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

TL;DR

CHERRY is a novel graph encoder-decoder model that significantly improves the accuracy of predicting virus-host interactions at the species level, outperforming existing methods and maintaining stability on short contigs.

Contribution

The paper introduces CHERRY, a new computational method using a graph encoder-decoder model for accurate virus-host interaction prediction, surpassing current state-of-the-art techniques.

Findings

CHERRY achieves 37% higher accuracy than previous methods.

It outperforms 11 popular host prediction tools.

CHERRY maintains stable performance on short contigs.

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.