Scaling up computational genomics with tree sequences

用树序列扩展计算基因组学

基本信息

批准号：
10585745
负责人：
PETER Lochhead RALPH
金额：
$ 60.57万
依托单位：
UNIVERSITY OF OREGON
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-05 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10585745
关键词：
Address Affect Agriculture Algorithms Architecture Area Astronomy Base Sequence Collection Communities Complex Computer software Computing Methodologies Culicidae Data Data Compression Data Set Development Disease Ecology Ensure Epidemiology Etiology Evolution Genealogical Tree Generations Genetic Genetic Processes Genetic Recombination Genetic Variation Genome Genomics Genotype Goals Haplotypes Health Benefit Historical Demography Human Genetics Human Genome Individual Internet Learning Libraries Maps Mathematics Methods Modeling Modernization Mutation Performance Phase Phenotype Population Population Genetics Population Sizes Positioning Attribute Process Production Records Research Running Sample Size Sampling Statistical Data Interpretation Structure Techniques Testing Time Training Trees Validation Variant Work algorithm development computer framework cost data format data structure deep learning design frontier genome-wide genomic data human disease improved interest interoperability learning strategy member multicore processor next generation novel strategies open source operation scale up sequence learning simulation statistics success supervised learning whole genome

项目摘要

Project Summary/Abstract Increasing sample size is a tremendously important factor in building our understanding of the genetics of human disease. As we discover that more and more diseases have a complex web of genetic causation, we need larger and larger genetic datasets to disentangle them, and to ultimately produce successful therapies. Driven in part by this need, the community is now assembling vast collections of human genome sequences, and millions of samples will soon be commonplace. There is a profound problem, however: our computational methods for storing, processing, and analyzing genomic data are lagging far behind. The algorithms and data structures underlying today’s computational methods were designed for thousands of samples, not millions. Without fundamental change in how we store and process genomic data, we will either not fully tap the potential of the data we collect, or the computational costs will be astronomical – or both. Nonhuman datasets, with applications in epidemiology, ecology, evolution, and agriculture, may not reach these sample sizes soon, but here we nevertheless face a related barrier. Simulation is increasingly important for tasks from hypothesis generation to parameter inference. However, current simulation methods only scale to tens or hundreds of thousands of individuals, inappropriate for many species of interest (e.g., mosquitos). This is crucial, since evolution and ecology in large populations differs from small ones, in ways that cannot be avoided by mathematical tricks (like rescaling). Our proposal addresses these critical needs by focusing on a new data structure: the “tree sequence”, which encodes genetic variation data using the population genetics processes that produced the data itself, by representing variation among contemporary samples using the underlying genealogical trees. This yields extraordinary levels of data compression, with file sizes hundreds of times smaller than current community standards. Since the tree sequence was introduced in 2016 it has led to performance increases of 2–4 orders of magnitude in genome simulation, calculation of statistics, and ancestry inference. Such sudden leaps in computational performance are vanishingly rare, and only possible through deep algorithmic advances. Our research plan builds on the extraordinary successes of tree sequence methods so far, scaling up three crucial layers of computational genomics: analysis, simulation, and inference. First, we will continue our development of highly efficient tree-sequence-based methods for fundamental operations in statistical and population genetics. Second, we will scale up genome simulations by integrating tree sequence methods into complex forward-time simulations and utilizing modern, multicore processors. Third, we will combine efficient simulations and the rich information contained in the tree sequence with cutting-edge deep-learning techniques to develop new inference methods. Together, we aim to revolutionize the way we work with and learn from population genetic variation data.

项目摘要/摘要增加样本量是建立我们对遗传学的理解的极为重要的因素人类疾病。当我们发现越来越多的疾病具有复杂的遗传原因网络时，我们需要越来越大的遗传数据集将其解散，并最终产生成功的疗法。社区的部分原因是，社区正在组装大量的人类基因组序列，数以百万计的样本很快将是司空见惯的。但是，有一个深刻的问题：我们的计算存储，处理和分析基因组数据的方法远远落后。算法和数据当今计算方法的基础结构是为数千个样本而不是数百万个设计设计的。没有我们如何存储和处理基因组数据的根本变化，我们将不会完全利用我们收集的数据的潜力，或计算成本将是天文学的。非人类数据集以及流行病学，生态学，进化和农业的应用程序可能无法达到这些样本量很快，但是在这里我们面临着相关的障碍。模拟越来越重要从假设生成到参数推断的任务。但是，当前的仿真方法仅规模对于数十或数十万个个人，不适合许多感兴趣的物种（例如蚊子）。这是至关重要的，因为大人群中的进化和生态与小群落不同，以无法可以通过数学技巧（例如重新制定）避免。我们的建议通过关注新的数据结构来满足这些关键需求：“树序列”，它使用产生数据本身的种群遗传学过程来编码遗传变异数据，通过使用潜在的家谱树代表当代样品之间的变化。这会产生数据压缩的非凡级别，文件尺寸比当前社区小数百倍标准。自2016年引入树序列以来，这导致了2-4个订单的性能提高基因组模拟，统计计算和祖先推断的幅度。如此突然跳进去计算性能消失了罕见，只有通过深层算法的进步才有可能。到目前为止，我们的研究计划以树序方法的非凡成功为基础计算基因组学的关键层：分析，仿真和推论。首先，我们将继续我们的开发高效的基于树序列的方法，用于统计和人口遗传学。其次，我们将通过集成树序列方法来扩展基因组模拟进入复杂的前进时间模拟并利用现代的多层处理器。第三，我们将结合有效的仿真和树序中包含的丰富信息，深入学习开发新推理方法的技术。我们共同旨在彻底改变我们与之合作的方式和从人口遗传变异数据中学习。