QUBBD: VIRAL EVOLUTION AND SPREAD OF INFECTIOUS DISEASE IN COMPLEX NETWORK: BIG DATA ANALYSIS AND MODELING

QUBBD：复杂网络中传染病的病毒进化和传播：大数据分析和建模

• 批准号: 10174114
• 负责人: Pavel Skums
• 金额: $ 23.99万
• 依托单位: GEORGIA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-12 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10174114
• 关键词: QUBBD; VIRAL; EVOLUTION; SPREAD; INFECTIOUS

Overview: Highly mutable RNA viruses, such as human immunodeficiency virus and hepatitis C virus are major causes of morbidity and mortality in the world. The hallmark of RNA viruses is their extremely high genetic diversity that allows them to rapidly establish new infections, escape host's immune system and develop drug resistance. Emergence of next-generation sequencing technologies promises to revolutionize the fields of virology and epidemiology by allowing to sample and characterize millions of intra-host viral variants in thousands of infected individuals. However, our understanding of mechanisms of disease spread and viral evolution are still limited due to the lack of computational methods for processing, integration and analysis of biomedical big data. The overarching goal of this project is to develop a comprehensive family of innovative algorithms and models that allow to describe, analyze, understand and predict complex multidimensional non-linear disease dynamics. Intellectual Merit: The proposed research will be conducted by an interdisciplinary team comprised of biologists, mathematicians, molecular epidemiologists and computer scientists with extensive expertise in the areas relevant to the project. The project will target highly important epidemiological and biomedical problems including development of efficient and scalable computational methods for surveillance of disease spread, modeling of epidemiological dynamics by incorporation of intra-host and inter-host evolutionary dynamics into a single framework and design of computational tools for utilization of data analysis results by health care professionals. Proposed algorithms and models will be validated using massive molecular and epidemiological data generated by project collaborators from CDC and Georgia Tech, as well as available from public sources. The algorithms will be distributed to the researchers and health care workers as free open-source packages and cloud-based online tools. In particular, they will be incorporated in the Global Health Outbreak and Surveillance Technology, a web-based data analysis system currently being developed at CDC. Research findings will be broadly disseminated via journal publications and conference presentations, including the International Symposium on Bioinformatics Research and Applications and Workshop on Computational Advances in Molecular Epidemiology organized by the Pis.

概述：高度可变的RNA病毒，如人类免疫缺陷病毒和丙型肝炎病毒， 世界上发病率和死亡率的主要原因。RNA病毒的特点是它们的 遗传多样性，使他们能够迅速建立新的感染，逃避宿主的免疫系统， 产生抗药性。下一代测序技术的出现有望彻底改变 病毒学和流行病学领域，允许采样和表征数百万宿主内病毒 成千上万的感染者。然而，我们对疾病传播机制的了解 和病毒进化仍然是有限的，由于缺乏计算方法的处理，整合和 生物医学大数据分析。该项目的总体目标是开发一个全面的家庭， 创新的算法和模型，允许描述，分析，理解和预测复杂的 多维非线性疾病动力学 智力优势：拟议的研究将由一个跨学科的团队进行， 生物学家、数学家、分子流行病学家和计算机科学家， 与项目相关的领域。该项目将针对非常重要的流行病学和生物医学 问题包括发展有效和可扩展的疾病监测计算方法 传播，通过纳入宿主内和宿主间进化的流行病学动态建模 动力学纳入一个单一的框架和设计的计算工具，利用数据分析结果， 医疗保健专业人员。提出的算法和模型将使用大量的分子和 来自CDC和格鲁吉亚理工学院的项目合作者生成的流行病学数据，以及可用的 从公共来源。这些算法将免费分发给研究人员和卫生保健工作者 开源包和基于云的在线工具。特别是，它们将被纳入全球 卫生爆发和监测技术，目前正在开发一个基于网络的数据分析系统 在疾控中心研究结果将通过期刊出版物和会议广泛传播 介绍，包括生物信息学研究和应用国际研讨会， 国际研究所举办的分子流行病学计算进展研讨会。

项目成果

Telescope: an interactive tool for managing large-scale analysis from mobile devices.

Telescope：一种用于管理移动设备大规模分析的交互式工具。

• DOI: 10.1093/gigascience/giz163
• 发表时间: 2020
• 期刊: GigaScience
• 影响因子: 9.2
• 作者: Brito,JaquelineJ;Mosqueiro,Thiago;Rotman,Jeremy;Xue,Victor;Chapski,DouglasJ;laHoz,JuanDe;Matias,Paulo;Martin,LanaS;Zelikovsky,Alex;Pellegrini,Matteo;Mangul,Serghei
• 通讯作者: Mangul,Serghei

Analysis of heterogeneous genomic samples using image normalization and machine learning.

• DOI: 10.1186/s12864-020-6661-6
• 发表时间: 2020-12-21
• 期刊: BMC genomics
• 影响因子: 4.4
• 作者: Basodi S;Baykal PI;Zelikovsky A;Skums P;Pan Y
• 通讯作者: Pan Y

Scalable Reconstruction of SARS-CoV-2 Phylogeny with Recurrent Mutations

具有反复突变的 SARS-CoV-2 系统发育的可扩展重建

• DOI: 10.1089/cmb.2021.0306
• 发表时间: 2021
• 期刊: Journal of Computational Biology
• 影响因子: 1.7
• 作者: Novikov, Daniel;Knyazev, Sergey;Grinshpon, Mark;Icer, Pelin;Skums, Pavel;Zelikovsky, Alex
• 通讯作者: Zelikovsky, Alex

Special Issue Preface: 13th International Symposium on BioinformaticsResearch and Applications (ISBRA 2017).

特刊前言：第十三届生物信息学研究与应用国际研讨会（ISBRA 2017）。

• DOI: 10.1089/cmb.2018.29012.zc
• 发表时间: 2018
• 期刊: Journal of computational biology : a journal of computational molecular cell biology
• 作者: Cai,Zhipeng;Skums,Pavel;Zelikovsky,Alexander
• 通讯作者: Zelikovsky,Alexander

Inference of mutability landscapes of tumors from single cell sequencing data.

• DOI: 10.1371/journal.pcbi.1008454
• 发表时间: 2020-11
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Tsyvina V;Zelikovsky A;Snir S;Skums P
• 通讯作者: Skums P