115):115rv1.
6. Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461(7261):218-23.
7. Tremblay-Servier M. Personalized medicine: the medicine of tomorrow. Foreword. Metab Clin Exp. 2013;62 Suppl 1:S1.
8. Hardy BJ, Seguin B, Goodsaid F, Jimenez-Sanchez G, Singer PA, Daar AS. The next steps for genomic medicine: challenges and opportunities for the developing world. Nat Rev Genet. 2008;9 Suppl 1:S23-7.
9. Mardis ER. The $1,000 genome, the $100,000 analysis? Genome Medicine. 2010;2(11).
10. Yuan Y, Failmezger H, Rueda OM, Ali HR, Graf S, Chin SF, et al. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci Transl Med. 2012;4(157):157ra43.
11. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234-48.
12. Brugmann A, Eld M, Lelkaitis G, Nielsen S, Grunkin M, Hansen JD, et al. Digital image analysis of membrane connectivity is a robust measure of HER2 immunostains. Breast Cancer Res Treat. 2012;132(1):41-9.
13. Gottret P, Schieber G. Health transitions, disease burdens, and health expenditure patterns. Health Financing Revisited: A Practitioner's Guide: The International Bank for Reconstruction and Development. 2006. p. 23-39.
14. Lu C, Schneider MT, Gubbins P, Leach-Kemon K, Jamison D, Murray CJ. Public financing of health in developing countries: a cross-national systematic analysis. Lancet. 2010;375(9723):1375-87.
15. Li A, Meyre D. Jumping on the Train of Personalized Medicine: A Primer for Non-Geneticist Clinicians: Part 2. Fundamental Concepts in Genetic Epidemiology. Curr Psychiatr Rev. 2014;10(4):101-17.
16. Li A, Meyre D. Jumping on the Train of Personalized Medicine A Primer for Non- Geneticist Clinicians Part 1. Fundamental Concepts in Molecular Genetics. Curr Psychiatr Rev. 2014;10(4):91-100.
17. Li A, Meyre D. Jumping on the Train of Personalized Medicine A Primer for Non-Geneticist Clinicians Part 3. Clinical Applications in the Personalized Medicine Area. Curr Psychiatr Rev. 2014;10(4):118-30.
18. Hood L. Systems Biology and P4 Medicine: Past, Present, and Future. Rambam Maimonides Med J. 2013;4(2).
19. Vecchio G, Fenech M, Pompa PP, Voelcker NH. Lab-on-a-Chip-Based High-Throughput Screening of the Genotoxicity of Engineered Nanomaterials. Small (Weinheim an der Bergstrasse, Germany). 2014.
20. Schadt EE. The changing privacy landscape in the era of big data. Mol Syst Biol. 2012;8:612.
21. Phillips KA, Ann Sakowski J, Trosman J, Douglas MP, Liang SY, Neumann P. The economic value of personalized medicine tests: what we know and what we need to know. Genet Med. 2014;16(3):251-7.
22. Hekim N, Coskun Y, Sinav A, Abou-Zeid AH, Agirbasli M, Akintola SO, et al. Translating biotechnology to knowledge-based innovation, peace, and development? Deploy a Science Peace Corps - an open letter to world leaders. Omics. 2014;18(7):415-20.
23. Ozdemir V, Badr KF, Dove ES, Endrenyi L, Geraci CJ, Hotez PJ, et al. Crowd-funded micro-grants for genomics and "big data": an actionable idea connecting small (artisan) science, infrastructure science, and citizen philanthropy. Omics. 2013;17(4):161-72.
24. Dove ES, Ozdemir V. All the post-genomic world is a stage: the actors and narrators required for translating pharmacogenomics into public health. Per Med. 2013;10(3):213-6.
25. Mbuagbaw L, van der Kop ML, Lester RT, Thirumurthy H, Pop-Eleches C, Ye C, et al. Mobile phone text messages for improving adherence to antiretroviral therapy (ART): an individual patient data meta-analysis of randomised trials. BMJ Open. 2013;3(12), e003950.
26. Hardin G. The Tragedy of the Commons. Science. 1968;162(3859):1243-8.
27. Ostrom E. Coping with Tragedies of the Commons. Ann Rev Politic Sci. 1999;2(1):493-535.
28. Ostrom E. Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge University Press; 1990.
29. De Vries R. How can we help? From "sociology in" to "sociology of" bioethics. J Law Med Ethics. 2004;32(2):279-92. 2.
30. Dove ES, Ozdemir V. The epiknowledge of socially responsible innovation. EMBO Rep. 2014;15(5):462-3.
31. Finishing the euchromatic sequence of the human genome. Nature. 2004;431(7011):931-45.
32. McDermott JE, Wang J, Mitchell H, Webb-Robertson BJ, Hafen R, Ramey J, et al. Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data. Expert Opin Med Diagn. 2013;7(1):37-51.
33. Kristensen VN, Lingjaerde OC, Russnes HG, Vollan HK, Frigessi A, Borresen-Dale AL. Principles and methods of integrative genomic analyses in cancer. Nat Rev Cancer. 2014;14(5):299-313.
34. Shendure J, Lieberman AE. The expanding scope of DNA sequencing. Nat Biotechnol. 2012;30(11):1084-94.
35. Pal A, McCarthy MI. The genetics of type 2 diabetes and its clinical relevance. Clin Genet. 2013;83(4):297-306.
36. Scholz MB, Lo CC, Chain PS. Next generation sequencing and bioinformatic bottlenecks: the current state of metagenomic data analysis. Curr Opin Biotechnol. 2012;23(1):9-15.
37. Berger B, Peng J, Singh M. Computational solutions for omics data. Nat Rev Genet. 2013;14(5):333-46.
38. Gomez-Cabrero D, Abugessaisa I, Maier D, Teschendorff A, Merkenschlager M, Gisel A et al. Data integration in the era of omics: current and future challenges. BMC Syst Biol. 2014;8(Suppl 2).
39. McShane LM, Cavenagh MM, Lively TG, Eberhard DA, Bigbee WL, Williams PM et al. Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration. BMC Med. 2013;11(1).
40. Brown NJ, MacDonald DA, Samanta MP, Friedman HL, Coyne JC. A critical reanalysis of the relationship between genomics and well-being. Proc Natl Acad Sci U S A. 2014;111(35):12705-9.
41. Wilson G, Aruliah DA, Brown CT, Chue Hong NP, Davis M, Guy RT et al. Best Practices for Scientific Computing. PLoS Biol. 2014;12(1).
42. Hannay JE, MacLeod C, Singer J, Langtangen HP, Pfahl D, Wilson G, editors. How Do Scientists Develop and Use Scientific Software? Washington, DC, USA: IEEE Computer Society; 2009.
43. Prabhu P, Jablin TB, Raman A, Zhang Y, Huang J, Kim H, et al. editors. A Survey of the Practice of Computational Science. New York, NY, USA: ACM; 2011.
44. Marshall E. Human genome 10th anniversary. Waiting for the revolution. Science. 2011;331(6017):526-9.
45. Cesario A, Auffray C, Russo P, Hood L. P4 Medicine Needs P4 Education. Curr Pharm Des. 2014;20(38):6071-2.
46. Schatz MC, Langmead B, Salzberg SL. Cloud computing and the DNA data race. Nat Biotech. 2010;28(7):691-3.
47. Schadt EE, Linderman MD, Sorenson J, Lee L, Nolan GP. Cloud and heterogeneous computing solutions exist today for the emerging big data problems in biology. Nat Rev Genet. 2011;12(3):224.
48. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, et al. A View of Cloud Computing. Commun ACM. 2010;53(4):50-8.
49. Marx V. Biology: The big challenges of big data. Nature. 2013;498(7453):255-60.
50. Hiltemann S, Mei H, de Hollander M, Palli I, van der Spek P, Jenster G, et al. CGtag: complete genomics toolkit and annotation in a cloud-based Galaxy. GigaScience. 2014;3(1):1.
51. Liu B, Madduri RK, Sotomayor B, Chard K, Lacinski L, Dave UJ, et al. Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses. J Biomed Inform. 2014;49:119-33.
52. Zheng G, Li H, Wang C, Sheng Q, Fan H, Yang S, et al. A platform to standardize, store, and visualize proteomics experimental data. Acta Biochim Biophys Sin. 2009;41(4):273-9.
53. Jo H, Jeong J, Lee M, Choi DH. Exploiting GPUs in Virtual Machine for BioCloud. BioMed Res Int. 2013;2013.
54. Yung LS, Yang C, Wan X, Yu W. GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies. Bioinformatics. 2011;27(9):1309-10.
55. Manavski SA, Valle G. CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment. BMC bioinformatics. 2008;9(Suppl 2).
56. McArt DG, Bankhead P, Dunne PD, Salto-Tellez M, Hamilton P, Zhang SD. cudaMap: a GPU accelerated program for gene expression connectivity mapping. BMC bioinformatics. 2013;14:305.
57. Schatz MC, Trapnell C, Delcher AL, Varshney A. High-throughput sequence alignment using Graphics Processing Units. BMC bioinformatics. 2007;8(1).
58. Solomonik E, Carson E, Knight N, Demmel J, editors. Tradeoffs Between Synchronization, Communication, and Computation in Parallel Linear Algebra Computations2014. New York, NY, USA: ACM; 2014.
59. Fadista J, Bendixen C. Genomic Position Mapping Discrepancies of Commercial SNP Chips. PloS one. 2012;7(2).
60. Merali Z. Computational science:...Error. Nature News. 2010;467(7317):775-7.
61. Robiou-du-Pont S, Li A, Christie S, Sohani ZN, Meyre D. Should we have blind faith in bioinformatics software? Illustrations from the SNAP web-based tool. PLoS One. 2015;10(3):e0118925.
62. Khan MA, Soto-Jimenez LM, Howe T, Streit A, Sosinsky A, Stern CD. Computational tools and resources for prediction and analysis of gene regulatory regions in the chick genome. Genesis. 2013;51(5):311-24.
63. Heath AP, Greenway M, Powell R, Spring J, Suarez R, Hanley D et al. Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets. Journal of the American Medical Informatics Association. JAMIA. 2014.
64. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004;5(10):R80.
65. Saito R, Smoot ME, Ono K, Ruscheinski J, Wang P-L, Lotia S, et al. A travel guide to Cytoscape plugins. Nat Methods. 2012;9(11):1069-76.
66. Dai L, Gao X, Guo Y, Xiao J, Zhang Z. Bioinformatics clouds for big data manipulation. Biology Direct. 2012;7(1).
67. Tenenbaum JD, Sansone SA, Haendel M. A sea of standards for omics data: sink or swim? J Am Med Inform Assoc. 2014;21(2):200-3.
68. Oberst A, Dillon CP, Weinlich R, McCormick LL, Fitzgerald P, Pop C, et al. Catalytic activity of the caspase-8-FLIP(L) complex inhibits RIPK3-dependent necrosis. Nature. 2011;471(7338):363-7.
69. Clarke R, Ressom HW, Wang A, Xuan J, Liu MC, Gehan EA, et al. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nat Rev Cancer. 2008;8(1):37-49.
70. Noble WS. How does multiple testing correction work? Nat Biotechnol. 2009;27(12):1135-7.
71. Dudoit S, Laan MJvd. Multiple Testing Procedures with Applications to Genomics. Springer Science & Business Media; 2007.
72. Miller RG, Jr. Simultaneous Statistical Inference. Springer New York; 2011.
73. Westfall PH, Troendle JF. Multiple testing with minimal assumptions. Biom J. 2008;50(5):745-55.
74. Westfall PH. Resampling-Based Multiple Testing: Examples and Methods for P-Value Adjustment. John Wiley & Sons; 1993.
75. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Methodol. 1995;57:289-300.
76. Parkhomenko E, Tritchler D, Beyene J. Genome-wide sparse canonical correlation of gene expression with genotypes. BMC Proc. 2007;1 Suppl 1:S119.
77. Yao F, Coquery J, Le Cao KA. Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets. BMC Bioinformatics. 2012;13:24.
78. Le Cao KA, Gonzalez I, Dejean S. integrOmics: an R package to unravel relationships between two omics datasets. Bioinformatics. 2009;25(21):2855-6.
79. Fan Y, Tang CY. Tuning parameter selection in high dimensional penalized likelihood. J R Stat Soc B. 2013;75(3):531-52.
80. Park H, Sakaori F, Konishi S. Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria. J Stat Comput Simul. 2013;84(7):1596-607.
81. Bühlmann P, Geer Svd. Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Science & Business Media; 2011.
82. Zhang C-H, Huang J. The sparsity and bias of the Lasso selection in high-dimensional linear regression. Ann Stat. 2008;36(4):1567-94.
83. Sass S, Buettner F, Mueller NS, Theis FJ. A modular framework for gene set analysis integrating multilevel omics data. Nucleic Acids Res. 2013;41(21):9622-33.
84. Minka TP, editor. Expectation Propagation for Approximate Bayesian Inference2001. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc; 2001.
85. Isci S, Dogan H, Ozturk C, Otu HH. Bayesian Network Prior: Network Analysis of Biological Data Using External Knowledge. Bioinformatics. 2013.
86. Reshetova P, Smilde AK, Kampen AHCv, Westerhuis JA. Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data. BMC Systems Biology. 2014;8(Suppl 2).
87. Dolédec S, Chessel D. Co-inertia analysis: an alternative method for studying species-environment relationships. Freshw Biol. 1994;31(3):277-94.
88. Fagan A, Culhane AC, Higgins DG. A multivariate analysis approach to the integration of proteomic and gene expression data. Proteomics. 2007;7(13):2162-71.
89. Culhane AC, Perriere G, Higgins DG. Cross-platform comparison and visualisation of gene expression data using co-inertia analysis. BMC Bioinformatics. 2003;4:59.
90. Meng C, Kuster B, Culhane AC, Gholami AM. A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics. 2014;15:162.
91. Alter O, Brown PO, Botstein D. Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms. Proc Natl Acad Sci U S A. 2003;100(6):3351-6.
92. Hartigan JA. Direct Clustering of a Data Matrix. J Am Stat Assoc. 1972;67(337):123-9.
93. Cheng Y, Church GM. Biclustering of expression data. Proceedings / International Conference on Intelligent Systems for Molecular Biology. ISMB Int Conf Intell Syst Mol Biol. 2000;8:93-103.
94. Tomescu OA, Mattanovich D, Thallinger GG. Integrative omics analysis. A study based on Plasmodium falciparum mRNA and protein data. BMC Syst Biol. 2014;8 Suppl 2:S4.
95. Hamid JS, Greenwood CMT, Beyene J. Weighted kernel Fisher discriminant analysis for integrating heterogeneous data. Comput Stat Data Anal. 2012;56(6):2031-40.
96. Haider S, Pal R. Integrated analysis of transcriptomic and proteomic data. Curr Genomics. 2013;14(2):91-110.
97. Chen G, Gharib TG, Huang CC, Taylor JM, Misek DE, Kardia SL, et al. Discordant protein and mRNA expression in lung adenocarcinomas. Mol Cell Proteomics. 2002;1(4):304-13.
98. Gygi SP, Rochon Y, Franza BR, Aebersold R. Correlation between protein and mRNA abundance in yeast. Mol Cell Biol. 1999;19(3):1720-30.
99. Yeung ES. Genome-wide correlation between mRNA and protein in a single cell. Angew Chem Int Ed Engl. 2011;50(3):583-5.
100. Van den Bulcke T, Lemmens K, Van de Peer Y, Marchal K. Inferring Transcriptional Networks by Mining 'Omics' Data. Curr Bioinforma. 2006;1(3):301-13.
101. Hwang D, Smith JJ, Leslie DM, Weston AD, Rust AG, Ramsey S, et al. A data integration methodology for systems biology: experimental verification. Proc Natl Acad Sci U S A. 2005;102(48):17302-7.
102. Nagarajan R, Scutari M, Lèbre S. Bayesian Networks in R: with Applications in Systems Biology. Springer Science & Business Media; 2013.
103. Friedman N, Linial M, Nachman I, Pe'er D. Using Bayesian networks to analyze expression data. J Comput Biol. 2000;7(3-4):601-20.
104. Huang S, Li J, Ye J, Fleisher A, Chen K, Wu T, et al. A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data. IEEE Trans Pattern Anal Mach Intell. 2013;35(6):1328-42.
105. Hoffman MM, Buske OJ, Wang J, Weng Z, Bilmes JA, Noble WS. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods. 2012;9(5):473-6.
106. Allen JD, Xie Y, Chen M, Girard L, Xiao G. Comparing Statistical Methods for Constructing Large Scale Gene Networks. PLoS One. 2012;7(1).
107. Hu P, Greenwood CM, Beyene J. Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models. BMC Bioinformatics. 2005;6:128.
108. Yoo S, Huang T, Campbell JD, Lee E, Tu Z, Geraci MW, et al. MODMatcher: Multi-Omics Data Matcher for Integrative Genomic Analysis. PLoS Comput Biol. 2014;10(8):e1003790.
109. Wang XF. Joint generalized models for multidimensional outcomes: a case study of neuroscience data from multimodalities. Biom J. 2012;54(2):264-80.
110. Batmanghelich NK, Dalca AV, Sabuncu MR, Polina G. Joint modeling of imaging and genetics. Inf Process Med Imaging. 2013;23:766-77.
111. O'Reilly PF, Hoggart CJ, Pomyen Y, Calboli FC, Elliott P, Jarvelin MR, et al. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS One. 2012;7(5):e34861.
112. Chu JH, Hersh CP, Castaldi PJ, Cho MH, Raby BA, Laird N, et al. Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD. BMC Syst Biol. 2014;8:78.
113. Grosdidier S, Ferrer A, Faner R, Pinero J, Roca J, Cosio B, et al. Network medicine analysis of COPD multimorbidities. Respir Res. 2014;15(1):111.
114. Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56-68.
115. Ozdemir V, Kolker E, Hotez PJ, Mohin S, Prainsack B, Wynne B, et al. Ready to put metadata on the post-2015 development agenda? Linking data publications to responsible innovation and science diplomacy. Omics. 2014;18(1):1-9.
116. Snyder M, Mias G, Stanberry L, Kolker E. Metadata checklist for the integrated personal OMICS study: proteomics and metabolomics experiments. Omics. 2014;18(1):81-5.
117. Kolker E, Ozdemir V, Martens L, Hancock W, Anderson G, Anderson N, et al. Toward more transparent and reproducible omics studies through a common metadata checklist and data publications. Omics. 2014;18(1):10-4.
118. Ioannidis JP, Khoury MJ. Improving validation practices in "omics" research. Science. 2011;334(6060):1230-2.
119. Hand DJ. Deconstructing Statistical Questions. J R Stat Soc Ser A Stat Soc. 1994;157(3):317-56.