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EthSEQ: ethnicity annotation from whole exome sequencing data

SUMMARY: Whole exome sequencing (WES) is widely utilized both in translational cancer genomics studies and in the setting of precision medicine. Stratification of individual’s ethnicity is fundamental for the correct interpretation of personal genomic variation impact. We implemented EthSEQ to provi...

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Detalles Bibliográficos
Autores principales: Romanel, Alessandro, Zhang, Tuo, Elemento, Olivier, Demichelis, Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5818140/
https://www.ncbi.nlm.nih.gov/pubmed/28369222
http://dx.doi.org/10.1093/bioinformatics/btx165
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author Romanel, Alessandro
Zhang, Tuo
Elemento, Olivier
Demichelis, Francesca
author_facet Romanel, Alessandro
Zhang, Tuo
Elemento, Olivier
Demichelis, Francesca
author_sort Romanel, Alessandro
collection PubMed
description SUMMARY: Whole exome sequencing (WES) is widely utilized both in translational cancer genomics studies and in the setting of precision medicine. Stratification of individual’s ethnicity is fundamental for the correct interpretation of personal genomic variation impact. We implemented EthSEQ to provide reliable and rapid ethnicity annotation from whole exome sequencing individual’s data, validated it on 1000 Genome Project and TCGA data (2700 samples) demonstrating high precision, and finally assessed computational performances compared to other tools. EthSEQ can be integrated into any WES based processing pipeline and exploits multi-core capabilities. AVAILABILITY AND IMPLEMENTATION: R package available at github.com/aromanel/EthSEQ and CRAN repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-58181402018-02-19 EthSEQ: ethnicity annotation from whole exome sequencing data Romanel, Alessandro Zhang, Tuo Elemento, Olivier Demichelis, Francesca Bioinformatics Applications Notes SUMMARY: Whole exome sequencing (WES) is widely utilized both in translational cancer genomics studies and in the setting of precision medicine. Stratification of individual’s ethnicity is fundamental for the correct interpretation of personal genomic variation impact. We implemented EthSEQ to provide reliable and rapid ethnicity annotation from whole exome sequencing individual’s data, validated it on 1000 Genome Project and TCGA data (2700 samples) demonstrating high precision, and finally assessed computational performances compared to other tools. EthSEQ can be integrated into any WES based processing pipeline and exploits multi-core capabilities. AVAILABILITY AND IMPLEMENTATION: R package available at github.com/aromanel/EthSEQ and CRAN repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2017-08-01 2017-03-27 /pmc/articles/PMC5818140/ /pubmed/28369222 http://dx.doi.org/10.1093/bioinformatics/btx165 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Romanel, Alessandro
Zhang, Tuo
Elemento, Olivier
Demichelis, Francesca
EthSEQ: ethnicity annotation from whole exome sequencing data
title EthSEQ: ethnicity annotation from whole exome sequencing data
title_full EthSEQ: ethnicity annotation from whole exome sequencing data
title_fullStr EthSEQ: ethnicity annotation from whole exome sequencing data
title_full_unstemmed EthSEQ: ethnicity annotation from whole exome sequencing data
title_short EthSEQ: ethnicity annotation from whole exome sequencing data
title_sort ethseq: ethnicity annotation from whole exome sequencing data
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5818140/
https://www.ncbi.nlm.nih.gov/pubmed/28369222
http://dx.doi.org/10.1093/bioinformatics/btx165
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