CAREER: Computational methods to improve our understanding of the diversity of genomic structural variation

职业：提高我们对基因组结构变异多样性的理解的计算方法

• 批准号: 2042518
• 负责人: Fereydoun Hormozdiari
• 金额: $ 50.37万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042518&HistoricalAwards=false
• 关键词: CAREER; Computational; methods; improve; our

Structural variations (SVs) are defined as medium and large genome rearrangements. A growing body of evidence has shown that SVs are a major contributing factor to diseases, complex traits, population genomics, and evolution. However, there are many unknowns about SVs including their diversity, complexity, distribution in a population, and exact impact in biology. The recent progress on genome technologies, especially high-throughput sequencing technologies, has provided an opportunity to investigate the complexity of SVs in genomes. However, a lack of computational approaches for efficient discovery and genotyping of different types of (complex) SVs has hindered our ability to comprehensively study the complexity and diversity of SVs in genomes. The goal of this project is to develop novel combinatorial methods to provide researchers with necessary tools to better capture the diversity of SVs and their potential biological impact. The results of this research will have application in a wide range of foci in genomics, from evolution to disease. This project will also achieve broader impact by providing training opportunities for both undergraduate and graduate students interested in computational genomics. This project seeks to develop novel computational methods to address some of the main challenges in studying SVs. As part of this project, novel combinatorial methods will be developed for efficient and accurate genotyping of any SV using ever changing sequencing technologies. This project will provide researchers with the necessary tools for ultra-efficient genotyping of a set of polymorphic SVs in a large cohort of sequenced samples using short-read sequencing technologies. Furthermore, novel mapping-free approaches for comparative SV discovery using long-read sequencing data will be developed. This will provide the necessary methods for studying the diverse set of SVs (including hard to detect and complex SVs) in sequenced samples of any species using these technologies. A combinatorial approach will also be developed to predict the functional impact of SVs by altering the chromatin structure of the genome. Finally, to establish the utility of these methods, these investigators will analyze publicly available data from diverse sets of species using the methods developed. The results of the projects will be available at www.hormozdiarilab.org.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

结构变异（SV）被定义为中等和大的基因组重排。越来越多的证据表明，SV是疾病、复杂性状、群体基因组学和进化的主要促成因素。然而，关于SV还有许多未知数，包括它们的多样性，复杂性，在种群中的分布以及在生物学中的确切影响。近年来基因组技术的发展，特别是高通量测序技术的发展，为研究基因组中SV的复杂性提供了机会。然而，缺乏有效的发现和不同类型的（复杂的）SV的基因分型的计算方法，阻碍了我们全面研究基因组中SV的复杂性和多样性的能力。该项目的目标是开发新的组合方法，为研究人员提供必要的工具，以更好地捕捉SV的多样性及其潜在的生物学影响。这项研究的结果将在基因组学的广泛领域中得到应用，从进化到疾病。该项目还将通过为对计算基因组学感兴趣的本科生和研究生提供培训机会来实现更广泛的影响。该项目旨在开发新的计算方法，以解决研究SV的一些主要挑战。作为该项目的一部分，将开发新的组合方法，使用不断变化的测序技术对任何SV进行有效和准确的基因分型。该项目将为研究人员提供必要的工具，用于使用短读测序技术在大型测序样本队列中对一组多态性SV进行超高效基因分型。此外，将开发使用长读序测序数据的比较SV发现的新的无映射方法。这将为使用这些技术研究任何物种的测序样品中的各种SV（包括难以检测和复杂的SV）提供必要的方法。还将开发一种组合方法，通过改变基因组的染色质结构来预测SV的功能影响。最后，为了确定这些方法的实用性，这些研究人员将使用开发的方法分析来自不同物种的公开数据。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

SVDSS: structural variation discovery in hard-to-call genomic regions using sample-specific strings from accurate long reads

• DOI: 10.1038/s41592-022-01674-1
• 发表时间: 2022-12-22
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Denti, Luca;Khorsand, Parsoa;Chikhi, Rayan
• 通讯作者: Chikhi, Rayan