Predicting age from the transcriptome of human dermal fibroblasts

Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the t...

Descripción completa

Detalles Bibliográficos
Autores principales: Fleischer, Jason G., Schulte, Roberta, Tsai, Hsiao H., Tyagi, Swati, Ibarra, Arkaitz, Shokhirev, Maxim N., Huang, Ling, Hetzer, Martin W., Navlakha, Saket
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Short Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6300908/
https://www.ncbi.nlm.nih.gov/pubmed/30567591
http://dx.doi.org/10.1186/s13059-018-1599-6
_version_ 1783381766859390976
author Fleischer, Jason G.
Schulte, Roberta
Tsai, Hsiao H.
Tyagi, Swati
Ibarra, Arkaitz
Shokhirev, Maxim N.
Huang, Ling
Hetzer, Martin W.
Navlakha, Saket
author_facet Fleischer, Jason G.
Schulte, Roberta
Tsai, Hsiao H.
Tyagi, Swati
Ibarra, Arkaitz
Shokhirev, Maxim N.
Huang, Ling
Hetzer, Martin W.
Navlakha, Saket
author_sort Fleischer, Jason G.
collection PubMed
description Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1599-6) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-6300908
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-63009082018-12-31 Predicting age from the transcriptome of human dermal fibroblasts Fleischer, Jason G. Schulte, Roberta Tsai, Hsiao H. Tyagi, Swati Ibarra, Arkaitz Shokhirev, Maxim N. Huang, Ling Hetzer, Martin W. Navlakha, Saket Genome Biol Short Report Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1599-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-20 /pmc/articles/PMC6300908/ /pubmed/30567591 http://dx.doi.org/10.1186/s13059-018-1599-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Short Report
Fleischer, Jason G.
Schulte, Roberta
Tsai, Hsiao H.
Tyagi, Swati
Ibarra, Arkaitz
Shokhirev, Maxim N.
Huang, Ling
Hetzer, Martin W.
Navlakha, Saket
Predicting age from the transcriptome of human dermal fibroblasts
title Predicting age from the transcriptome of human dermal fibroblasts
title_full Predicting age from the transcriptome of human dermal fibroblasts
title_fullStr Predicting age from the transcriptome of human dermal fibroblasts
title_full_unstemmed Predicting age from the transcriptome of human dermal fibroblasts
title_short Predicting age from the transcriptome of human dermal fibroblasts
title_sort predicting age from the transcriptome of human dermal fibroblasts
topic Short Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6300908/
https://www.ncbi.nlm.nih.gov/pubmed/30567591
http://dx.doi.org/10.1186/s13059-018-1599-6
work_keys_str_mv AT fleischerjasong predictingagefromthetranscriptomeofhumandermalfibroblasts
AT schulteroberta predictingagefromthetranscriptomeofhumandermalfibroblasts
AT tsaihsiaoh predictingagefromthetranscriptomeofhumandermalfibroblasts
AT tyagiswati predictingagefromthetranscriptomeofhumandermalfibroblasts
AT ibarraarkaitz predictingagefromthetranscriptomeofhumandermalfibroblasts
AT shokhirevmaximn predictingagefromthetranscriptomeofhumandermalfibroblasts
AT huangling predictingagefromthetranscriptomeofhumandermalfibroblasts
AT hetzermartinw predictingagefromthetranscriptomeofhumandermalfibroblasts
AT navlakhasaket predictingagefromthetranscriptomeofhumandermalfibroblasts

Ejemplares similares