Learning about the evolution of structural variations from genomic and transcriptomic data

从基因组和转录组数据中了解结构变异的演变

• 批准号: 10270302
• 负责人: Raquel Assis
• 金额: $ 37.01万
• 依托单位: FLORIDA ATLANTIC UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10270302
• 关键词: Animals; Automobile Driving; Biological Assay; Biomedical Research; Birth; Cessation of life; Color Visions; Communities; Complex; Computer software; DNA; Data; Decision Trees; Development; Disease; Drosophila genus; Event; Evolution; Future; Gene Deletion; Gene Duplication; Gene Expression; Genome; Genomics; Goals; Human; Individual; Investigation; Investigative Techniques; Learning; Life; Light; Malignant Neoplasms; Mammals; Methods; Modeling; Mutation; Natural Selections; Nature; Organism; Outcome; Pattern; Phylogenetic Analysis; Plants; Play; Poaceae; Population; Recording of previous events; Role; Shapes; Structure; Taxonomy; Techniques; Time; Tissues; Trees; Variant; Work; base; design; follow-up; gene translocation; genomic variation; human disease; innovation; interest; migration; open source; statistical and machine learning; structural genomics; transcriptomics

PROJECT SUMMARY Structural variations are key drivers of both evolutionary adaptation and human disease. My group develops and applies computational and statistical approaches for understanding the evolution of structural variations from patterns in their genomic and transcriptomic data. During the past few years, our studies have focused primarily on gene duplication, which represents the most common type of structural variation observed in nature. In particular, we investigated the origins of evolutionary innovation after gene duplication, a problem of long- standing interest in the evolutionary genomics community. To answer this question, we designed the first method for classifying evolutionary outcomes of duplicate genes from phylogenetic comparisons of their gene expression profiles. By applying this decision tree method to multi-tissue gene expression data, we were able to classify evolutionary outcomes of duplicate genes in Drosophila, mammals, and grasses. These studies revealed frequent tissue-specific expression divergence after duplication, as well as sequence and expression differences within and among taxa that are consistent with natural selection. In a follow-up population-genomic analysis, we demonstrated that natural selection indeed plays an important role in the evolutionary outcomes of young duplicate genes in Drosophila. Later, we developed analogous decision tree classifiers for two additional types of structural variations: gene deletion and translocation. Applications of our methods to sequence and expression data from multiple tissues and developmental stages in Drosophila uncovered rapid divergence concordant with adaptation, suggesting that natural selection shapes the evolutionary trajectories of structural variations generated by deletion and translocation as well. However, our recent analyses revealed that there are many limitations of these decision tree methods, including sensitivity to gene expression stochasticity, lack of statistical support, and inability to predict parameters driving the evolution of structural variations. Thus, during the next five years, my group will develop a suite of tailored model-based statistical and machine learning approaches for classifying the evolutionary outcomes and predicting the evolutionary parameters of structural variations arising from duplication, deletion, inversion, and translocation events. Our preliminary studies indicate that these techniques will be much more powerful and accurate than previous approaches, and will therefore compose major advancements in evolutionary investigations of structural variations. In addition to implementing our methods in open source software packages, we will apply them to assay the evolutionary implications of different types of structural variations in humans and several other animal and plant taxa. Comparisons will be made among different types of structural variations, their evolutionary outcomes, and taxonomic groups. The major goal of these studies will be to ascertain the general rules by which different types of structural variation contribute to evolutionary innovation. Together, these studies will shed light on how gene duplication, deletion, inversion, and translocation work in concert to generate a diversity of complex adaptations across the tree of life.

项目摘要 结构变异是进化适应和人类疾病的关键驱动力。我的团队发展， 应用计算和统计方法来理解结构变化的演变， 基因组和转录组数据中的模式。在过去的几年里，我们的研究主要集中在 基因复制是自然界中最常见的结构变异类型。在 特别是，我们研究了基因复制后进化创新的起源，这是一个长期存在的问题， 一直对进化基因组学感兴趣为了回答这个问题，我们设计了第一种方法 用于根据重复基因表达的系统发育比较对重复基因的进化结果进行分类 数据区.通过将这种决策树方法应用于多组织基因表达数据，我们能够分类 果蝇、哺乳动物和草类中重复基因的进化结果。这些研究揭示 重复后频繁的组织特异性表达分歧，以及序列和表达差异 与自然选择一致的分类群内部和之间。在后续的人群基因组分析中，我们 证明了自然选择确实在年轻人的进化结果中起着重要作用。 果蝇中的重复基因后来，我们为另外两种类型开发了类似的决策树分类器 结构变异：基因缺失和易位。我们的方法在序列和表达式中的应用 来自果蝇多个组织和发育阶段的数据揭示了与 适应，这表明自然选择塑造了结构变化的进化轨迹 也是由缺失和易位产生的。然而，我们最近的分析显示， 这些决策树方法的局限性，包括对基因表达随机性的敏感性，缺乏统计分析， 支持，以及无法预测驱动结构变化演变的参数。因此，在接下来的 五年后，我的团队将开发一套定制的基于模型的统计和机器学习方法， 对进化结果进行分类，并预测结构变异的进化参数 复制、缺失、倒位和易位事件。我们的初步研究表明， 技术将比以前的方法更强大和准确，因此将组成 结构变异的进化研究的重大进展。除了实施我们的 方法在开源软件包，我们将应用它们来分析不同的进化含义 人类和其他几种动物和植物类群的结构变异类型。将进行比较 不同类型的结构变异，它们的进化结果和分类群体之间的关系。主要 这些研究的目标是确定不同类型的结构变异的一般规律， 有助于进化创新。总之，这些研究将揭示基因复制，缺失， 反转和易位协同工作，在整个生命之树中产生了复杂的适应多样性。