III:Small: Expressiveness of Genome Graphs: Construction, Comparison, and Heterogeneity

一个人基因组中的差异(也称为变异)有助于确定特定的特征，如他们对疾病的敏感性、他们对药物的反应以及他们生物学上的其他重要方面。同样，细菌和病毒基因组的差异有助于确定它们的具体特征，例如，它们是否对人类或动物有害。在研究生物体的生物学时，了解和考虑这些遗传差异是很重要的，因为它们在生物体中的单个细胞如何发挥作用方面发挥着如此重要的作用。基因组测序技术的进步已经在包括人类在内的许多生物中产生了这样的差异的巨大目录。由于缺乏有效的计算方法，这些丰富的基因组信息库无法完全整合和分析，现有的方法在从基因组差异集合中得出结论时存在计算效率低下和准确性失误的问题。该项目将开发新的计算方法，以提高存储、比较和评估基因组差异目录的准确性并减少计算资源需求。这将导致新的科学软件，将更好地组织差异目录，使计算分析更容易处理。它还将产生更准确地衡量个体群体多样性的软件，以及支持更好地在群体之间进行比较的软件。该项目将通过对癌症肿瘤进行亚型划分，评估不同类型肿瘤中细胞的多样性，以及比较在不同环境中发现的细菌种群来验证这些方法。该项目将产生更快、更准确的软件来分析许多基因组差异，这将促进我们对基因组变异如何影响人类健康和生物过程的理解。为了更好地解释在这个项目中开发的创新和研究基因组差异的重要性，该项目还将制作一系列教育视频，帮助其他人了解该项目开发的技术背后的主要思想。基因组图已成为分析基因组变异集合的重要数据结构。这些图中的节点(或边)用基因组序列(字符串)标记，图中的路径表示图所代表的种群中存在的子串。它们可以用来代表一个生物体种群的“参考”基因组。基因组图已被用来减少参考基因组中的偏差，形成更具包容性的参考基因组，并减少执行基因组序列分析的空间和时间要求。出于这个原因，许多工具正在被调整以使用基因组图作为参考，而不是传统的线性(单序列)参考。虽然基因组图在这些领域一直被证明是有用的，但与之相关的一些问题的算法存在计算尺度不佳和缺乏形式化的问题。该项目将开发和验证几个核心基因组图问题的算法，特别是(目标1)构建基因组图，(目标2)比较基因组图，以及(目标3)评估基因组图的复杂性。该项目将用来解决这些问题的框架是创新的，因为它涉及到利用图流分解和基因组图之间未被探索的联系。这种方法揭示了基因组图和它们所代表的字符串群之间的自然关系。基因组图表达能力的这种全球视角是该项目将探索的公式的核心。该项目将解决桥梁图论和基因组学的问题，导致这些领域之间更大的互动和联系。我们的算法将允许基因组图更准确地反映所需的种群，将允许来自多个基因组的信息更好地整合，并将促进利用大量基因组变异所需的信息学工具。该项目将应用和评估这些算法，以(1)改进用于绘制基因组群体的序列比对，(2)改进癌症肿瘤序列和元基因组样本的聚集，以及(3)更好地模拟转移性癌症样本中的异质性进展。开发的算法将在一个开源库中实现，以鼓励它们在其他系统中使用。最后，该项目将创建开源的免费教学视频，向更广泛的受众介绍泛基因组学、基因组图和开发的算法等概念。该奖项反映了NSF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。

Differences (also known as variants) in a person's genome help to determine specific characteristics such as their susceptibility to disease, their response to drugs, and other significant aspects of their biology. Similarly, differences in the genomes of bacteria and viruses help to determine their specific characteristics such as, for example, whether they are harmful to humans or animals. These genetic differences are important to understand and to take into account when studying the biology of an organism because they play such an important role in how even individual cells in the organism function. Advances in genome sequencing technology have generated huge catalogs of such differences in many organisms, including humans. These rich repositories of genomic information cannot be fully integrated and analyzed due to a lack of effective computational methods, and existing methods suffer from computational inefficiencies and lapses of accuracy when drawing conclusions from collections of genomic differences. This project will develop new computational methods to increase accuracy and decrease computational resource requirements for storing, comparing, and evaluating catalogs of genomic differences. It will result in new scientific software that will better organize catalogs of differences to make computational analyses more tractable. It will also result in software that more accurately measures the diversity of a population of individuals and software that supports making better comparisons between populations. The project will validate these methods by subtyping cancer tumors, assessing the diversity of cells in various types of tumors, and by comparing populations of bacteria found in different environments. The project will result in faster, more accurate software for the analysis of many genomic differences that will advance our understanding of how genomic variants affect human health and biological processes. To better explain the innovations developed during this project and the importance of studying genomic differences, the project will also produce a series of educational videos that will help other people understand the main ideas behind the techniques developed in this project.Genome graphs have emerged as an important data structure in the analysis of collections of genomic variants. These are graphs in which nodes (or edges) are labeled with genomic sequences (strings) and paths in the graph represent substrings that are present in the population that the graph represents. They can be used as representations of a “reference” genome for a population of organisms. Genome graphs have been used to reduce bias in the reference genome, form more inclusive reference genomes, and to reduce space and time requirements to perform genomic sequence analyses. For this reason, many tools are being adapted to use genome graphs as references in lieu of traditional linear (single sequence) references. While genome graphs have consistently proved useful in these areas, the algorithms for a number of problems associated with them suffer from poor computational scaling and lack of formalization. The project will develop and validate algorithms for several central genome graph problems, specifically to (goal 1) construct genome graphs, to (goal 2) compare genome graphs, and to (goal 3) assess the complexity of genome graphs. The framework that the project will use to solve these problems is innovative in that it involves exploiting the under-explored connection between graph flow decompositions and genome graphs. This approach reveals natural relationships between genome graphs and the population of strings they represent. This global view of the expressive power of a genome graph is central to the formulations that the project will explore. The problems that the project will tackle bridge graph theory and genomics, leading to greater interactions and connections between those fields. Our algorithms will allow genome graphs to more accurately reflect desired populations, will allow information from multiple genomes to be better integrated, and will advance the informatics tools needed to exploit large collections of genomic variants. The project will apply and evaluate these algorithms to (1) improve sequence alignment for mapping populations of genomes, (2) improve clustering of cancer tumor sequences and metagenomic samples, and (3) better model the progression of heterogeneity in metastatic cancer samples. The developed algorithms will be implemented in an open-source library to encourage their use in other systems. Finally, the project will create open-source, free instructional videos to introduce concepts such as pan-genomics, genome graphs, and the developed algorithms to a wider audience.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.