SeqFeatR for the Discovery of Feature-Sequence Associations

Area of Science:

• Genomics
• Bioinformatics
• Immunology

Background:

• Specific selection pressures drive genomic mutations.
• Identifying associations between mutations and selection pressures is crucial for biological research.
• Existing tools may lack comprehensive statistical and visualization capabilities for sequence-feature analysis.

Purpose of the Study:

• To introduce SeqFeatR, an open-source software for identifying associations between mutation patterns and specific selection pressures (features).
• To demonstrate the utility of SeqFeatR in discovering T cell epitopes from viral protein sequences for specific HLA types.
• To highlight SeqFeatR's support for frequentist and Bayesian methods and its novel visualization features.

Main Methods:

• Development of SeqFeatR as an R package.
• Implementation of frequentist and Bayesian statistical methods for sequence-feature association discovery.
• Integration of advanced visualization tools for statistical analysis results.

Main Results:

• SeqFeatR successfully identifies associations between mutation patterns and biological features.
• The software facilitates the discovery of T cell epitopes in viral sequences.
• Demonstrated utility of SeqFeatR's statistical and visualization functions using real data.

Conclusions:

• SeqFeatR is a valuable open-source tool for analyzing sequence-feature associations.
• The software enhances the discovery of biologically relevant mutations and epitopes.
• SeqFeatR provides accessible statistical and visualization capabilities for researchers.