Developing bioinformatics approaches for the anaylsis of influenza virus whole genome sequence data.

开发用于分析流感病毒全基因组序列数据的生物信息学方法。

• 批准号: 1800166
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1800166
• 关键词: Developing; bioinformatics; approaches; anaylsis; influenza

Influenza viruses have a key impact on human and animal health worldwide, infecting humans and livestock and seeding pandemics. Sequencing technologies have enabled us to monitor and understand the genetic variation of these viruses at unprecedented scale. However, since it is now possible to routinely sequence hundreds or thousands of virus whole genomes, new bioinformatics approaches are required for analysis and processing of data at this scale. Methodological development in this area has become increasingly important for the purposes of understanding how influenza viruses evolve and spread. The influenza virus genome is composed of 8 segments which continuously mutate and reassort, which result in antigenic drift and antigenic shift. Many standard bioinformatics approaches in microbial genomics were designed for data derived from bacteria that evolve at a much slower rate. As such, many approaches, from classification to mapping, can perform sub-optimally. Simultaneously, modern phylodynamic methods can provide important information, but many are not designed for big data applications.This project was focused on the development of cutting-edge bioinformatics methods and modelling for the processing and analysis of influenza virus whole genome sequencing data, working with world-class researchers from University of Cardiff and collaborators across the UK. The student designed bioinformatics algorithms and modelling approaches for several parts of the whole genome sequencing pipeline, including sequence assembly, classification, and phylodynamic modelling.

流感病毒对全球人类和动物健康具有关键影响，感染人类，牲畜和种子大流行。测序技术使我们能够以前所未有的规模监测和了解这些病毒的遗传变异。但是，由于现在可以常规地对数百或数千种病毒整个基因组进行序列，因此需要新的生物信息学方法来分析和处理此规模的数据。对于了解流感病毒如何发展和传播的目的，该领域的方法论发展变得越来越重要。流感病毒基因组由8个片段组成，这些片段连续突变和重新延伸，从而导致抗原漂移和抗原移位。微生物基因组学中许多标准的生物信息学方法都是为源自细菌得出的数据而设计的，这些数据以较慢的速率进化。因此，从分类到映射的许多方法都可以在次优的情况下执行。同时，现代的系统动力学方法可以提供重要的信息，但许多人不是为大数据应用而设计的。本项目的重点是开发尖端的生物信息学方法和用于处理和分析流感病毒整体基因组测序数据的建模，与Cardiff大学的世界级研究人员合作，并与Cardiff University and Complorators一起工作。该学生为整个基因组测序管道的多个部分设计了生物信息学算法和建模方法，包括序列组装，分类和系统动力学建模。

项目成果

Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications.

• DOI: 10.1093/bioinformatics/btz814
• 发表时间: 2020-03-01
• 期刊: Bioinformatics (Oxford, England)
• 作者: Southgate JA;Bull MJ;Brown CM;Watkins J;Corden S;Southgate B;Moore C;Connor TR
• 通讯作者: Connor TR