Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Vitali, Francesca, Li, Qike, Schissler, A Grant, Berghout, Joanne, Kenost, Colleen, Lussier, Yves A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
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
_version_ 1783428652563693568
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
work_keys_str_mv AT vitalifrancesca developingapersonalomeforprecisionmedicineemergingmethodsthatcomputeinterpretableeffectsizesfromsinglesubjecttranscriptomes
AT liqike developingapersonalomeforprecisionmedicineemergingmethodsthatcomputeinterpretableeffectsizesfromsinglesubjecttranscriptomes
AT schissleragrant developingapersonalomeforprecisionmedicineemergingmethodsthatcomputeinterpretableeffectsizesfromsinglesubjecttranscriptomes
AT berghoutjoanne developingapersonalomeforprecisionmedicineemergingmethodsthatcomputeinterpretableeffectsizesfromsinglesubjecttranscriptomes
AT kenostcolleen developingapersonalomeforprecisionmedicineemergingmethodsthatcomputeinterpretableeffectsizesfromsinglesubjecttranscriptomes
AT lussieryvesa developingapersonalomeforprecisionmedicineemergingmethodsthatcomputeinterpretableeffectsizesfromsinglesubjecttranscriptomes