Detecting structural variants in a large population of samples through high-throughput sequencing data

通过高通量测序数据检测大量样本中的结构变异

• 批准号: 10797960
• 负责人: Xin Zhou
• 金额: $ 5.26万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10797960
• 关键词: Algorithms; Area; Base Pairing; Complex; Copy Number Polymorphism; DNA sequencing; Data; Data Set; Detection; Development; Diploidy; Disease; Etiology; Generations; Genetic; Genetic Predisposition to Disease; Genetic Variation; Genomics; Haplotypes; Hereditary Disease; High-Throughput Nucleotide Sequencing; Human Genome; Individual; Link; Linkage Disequilibrium Mapping; Malignant Neoplasms; Maps; Methods; Patients; Pattern; Phase; Phenotype; Population; Population Control; Research; Risk; Sampling; Technology; Third Generation Sequencing; Variant; computerized tools; design; genome wide association study; genome-wide; improved; insight; single-cell RNA sequencing; tool; transcriptome sequencing; whole genome

PROJECT SUMMARY The mapping of the human genome and genome wide association studies have provided great insights in our understanding of the genetic etiology of hereditary diseases; however, critical gaps remain. A type of genetic variations that has been difficult to detect in genomic studies has been Structural Variants (SVs), disruptions involving more than 50 base pairs. SVs have been implicated in a lot of inherited diseases and cancers, yet their detection remains challenging with conventional DNA sequencing methods. Developments in third- generation sequencing (linked-read and long-read sequencing) and single-cell RNA sequencing (scRNA-seq) provide an opportunity to greatly improve the detection of SVs and Copy Number Variations (CNVs), one common type of SVs. However, existing computational tools do not fully take advantage of the potential and the opportunities that these technologies offer. In this project, drawing from our unique expertise in this rapidly evolving area, we propose the development of a new generation of tools that will improve greatly the detection and phasing of SVs from a large population of samples. We will develop computational tools to generate a high-quality diploid assembly from each individual and to combine data from large populations of controls and patients to characterize SVs that confer risk for any particular disease. We will further design a haplotype- based linkage disequilibrium (LD) mapping approach at the whole genome scale to identify unique sharing haplotype patterns and provide a new perspective for complex disease studies. Detecting SVs in combination with small variants will further allow us to explain the etiology of complex diseases. We will also develop algorithms to detect CNVs from scRNA-seq datasets, which have application in cancer studies. Successful completion of this project will constitute a major step forward in uncovering the genetic cause of complex diseases and cancers.

项目总结

项目成果

ADEPT: Autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering.

• DOI: 10.1016/j.isci.2023.106792
• 发表时间: 2023-06-16
• 期刊: ISCIENCE
• 影响因子: 5.8
• 作者: Hu, Yunfei;Zhao, Yuying;Schunk, Curtis T.;Ma, Yingxiang;Derr, Tyler;Zhou, Xin Maizie
• 通讯作者: Zhou, Xin Maizie

Leveraging cross-source heterogeneity to improve the performance of bulk gene expression deconvolution.

利用跨源异质性来提高批量基因表达反卷积的性能。

• DOI: 10.1101/2024.04.07.588458
• 发表时间: 2024
• 期刊: bioRxiv : the preprint server for biology
• 作者: Shen,Wenjun;Liu,Cheng;Hu,Yunfei;Lei,Yuanfang;Wong,Hau-San;Wu,Si;Zhou,XinMaizie
• 通讯作者: Zhou,XinMaizie