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 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.

Keywords: deep learning; graph convolutional network; link prediction; phage host prediction.

Figure 1

The key components of CHERRY. (A) The multimodal knowledge graph. The triangle represents the prokaryotic node and circle represents virus nodes. Different colors represent different taxonomic labels of the prokaryotes. I–III illustrate graph convolution using neighbors of increasing orders. (B) The graph convolutional encoder of CHERRY. (C) The decoder of CHERRY.

Figure 2

The benchmark dataset for virus–host interactions. (A) The viruses and their hosts in the training and test set, respectively. (B) Similarity between viruses in the training and test sets.

Figure 3

Visualization of the multimodal graph. The colors of the nodes represent their labels. For prokaryotic nodes, the labels represent their species. For virus nodes, the labels represent their hosts’ species. Because there are a large number of labels, this graph only colors the top eight labels with the largest number of nodes. All others are gray.

Figure 4

Host prediction performance on the benchmark dataset. Y-axis: accuracy. (A) 10-fold cross-validation on the training set. X-axis represents different types of graphs and BLAST-based host prediction. without graph: training with only decoder. Virus–virus: the knowledge graph only contains virus–virus edges. virus–prokaryote: the knowledge graph only contains virus–prokaryote edges. random sampling: the model is trained on the complete graph with a randomly sampled negative set. complete graph: the model is trained on the complete graph with negative sampling. Error bar represents the highest, lowest and average accuracy of the 10-fold cross-validation. (B) Comparison of host prediction accuracy on the test set from species to phylum. Tools that can output predictions at the species level (PHIST, PHIAF, vHULK, DeepHost, VHM-net and CHERRY) are grouped together and ordered based on their species-level performance.

Figure 5

The impact of training-vs-test sequence similarity on host prediction at the genus level. X-axis: maximum dashing similarity. Left Y-axis: accuracy (line segments). Right Y-axis: number of test viruses under each similarity cutoff (gray bars).

Figure 6

Host prediction accuracy for viruses that lack significant alignments against the prokaryotes. X-axis: taxonomic rankings. Y-axis: accuracy.

Figure 7

A case study for a sub-graph with heterogeneous labels. Triangles: prokaryotic nodes. Circles: virus nodes. White nodes: test viruses. Nodes with other colors: training samples. Different colors represent different species/labels. The open-end edges adjacent to the nodes indicate that these nodes have more connections.

Figure 8

Host prediction performance on contigs. X-axis: length of the input contigs. Y-axis: accuracy.

Figure 9

Host prediction results for different groups of viruses. X-axis: taxonomic rank. Y-axis: accuracy. ALL: the accuracy on the whole dataset, which is the same as Figure 4 (B). Other: accuracy for viruses that do not belong to Caudovirales.

Figure 10

The experimental results of top- prediction. (A): Tendency of the prediction score. X-axis: the sorted index by . Y-axis: average score of the top- prediction. (B): The accuracy using top- prediction.

Figure 11

The experimental results on the MetaHiC dataset. For each bin, we use the lowest rank of the assigned taxon as the host label for the phage contigs in the bin. Phage contigs from the same bins have the same label. (A): The number of phage contigs with host labels at different taxonomic ranks. (B): The host prediction accuracy (Y-axis) on the 6545 phage contigs. The comparison includes six tools that can predict hosts at species level.

Figure 12

Host prediction on the glacier metagenomic data. The numbers without parentheses represent the number of viruses. The numbers with parentheses represent the number of viruses with the same predicted hosts. For example, 12 viruses have predictions by both CHERRY and BLASTN and 11 of them have the same predicted hosts.

Figure 13

Host prediction on the gut metagenomic data. The numbers without parentheses represent the number of viruses. The numbers with parentheses represent the number of viruses with the same predicted hosts.

Figure 14

The precision-recall curve of predicting viruses infecting targeted prokaryotes. X-axis: recall, Y-axis: precision. The performance for three thresholds 0.95, 0.9 and 0.8 are marked with the the cross sign on the curve.