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PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform

Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, w...

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Autores principales: Wang, Yimin, Ling, Yunchao, Gong, Jiao, Zhao, Xiaohan, Zhou, Hanwen, Xie, Bo, Lou, Haiyi, Zhuang, Xinhao, Jin, Li, Fan, Shaohua, Zhang, Guoqing, Xu, Shuhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825616/
https://www.ncbi.nlm.nih.gov/pubmed/36243989
http://dx.doi.org/10.1093/nar/gkac905
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author Wang, Yimin
Ling, Yunchao
Gong, Jiao
Zhao, Xiaohan
Zhou, Hanwen
Xie, Bo
Lou, Haiyi
Zhuang, Xinhao
Jin, Li
Fan, Shaohua
Zhang, Guoqing
Xu, Shuhua
author_facet Wang, Yimin
Ling, Yunchao
Gong, Jiao
Zhao, Xiaohan
Zhou, Hanwen
Xie, Bo
Lou, Haiyi
Zhuang, Xinhao
Jin, Li
Fan, Shaohua
Zhang, Guoqing
Xu, Shuhua
author_sort Wang, Yimin
collection PubMed
description Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, we developed the database PGG.SV to provide a practical platform for both regionally and globally representative structural variants. In its current version, PGG.SV archives 584 277 SVs obtained from whole-genome sequencing data of 6048 samples, including 1030 long-read sequencing genomes representing 177 global populations. PGG.SV provides (i) high-quality SVs with fine-scale and precise genomic locations in both GRCh37 and GRCh38, covering underrepresented SVs in existing sequencing and microarray data; (ii) hierarchical estimation of SV prevalence in geographical populations; (iii) informative annotations of SV-related genes, potential functions and clinical effects; (iv) an analysis platform to facilitate SV-based case-control association studies and (v) various visualization tools for understanding the SV structures in the human genome. Taken together, PGG.SV provides a user-friendly online interface, easy-to-use analysis tools and a detailed presentation of results. PGG.SV is freely accessible via https://www.biosino.org/pggsv.
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spelling pubmed-98256162023-01-10 PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform Wang, Yimin Ling, Yunchao Gong, Jiao Zhao, Xiaohan Zhou, Hanwen Xie, Bo Lou, Haiyi Zhuang, Xinhao Jin, Li Fan, Shaohua Zhang, Guoqing Xu, Shuhua Nucleic Acids Res Database Issue Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, we developed the database PGG.SV to provide a practical platform for both regionally and globally representative structural variants. In its current version, PGG.SV archives 584 277 SVs obtained from whole-genome sequencing data of 6048 samples, including 1030 long-read sequencing genomes representing 177 global populations. PGG.SV provides (i) high-quality SVs with fine-scale and precise genomic locations in both GRCh37 and GRCh38, covering underrepresented SVs in existing sequencing and microarray data; (ii) hierarchical estimation of SV prevalence in geographical populations; (iii) informative annotations of SV-related genes, potential functions and clinical effects; (iv) an analysis platform to facilitate SV-based case-control association studies and (v) various visualization tools for understanding the SV structures in the human genome. Taken together, PGG.SV provides a user-friendly online interface, easy-to-use analysis tools and a detailed presentation of results. PGG.SV is freely accessible via https://www.biosino.org/pggsv. Oxford University Press 2022-10-16 /pmc/articles/PMC9825616/ /pubmed/36243989 http://dx.doi.org/10.1093/nar/gkac905 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://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 Database Issue
Wang, Yimin
Ling, Yunchao
Gong, Jiao
Zhao, Xiaohan
Zhou, Hanwen
Xie, Bo
Lou, Haiyi
Zhuang, Xinhao
Jin, Li
Fan, Shaohua
Zhang, Guoqing
Xu, Shuhua
PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform
title PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform
title_full PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform
title_fullStr PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform
title_full_unstemmed PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform
title_short PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform
title_sort pgg.sv: a whole-genome-sequencing-based structural variant resource and data analysis platform
topic Database Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9825616/
https://www.ncbi.nlm.nih.gov/pubmed/36243989
http://dx.doi.org/10.1093/nar/gkac905
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