Mining evolutionary network of influenza viruses : pipeline for co-mutational analysis of influenza viruses’ sequences

Influenza is a persistent threat to humans, resulting in millions of cases of severe illnesses and about 250,000 to 500,000 deaths. Consequently, it also causes tremendous economic losses as vaccines are produced yearly to counter against influenza. However, a vaccine loses its effectiveness in the...

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Main Author: Lim, Chun Wei
Other Authors: Kwoh Chee Keong
Format: Final Year Project
Language:English
Published: 2019
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Online Access:http://hdl.handle.net/10356/76438
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-764382023-03-03T20:27:17Z Mining evolutionary network of influenza viruses : pipeline for co-mutational analysis of influenza viruses’ sequences Lim, Chun Wei Kwoh Chee Keong School of Computer Science and Engineering Bioinformatics Research Centre Fransiskus Xaverius Ivan DRNTU::Engineering::Computer science and engineering Influenza is a persistent threat to humans, resulting in millions of cases of severe illnesses and about 250,000 to 500,000 deaths. Consequently, it also causes tremendous economic losses as vaccines are produced yearly to counter against influenza. However, a vaccine loses its effectiveness in the following year, since influenza evolves virulently. Therefore, the vaccine must be updated each year to stay effective. Hence, there is a demand to outline the evolution of influenza viruses for better selection of vaccines and predict pandemic strains. In this project, we aim to explore and perfect a novel method that relies on phylogenetic tree which are sampled from sequences data for mutations identification. This method is applied to the hemagglutinin of influenza H3N2. With network analyses integrated in the pipeline, not only does this approach allows us to recover site-pairs that potentially co-mutate, we are able to extract the direction of the relationship as well. From these recovery, we are able to facilitate the design of vaccines to better combat the threat of influenza viruses. We benchmarked the proposed method against a simulated set of data to identify the significant co-mutational site-pairs. The simulated data is built by simulating the substitution processes along a fixed phylogeny with a given nucleotide substitution model and suitable parameters. The results show that the proposed pipeline is robust and is capable of identifying significant dominant mutations during evolution. Bachelor of Engineering (Computer Science) 2019-03-04T13:10:22Z 2019-03-04T13:10:22Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/76438 en Nanyang Technological University 54 p. application/pdf application/pdf application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Lim, Chun Wei
Mining evolutionary network of influenza viruses : pipeline for co-mutational analysis of influenza viruses’ sequences
description Influenza is a persistent threat to humans, resulting in millions of cases of severe illnesses and about 250,000 to 500,000 deaths. Consequently, it also causes tremendous economic losses as vaccines are produced yearly to counter against influenza. However, a vaccine loses its effectiveness in the following year, since influenza evolves virulently. Therefore, the vaccine must be updated each year to stay effective. Hence, there is a demand to outline the evolution of influenza viruses for better selection of vaccines and predict pandemic strains. In this project, we aim to explore and perfect a novel method that relies on phylogenetic tree which are sampled from sequences data for mutations identification. This method is applied to the hemagglutinin of influenza H3N2. With network analyses integrated in the pipeline, not only does this approach allows us to recover site-pairs that potentially co-mutate, we are able to extract the direction of the relationship as well. From these recovery, we are able to facilitate the design of vaccines to better combat the threat of influenza viruses. We benchmarked the proposed method against a simulated set of data to identify the significant co-mutational site-pairs. The simulated data is built by simulating the substitution processes along a fixed phylogeny with a given nucleotide substitution model and suitable parameters. The results show that the proposed pipeline is robust and is capable of identifying significant dominant mutations during evolution.
author2 Kwoh Chee Keong
author_facet Kwoh Chee Keong
Lim, Chun Wei
format Final Year Project
author Lim, Chun Wei
author_sort Lim, Chun Wei
title Mining evolutionary network of influenza viruses : pipeline for co-mutational analysis of influenza viruses’ sequences
title_short Mining evolutionary network of influenza viruses : pipeline for co-mutational analysis of influenza viruses’ sequences
title_full Mining evolutionary network of influenza viruses : pipeline for co-mutational analysis of influenza viruses’ sequences
title_fullStr Mining evolutionary network of influenza viruses : pipeline for co-mutational analysis of influenza viruses’ sequences
title_full_unstemmed Mining evolutionary network of influenza viruses : pipeline for co-mutational analysis of influenza viruses’ sequences
title_sort mining evolutionary network of influenza viruses : pipeline for co-mutational analysis of influenza viruses’ sequences
publishDate 2019
url http://hdl.handle.net/10356/76438
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