CCF-BSF: AF: Small: Collaborative Research: Algorithmic Techniques for Inferring Transmission Networks from Noisy Sequencing Data

CCF-BSF：AF：小型：协作研究：从噪声排序数据推断传输网络的算法技术

• 批准号: 1618347
• 负责人: Ion Mandoiu
• 金额: $ 20万
• 依托单位: University of Connecticut
• 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: 2016-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618347&HistoricalAwards=false
• 关键词: CCF; BSF; AF; Small; Collaborative

Many viruses encode their genome in RNA and exhibit high genomic diversity within their hosts. Advances in sequencing technologies have made it feasible to track viral transmissions and timely detect outbreaks on a global scale. The goal of this project is to develop a comprehensive set of predictive mathematical models and accurate computational methods for integrated analysis of the massive epidemiological and sequencing datasets generated by emerging molecular surveillance programs. Research results will be broadly disseminated via journal publications and presentations at international conferences, including the Workshop on Computational Advances in Molecular Epidemiology organized by the PIs. Prototype implementations of developed algorithms will be distributed as open-source packages and incorporated in the cloud-based Global Hepatitis Outbreak and Surveillance Toolkit (GHOST) developed at CDC. The project will provide ample opportunities for promoting participation of women and underrepresented groups in bioinformatics and molecular epidemiology research at GSU, UCONN, Georgia Tech and Tel Aviv University. An important aspect of the project is to disseminate core concepts and ideas from Computer Science and Computational Biology to wide target audiences including: (1) teaching Computer Science, in an informal setting, to middle and high school students, and (2) incorporating computational thinking into Life Science curriculum at the undergraduate university level. The proposed research and education activities will leverage the extensive expertise of an interdisciplinary team comprised of computer scientists, mathematicians, and molecular epidemiologists to develop accurate mathematical models and computational methods for key problems in molecular epidemiology including deconvolution and inference of viral variants from error-prone pooled sequencing data, inference of relatedness between viral samples and transmission networks, inference of transmission event times and network parameters, as well as predictive modeling of transmission network dynamics. The team will carry out extensive algorithm validation on massive molecular surveillance datasets generated at CDC and develop robust prototype software implementations.

许多病毒将其基因组编码为RNA，并在其宿主内表现出高度的基因组多样性。测序技术的进步使得在全球范围内追踪病毒传播和及时发现疫情成为可能。 该项目的目标是开发一套全面的预测数学模型和精确的计算方法，用于对新兴分子监测计划产生的大量流行病学和测序数据集进行综合分析。研究结果将通过期刊出版物和在国际会议上的演讲广泛传播，包括由PI举办的分子流行病学计算进展研讨会。已开发算法的原型实现将作为开源软件包分发，并纳入CDC开发的基于云的全球肝炎爆发和监测工具包（GHOST）中。 该项目将为促进妇女和代表性不足的群体参与佐治亚州立大学、康涅狄格大学、格鲁吉亚理工学院和特拉维夫大学的生物信息学和分子流行病学研究提供充分的机会。 该项目的一个重要方面是向广泛的目标受众传播计算机科学和计算生物学的核心概念和思想，包括：（1）在非正式环境中向初中和高中学生教授计算机科学，以及（2）将计算思维纳入本科大学水平的生命科学课程。拟议的研究和教育活动将利用由计算机科学家、数学家和分子流行病学家组成的跨学科团队的广泛专业知识，为分子流行病学的关键问题开发准确的数学模型和计算方法，包括从易出错的合并测序数据中解卷积和推断病毒变体，推断病毒样本与传播网络之间的相关性，传输事件时间和网络参数的推断，以及传输网络动态的预测建模。该团队将对CDC生成的大规模分子监测数据集进行广泛的算法验证，并开发强大的原型软件实现。