Tracking mutational semantics of SARS-CoV-2 genomes

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Genomic datasets, like those from SARS-CoV-2, are sequential and dynamic, presenting unique analytical challenges.
• Traditional methods may not fully capture the temporal evolution and complex associations within viral genomes.

Purpose of the Study:

• To apply Natural Language Processing (NLP) algorithms to SARS-CoV-2 genomic data.
• To reveal viral characteristics and evolutionary patterns.
• To identify mutations relevant to patient health outcomes.

Main Methods:

• Utilized fundamental NLP algorithms for processing SARS-CoV-2 genome sequences and mutations.
• Employed dynamic topic modeling to probe temporal mutational signatures.
• Applied semantic drift analysis to trace mutation associations in genomic records.

Main Results:

• Demonstrated NLP's applicability in analyzing viral evolution and mutation dynamics.
• Successfully traced mutation associations and semantic drift within genomic data.
• Identified potential links between specific mutations and patient health status.

Conclusions:

• NLP offers a novel framework for understanding viral evolution and mutation impact.
• The approach can identify significant mutations and their relevance to disease.
• This methodology aids in detecting mutations of concern and understanding their clinical relevance.