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

III：中：使用单细胞 DNA 测序数据对癌症中的 SNV 和 CNA 进行可扩展的进化分析

• 批准号: 2106837
• 负责人: Luay Nakhleh
• 金额: $ 117.34万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106837&HistoricalAwards=false
• 关键词: III; Medium; Scalable; Evolutionary; Analysis

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.

癌症是一种由个体基因组突变驱动的疾病。这些突变包括单核苷酸变体或SNV，其改变基因组中的单个核苷酸。它们还包括拷贝数畸变或CNA，其导致基因组中DNA片段的缺失或扩增。在从癌症患者获得的基因组数据中检测这些突变使我们能够更好地了解和开发癌症治疗方法。该项目旨在开发使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的工具。该项目的性质是跨学科的，将有助于培养学生在多个学科的接口，以及为整个社区提供软件。该项目将导致从单细胞DNA测序数据中检测SNV和CNA的可扩展方法。这将通过四个方面来实现。在第一阶段，该项目将开发SNV和突变树的同时推理方法。这里，将包括超越无限站点假设的模型。在第二阶段，该项目将为CNA存在下的基因组进化产生新的模型和推理方法。在第三阶段，该项目将设计新的分而治之的技术，将第一阶段和第二阶段的方法扩展到全基因组数据和从数千个细胞中获得的数据。在推力4中，所有的方法都将在合成和生物数据上进行评估，开源的实施将公开发布。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

MoTERNN: Classifying the Mode of Cancer Evolution Using Recursive Neural Networks

• DOI: 10.1101/2022.08.21.504710
• 发表时间: 2022-08
• 期刊: bioRxiv
• 作者: M. Edrisi;Huw A. Ogilvie;Meng Li;L. Nakhleh