Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes
AffiliationUniv Arizona, Ctr Biomed Informat & Biostat
MetadataShow full item record
PublisherOXFORD UNIV PRESS
CitationFrancesca Vitali, Qike Li, A Grant Schissler, Joanne Berghout, Colleen Kenost, Yves A Lussier, Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes, Briefings in Bioinformatics, Volume 20, Issue 3, May 2019, Pages 789–805, https://doi.org/10.1093/bib/bbx149
JournalBRIEFINGS IN BIOINFORMATICS
Rights© The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractThe development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile (personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.
NoteOpen access article
VersionFinal published version
SponsorsNational Institute of Health (NIH)/Office of the Director Precision Medicine Initiative [1UG3OD023171-01]; Precision Medicine Initiative of the Center for Biomedical Informatics and Biostatistics of the University of Arizona Health Sciences; NIH/National Heart, Lung, and Blood Institute [HL126609-01, HL132523, U01 HL125208]; NIH/National Cancer Institute [P30CA023074, 1R01CA190696-01]; NIH/National Institute of Allergy and Infectious Diseases [U01AI122275-01]
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