Scaling up computational genomics with tree sequences

项目摘要/摘要 增加样本量是建立我们对遗传的理解的一个非常重要的因素。 人类疾病。随着我们发现越来越多的疾病具有复杂的遗传原因网络，我们 需要越来越大的基因数据集来解开它们，并最终产生成功的治疗方法。 在一定程度上受到这种需求的推动，该社区现在正在收集大量的人类基因组序列， 数以百万计的样本很快就会变得司空见惯。非人类数据集，在流行病学中的应用， 生态学和进化论也将紧随其后。然而，有一个深刻的问题：我们的计算 存储、处理、模拟和分析基因组数据的方法远远落后于我们的能力 收集这样的数据。今天的计算方法背后的算法和数据结构是设计的 数以千计的样本，而不是数百万个，我们正处于被即将到来的海啸淹没的危险中 数据。如果我们存储和处理基因组数据的方式没有根本性的改变，我们要么不能充分利用 我们收集的数据的潜力，或者计算成本将是天文数字--或者两者兼而有之。 我们的建议通过关注一种新的数据结构来满足这一关键需求：简洁的树序列。 该数据结构(简写为“树序列”)使用种群GE-1对遗传变异数据进行编码。 产生数据本身的嵌套过程-通过以下方式表示当代样本之间的差异 在潜在的家谱树的分支上的突变。这产生了超高水平的数据通信- fiLE的大小比当前的社区标准小几百倍。由于树序列 于2016年推出，在各种应用中实现了2-4个数量级的性能提升 基因组模拟、统计计算和祖先推断的研究。计算如此突然的飞跃- 常规性能非常罕见，只有通过深入的算法进步才有可能实现。 我们的研究计划建立在迄今为止树序列方法的非凡成功的基础上，扩大了三个 计算基因组学的关键层面：分析、模拟和推断。首先，我们将继续我们的 基于树序列的高效fi方法在统计和数据处理中的应用 种群遗传学。其次，我们将通过将树序列方法整合到 复杂的正向时间模拟，利用现代多核处理器。第三，我们将结合EffiEncient 用前沿的深度学习方法改进现有的推理方法的基因组模拟，两者 来自基因组数据的树序列，以及来自新的树序列编码的种群参数 基因分型数据。我们共同致力于彻底改变我们处理种群遗传变异数据的方式，以及 我们如何利用它来了解人类的健康和进化过程。 我们经验丰富的跨学科团队致力于生产经过严格测试和验证的软件 以及通过包容性和开放开发实现可访问、可互操作和可重复使用的数据格式。

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. Nonhuman datasets, with applications in epidemiology, ecology, and evolution, will not be far behind. There is a profound problem, however: our computational methods for storing, processing, simulating, and analyzing genomic data are lagging far behind our ability to collect such data. The algorithms and data structures underlying today's computational methods were designed for thousands of samples, not millions, and we are in danger of being overwhelmed by the impending tsunami of data. Without a 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. Our proposal addresses this critical need by focusing on a new data structure: the succinct tree sequence. This data structure (the “tree sequence”, for brevity) encodes genetic variation data using the population ge- netics processes that produced the data itself – by representing variation among contemporary samples via mutations on the branches of the underlying genealogical trees. This yields extraordinary levels of data com- pression, 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 the diverse applica- tions of genome simulation, calculation of statistics, and ancestry inference. Such sudden leaps in computa- tional 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, utilizing modern, multicore processors. Third, we will combine efficient genome simulations with cutting-edge deep-learning methods to improve existing inference methods, both of tree sequences from genomic data, and of population parameters from novel tree-sequence encodings of genotype data. Together, we aim to revolutionize the way we work with population genetic variation data, and how we use it to understand human health and evolutionary processes. Our experienced, interdisciplinary team is committed to producing rigorously tested and validated software and accessible, interoperable, and reusable data formats through inclusive and open development.