Scaling up computational genomics with tree sequences

项目总结/摘要 增加样本量是建立我们对遗传学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有一个复杂的遗传因果关系网， 需要越来越大的基因数据集来解开它们，并最终产生成功的疗法。 在这一需求的推动下，科学界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。非人类数据集及其在流行病学中的应用， 生态学和进化论也不会落后太远。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， 收集这些数据。设计了当今计算方法的算法和数据结构 我们需要数千个样本，而不是数百万个样本，我们正处于被即将到来的海啸淹没的危险之中。 的数据.如果我们不从根本上改变存储和处理基因组数据的方式， 我们收集的数据的潜力，或计算成本将是天文数字-或两者兼而有之。 我们的建议通过关注一种新的数据结构来解决这一关键需求：简洁的树序列。 这种数据结构（简称“树序列”）使用群体遗传学编码遗传变异数据。 产生数据本身的netics过程-通过表示当代样本之间的变化， 潜在系谱树分支上的突变。这将产生非凡的数据COM水平- 压缩，文件大小比当前社区标准小数百倍。由于树序列 在2016年推出，它使各种应用的性能提高了2-4个数量级， 基因组模拟，统计计算和祖先推断的基础。如此突然的计算飞跃- 传统的性能是非常罕见的，只有通过深入的算法进步才有可能。 我们的研究计划建立在迄今为止树序列方法取得的非凡成功的基础上， 计算基因组学的关键层：分析、模拟和推理。首先，我们将继续 开发基于树序列的高效方法，用于统计和 群体遗传学其次，我们将通过将树序列方法整合到 复杂的前向时间模拟，利用现代多核处理器。第三，我们将联合收割机 基因组模拟与尖端的深度学习方法，以改善现有的推理方法， 从基因组数据的树序列，和人口参数从新的树序列编码， 基因型数据。我们的目标是彻底改变我们处理群体遗传变异数据的方式， 我们如何用它来了解人类健康和进化过程。 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 以及可访问、可互操作和可重用的数据格式。

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.