Are you struggling to predict virus hosts from viral genomes? COMPARE.EDU.VN offers a comprehensive comparison of different feature representations to streamline your analysis. Discover how nucleotide, amino acid, and protein domain features enhance prediction accuracy with our in-depth evaluation, offering a transformative solution for computational host assignment. Enhance accuracy, refine host prediction, and simplify intricate genomic data interpretation.
1. Introduction: The Challenge of Virus Host Prediction
Determining which virus infects which host species is a pivotal challenge in virology. This knowledge is fundamental to understanding the impact of viruses on cellular life and their roles in ecosystems worldwide. From the human microbiome to marine environments, viruses play a crucial role in the regulation of biogeochemical cycles, as well as acting as animal and plant pathogens.
The advent of metagenomics has spurred a rapid increase in virus discovery. Over half of all known viral genomes have been deposited in databases in the last few years. This expansion is a significant step toward cataloging the Earth’s virosphere. However, the indiscriminate nature of metagenomics means that most newly discovered viruses lack identified hosts. In the IMG/VR databases, for example, less than 5% of over 700,000 viral genomes have associated hosts. The absence of high-throughput methods for reliable virus-host associations necessitates fast, accurate computational tools to annotate new viral genomes with host taxon information.
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2. Current Computational Approaches
Existing computational approaches to virus host prediction include:
- Searching for homologous sub-sequences in hosts, such as prophage or CRISPR-Cas spacers.
- Looking for co-abundance between virus and host.
- Distance-based metrics of oligo-nucleotide or k-mer composition with potential host genomes or reference virus genomes.
- Machine learning methods using various sequence-derived features.
While the first strategy provides high-confidence predictions, it is limited by the alignment approaches at low sequence similarity. K-mer profile comparison, although alignment-free, loses discriminative power due to a lack of contrast in high-dimensional space. Additionally, all methods relying on reference genomes are constrained by available database genomes.
Machine learning approaches offer alternatives that are independent of reference genomes or alignment, relying instead on labeled training examples. Machine learning techniques are widely used in computational biology to analyze large biological sequence datasets due to their ability to find weak patterns in complex and noisy data without requiring prior knowledge of the specific mechanisms responsible for the phenotype of interest. Measurable attributes, termed ‘features,’ are critical for successful machine learning, typically represented in fixed-length numerical vectors encapsulating the discriminative information contained in viral genomes.
3. The Role of Viral Genome Features in Host Prediction
To date, most machine learning approaches to virus host prediction have used features derived from oligo-nucleotide or k-mer biases known to correlate with their host genomes. These include CG bias, CpG bias, and di-codon bias. Di-nucleotide features, in particular, have been used across a range of virus host prediction tasks, from training on a single virus species or genera with multiple hosts to training on host taxa with multiple viruses. The potential for improved prediction by extending the length of nucleotide k-mers has also been demonstrated.
The nucleotide sequence contains the information needed for a virus to exploit its host, including regulatory RNAs and amino acid sequences. The latter, through their biochemical properties, fold into three-dimensional structures with functional properties mediated through molecular interactions. Though this ‘functional’ information is present in the nucleotide sequence, it is not always easily extracted by machine learning approaches. To date, only a few machine learning approaches have demonstrated the potential of using alternative representations of the genome for virus host prediction.
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4. Exploiting the Coevolutionary Virus-Host Relationship
The use of features derived from viral genomes for host prediction is based on the observation that over time, the coevolutionary virus-host relationship embeds a host-specific signal in the virus genome. As obligate intracellular parasites, viruses must enter a host cell, subvert its defense systems, and exploit its cellular systems to replicate. To achieve this, the virus must make hundreds to thousands of molecular interactions with the host system while evading the host immune response. This antagonistic relationship drives a coevolutionary ‘arms race,’ imprinting a host-specific signal in the viral genome.
Most virus-host interactions are protein-protein interactions mediated through both domain-domain interactions and domain-motif interactions. There are many known examples of viruses converging on host short linear motifs (SLiMs) to directly mimic host molecular interfaces. Pathogens across different domains of life that infect the same host mimic the same host motifs.
The phylogenetic signal due to the evolutionary relationship between viruses that infect the same host can also be predictive. This is because the coevolutionary process tends to make virus and host phylogenetic trees congruent, despite frequent host-switching. Host-switching tends to be preferentially biased to closely related hosts because a virus must evade the immune response and exploit the new host’s system for replication to successfully jump host species. Alignment-free phylogenetic analysis has shown that k-mer composition contains sufficient phylogenetic signal to reliably infer evolutionary relationships. Protein domains have also been used to infer phylogeny.
5. The Goal: Comparing Feature Representations for Virus Host Prediction
The primary goal of this study is to compare the predictive power of a wider range of features than those generated from nucleotide sequences alone. The hypothesis is that features derived from transformations of nucleotide sequences to other representations have the potential to improve prediction over nucleotide sequences. By adding biological information in the form of translated amino acid sequences, physio-chemical properties of amino acid residues, and predicted protein domains, we aim to make the complex nature of both the evolutionary and host-mimicry information more easily accessible to machine learning algorithms.
To achieve this, genome sequences are transformed into higher-level sequence representations, adding biological information in the form of:
- Translation of nucleotide to amino acid sequences.
- Physio-chemical properties of amino acid residues – a more functional representation that allows for conservative amino acid substitution.
- Predicted protein domains – distinct structural/functional subunits of a protein associated with specific functions.
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6. Methodology: A Supervised Machine Learning Workflow
Based on these four representations of the viral genomes and using a supervised machine learning workflow, all levels of feature representation are predictive of host taxonomic information for both prokaryote and eukaryote hosts. By using a novel phylogenetically-aware ‘holdout’ method, the contribution of phylogenetic and convergent signals to prediction are investigated. Using a kernel combination method to improve prediction demonstrates that the information embedded by these different genome representations is complementary and can be combined to improve predictions.
The study’s results demonstrate that features capturing the different layered biological signals arising from multiple types of the viral-host molecular interactions have the prospect of improving the accuracy of virus host prediction across a broad range of applications.
7. Results: Predictive Capacity Across Hosts
Datasets for different host taxa, or labels, were created using sequences for positive and negative viruses, that is, viruses that are either known or not known to infect the labelled taxa. To ameliorate the problems caused by overlapping or redundant data, a minimum distance was maintained between sequences by using only the reference sequence for each viral species. The Virus Host Database was used to identify known species-level virus host interactions for both prokaryote and eukaryote hosts at different host taxonomic levels. A balanced binary dataset was created for each host taxa for which there were more than a minimum number of known interacting viruses. Known interactions made up the positive labelled class. The negative class was drawn from the remaining viruses that infect hosts in the parent taxa of the positive class.
For the prokaryote hosts, this resulted in 65 datasets (all for bacteria hosts), corresponding to Baltimore class I, dsDNA viruses. For the eukaryote hosts, the following two strategies were used: combining viruses from all Baltimore classes; and combining all RNA viruses, respectively, for a particular host taxon into a single dataset. This resulted in a total of 116 eukaryote datasets covering 57 host taxa over all taxonomic ranks, from kingdom to species level and the different Baltimore, and combined classes of the viruses. These include 48 datasets comprising all RNA viruses for host groups that include many at family, genus and species level.
Each of the 224 datasets was randomly split into training and test partitions with a ratio of 0.8 to 0.2 prior to extracting the 20 different feature set matrices from the viral genomes. Each of these feature matrices was used to train and test an SVM classifier, resulting in AUC scores for over 3740 classifiers.
8. Genome Representations and Feature Sets
The 20 feature sets generated from the four representations of the viral genomes are shown in the table below.
9. Predictive Power of All Genome Representations
To test the predictive capacity of the different levels of the genome representation, a binary classifier was trained and tested for each of the 20 feature sets on all of the datasets. The results of the evaluation of all the classifiers demonstrate that all levels of genome representation contain a signal predictive of host taxa across the host tree.
Heatmaps comparing the AUC scores for the prokaryote and eukaryote classifiers show that, apart from DNA k-mers of length 1, all feature sets are consistently predictive. Omitting results for DNA k-mers of length 1, 82% of the dataset-featureset combinations have an AUC of 0.75 or more (74% with AUC of 0.8 or more). Any AUC score above 0.5 (random classification) indicates the presence of a predictive signal. A score of 1 demonstrates the potential for a perfect classifier where all predictions are correct. Most host taxa have many feature sets that contain a predictive signal (146 out of the 180 datasets have at least one feature set with a score of greater than 0.90). Some hosts are more challenging to predict, with none of the feature sets giving good performance (6 out of the 180 datasets have a maximum score of less than 0.80). This is most apparent at the lower taxonomic ranks of species and genus where the goal is to separate the viruses of more similar hosts and for some Baltimore classes. Overall, the results show that a genomic signature that predicts host taxonomy is present at all levels of biological information representation tested in our study.
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10. Results for Bacteria Datasets
The heatmap below shows the results for all the bacteria datasets for all the feature sets.
The heatmap shows that all feature sets contain some predictive signal with an AUC > 0.5 for the majority of the bacteria datasets. The rows each correspond to a dataset and are ordered by taxonomic rank (indicated by the colour bar on the right) and each column a feature set.
11. Results for Eukaryote Datasets
The heatmap below shows the results for all the eukaryote datasets across all the feature sets.
The heatmap shows that most of the feature sets contain some predictive signal, AUC > 0.5, for the majority of the eukaryote datasets and for all Baltimore groupings (indicated by the inner colour bar on the right). Each row corresponds to a dataset and are ordered by taxonomic rank (indicated by the outer colour bar on the right) and each column corresponds to a feature set.
12. The Impact of K-mer Length on Prediction Accuracy
The effect of k-mer length on prediction accuracy was tested using a range of k-mer lengths for the sequence representations of the genomes (nucleic acid, amino acid, and physio-chemical properties). The results show that for all feature levels, prediction improves with increasing k-mer length. This is despite the exponential growth in the size of the feature sets.
13. K-mer Length and Bacteria Datasets
The boxplots below show how prediction improves with increasing k-mer length for all representations of the genome and that prediction gets more difficult at lower taxonomic ranks.
14. K-mer Length and Eukaryote Datasets
The boxplots below show how prediction improves with increasing k-mer length comparing prediction across the different Baltimore groupings. These boxplots show how prediction improves with increasing k-mer length for all representations of the genome and that prediction gets more difficult at lower taxonomic ranks.
Prediction appears to be more difficult for the eukaryote datasets. This is perhaps due to the fact that eukaryote hosts are infected by viruses from across all seven Baltimore classes. The alternative replication/life-cycle strategies used by viruses from different classes will involve dissimilar sets of molecular interactions. It is therefore likely that they will acquire disparate host-derived signatures in their genomes, making the classification task more challenging. The problem is further exacerbated by the size of the datasets with few host taxa being available below the rank class when split on Baltimore class to meet the minimum dataset size.
15. Relationship Between AUC Scores and Dataset Size
Comparing classifiers for datasets moving from higher to lower taxonomic levels, prediction becomes less accurate and less consistent across all the feature sets. One possible reason for this drop in predictive power (and increased variance) is the decrease in size of the datasets. This is confirmed by comparing the AUC scores against dataset size. Although many of the smaller datasets achieve a high AUC, the worst performing classifiers all correspond to smaller datasets.
The scatterplot shows that most of the classifiers achieve good AUC scores (above 0.85). This is the case even for the small datasets and for those at family level and below.
16. Phylogenetic and Convergent Signals
Experiments were performed to determine if we were finding more than just a phylogenetic signal embedded in the virus genomes. A novel cross-validation method was developed where, rather than stratifying data randomly into training and test sets, one complete virus family was withheld from training and then used to test the resulting classifier. The aim was, as far as possible, to holdout a group of closely related viruses.
17. Creating Holdout Datasets
The image below shows an example of how a holdout dataset was created.
18. Comparing Holdout and Standard Classifiers
To assess the performance of these ‘holdout classifiers’, they were compared with previous classifiers (referred to as ‘all’), where a random split of all the viruses was used to form both the training and test sets. Interestingly, while a small drop in AUC performance was observed across all feature sets, the majority of the ‘holdout’ classifiers retained a predictive signal.
Comparison of holdout and the standard (labelled ‘all’) classifiers for each dataset. For the majority of datasets there was a small loss in predictive power, implying that both classifiers are learning a shared signal. In a minority of cases there was a complete loss in predictive power implying the lack of a common signal.
19. The Signal Loss for Holdout Classifiers
The violin plots below show the ratios of the AUC scores for holdout to standard classifiers for each dataset, illustrating the variation in signal loss for the different feature sets.
20. Complementary Information in Different Genome Representations
To check whether these alternative features are redundant or provide complementary information, feature sets from the different genome levels were combined. A property of kernels, as used by SVMs, is the fact that it is straightforward to combine feature sets by creating composite kernels. The most predictive kernels from the DNA_9, AA_4, PC_5, and Domain feature sets were combined in different linear combinations.
21. Combined Kernel Classifiers
The image below demonstrates how prediction can improve with the number of kernels contributing to the SVM classifier.
22. False Positive Rate (FPR) vs. True Positive Rate (TPR)
By adjusting the contribution of the different kernels, the specificity (1- FPR) and sensitivity (TPR) of the classifier can be altered.
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23. Discussion: Key Findings and Implications
The aim of this study was to compare the predictive power of a wide range of features for use in machine learning approaches to virus host prediction. The results show that features derived from all four representations of viral genomes are predictive across the host tree. Combining a broader range of features that encapsulate the multiple layers of information held within viral genomes can lead to improved accuracy of virus-host prediction.
The majority of previous machine learning approaches to virus-host prediction have focused on information from nucleotide sequences only, which although predictive of host, ignore the rich information contained within alternative representations of the genomes. Through a process of convergent evolution, viruses are known to mimic their host’s molecular interfaces at domain-domain and domain-motif interaction sites. Such mimicry will be reflected in the amino acid sequence and domain content. The results show that features derived from these genome representations can be successfully used for prediction, as demonstrated in previous studies.
There is no universal best feature set, and some datasets are more challenging to predict with none of the feature sets achieving good performance. This is most apparent at the lower taxonomic ranks of species and genus where the goal is to separate the viruses of more similar hosts. Some Baltimore classes are easier to predict than others.
The novel holdout method suggests that the predictive signal embedded in viral genomes is made up of both phylogenetic and convergent signals. By removing the signal coming from the phylogenetic relationships between the viruses infecting a host, it was found that the majority of the ‘holdout’ classifiers still contained a predictive signal.
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24. Impact of K-mer Length and Molecular Interactions
Increasing the length of the k-mers improves prediction with all sequence representations. This aligns with findings that longer nucleotide sequences co-occur in viruses and their host across all classes of viruses and host.
Longer k-mer features and domains—which only occur once or a few times in a genome—have the capacity to encode information about local virus-host molecular interactions, such as motif-domain or domain-domain interfaces. Changes in the occurrence of these local features caused by single mutations as a virus adapts to its host will have a big impact on k-mer composition, whereas global genome-wide biases will take many mutations over the whole genome to have a significant effect on the k-mer composition.
25. Challenges and Future Directions
Machine learning requires suitable training examples, which constrains predictions to the small fraction of cellular life that have many known viruses. This data is biased towards well-studied organisms. To overcome this, hosts were pooled into higher taxa. For all feature sets, prediction gets more difficult for datasets at lower taxonomic ranks.
Several factors limit the specificity of predictions, and as virus host interactions tend to be species-specific, this will limit the applications of this approach. While this study was restricted to using species reference sequences, a wider study using all available host-labeled data from databases should enable higher-resolution predictions.
In this study, sequence composition derived features were limited to fixed k-mers, not allowing mismatches. Using a motif representation or relaxed k-mers with mismatches may be better at generalizing across closely related sub-sequences and ultimately improve performance.
Future development and deployment of classifiers for different virus host prediction domains would require task-dependent optimization of the models and their operating thresholds. Various model optimizations are possible, including combining multiple feature sets. Kernel weights would need to be optimized with respect to the most important error metric for the task in hand.
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26. Conclusion: Enhancing Virus Host Prediction with Diverse Features
In conclusion, the results demonstrate that features derived from all four representations of viral genomes are predictive across the host tree. Combining a broader range of features that encapsulate the multiple layers of information held within viral genomes can lead to improved accuracy of virus-host prediction. This use of complementary features will lead to higher confidence assignments about host taxon information for the increasing numbers of viruses with unassigned hosts from metagenomics studies and, for example, to identify the reservoir source of a spillover event. Furthermore, the local nature of domain and longer k-mer features has the potential to be informative of the mechanisms leading to virus host specificity.
27. FAQ: Virus Host Prediction
Q1: What is virus host prediction and why is it important?
Virus host prediction is the process of identifying which host species a virus infects. It’s crucial for understanding viral ecology, disease emergence, and developing effective control strategies.
Q2: What are the main computational approaches for virus host prediction?
The main approaches include homology-based methods, co-abundance analysis, distance-based metrics of k-mer composition, and machine learning techniques.
Q3: Why are machine learning methods increasingly used in virus host prediction?
Machine learning can identify complex patterns in viral genomes without requiring prior knowledge of specific mechanisms, making it ideal for analyzing large datasets.
Q4: What types of features are used in machine learning for virus host prediction?
Common features include nucleotide biases, k-mer frequencies, amino acid compositions, and protein domain information.
Q5: How does the length of k-mers affect the accuracy of virus host prediction?
Generally, longer k-mers provide more specific information and can improve prediction accuracy, although this can also increase computational complexity.
Q6: What is the significance of phylogenetic signals in virus host prediction?
Phylogenetic signals reflect the evolutionary relationships between viruses and their hosts, which can be predictive of host specificity due to coevolutionary processes.
Q7: How do convergent signals contribute to virus host prediction?
Convergent signals indicate that viruses infecting the same host may mimic host molecular interfaces, providing additional predictive power.
Q8: What are the limitations of current virus host prediction methods?
Limitations include reliance on reference genomes, biases in available data, and difficulties in discriminating between closely related hosts.
Q9: How can different genome representations be combined to improve virus host prediction?
Combining nucleotide sequences, amino acid properties, and protein domain information can capture multiple layers of information and improve prediction accuracy.
Q10: What future developments can enhance virus host prediction?
Future advancements include using motif representations, optimizing machine learning models, and incorporating more comprehensive datasets to improve the accuracy and scope of predictions.
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