RAPID: Real-time phylogenetic inference and transmission cluster analysis of COVID-19

RAPID：COVID-19 的实时系统发育推断和传播聚类分析

• 批准号: 2028040
• 负责人: Alexander Moshiri
• 金额: $ 20万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028040&HistoricalAwards=false
• 关键词: RAPID; Real; time; phylogenetic; inference

As the COVID-19 pandemic spreads rapidly around the world, public health officials need to be able to answer questions such as “How is COVID-19 spreading through the population?” and “How many individual outbreaks exist within a given community?”. With increasing access to sequencing technologies, scientists can analyze the genome sequences of collected SARS-CoV-2 viral samples in order to gain information about to aid in the development of vaccines and drugs as well as to infer the most likely evolutionary history of the virus, which can help epidemiologists track the spread of the virus across populations. The epidemiological use of the evolutionary history of the virus is only useful if it can be updated in real-time, but as the sheer volume of available data rapidly grows, scientists will require scalable computational tools to conduct these analyses. The goal of this project is to develop novel algorithms, software tools, and hardware systems that will scale to the massive amounts of data that are rapidly being generated in this pandemic, which will in turn aid in phylogenomic analysis of the virus, the effective tracking of the spread of the virus as well as in the development of novel vaccines and drugs in this pandemic. As a broader impact, this project will help with replicability and reproducibility of genetic and epidemiological research results. Furthermore, the existence of such a system will aid in fighting future viral outbreaks. This project provides professional development opportunities for an early career scientist.The standard viral phylogenetic inference workflow consists of quality checking and filtering, multiple sequence alignment, phylogenetic inference, phylogenetic rooting, phylogenetic dating, and transmission clustering. The researchers have identified that the computational bottlenecks of the workflow are multiple sequence alignment and phylogenetic inference, which scale poorly as a function of the number of input sequences. The objective of this project is the development of a user-friendly, scalable, and modular workflow for conducting a real-time computational phylogenetic analysis of assembled viral genomes, with a primary focus of SARS-CoV-2. The project solution includes: (1) the development of a novel software tool for orchestrating the automated end-to-end workflow, (2) the development of novel algorithms (and software implementations of these algorithms) to speed up the computational bottlenecks of the workflow, (3) the development of novel hardware systems for accelerating the workflow, and (4) a real-time publicly-accessible repository in which researchers can access the most up-to-date analysis results (with intermediate files) of all SARS-CoV-2 genomes currently available to prevent repeat computation efforts. The analysis infrastructure that will be built in this project will be broadly applicable to any viral pathogen for which phylogenetic inference is biologically and epidemiologically meaningful.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.

随着新冠肺炎疫情在世界范围内迅速蔓延，公共卫生官员需要能够回答这样的问题：“新冠肺炎是如何在人群中传播的？”以及“在一个给定的社区内存在多少个单独的疫情？”随着越来越多的人获得测序技术，科学家可以分析收集的SARS-CoV-2病毒样本的基因组序列，以获得有关帮助开发疫苗和药物的信息，并推断病毒最可能的进化史，这可以帮助流行病学家跟踪病毒在人群中的传播。病毒进化历史的流行病学应用只有在能够实时更新的情况下才有用，但随着可用数据的绝对数量迅速增长，科学家将需要可扩展的计算工具来进行这些分析。该项目的目标是开发新的算法、软件工具和硬件系统，以适应在这场大流行中迅速产生的大量数据，这反过来将有助于对病毒的系统学分析，有效跟踪病毒的传播，以及在这场大流行中开发新的疫苗和药物。作为更广泛的影响，该项目将有助于遗传和流行病学研究成果的可复制性和再现性。此外，这种系统的存在将有助于抗击未来的病毒爆发。该项目为早期职业科学家提供了职业发展机会。标准的病毒系统发育推断工作流程包括质量检查和筛选、多序列比对、系统发育推断、系统发育起源、系统发育年代测定和传播聚类。研究人员已经发现，工作流程的计算瓶颈是多序列比对和系统发育推理，它们作为输入序列数量的函数的可扩展性很差。该项目的目标是开发一种用户友好、可扩展和模块化的工作流程，用于对组装的病毒基因组进行实时计算系统发育分析，主要重点是SARS-CoV-2。项目解决方案包括：(1)开发一种新的软件工具来协调自动化的端到端工作流程，(2)开发新的算法(以及这些算法的软件实现)以加快工作流程的计算瓶颈，(3)开发用于加速工作流程的新硬件系统，以及(4)一个实时的公众可访问的储存库，研究人员可以在其中获取目前所有SARS-CoV-2基因组的最新分析结果(包括中间文件)，以防止重复计算工作。该项目将建立的分析基础设施将广泛适用于任何系统发育推断具有生物学和流行病学意义的病毒病原体。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

FPGA Acceleration of Pairwise Distance Calculation for Viral Transmission Clustering

病毒传播聚类的成对距离计算的 FPGA 加速

• 发表时间: 2021
• 期刊: IEEE Biomedical Circuits and Systems Conference
• 作者: Salamat, Sahand;Moshiri, Niema;Rosing, Tajana
• 通讯作者: Rosing, Tajana

The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction.

• DOI: 10.1038/s41598-022-09035-w
• 发表时间: 2022-03-24
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Moshiri N;Fisch KM;Birmingham A;DeHoff P;Yeo GW;Jepsen K;Laurent LC;Knight R
• 通讯作者: Knight R

ViralMSA: massively scalable reference-guided multiple sequence alignment of viral genomes

• DOI: 10.1093/bioinformatics/btaa743
• 发表时间: 2021-03-01
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Moshiri, Niema