Predicting the antigenicity and reassortment probability of influenza viruses

• Main Author: Ong, Wenqi
• Other Authors: Kwoh Chee Keong
• Format: Final Year Project
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: http://hdl.handle.net/10356/76931
• Institution: Nanyang Technological University

The emergence of novel combinations of influenza virus strains has been the main cause of pandemics as it outpaces the development of flu vaccines to bring the spread of the influenza virus under control. Numerous studies have shown that influenza viruses possess the ability commonly known as influenza reassortment, In this process, viruses rearrange their genetic components to produce never-before-seen genome combinations which render current vaccines ineffective. With data and knowledge on the influenza virus becoming more abundant and accessible in the field of bioinformatics, there is accordingly an increase in the development of computational models for analysis and prediction based on various characteristics of the influenza virus, such as influenza antigenicity. These analytical and predictive models accelerate researchers in the development of effective vaccines in the long run. As such, the aim of this project is to aid the aforementioned through developing two web applications, by proposing and applying various computation methods to data. In the first web application, by providing an antigenic database consisting of virus information and HI assays data, researchers are able to upload/retrieve data to predict the antigenic similarity between viruses using computational analysis methods such as context-free encoding scheme. In the second web application, by providing a database of whole genome protein sequences in full length, researchers are able to predict the reassortment probability of influenza viruses using methods such as host-prediction-based probability estimation. Computation results generated via the various methods are then furnished and visualised in an intuitive way to empower researchers in accomplishing insightful and accurate inferences for vaccine development.