Inference of homology by Blast visualization of influenza genome set

• Main Author: Tan, Jun Qiu
• Other Authors: Kwoh Chee Keong
• Format: Final Year Project
• Language: English
• Published: 2017
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: http://hdl.handle.net/10356/70492
• Institution: Nanyang Technological University

The Spanish Flu, a strain of the influenza virus H1N1, is estimated to have killed 50-100 million people in the 1918 influenza pandemic. Studies have shown that this is a very pathogenic strain of human influenza whereby gene segments have persisted in new reassortant strains, threatening to cause great harm to public health. Knowledge of the ancestral history/homology of such new reassortant strains can help shed light on understanding how these new strains came to be which may help researchers develop new insights in preventing the spread of future influenza pandemics. Sequence similarity analysis through BLAST is one of the first few steps to understanding sequence homology but current BLAST visualizations can be intimidating and confusing. The aim of this project is to develop a web application that improves upon current BLAST visualizations by filtering results based on year and summarizing additional supplementary data from BLAST hits (Country, Host, Year, Segment) into insightful charts that provide a quick overview of the results that BLAST has created. This would provide users with sufficient background knowledge for better inference about the sequence homology of the query Influenza genome set as well as potentially assisting in the development of insightful hypotheses.