EAGER: Novel Computational Models and Algorithms for Mapping Link-Read Sequencing Data

创新的低成本和低输入的DNA文库制备技术，使用微流控分子条形码，已经开发出来。这些技术，也被称为Linked-Read测序，提供了比存在于同一DNA分子上的标准短读段更多的长距离信息。有限数量的可用工具无法满足社区快速增长的需求，因为它们专注于简单的应用程序，缺乏所需的灵敏度，并且计算成本高昂。该项目将开发用于将Linked-Reads映射到参考基因组的敏感算法。该方法也有潜力解决其他具有挑战性的问题，如准确发现和组装的结构变异和解决基因组中的重复区域。虽然有大量的计算方法使用标准的短读段测序技术，但还没有一套全面的可扩展的算法，可以有效地应用于Linked-Read测序技术。该项目将开发新的概率模型和算法，以解决几个计算上具有挑战性的问题，利用链接阅读测序技术。特别是，该项目将开发一种新的概率框架和一种敏感的算法，用于将Linked-Reads映射到参考基因组，并应用于敏感的结构变异检测。该项目的成功完成将提供可应用于大规模全基因组数据的快速和可扩展的计算方法。计划中的方法、工具和相关测试数据集将在GitHub上免费提供，并提供大量文档供社区使用和受益。该奖项反映了NSF的法定使命，并被认为值得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来支持。

Innovative low-cost and low-input DNA library preparation techniques, using microfluidic molecular barcoding, have been developed. These technologies, also known as Linked-Read sequencing, offer additional long-range information over standard short-reads that are present on the same DNA molecule. The limited number of available tools do not meet the fast-growing needs of the community, because they focus on simple applications, lack the desired sensitivity, and are computationally expensive. This project will develop sensitive algorithms for mapping Linked-Reads to the reference genome. The method has also potential in solving other challenging problems such as the accurate discovery and assembly of structural variants and resolving repeat regions in the genome.While there are an enormous amount of computational methods using standard short-read sequencing technologies, there is yet no comprehensive set of scalable algorithms that can be applied effectively to Linked-Read sequencing technologies. This project will develop novel probabilistic models and algorithms to address several computationally challenging problems, leveraging Linked-Read sequencing technologies. In particular, the project will develop a novel probabilistic framework and a sensitive algorithm for mapping Linked-Reads to the reference genome with applications to sensitive structural variation detection. Successful completion of this project will provide fast and scalable computational methods that can be applied to large-scale whole-genome data. The planned methods, tools and related test data sets will be made freely available online with extensive documentation on GitHub for the community to use and benefit.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.