The team of Xu Shuhua/Zhang Guoqing/Fan Shaohua released the PGG.SV of the human genome structural variation database and computational analysis platform

On October 16, 2022, the internationally renowned academic journal Nucleic Acids Research The human genome structural variation PGG.
SV database developed by Professor Xu Shuhua's team from the School of Life Sciences of Fudan University, Zhang Guoqing, researcher at the Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, and Fan Shaohua, a researcher from the School of Life Sciences, Fudan University, was published online the article is titled "PGG.
SV: A whole-genome-sequencing-based structural variant resource and data analysis platform"
。 By collecting whole genome sequencing data of the global population, the database focuses on the mining and integration of genomic structural variation data, providing a comprehensive platform
for data acquisition, information query and online analysis for the study of human genome structural variation.

Genomic structural variants (SVs) mainly include DNA deletions, insertions, fragment duplications and other variant types of large fragments on the genome, and a large number of studies have shown that SV is associated with cancer, Autism, neurodevelopmental disorders and other complex genetic diseases are related, and have been attracting attention
in the fields of medicine and genetics in recent years.
With the advancement and popularization of genome sequencing technology, a large number of structural variants have been continuously discovered and studied, and some structural variants with strong pathogenicity have gradually been verified
.
The research team aims to construct a representative and diverse dataset of genomic structural variation in healthy people, on the one hand, to provide reliable control samples for the study of structural variation in patients with genetic diseases, and on the other hand, to annotate and predict the function of variation, which will also effectively narrow the screening scope of pathogenic mutations and provide effective guidance and assistance
to researchers in related fields.

Due to the significant differences and diversity of structural variants among different regions and ethnic groups, and the existing databases and public datasets each adopt different analysis processes, there has been a lack of a structural variation resource and analysis platform with population samples and next-generation sequencing data, especially the coverage of East Asian population samples is seriously insufficient
.
Using the integration of large-scale sequencing data, the research team covered a total of 6,048 whole genome sequencing samples from 177 representative regions and ethnic groups around the world, especially the in-depth analysis of China's rich ethnic diversity characteristics, covering China for the first time 50 ethnic minorities
.
As of publication, the database contains a total of 584,277 structural variants, which will continue to be added
in the future.
In addition, PGG.
SV includes the third-generation long-reads sequencing data for the first time, which has greater advantages in the detection of structural variations, especially in the detection and determination of insertion sequences, and its effect is significantly better than second-generation sequencing technology
.
Previous databases of large-scale structural variants were built
on next-generation sequencing or gene chip data.
The research team generated and collected 1,030 third-generation sequencing genomes, and for the first time used the combination of third-generation sequencing and second-generation sequencing to construct a structural variation database, which greatly improved the quantity and quality
of structural variation detection results.

1 PGG.
SV Schematic diagram of data processing process

In terms of database function, PGG.
SV provides a concise and friendly query function, providing accurate display of the genomic location of different population structural variations, as well as statistical information
such as frequency differences between various ethnic groups around the world.
Taking advantage of the advantages previously accumulated by the research group, PGG.
SV linked with PGG.
SNV and other databases previously developed by Professor Xu Shuhua's team, and with the help of linkage imbalance and genomic spatial location information, single nucleotide mutation (SNV Detailed results are combined with structural variation to enhance the parsing capabilities
of data diversity.
In addition, PGG.
SV provides rich clinical effect analysis and predictive analysis capabilities, providing prediction and enrichment analysis of genes and functions based on genes and regulatory elements associated with structural variants, as well as tools for retrieving relevant structural variants by specific diseases and phenotypes, so that users with clinical research needs can use
.

Finally, PGG.
SV supports rich online analysis and visualization capabilities
.
On the one hand, the research team provides a comparison and annotation of the structural variation results submitted by the user, so that the user can understand the difference between his own target sample and the control sample provided by the database; On the other hand, the research team also provides structural variation visualization capabilities, which can retrieve user-submitted DNA sequences on the human genome, display the genomic location of related variants, and provide detailed visualization
of changes in the spatial structure of variations.

2 PGG.
SV interface diagram

Overall, PGG.
SV provides a high-quality data resource for population genomic structural variation, greatly improves the detection and display of human genome structural variation information based on next-generation sequencing data, especially for the first time comprehensively covers the diversity of structural variation in East Asian populations and Chinese populations, and provides annotations
of related genes and potential clinical effects 。 In addition, the platform provides a variety of online analysis capabilities, including case-control studies, as well as visualization tools
for structural variation in the human genome.

Yimin Wang, Ling Zunchao, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, and Jiao Gong, Institute of Human Phenome, Fudan University, are the co-first authors
of this paper.
Professor Xu Shuhua, researcher Zhang Guoqing and researcher Fan Shaohua are the co-corresponding authors
of this paper.
The research work has been supported
by the Basic Science Center of the National Foundation of China, the National Natural Science Foundation of China, the Pilot Project of the Chinese Academy of Sciences, the Newton Fund of the Royal Society, and the Shanghai Municipal Major Special Project of the Human Phenotype.

Original link: https://academic.
oup.
com/nar/advance-article/doi/10.
1093/nar/gkac905/6761741?login=false