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

许多病毒在RNA中编码它们的基因组，并在宿主体内显示出高度的基因组多样性。测序技术的进步使在全球范围内跟踪病毒传播和及时检测疫情成为可能。该项目的目标是开发一套全面的预测数学模型和准确的计算方法，用于对新兴分子监测项目产生的海量流行病学和测序数据集进行综合分析。研究成果将通过期刊出版物和在国际会议上的演讲广泛传播，包括由私人投资机构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程序包分发，并纳入CDC开发的基于云的全球肝炎暴发和监测工具包(GHOST)。该项目将为促进妇女和代表性不足的群体参与格拉斯哥州立大学、康涅狄格州大学、佐治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大量机会。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)在非正式环境下向初中生教授计算机科学，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，为分子流行病学中的关键问题开发准确的数学模型和计算方法，包括从容易出错的集合测序数据中对病毒变异进行去卷积和推断，推断病毒样本与传播网络之间的相关性，推断传播事件时间和网络参数，以及传播网络动态的预测建模。该团队将对疾控中心生成的海量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。

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.