Software for exploring all forms of genetic variation in any species

用于探索任何物种中所有形式的遗传变异的软件

• 批准号: 9749979
• 负责人: Aaron R Quinlan
• 金额: $ 45.75万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9749979
• 关键词: Address; Alleles; Animal Model; Biology; Catalogs; Cell physiology; ChIP-seq; Complex Analysis; Computer software; Copy Number Polymorphism; DNA sequencing; Data Set; Databases; Diploidy; Disease; Genes; Genetic; Genetic Research; Genetic Variation; Genetic study; Genome; Genotype; Haploidy; Haplotypes; Human; Human Genome; Inheritance Patterns; Investigation; Laboratories; Livestock; Mendelian disorder; Methods; Microscope; Mining; Modernization; Nucleotides; Organism; Performance; Phenotype; Ploidies; Polyploidy; Publishing; Research; Research Personnel; Research Support; Sampling; Software Framework; Structure; System; Technology; Variant; base; biological research; blind; crosslinking and immunoprecipitation sequencing; data integration; design; experience; experimental study; flexibility; foot; genome analysis; genome annotation; genome-wide; genome-wide analysis; human disease; indexing; innovation; insertion/deletion mutation; insight; software systems; success; tool; trait; transcriptome sequencing; web based interface

Modern DNA sequencing technologies have revolutionized the design of experiments investigating the biology of the genome and the genetic basis of traits. Arguably the most powerful application of these technologies has been the creation of exquisitely detailed catalogs describing the landscape of genetic variation in multiple species. However, discovery of genetic variation is merely the beginning. Exploration and analysis of the resulting catalogs is required to catalyze new insights into the relationship between genotype and phenotype. This proposal is motivated by two fundamental limitations inhibiting discovery from genetic variation datasets. First, existing software for mining variation to understand disease and other traits does not scale to large datasets involving thousands of samples. Second, most existing tools are focused on human studies; consequently, this inhibits the application of modern DNA sequencing to genetic studies of model organisms, livestock genetics, and newly sequenced species. We propose to solve these challenges by building upon our GEMINI framework. Since 2012, we have maintained GEMINI as a powerful software framework for exploring genome variation. GEMINI's strength is that it integrates genetic variation with a diverse set of genome annotations into a database to facilitate variant prioritization. It allows researchers to conduct complex analyses with simple queries based on sample genotypes, phenotypes, inheritance patterns, and genome annotations. GEMINI has quickly become a very popular tool for rare human disease research leading to discoveries by multiple labs, including our own. Despite its power and popularity, GEMINI has three important limitations. It was not designed for studies involving genetic variation from more than a few hundred samples. Furthermore, its focus is the analysis of single-nucleotide (SNP) and insertion-deletion (INDEL); it is blind to structural and copy number variation. Finally, GEMINI can only analyze genetic variation datasets for the human genome; no other species or genome builds are supported. Therefore, this proposal seeks to provide geneticists studying any species with a powerful, flexible and simple to use software system that is fast and scalable enough to support genetic research for many years to come. We will do this but achieving the following Specific Aims: (1) Develop a scalable, high performance genotype and haplotype query engine to empower large scale genome studies. (2) Devise new methods for genotyping, integrating and prioritizing structural variation. (3) Enable scalable, flexible genome analysis in any species and genome build. In summary, by completing these aims, the proposed research will provide geneticists studying any species with a powerful, flexible and simple to use software system that is fast and scalable enough to support genetic research for many years to come.

现代DNA测序技术彻底改变了研究生物多样性的实验设计 基因组生物学和性状的遗传基础。可以说，这些功能最强大的应用程序 技术已经创建了描述基因景观的精致详细的目录 多种物种的变异。然而，基因变异的发现仅仅是个开始。探索和 需要对产生的目录进行分析，以催化对基因型之间关系的新见解 和表型。这一建议的动机是两个根本的限制，阻碍了对基因的发现 变异数据集。首先，现有的用于挖掘变异以了解疾病和其他特征的软件不能 扩展到涉及数千个样本的大型数据集。其次，现有的大多数工具都是以人为中心 因此，这阻碍了现代DNA测序在模型遗传学研究中的应用 生物体、家畜遗传学和新测序物种。 我们建议通过建立我们的双子座框架来解决这些挑战。自2012年以来，我们已经 保持双子座作为探索基因组变异的强大软件框架。双子座的力量是 它将遗传变异和一组不同的基因组注释整合到一个数据库中，以促进变异 确定优先顺序。它允许研究人员使用基于样本的简单查询进行复杂的分析 基因类型、表型、遗传模式和基因组注释。双子座很快就变成了一个非常 用于罕见人类疾病研究的流行工具，导致多个实验室的发现，包括我们自己的实验室。 尽管双子座的力量和受欢迎程度很高，但它有三个重要的局限性。它不是为 涉及数百个样本的遗传变异的研究。此外，它的重点是 单核苷酸(SNP)和插入-缺失(Indel)分析；它对结构和拷贝数是盲目的 变种。最后，双子座只能分析人类基因组的遗传变异数据集；不能分析其他物种 或者支持基因组构建。因此，这项提议试图为研究任何物种的遗传学家提供 具有强大、灵活且简单易用的软件系统，该软件系统具有足够快的速度和足够的可扩展性，可支持 在未来的许多年里进行研究。我们将这样做，但要实现以下具体目标： (1)开发可扩展、高性能的基因和单倍型查询引擎，为用户赋能 大规模的基因组研究。 (2)设计新的基因分型、整合和区分结构变异的方法。 (3)在任何物种和基因组构建中实现可扩展、灵活的基因组分析。 总之，通过完成这些目标，拟议的研究将为遗传学家提供研究任何 具有强大、灵活且简单易用的软件系统的物种，该软件系统具有足够快的速度和足够的可扩展性来支持 在未来的许多年里进行基因研究。