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Updated: May 15, 2025
07:15
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
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Predicting viral host codon fitness and path shifting through tree-based learning on codon usage biases and genomic
Shuquan Su1,2,3, Zhongran Ni4,5, Tian Lan2
1Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, China.
Scientific Reports
|April 10, 2025
Summary
This study introduces a quantitative method to measure viral codon fitness (VCF) and its shifts, crucial for understanding pathogen spread. Machine learning models predict VCF using viral genomic properties, revealing the wobble position
Area of Science:
- Virology
- Genomics
- Bioinformatics
Background:
- Viral codon fitness (VCF) and its shifts are vital for pathogen epidemiology but lack quantitative study.
- Understanding host-virus interactions at the genomic level is crucial for predicting viral behavior.
Purpose of the Study:
- To develop and apply quantitative measurements for viral codon fitness (VCF) and its shifts.
- To investigate the predictive power of viral genomic properties, including relative synonymous codon usage (RSCU), on host codon fitness.
- To establish a human virus codon fitness (HVCF) score for assessing viruses infecting humans.
Main Methods:
- Utilized tree-based machine learning models to predict virus host codon fitness based on viral genomic properties and RSCU.
- Performed statistical analysis on RSCU data, focusing on the wobble position of virus codons.
- Developed a bioinformatics tool to simulate codon-based virus fitness shifting.
- Evaluated HVCF scores for human and non-human viruses, including SARS-CoV-2, and compared related bat coronaviruses.
Main Results:
- Relative synonymous codon usage (RSCU) and other genomic features accurately predict virus host codon fitness.
- The wobble position of viral codons is critical for distinguishing host codon fitness.
- No significant shift towards human-non-infectious codon fitness was observed in SARS-CoV-2.
- Tylonycteris bat coronavirus HKU4 relatives show potential similarities to SARS-CoV-2 in human codon fitness.
- Synonymous mutations are abundant in predicted codon fitness shifting pathways.
Conclusions:
- The developed models reliably characterize host codon fitness and can be translated into a human virus codon fitness (HVCF) score.
- HVCF analysis provides insights into virus-host interactions and evolutionary trajectories.
- Findings suggest potential evolutionary links between bat coronaviruses and SARS-CoV-2 regarding human codon fitness.
- Synonymous mutations offer new perspectives for virus evolution research and environmental surveillance.
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