Scalable Coalescent Inference for Large Data Sets

适用于大型数据集的可扩展合并推理

基本信息

批准号：
10192760
负责人：
Julia Palacios
金额：
$ 30.48万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-05 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10192760
关键词：
Address Algorithms Area Bayesian Analysis Biological Biology Bison Cessation of life Communicable Diseases Computer software Computing Methodologies DNA DNA Sequence Data Data Set Development Dimensions Encapsulated Ensure Evolution Explosion Frequencies Genealogical Tree Genealogy Genes Genetic Genetic Phenomena Genetic Variation Genetic study Goals Human Investigation Label Mathematics Methodology Methods Modeling Modernization Molecular Molecular Analysis North America Phylogenetic Analysis Population Population Genetics Process Processed Genes Property Public Health Recording of previous events Research Research Personnel Resolution Sample Size Sampling Shapes Site Statistical Methods Statistical Models Stochastic Processes Structure Time Trees Uncertainty ZIKA base cancer genomics genomic data improved innovation large datasets mathematical model next generation sequencing novel open source simulation statistics theories tool

项目摘要

Mathematical and statistical modeling of gene genealogies-trees that reflect ancestral relationships among sampled molecular sequences-is central to many biological fields, including population genetics, phylodynamics of infectious disease, paleogenomics, phylogenetics, and cancer genomics. Kingman's n-coalescent is a stochastic process of gene genealogies whose parameters depend on an evolutionary model. Inference of model parameters then contributes to an understanding of the phenomena that have given rise to the sequences. Though many sophisticated methods have been developed to date, major statistical and computational challenges remain because the state space of genealogies grows superexponentially with the number of samples. We are no longer data-limited but instead, we lack computational and statistical methods for analysis of large scale emerging genomic data sets. The long-term goal of the researchers is to develop statistically consistent and computationally efficient coalescent methods for exact inference of evolutionary parameters from next-generation sequencing datasets. The objective of this research is to apply the notion of lumpability of Kingman's n-coalescent to address the state-space explosion problem of coalescent methods. The basic idea is to model a coarser resolution of the underlying genealogy and reduce the cardinality of the hidden state space. These coarser coalescent models include Tajima's coalescent and the pure-death process coalescent. The specific aims include (1) prove theorems for coalescent models and provide theoretical and practical tools for addressing computational challenges when modeling different resolutions or "lumpings" of Kingman's coalescent; (2) develop scalable methods for inference of evolutionary parameters using different coalescent models; (3) theoretically and empirically validate the inference methods, applying them in simulations and in molecular sequences from infectious diseases such as Zika, as well as ancient DNA samples from bison in North America and ancient and modern human samples; (4) implement the novel methods in open source software, ensuring fast dissemination of the methodology among researchers. The research is innovative in many distinct ways. First, Tajima's coalescent has not yet been exploited for inference despite the potential based on the smaller state space. Second, the methods developed here will allow inference from data sets that have not been exploited before because of computational limitations. Third, we will not only provide a suite of tools ready for application but we will also provide statistical results supporting our implementations. Our proposed research on scalable modeling of genealogical trees will be significant in a number eJf fields, including the theory of evolutionary trees, statistical inference in population genetics and phylogenetics, and the analysis of molecular sequences from infectious disease and ancient DNA.

基因家谱树的数学和统计模型，反映了采样的祖先关系分子序列 - 许多生物领域的中心疾病，古生物学，系统发育学和癌症基因组学。金曼的n-钙化是基因的随机过程参数的家谱取决于进化模型。然后，模型参数的推断有助于了解引起序列的现象。尽管许多复杂的方法已经迄今为止开发的主要统计和计算挑战仍然存在，因为家谱的状态空间不断增长与样品数量的数量相大。我们不再是数据限制的，而是我们缺乏计算和用于分析大型新兴基因组数据集的统计方法。研究人员的长期目标是开发统计一致和计算有效的合并方法，以精确推断进化论来自下一代测序数据集的参数。这项研究的目的是应用金曼的N-钙化问题解决了共同方法的状态空间爆炸问题。基本想法是为基础谱系的更粗糙分辨率建模，并降低隐藏状态空间的基础性。这些更粗糙的合并模型包括塔吉马的合并和纯死亡过程合并。具体目标包括（1）证明用于合并模型的定理，并提供理论和实用的工具来解决在建模金曼（Kingman）合并的不同决议或“大小”时，都会面临计算挑战；（2）发展使用不同的合并模型推断进化参数的可扩展方法；（3）理论上从经验上验证推理方法，将其应用于模拟和分子序列中寨卡病等疾病，以及北美野牛的古老DNA样本以及古代和现代人类样品；（4）在开源软件中实施新方法，确保该方法的快速传播在研究人员中。这项研究以许多不同的方式具有创新性。首先，塔吉马的融合尚未尽管存在基于较小的状态空间的潜力，但仍利用推断。第二，这里开发的方法将允许从由于计算限制而从未被利用的数据集进行推断。第三，我们不仅将提供一套准备应用程序的工具，而且我们还将提供统计结果，以支持我们实施。我们对族谱树的可扩展建模的拟议研究将在许多EJF中很重要领域，包括进化树理论，人口遗传学和系统发育学的统计推断以及来自传染病和古代DNA的分子序列的分析。