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FET: Small: Accurate and Scalable Methods for Analysis of Complex Genomic Populations

FET：小型：用于分析复杂基因组群体的准确且可扩展的方法

基本信息

批准号：
2109983
负责人：
Haris Vikalo
金额：
$ 50万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2109983&HistoricalAwards=false
关键词：
FET Small Accurate Scalable Methods

项目摘要

Genetic material in living cells and viruses experiences mutations which lead to emergence of diverse genomic populations. In humans, whether inherited or acquired over a lifetime of an individual, genetic mutations impact the individual’s health by causing genetic disorders and rendering the individual predisposed to complex diseases. The onset and progression of cancer, for example, is in part driven by somatic mutations that accumulate over time, creating one or more populations of tumor cells. Genetic variations also occur in viruses where they lead to emergence of rich populations whose spectrum is reflective of the proliferative advantage that particular mutations provide to strains present in the population. Therefore, inferring the composition and studying evolution of genomic populations reveal valuable information about genetic signatures of diseases and generally suggest directions for medical and pharmaceutical research. This research effort will benefit the field and society more broadly through coordinated efforts in innovating in education, enhancing diversity, engaging the community, and disseminating results to a wider public.The aims of this project lie along three integrated research directions: First, one direction is enabling accurate and scalable analysis of diverse mixtures of genomic sequences characterized by accumulated point mutations and insertions/deletions. As part of this research thrust, investigators will introduce deep learning and community detection paradigms to the problems of haplotype assembly and reconstructing viral populations, relying on domain knowledge to inform the design of novel algorithms in those settings. Second, another direction is designing algorithmic frameworks for the reconstruction of the mixtures of sequences characterized by structural variations, such as the variations of copy numbers. Drawing upon ideas from stochastic geometry and mixed integer optimization, investigators will provide novel methods for accurate discovery of the composition of such mixtures, with a focus on mapping intra-tumor heterogeneous genomic landscapes in cancer cells. Third, the final direction involves development of methods for studying dynamics of genomic mixtures that the first two thrusts explore in static settings. In particular, the last research thrust aims to enable tracking the evolution of viral populations in infection networks and the discovery of ancestral relationships between somatic mutations in clonal components of tumor cells.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.

活细胞和病毒中的遗传物质经历突变，导致不同基因组群体的出现。在人类中，无论是遗传的还是在个体一生中获得的，基因突变通过引起遗传疾病并使个体易患复杂疾病来影响个体的健康。例如，癌症的发生和进展部分是由随着时间的推移积累的体细胞突变驱动的，从而产生一个或多个肿瘤细胞群。遗传变异也发生在病毒中，它们导致出现丰富的群体，其谱反映了特定突变为群体中存在的菌株提供的增殖优势。因此，推断基因组群体的组成和研究进化揭示了有关疾病遗传特征的有价值的信息，并通常为医学和药学研究提供方向。该研究工作将通过在教育创新、增强多样性、参与社区和向更广泛的公众传播结果方面的协调努力，更广泛地造福该领域和社会。该项目的目标沿着沿着三个综合研究方向：第一，一个方向是能够准确和可扩展地分析以累积点突变和插入/缺失为特征的基因组序列的各种混合物。作为这项研究的一部分，研究人员将引入深度学习和社区检测范式来解决单倍型组装和重建病毒种群的问题，依靠领域知识来设计这些环境中的新算法。第二，另一个方向是设计算法框架，用于重建以结构变异（如拷贝数变异）为特征的序列混合物。利用随机几何和混合整数优化的思想，研究人员将提供准确发现此类混合物组成的新方法，重点是绘制癌细胞中肿瘤内异质基因组景观。第三，最后一个方向涉及的方法，研究动态的基因组混合物，前两个推力在静态设置探索的发展。特别是最后一个研究重点，旨在追踪感染网络中病毒种群的进化，并发现肿瘤细胞克隆组分中体细胞突变之间的祖先关系。该奖项反映了NSF的法定使命，并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，被认为值得支持。