Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes

By Rui Chen, G. Mias, J. Li-Pook-Than, Lihua Jiang, Hugo Y. K. Lam, Rong Chen, E. Miriami, Konrad J. Karczewski, M. Hariharan, F. Dewey and 30 more. Cell, 2012

1,224 citations recorded by the source corpus.

SUMMARY 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 II 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, discovered 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 disease states by connecting genomic information with additional dynamic omics activity.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/df4a7ef7e4d2da9c5c939ae3de7853152ccc81f1

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Citations in the corpus

Next-generation sequencing data interpretation: enhancing reproducibility and accessibility (2012, background): “These individual applications can be combined into larger studies, such as the recently published genomic profiling of a human individual whose genome was sequenced and gene expression tracked over an extended period in a series of RNA-seq experiments 7 .”
Big Data Analytics in Healthcare (2015, methodology): “The integrative personal omics profile (iPOP) combines physiological monitoring and multiple high-throughput methods for genome sequencing to generate a detailed health and disease states of a subject [23].”
Big Data: Astronomical or Genomical? (2015, background): “For medicine, just having the genome will not be sufficient: for each individual, it will need to be coupled with other relevant ‘ omics data sets, some collected periodically and from different tissues, to compare healthy and diseased states [26].”
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Challenges of Big Data Analysis (2013, background): “For example, the large amount of genome sequencing data now make it possible to uncover the genetic markers of rare disorders (Worthey et al., 2010; Chen et al., 2012) and ﬁnd associations between diseases and rare sequence variants (Cohen et al., 2004; Han and Pan, 2010).”
Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery (2018, background): “For “omics” data to be meaningful and to revolutionise clinical medicine, clinical phenotype data has to be integrated with genomic, transcriptomic, proteomic and epigenomic data [33,193,209].”
Integrative approaches for finding modular structure in biological networks (2013, background): “The second group of methods for active module identification emulates the related concepts of diffusion flow and network propagation 36,37,45,67–72 .”
Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2015, methodology): “…multiple data types were used for evaluating the health status of the individual patients [25] including the widely popularized “Snyderome” project [26], 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 Science of the Individual (2013, background): “Take, for example, a recent study on personal genomics (Chen et al., 2012).”