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Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes
Open accessAbstract
Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.
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“However, a single layer of ‘omics’ can only provide limited insights into the biological mechanisms of a disease.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Chen et al., 2012
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“none of these approaches are truly integrative on the population scale and compare the predictive nature and value of the various data types in the context of biomedicine.”
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Flagged first, then by citations
2 of the 108 references couldn’t be matched to a known paper.
Research works printed but not linked: 20
- Adzhubei, I.A., Schmidt, S., Peshkin, L., Ramensky, V.E., Gerasimova, A., Bork, P., Kondrashov, A.S., and Sunyaev, S.R. (2010). A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249. · DOI · Paper page
- Ahdesmäki, M., Lähdesmäki, H., Gracey, A., Shmulevich, L., and Yli-Harja, O. (2007). Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data. BMC Bioinformatics 8, 233. · DOI
- Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al.; The Gene Ontology Consortium. (2000). Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29. · DOI · Paper page
- Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc., B 57, 289–300. · DOI · Paper page
- Croft, D., O'Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., Gopinath, G., Jassal, B., et al. (2011). Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 39 (Database issue), D691–D697. · DOI · Paper page
- Hindson, B.J., Ness, K.D., Masquelier, D.A., Belgrader, P., Heredia, N.J., Makarewicz, A.J., Bright, I.J., Lucero, M.Y., Hiddessen, A.L., Legler, T.C., et al. (2011). High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal. Chem. 83, 8604–8610. · DOI · Paper page
- Hocke, K., and Kämpfer, N. (2009). Gap filling and noise reduction of unevenly sampled data by means of the Lomb-Scargle periodogram. Atmos. Chem. Phys. 9, 4197–4206. · DOI
- Huang, W., Sherman, B.T., and Lempicki, R.A. (2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13. · DOI · Paper page
- Kersey, P.J., Duarte, J., Williams, A., Karavidopoulou, Y., Birney, E., and Apweiler, R. (2004). The International Protein Index: an integrated database for proteomics experiments. Proteomics 4, 1985–1988. · DOI
- Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., and Durbin, R.; 1000 Genome Project Data Processing Subgroup. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079. · DOI · Paper page
- Lomb, N. (1976). Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39, 447–462. · DOI
- Maere, S., Heymans, K., and Kuiper, M. (2005). BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448–3449. · DOI
- Parkhomchuk, D., Borodina, T., Amstislavskiy, V., Banaru, M., Hallen, L., Krobitsch, S., Lehrach, H., and Soldatov, A. (2009). Transcriptome analysis by strand-specific sequencing of complementary DNA. Nucleic Acids Res. 37, e123. · DOI
- Scargle, J.D. (1982). Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data. Astrophys. J. 263, 835–853. · DOI
- Scargle, J.D. (1989). Studies in astronomical time series analysis. III-Fourier transforms, autocorrelation functions, and cross-correlation functions of unevenly spaced data. Astrophys. J. 343, 874–887. · DOI
- Trapnell, C., Pachter, L., and Salzberg, S.L. (2009). TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111. · DOI
- Trapnell, C., Williams, B.A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M.J., Salzberg, S.L., Wold, B.J., and Pachter, L. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515. · DOI · Paper page
- Van Dongen, H.P., Olofsen, E., VanHartevelt, J.H., and Kruyt, E.W. (1999). A procedure of multiple period searching in unequally spaced time-series with the Lomb-Scargle method. Biol. Rhythm Res. 30, 149–177. · DOI
- Yang, R., Zhang, C., and Su, Z. (2011). LSPR: an integrated periodicity detection algorithm for unevenly sampled temporal microarray data. Bioinformatics 27, 1023–1025. · DOI
- Zhao, W., Agyepong, K., Serpedin, E., and Dougherty, E.R. (2008). Detecting periodic genes from irregularly sampled gene expressions: a comparison study. EURASIP J. Bioinform. Syst. Biol. 2008, 769293. · DOI
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Self-citationMardinoğlu, A., Wu, H., Björnson, E., Zhang, C., Hakkarainen, A., Räsänen, S., Lee, S., Mancina, R. M., Bergentall, M., Pietiläinen, K. H., et al. (2018) · Cell Metabolism · doi:10.1016/j.cmet.2018.01.005 · Cited by 465
Self-citationMolecular genetic testing and the future of clinical genomics
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–Omic and Electronic Health Record Big Data Analytics for Precision Medicine
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Picache, J. A., Rose, B. S., Balinski, A., Leaptrot, K. L., Sherrod, S. D., May, J. C., & MCLEAN, J. A. J. (2018) · Chemical Science · doi:10.1039/c8sc04396e · Cited by 289
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Bush, W. S., Oetjens, M. T., & Crawford, D. C. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.36 · Cited by 269
Application of ’omics technologies to biomarker discovery in inflammatory lung diseases
Wheelock, C. E., Goss, V. M., Balgoma, D., Nicholas, B., Brandsma, J., Skipp, P. J. S., Snowden, S. G., Burg, D. W., D’Amico, A., Horváth, I., et al. (2013) · European Respiratory Journal · doi:10.1183/09031936.00078812 · Cited by 275
Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple ‘Omic’ Layers
Yugi, K., Kubota, H., Hatano, A., & Kuroda, S. (2016) · Trends in Biotechnology · doi:10.1016/j.tibtech.2015.12.013 · Cited by 258
The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery
Dzobo, K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820472-6.00041-4 · Cited by 300
Breton, C. V., Marsit, C. J., Faustman, E. M., Nadeau, K. C., Goodrich, J. M., Dolinoy, D. C., Herbstman, J. B., Holland, N., LaSalle, J. M., Schmidt, R. J., et al. (2017) · Environmental Health Perspectives · doi:10.1289/ehp595 · Cited by 271
Self-citationDissemination of scientific software with Galaxy ToolShed
Blankenberg, D. J., Kuster, G. V., Bouvier, E., Baker, D., Afgan, E., Stoler, N., Galaxy Team, Taylor, J., & Nekrutenko, A. (2014) · Genome biology · doi:10.1186/gb4161 · Cited by 258
Pharmacogenomics and Individualized Medicine: Translating Science Into Practice
Crews, K. R., Hicks, J. K., Pui, C.-H., Relling, M. V., & Evans, W. E. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.120 · Cited by 255
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Tan, P., Chen, X., Zhang, H., Wei, Q., & Luo, K. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.01.005 · Cited by 256
Ritchie, S. C., Würtz, P., Nath, A. P., Abraham, G., Havulinna, A. S., Fearnley, L. G., Sarin, A., Kangas, A. J., Soininen, P., Aalto, K., et al. (2015) · Cell Systems · doi:10.1016/j.cels.2015.09.007 · Cited by 255
Real-time metabolome profiling of the metabolic switch between starvation and growth
Link, H., Fuhrer, T., Gerosa, L., Zamboni, N., & Sauer, U. (2015) · Nature Methods · doi:10.1038/nmeth.3584 · Cited by 251
Ahmad, T., Lund, L. H., Rao, P., Ghosh, R., Warier, P., Vaccaro, B. J., Dahlström, U., O’Connor, C. M., Felker, G. M., & Desai, N. R. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.008081 · Cited by 255
Genomic Medicine: A Decade of Successes, Challenges, and Opportunities
McCarthy, J. J., McLeod, H. L., & Ginsburg, G. S. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3005785 · Cited by 242
Wearable sensors enable personalized predictions of clinical laboratory measurements
Dunn, J., Kidzinski, L., Runge, R., Witt, D., Hicks, J. L., Schüssler-Fiorenza Rose, S. M., Li, X., Bahmani, A., Delp, S. L., Hastie, T., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01339-0 · Cited by 244
Self-citationPersonalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance
de Castro, D. G., Clarke, P. A., Al‐Lazikani, B., & Workman, P. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.237 · Cited by 237
Nakayasu, E. S., Nicora, C., Sims, A. M., Burnum-Johnson, K., Kim, Y., Kyle, J. E., Matzke, M. M., Shukla, A. K., Chu, R., Schepmoes, A., et al. (2016) · mSystems · doi:10.1128/msystems.00043-16 · Cited by 235
Sagner, M., McNeil, A., Puska, P., Auffray, C., Price, N. D., Hood, L., Lavie, C. J., Han, Z., Zhu, C. H., Brahmachari, S. K., et al. (2016) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2016.08.002 · Cited by 243
Contrepois, K., Jiang, L., & Snyder, M. P. (2015) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m114.046508 · Cited by 230
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Jiang, C., Wang, X., Li, X., Inlora, J., Wang, T., Liu, Q., & Snyder, M. (2018) · Cell · doi:10.1016/j.cell.2018.08.060 · Cited by 218
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Wild, C. P., Scalbert, A., & Herceg, Z. (2013) · Environmental and Molecular Mutagenesis · doi:10.1002/em.21777 · Cited by 239
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Wang, I. X., So, E., Devlin, J. L., Zhao, Y., Wu, M., & Cheung, V. G. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.10.002 · Cited by 225
Characterizing Extracellular Vesicles and Their Diverse RNA Contents
Veziroglu, E. M., & Mias, G. I. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00700 · Cited by 229
Self-citationA public resource facilitating clinical use of genomes
Ball, M. P., Thakuria, J. V., Zaranek, A. W., Clegg, T., Rosenbaum, A. M., Wu, X., Angrist, M. H., Bhak, J., Bobe, J. R., Callow, M. J., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1201904109 · Cited by 224
Self-citationNext-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications
Kulski, J. K. (2016) · InTech eBooks · doi:10.5772/61964 · Cited by 248
Beltran, P. M. J., Federspiel, J. D., Sheng, X., & Cristea, I. M. (2017) · Molecular Systems Biology · doi:10.15252/msb.20167062 · Cited by 218
Tebani, A., Afonso, C., Marret, S., & Bekri, S. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17091555 · Cited by 228
Integrative Personal Omics Profiles during Periods of Weight Gain and Loss
Piening, B., Zhou, W., Contrepois, K., Röst, H. L., Gu, G. J., Mishra, T., Hanson, B., Bautista, E. J., Leopold, S. R., Yeh, C. Y., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2017.12.013 · Cited by 212
Self-citationShameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbv118 · Cited by 198
Self-citationCarlberg, C. (2019) · Nutrients · doi:10.3390/nu11030676 · Cited by 211
Human diseases through the lens of network biology
Furlong, L. I. (2012) · Trends in Genetics · doi:10.1016/j.tig.2012.11.004 · Cited by 196
Zhang, A., Sun, H. J., Xu, H., Qiu, S., & Wang, X. (2013) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2012.0090 · Cited by 198
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Le-Niculescu, H., Levey, D. F., Ayalew, M., Palmer, L., Gavrin, L. M., Jain, N., Winiger, E., Bhosrekar, S., Shankar, G., Radel, M., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.95 · Cited by 193
Elenko, E., Underwood, L., & Zohar, D. (2015) · Nature Biotechnology · doi:10.1038/nbt.3222 · Cited by 191
Multiplexed Target Detection Using DNA-Binding Dye Chemistry in Droplet Digital PCR
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Metadata
- Authors
- Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y. K., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., Cheng, Y., Clark, M. J., Im, H., Habegger, L., Balasubramanian, S., O'Huallachain, M., Dudley, J. T., Hillenmeyer, S., Haraksingh, R., Sharon, D., Euskirchen, G., Lacroute, P., Bettinger, K., Boyle, A. P., Kasowski, M., Grubert, F., Seki, S., Garcia, M., Whirl-Carrillo, M., Gallardo, M., Blasco, M. A., Greenberg, P. L., Snyder, P., Klein, T. E., Altman, R. B., Butte, A. J., Ashley, E. A., Gerstein, M., Nadeau, K. C., Tang, H., & Snyder, M.
- Affiliations
- Stanford Medicine (US)
- Yale University (US)
- Stanford University (US)
- Spanish National Cancer Research Centre (ES)
- Corresponding author
- Snyder, Michael
- ORCID
- Chen, Rui
- Mias, George I.
- Jiang, Lihua
- Lam, Hugo Y.K.
- Chen, Rong
- Karczewski, Konrad J.
- Hariharan, Manoj
- Cheng, Yong
- Im, Hogune
- Balasubramanian, Suganthi
- Dudley, Joel T.
- Haraksingh, Rajini
- Boyle, Alan P.
- Kasowski, Maya
- Seki, Scott
- Greenberg, Peter L.
- Klein, Teri E.
- Altman, Russ B.
- Butte, Atul J.
- Ashley, Euan A.
- Gerstein, Mark
- Nadeau, Kari C.
- Tang, Hua
- Snyder, Michael
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 148
- Issue
- 6
- Pages
- 1293-1307
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC3341616
- OpenAlex
- W2079859830
- Cited by
- 1,291
- References
- 108
- Citations by year
- 2026: 39 · 2025: 44 · 2024: 56 · 2023: 47 · 2022: 89 · 2021: 79 · 2020: 62 · 2019: 84 · 2018: 108 · 2017: 72 · 2016: 100 · 2015: 121 · 2014: 125 · 2013: 169 · 2012: 93
- FWCI
- 65.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (OD004613)
- National Heart, Lung, and Blood Institute (K08HL083914, T32HL094274)
- National Human Genome Research Institute (P50HG002357, U54HG004558, T32HG000044)
- National Institute of General Medical Sciences (R24GM061374, R24-GM61374, T32GM007205)
- U.S. National Library of Medicine (T15LM007033)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Keywords
- multi-omics integration, type 2 diabetes risk, RNA editing, autoantibodies, transcriptomics, proteomics, metabolomics, personalized medicine
- MeSH terms
- Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, Mutation, Respiratory Syncytial Viruses, Rhinovirus, Genome, Human, Gene Expression Profiling, Genomics, Proteomics, Metabolomics, Precision Medicine
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