CHERRY: a Computational metHod for accuratE pRediction of virus–pRokarYotic interactions using a graph encoder–decoder model

Abstract Prokaryotic viruses, which infect bacteria and archaea, are key players in microbial communities. Predicting the hosts of prokaryotic viruses helps decipher the dynamic relationship between microbes. Experimental methods for host prediction cannot keep pace with the fast accumulation of seq...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 23; no. 5
Main Authors: Shang, Jiayu, Sun, Yanni
Format: Journal Article
Language:English
Published: England Oxford University Press 20.09.2022
Oxford Publishing Limited (England)
Subjects:
Accuracy
Amino acid sequence
Archaea
Bacteria
Coders
Computer applications
Experimental methods
Knowledge representation
Microbial activity
Microorganisms
Next-generation sequencing
Nucleotide sequence
Phages
Predictions
Problem Solving Protocol
Viruses
graph convolutional network
deep learning
link prediction
phage host prediction
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary:Abstract Prokaryotic viruses, which infect bacteria and archaea, are key players in microbial communities. Predicting the hosts of prokaryotic viruses helps decipher the dynamic relationship between microbes. Experimental methods for host prediction cannot keep pace with the fast accumulation of sequenced phages. Thus, there is a need for computational host prediction. Despite some promising results, computational host prediction remains a challenge because of the limited known interactions and the sheer amount of sequenced phages by high-throughput sequencing technologies. The state-of-the-art methods can only achieve 43% accuracy at the species level. In this work, we formulate host prediction as link prediction in a knowledge graph that integrates multiple protein and DNA-based sequence features. Our implementation named CHERRY can be applied to predict hosts for newly discovered viruses and to identify viruses infecting targeted bacteria. We demonstrated the utility of CHERRY for both applications and compared its performance with 11 popular host prediction methods. To our best knowledge, CHERRY has the highest accuracy in identifying virus–prokaryote interactions. It outperforms all the existing methods at the species level with an accuracy increase of 37%. In addition, CHERRY’s performance on short contigs is more stable than other tools.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbac182