Host-parasite relations of bacteria and phages can be unveiled by oligostickiness, a measure of relaxed sequence similarity

Bioinformatics approaches for unveiling virus-host interactions

The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.

DeePhage: distinguishing virulent and temperate phage-derived sequences in metavirome data with a deep learning approach

BACKGROUND

Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for elucidating their different roles in interactions with bacterial hosts and regulation of microbial communities. However, there is no experimental or computational approach to effectively classify their sequences in culture-independent metavirome. We present a new computational method, DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment.

FINDINGS

DeePhage uses a "one-hot" encoding form to represent DNA sequences in detail. Sequence signatures are detected via a convolutional neural network to obtain valuable local features. The accuracy of DeePhage on 5-fold cross-validation reaches as high as 89%, nearly 10% and 30% higher than that of 2 similar tools, PhagePred and PHACTS. On real metavirome, DeePhage correctly predicts the highest proportion of contigs when using BLAST as annotation, without apparent preferences. Besides, DeePhage reduces running time vs PhagePred and PHACTS by 245 and 810 times, respectively, under the same computational configuration. By direct detection of the temperate viral fragments from metagenome and metavirome, we furthermore propose a new strategy to explore phage transformations in the microbial community. The ability to detect such transformations provides us a new insight into the potential treatment for human disease.

CONCLUSIONS

DeePhage is a novel tool developed to rapidly and efficiently identify 2 kinds of phage fragments especially for metagenomics analysis. DeePhage is freely available via http://cqb.pku.edu.cn/ZhuLab/DeePhage or https://github.com/shufangwu/DeePhage.

Prediction of virus-host infectious association by supervised learning methods

Background

The study of virus-host infectious association is important for understanding the functions and dynamics of microbial communities. Both cellular and fractionated viral metagenomic data generate a large number of viral contigs with missing host information. Although relative simple methods based on the similarity between the word frequency vectors of viruses and bacterial hosts have been developed to study virus-host associations, the problem is significantly understudied. We hypothesize that machine learning methods based on word frequencies can be efficiently used to study virus-host infectious associations.

Methods

We investigate four different representations of word frequencies of viral sequences including the relative word frequency and three normalized word frequencies by subtracting the number of expected from the observed word counts. We also study five machine learning methods including logistic regression, support vector machine, random forest, Gaussian naive Bayes and Bernoulli naive Bayes for separating infectious from non-infectious viruses for nine bacterial host genera with at least 45 infecting viruses. Area under the receiver operating characteristic curve (AUC) is used to compare the performance of different machine learning method and feature combinations. We then evaluate the performance of the best method for the identification of the hosts of contigs in metagenomic studies. We also develop a maximum likelihood method to estimate the fraction of true infectious viruses for a given host in viral tagging experiments.

Results

Based on nine bacterial host genera with at least 45 infectious viruses, we show that random forest together with the relative word frequency vector performs the best in identifying viruses infecting particular hosts. For all the nine host genera, the AUC is over 0.85 and for five of them, the AUC is higher than 0.98 when the word size is 6 indicating the high accuracy of using machine learning approaches for identifying viruses infecting particular hosts. We also show that our method can predict the hosts of viral contigs of length at least 1kbps in metagenomic studies with high accuracy. The random forest together with word frequency vector outperforms current available methods based on Manhattan and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$d_{2}^{*}$\end{document}d2∗ dissimilarity measures. Based on word frequencies, we estimate that about 95% of the identified T4-like viruses in viral tagging experiment infect Synechococcus, while only about 29% of the identified non-T4-like viruses and 30% of the contigs in the study potentially infect Synechococcus.

Conclusions

The random forest machine learning method together with the relative word frequencies as features of viruses can be used to predict viruses and viral contigs for specific bacterial hosts. The maximum likelihood approach can be used to estimate the fraction of true infectious associated viruses in viral tagging experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1473-7) contains supplementary material, which is available to authorized users.

Metagenomic exploration of viruses throughout the Indian Ocean

The characterization of global marine microbial taxonomic and functional diversity is a primary goal of the Global Ocean Sampling Expedition. As part of this study, 19 water samples were collected aboard the Sorcerer II sailing vessel from the southern Indian Ocean in an effort to more thoroughly understand the lifestyle strategies of the microbial inhabitants of this ultra-oligotrophic region. No investigations of whole virioplankton assemblages have been conducted on waters collected from the Indian Ocean or across multiple size fractions thus far. Therefore, the goals of this study were to examine the effect of size fractionation on viral consortia structure and function and understand the diversity and functional potential of the Indian Ocean virome. Five samples were selected for comprehensive metagenomic exploration; and sequencing was performed on the microbes captured on 3.0-, 0.8- and 0.1 µm membrane filters as well as the viral fraction (<0.1 µm). Phylogenetic approaches were also used to identify predicted proteins of viral origin in the larger fractions of data from all Indian Ocean samples, which were included in subsequent metagenomic analyses. Taxonomic profiling of viral sequences suggested that size fractionation of marine microbial communities enriches for specific groups of viruses within the different size classes and functional characterization further substantiated this observation. Functional analyses also revealed a relative enrichment for metabolic proteins of viral origin that potentially reflect the physiological condition of host cells in the Indian Ocean including those involved in nitrogen metabolism and oxidative phosphorylation. A novel classification method, MGTAXA, was used to assess virus-host relationships in the Indian Ocean by predicting the taxonomy of putative host genera, with Prochlorococcus, Acanthochlois and members of the SAR86 cluster comprising the most abundant predictions. This is the first study to holistically explore virioplankton dynamics across multiple size classes and provides unprecedented insight into virus diversity, metabolic potential and virus-host interactions.