Related Experiment Video
Updated: May 16, 2025
07:06
Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach
Published on: December 1, 2011
13.1K
ORF1ab codon frequency model predicts host-pathogen relationship in orthocoronavirinae.
Phillip E Davis1, Joseph A Russell1
1MRIGlobal, Gaithersburg, MD, United States.
Frontiers in Bioinformatics
|April 2, 2025
Summary
Predicting virus traits from genetic sequences is crucial for epidemiology. New models accurately forecast human-pathogen phenotypes in Orthocoronavirinae (coronavirus) using Orf1ab codon frequencies, highlighting translation
Area of Science:
- Virology
- Genomics
- Epidemiology
Background:
- Predicting viral phenotypic properties from sequence data is a key challenge in viral epidemiology.
- The Orthocoronavirinae (coronavirus) subfamily presents a significant focus for such predictions due to its impact on human health.
Purpose of the Study:
- To develop and validate predictive models for human-pathogen phenotypes in viruses using only sequence data.
- To identify critical sequence features that drive predictive model performance.
Main Methods:
- Utilized Orf1ab codon frequencies from viral sequences as input features.
- Developed machine learning models to predict human-pathogen phenotypes.
- Evaluated model performance using average precision and recall metrics on independent test sets.
Main Results:
- Achieved 76.74% average precision and 85.96% average recall for predicting human-pathogen phenotypes in Orthocoronavirinae.
- Identified a small subset of 5 codons as critical features for model success.
- Demonstrated that alternative sequence features and representations performed poorly.
Conclusions:
- Orf1ab codon frequencies are powerful predictors of viral human-pathogen phenotypes within the Orthocoronavirinae clade.
- The findings suggest a significant role for translation in virus-host interactions and provide a foundation for future research into viral adaptation and pathogenicity.
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