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Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6585155/ https://www.ncbi.nlm.nih.gov/pubmed/29272327 http://dx.doi.org/10.1093/bib/bbx149 |
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author | Vitali, Francesca Li, Qike Schissler, A Grant Berghout, Joanne Kenost, Colleen Lussier, Yves A |
author_facet | Vitali, Francesca Li, Qike Schissler, A Grant Berghout, Joanne Kenost, Colleen Lussier, Yves A |
author_sort | Vitali, Francesca |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6585155 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-65851552019-06-25 Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes Vitali, Francesca Li, Qike Schissler, A Grant Berghout, Joanne Kenost, Colleen Lussier, Yves A Brief Bioinform Paper 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. Oxford University Press 2017-12-18 /pmc/articles/PMC6585155/ /pubmed/29272327 http://dx.doi.org/10.1093/bib/bbx149 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Paper Vitali, Francesca Li, Qike Schissler, A Grant Berghout, Joanne Kenost, Colleen Lussier, Yves A Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes |
title | Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes |
title_full | Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes |
title_fullStr | Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes |
title_full_unstemmed | Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes |
title_short | Developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes |
title_sort | developing a ‘personalome’ for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes |
topic | Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6585155/ https://www.ncbi.nlm.nih.gov/pubmed/29272327 http://dx.doi.org/10.1093/bib/bbx149 |
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