Presentation Title

Comparison of West Nile Proteome Sequences using Co-Occurrence Networks

Presenter Information

Lavanya UppalaFollow

Advisor Information

Kathryn Cooper

Location

MBSC 201

Presentation Type

Poster

Start Date

6-3-2020 10:45 AM

End Date

6-3-2020 12:00 PM

Abstract

According to the CDC, West Nile virus is the leading cause of mosquito-borne disease in the continental US, with approximately 80% of cases being asymptomatic. Creating a platform utilizing publicly available big genomic and health data in order to predict, surveil, and manage viral infectious diseases like West Nile, may help mitigate the spread of such illnesses on a national scale. This can be done by using various databases, such as the one maintained by the NCBI. This Virus Variation Database houses 143 different protein sequences of West Nile virus strains collected from humans from around the world, ranging from 1953 to present. Combining this database with bioinformatics methodologies to compare virus strains, helps track the evolution of the West Nile genome and helps visualize widespread patterns. For instance, building co-occurrence networks allows for the comparison of a specific strain against a base protein sequence. Mapping these networks shows which proteins, frameshift mutations, and changes in evolutionarily conserved nucleotides are shared amongst lethal strains. These common traits can then be analyzed for their correlations to the virus strain's pathogenicity or overall ability to cause disease. Although West Nile virus mutates fairly slowly, identifying the segments of the virus’s genome or proteins that makes a single strain more lethal than another, could facilitate the creation of a treatment plan or vaccine for symptomatic strains. Applications of the research, by way of constructing such a platform, include providing governments and healthcare professionals additional information as to the lethality of an emergent virus strain.

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COinS
 
Mar 6th, 10:45 AM Mar 6th, 12:00 PM

Comparison of West Nile Proteome Sequences using Co-Occurrence Networks

MBSC 201

According to the CDC, West Nile virus is the leading cause of mosquito-borne disease in the continental US, with approximately 80% of cases being asymptomatic. Creating a platform utilizing publicly available big genomic and health data in order to predict, surveil, and manage viral infectious diseases like West Nile, may help mitigate the spread of such illnesses on a national scale. This can be done by using various databases, such as the one maintained by the NCBI. This Virus Variation Database houses 143 different protein sequences of West Nile virus strains collected from humans from around the world, ranging from 1953 to present. Combining this database with bioinformatics methodologies to compare virus strains, helps track the evolution of the West Nile genome and helps visualize widespread patterns. For instance, building co-occurrence networks allows for the comparison of a specific strain against a base protein sequence. Mapping these networks shows which proteins, frameshift mutations, and changes in evolutionarily conserved nucleotides are shared amongst lethal strains. These common traits can then be analyzed for their correlations to the virus strain's pathogenicity or overall ability to cause disease. Although West Nile virus mutates fairly slowly, identifying the segments of the virus’s genome or proteins that makes a single strain more lethal than another, could facilitate the creation of a treatment plan or vaccine for symptomatic strains. Applications of the research, by way of constructing such a platform, include providing governments and healthcare professionals additional information as to the lethality of an emergent virus strain.