ArticlePLoS pathogens2021
Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning.
Article in PLoS pathogens, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 41 citations in OpenAlex.
- Systematic multi-reference vertebrate ACE2 sequence similarity analysis predicts species susceptibility to SARS-related sarbecoviruses.Scientific reports · 2026Article
- A Unified Framework to Prioritize RNA Virus Cross-Species Transmission Risk Across an Expansive Host Landscape.Viruses · 2026Article
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- Differences in codon usage between host-species-specific rabies virus clades are driven by non-GC3 nucleotide composition.Virus evolution · 2026Article
- Charting the virosphere: computational synergies of AI and bioinformatics in viral discovery and evolution.Journal of virology · 2025Review
- Deciphering the Code of Viral-Host Adaptation Through Maximum-Entropy Nucleotide Bias Models.Molecular biology and evolution · 2025Article
- Viral genomic features predict Orthopoxvirus reservoir hosts.Communications biology · 2025Article
- ORF1ab codon frequency model predicts host-pathogen relationship in orthocoronavirinae.Frontiers in bioinformatics · 2025Article
- Viral genomic features predictbioRxiv : the preprint server for biology · 2024Article
- Features that matter: Evolutionary signatures can predict viral transmission routes.PLoS pathogens · 2024Article
- Compositional features analysis by machine learning in genome represents linear adaptation of monkeypox virus.Frontiers in genetics · 2024Article
- Evidence for an ancient aquatic origin of the RNA viral orderProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Risk Assessment of the Possible Intermediate Host Role of Pigs for Coronaviruses with a Deep Learning Predictor.Viruses · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Classification of group A rotavirus VP7 and VP4 genotypes using random forest.Frontiers in genetics · 2023Article
- Artificial Intelligence Models for Zoonotic Pathogens: A Survey.Microorganisms · 2022Review
- Assessing the risk of human-to-wildlife pathogen transmission for conservation and public health.Ecology letters · 2022Review
- Tracking the amino acid changes of spike proteins across diverse host species of severe acute respiratory syndrome coronavirus 2.iScience · 2022Article
- Infectious disease macroecology: parasite diversity and dynamics across the globe.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Spiking dependence of SARS-CoV-2 pathogenicity on TMPRSS2.Journal of medical virology · 2021Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.