III: Medium: Scalable Evolutionary Analysis of SNVs and CNAs in Cancer Using Single-Cell DNA Sequencing Data

癌症是一种由个体基因组突变引起的疾病。这些突变包括单核苷酸变体，或SNV，它改变了基因组中的单核苷酸。它们还包括拷贝数异常，或CNA，这会导致基因组中DNA片段的缺失或扩增。检测从癌症患者获得的基因组数据中的这些突变使我们能够更好地理解和开发癌症的治疗方法。该项目旨在开发工具，利用从许多单个细胞获得的大量基因组序列来完成这一检测任务。该项目本质上是跨学科的，将帮助培训多学科之间的学生，并为整个社区提供软件。该项目将产生可扩展的方法，用于从单细胞DNA测序数据中检测SNV和CNA。这将通过四次推进来实现。在推力1中，该项目将开发同时推断SNV和突变树的方法。这里，超越无限站点假设的模型将被包括在内。在推力2中，该项目将产生在CNA存在的情况下基因组进化的新模型和推断方法。在推力3中，该项目将设计新的分而治之的技术，将推力1和2的方法扩展到全基因组数据和从数千个细胞获得的数据。在推力4中，所有方法都将基于合成和生物数据进行评估，开源实施将公开发布。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。

Cancer is a disease that is driven by mutations in the genome of an individual. These mutations include single nucleotide variants, or SNVs, which alter a single nucleotide in the genome. They also include copy number aberrations, or CNAs, which result in the deletion or amplification of stretches of DNA in the genome. Detecting these mutations in genomic data obtained from cancer patients allows us to better understand and develop treatments for cancer. This project aims to develop tools for accomplishing this detection task using large amounts of genomic sequences obtained from many individual cells. The project by its nature is interdisciplinary and will help train students at the interface of multiple disciplines as well as provide software for the community at large. The project will result in scalable methods for SNV and CNA detection from single-cell DNA sequencing data. This will be accomplished through four thrusts. In thrust 1, the project will develop methods for simultaneous inference of SNVs and mutation trees. Here, models beyond the infinite-sites assumption will be included. In thrust 2, the project will produce new models and inference methods for genome evolution in the presence of CNAs. In thrust 3, the project will devise novel divide-and-conquer techniques to scale the methods of thrusts 1 and 2 to whole-genome data and data obtained from thousands of cells. In thrust 4, all methods will be evaluated on synthetic and biological data, and open-source implementation will be released publicly.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.