IIBR Informatics: Taming Complexity Through Simulations: Scalable Inference Under the Coalescent with Recombination

几十亿年来，物种一直在进化、分化和适应环境。虽然我们无法直接了解物种的历史，但它们的基因组提供了许多信号，让我们能够重建这段历史。了解基因组的进化有助于阐明物种是如何进化和分化的，基因是如何出现和进化的，以及特征是如何进化的。然而，基因组的进化是一个非常复杂的过程，导致基因组中不同区域有不同的进化历史。导致这种情况的一个过程是重组。该项目旨在开发在重组存在的情况下推断基因和基因组进化史的方法。目前，由于在推导数学模型和计算上可行的推理解决方案方面的挑战，这项任务对于大基因组集是不可行的。该项目将通过允许在存在重组的情况下自动推导和推断一组基因组的进化历史来实现这一任务。该项目将支持研究生和博士后指导，并将允许扩大对计算机的参与，特别是考虑到其跨学科性质。该项目获得的结果将促进新型基因组分析，从而促进生物学发现。该项目的目的是设计方法，使进化历史(拓扑和参数)的推断在一个称为多物种结合重组和迁移(MSC-RM)的模型下具有实用性和可扩展性。这个模型允许分析由不同物种和物种内不同个体的基因组序列组成的数据，同时考虑到重组、不完全谱系排序和基因流，以及DNA序列进化的各种模型。为了推断物种系统发育的拓扑结构，采用了深度学习的方法，其中神经网络是根据模拟数据进行训练的。为了推断系统发育的参数(发散时间和种群大小)，根据模拟数据建立了隐马尔可夫模型，并利用二次向前算法计算了似然率的代理。这种新技术的组合有助于在MSC-RM模型下实现自动化和可扩展的推理。所有方法都将在开放源码中实施并公开提供，所有结果将通过出版物、公开讲座和教程传播。该项目的结果将在http://bioinfocs.rice.edu.This上公布，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Species have been evolving, diverging, and adapting to their environments for billions of years. While we have no direct access to the history of species, their genomes provide much signal that allows us to reconstruct this history. Understanding the evolution of genomes helps shed light on how species evolve and diverge, how genes emerge and evolve, and how traits evolve. However, the evolution of genomes is a very complex process that results in scenarios where different regions in the genomes have different evolutionary histories. One process that leads to such a scenario is recombination. This project aims to develop methods for inferring evolutionary histories of genes and genomes in the presence of recombination. Currently this task is not doable for large sets of genomes due to challenges with deriving mathematical models and computationally feasible inference solutions. This project will enable this task by allowing for automatically deriving and inferring the evolutionary history of a set of genomes in the presence of recombination. The project will support graduate student and post-doc mentoring, and will allow for broadening participation in computing, especially given its interdisciplinary nature. Results obtained by this project will facilitate new types of genomic analyses and, consequently, biological discoveries. The aim of this project is to devise methods that make practical and scalable the inference of evolutionary histories (topologies and parameters) under a model called the multispecies coalescent with recombination and migration (MSC-RM). This model allows for analyzing data that consists of genomics sequences from different species and different individuals within species while accounting simultaneously for recombination, incomplete lineage sorting, and gene flow, in addition to various models of DNA sequence evolution. For inferring the topology of the species phylogeny, a deep learning approach is taken, where a neural network is trained on simulated data. For inferring the phylogeny’s parameters (divergence times and population sizes), a hidden Markov model is built from simulated data, and a proxy to the likelihood is computed by means of the quadratic Forward algorithm. This combination of novel techniques helps achieve automated and scalable inference under the MSC-RM model. All methods will be implemented and made publicly available in open source, and all results will be disseminated via publications, public lectures, and tutorials. Results of this project will be available at http://bioinfocs.rice.edu.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.