AF: Small: Reconstructing Mixtures of DNA Sequences from High-Throughput Sequencing Data

AF：小：从高通量测序数据重建 DNA 序列混合物

• 批准号: 1618427
• 负责人: Haris Vikalo
• 金额: $ 40万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618427&HistoricalAwards=false
• 关键词: AF; Small; Reconstructing; Mixtures; DNA

In a wide range of problems in genomics and personalized medicine, it is of critical importance to accurately reconstruct distinct nucleotide sequences present in a heterogeneous mixture. Examples include viral quasispecies reconstruction, mapping repertoire of immune cells, and haplotyping. While recent advancements in high-throughput DNA sequencing have enabled affordable studies of genetic variations, technological limitations of sequencing platforms as well as potentially non-uniform frequencies of the sequences in a mixture render the analysis of heterogeneous mixtures a challenging and computationally intensive task.This research aims to develop fast and accurate algorithms for reconstruction and frequency estimation of sequences in diverse mixtures that will assist practitioners in pharmacogenomics and personalized medicine. The project includes a focus on fostering diversity, dissemination of new interdisciplinary research across disciplines, and enrichment of the educational experience of participating engineering students.Specific goals of the project include: First, the design and analysis of matrix factorization methods for accurate and efficient reconstruction of distinct sequences present in a heterogeneous mixture and for estimation of their frequencies. In the proposed framework, sequence reconstruction is formulated as the problem of factorizing structured, partially observed low-rank matrices and efficiently solved by exploiting salient features of high-throughput sequencing data. Second, the development of a methodology for the analysis of dynamically evolving mixtures of sequences temporally sampled by means of high-throughput sequencing. This research thrust will lead to novel sequence reconstruction methods capable of tracking the evolution of sequences over time and accurate identification of their frequencies. The third and final goal is the development of algorithmic solutions to specific sequence diversity analysis problems that fully exploit structural features of the respective applications and thus enable superior performance.

在基因组学和个体化医学的广泛问题中，准确地重建存在于异质混合物中的不同核苷酸序列是至关重要的。例子包括病毒准种重建，免疫细胞的映射库，和单倍型。虽然高通量DNA测序的最新进展使人们能够负担得起遗传变异的研究，测序平台的技术限制以及潜在的非混合物中序列的均匀频率使得异质混合物的分析成为一项具有挑战性和计算密集型的任务。本研究旨在开发快速准确的算法，用于不同混合物中序列的重建和频率估计，在药物基因组学和个性化医疗方面协助从业者。该项目包括一个重点是促进多样性，跨学科的新的跨学科研究的传播，并参与工程学生的教育经验的丰富，该项目的具体目标包括：第一，矩阵分解方法的设计和分析，准确和有效地重建不同的序列存在于一个异质混合物和估计其频率。在所提出的框架中，序列重构被配制成分解结构化的、部分观察到的低秩矩阵的问题，并通过利用高通量测序数据的显著特征来有效地解决。第二，开发一种方法，用于分析动态演变的混合物的序列时间采样的高通量测序的手段。这项研究的推力将导致新的序列重建方法，能够跟踪序列随时间的演变和准确识别其频率。第三个也是最后一个目标是开发针对特定序列多样性分析问题的算法解决方案，其充分利用相应应用的结构特征，从而实现上级性能。