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

基本信息

批准号：
1619110
负责人：
Aleksandr Zelikovskiy
金额：
$ 20万
依托单位：
Georgia State University Research Foundation, Inc.
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 2020-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619110&HistoricalAwards=false
关键词：
CCF BSF AF Small Collaborative

项目摘要

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.

许多病毒在RNA中编码其基因组，并在其宿主中表现出较高的基因组多样性。测序技术的进步使跟踪病毒传播并及时检测全球范围的爆发变得可行。该项目的目的是开发一组全面的预测数学模型和准确的计算方法，用于对新兴分子监视程序产生的大规模流行病学和测序数据集进行综合分析。研究结果将通过国际会议上的杂志出版物和演讲大致传播，包括PIS组织的分子流行病学计算进步研讨会。开发算法的原型实现将以开源包分配，并纳入CDC开发的基于云的全球肝炎爆发和监视工具包（Ghost）。该项目将为促进妇女和代表性不足的群体在GSU，UCONN，GEORGIA TECH和TEL AVIV大学的生物信息学和分子流行病学研究中提供足够的机会。该项目的一个重要方面是将核心概念和思想传播到从计算机科学和计算生物学到广泛的目标受众，包括：（1）在非正式的环境中向中学和高中生教授计算机科学，以及（2）将计算思维纳入本科大学级别的生活科学课程。 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网络参数以及传输网络动力学的预测建模。该团队将对CDC生成的大量分子监视数据集进行广泛的算法验证，并开发可靠的原型软件实现。