Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes
Year: 2017
Type: article
Abstract: The 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.
Source: Briefings in Bioinformatics
DOI: 10.1093/bib/bbx149
Institutions: University of Arizona, BIO5 Institute, University of Nevada, Reno
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,885
Funders: Division of Intramural Research, National Institute of Allergy and Infectious Diseases, University of Arizona, National Institutes of Health, Health Sciences, University of Arizona, National Heart, Lung, and Blood Institute, NIH Office of the Director, National Cancer Institute, National Institute of Allergy and Infectious Diseases
Awards: G1361187950, G1717768068, G179895613, G1889358111, Immuno-Genetic Basis for Human Disseminated Coccidioidomycosis, G2071803158, G2277093813, University of Arizona - Banner Health Precision Medicine Initiative Cohort Enrollment Center, G2767500979, G2986134023, G4106873893, G4690227025, G6170531065, G7601354740, G7608945919, G9000025088, G9000081785, G9000084197, G9000111835, G9000164432, G9000177445, Cytoskeletal Regulation of Lung Endothelial Pathobiology in ARDS
OpenAlex ID: W2778770310