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Morris R, Wang S. Building a pathway to One Health surveillance and response in Asian countries. SCIENCE IN ONE HEALTH 2024; 3:100067. [PMID: 39077383 PMCID: PMC11262298 DOI: 10.1016/j.soh.2024.100067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/27/2024] [Indexed: 07/31/2024]
Abstract
To detect and respond to emerging diseases more effectively, an integrated surveillance strategy needs to be applied to both human and animal health. Current programs in Asian countries operate separately for the two sectors and are principally concerned with detection of events that represent a short-term disease threat. It is not realistic to either invest only in efforts to detect emerging diseases, or to rely solely on event-based surveillance. A comprehensive strategy is needed, concurrently investigating and managing endemic zoonoses, studying evolving diseases which change their character and importance due to influences such as demographic and climatic change, and enhancing understanding of factors which are likely to influence the emergence of new pathogens. This requires utilisation of additional investigation tools that have become available in recent years but are not yet being used to full effect. As yet there is no fully formed blueprint that can be applied in Asian countries. Hence a three-step pathway is proposed to move towards the goal of comprehensive One Health disease surveillance and response.
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Affiliation(s)
- Roger Morris
- Massey University EpiCentre and EpiSoft International Ltd, 76/100 Titoki Street, Masterton 5810, New Zealand
| | - Shiyong Wang
- Health, Nutrition and Population, World Bank Group, Washington, DC, USA
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Pan J, Zhang Z, Li Y, Yu J, You Z, Li C, Wang S, Zhu M, Ren F, Zhang X, Sun Y, Wang S. A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts. Brief Bioinform 2024; 25:bbae119. [PMID: 38555472 PMCID: PMC10981679 DOI: 10.1093/bib/bbae119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/23/2024] [Accepted: 03/02/2024] [Indexed: 04/02/2024] Open
Abstract
Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.
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Affiliation(s)
- Jie Pan
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhen Zhang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Ying Li
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Jiaoyang Yu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Chenyu Li
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shixu Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Minghui Zhu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Fengzhi Ren
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Xuexia Zhang
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Yanmei Sun
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shiwei Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
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Yin H, Wu S, Tan J, Guo Q, Li M, Guo J, Wang Y, Jiang X, Zhu H. IPEV: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning. Gigascience 2024; 13:giae018. [PMID: 38649300 PMCID: PMC11034026 DOI: 10.1093/gigascience/giae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/14/2024] [Accepted: 03/25/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses. FINDINGS We present IPEV, a novel method to distinguish prokaryotic and eukaryotic viruses in viromes, with a 2-dimensional convolutional neural network combining trinucleotide pair relative distance and frequency. Cross-validation assessments of IPEV demonstrate its state-of-the-art precision, significantly improving the F1-score by approximately 22% on an independent test set compared to existing methods when query viruses share less than 30% sequence similarity with known viruses. Furthermore, IPEV outperforms other methods in accuracy on marine and gut virome samples based on annotations by sequence alignments. IPEV reduces runtime by at most 1,225 times compared to existing methods under the same computing configuration. We also utilized IPEV to analyze longitudinal samples and found that the gut virome exhibits a higher degree of temporal stability than previously observed in persistent personal viromes, providing novel insights into the resilience of the gut virome in individuals. CONCLUSIONS IPEV is a high-performance, user-friendly tool that assists biologists in identifying and classifying prokaryotic and eukaryotic viruses within viromes. The tool is available at https://github.com/basehc/IPEV.
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Affiliation(s)
- Hengchuang Yin
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Shufang Wu
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Jie Tan
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Qian Guo
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Mo Li
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Jinyuan Guo
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
| | - Yaqi Wang
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Xiaoqing Jiang
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
- Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China
| | - Huaiqiu Zhu
- Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China
- School of Life Sciences, Peking University, Beijing 100871, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
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Ming Z, Chen X, Wang S, Liu H, Yuan Z, Wu M, Xia H. HostNet: improved sequence representation in deep neural networks for virus-host prediction. BMC Bioinformatics 2023; 24:455. [PMID: 38041071 PMCID: PMC10691023 DOI: 10.1186/s12859-023-05582-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/24/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand. RESULTS To overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of "Rabies lyssavirus" and an in-house dataset of "Flavivirus". Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability. CONCLUSION HostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.
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Affiliation(s)
- Zhaoyan Ming
- School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China
| | - Xiangjun Chen
- Polytechnic Institute, Zhejiang University, Hangzhou, 310058, China
| | - Shunlong Wang
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Wuhan, 430071, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Hong Liu
- Institute of Biomedicine, Shandong University of Technology, Zibo, 255000, China
| | - Zhiming Yuan
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Wuhan, 430071, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Minghui Wu
- School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China.
| | - Han Xia
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Wuhan, 430071, China.
- University of Chinese Academy of Sciences, Beijing, 100190, China.
- Hubei Jiangxia Laboratory, Wuhan, 430200, China.
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Hatibi N, Dumont-Lagacé M, Alouani Z, El Fatimy R, Abik M, Daouda T. Misclassified: identification of zoonotic transition biomarker candidates for influenza A viruses using deep neural network. Front Genet 2023; 14:1145166. [PMID: 37576548 PMCID: PMC10415530 DOI: 10.3389/fgene.2023.1145166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 04/25/2023] [Indexed: 08/15/2023] Open
Abstract
Introduction: Zoonotic transition of Influenza A viruses is the cause of epidemics with high rates of morbidity and mortality. Predicting which viral strains are likely to transition from their genetic sequence could help in the prevention and response against these zoonotic strains. We hypothesized that features predictive of viral hosts could be leveraged to identify biomarkers of zoonotic viral transition. Methods: We trained deep learning models to predict viral hosts based on the virus mRNA or protein sequences. Our multi-host dataset contained 848,630 unique nucleotide sequences obtained from the NCBI Influenza Virus and Influenza Research Databases. Each sequence, representing one gene from one viral strain, was classified into one of the three host categories: Avian, Human, and Swine. Trained models were analyzed using various neural network interpretation methods to identify interesting candidates for zoonotic transition biomarkers. Results: Using mRNA sequences as input led to higher prediction accuracies than amino acids, suggesting that the codon sequence contains information relevant to viral hosts that is lost during protein translation. UMAP visualization of the latent space of our classifiers showed that viral sequences clustered according to their host of origin. Interestingly, sequences from pandemic zoonotic viral strains localized at the margins between hosts, while zoonotic sequences incapable of Human-to-Human transmission localized with non-zoonotic viruses from the same host. In addition, host prediction for pandemic zoonotic sequences had low prediction accuracy, which was not the case for the other zoonotic strains. This supports our hypothesis that ambiguously predicted viral sequences bear features associated with cross-species infectivity. Finally, we compared misclassified sequences to well-classified ones to extract interesting candidates for zoonotic transition biomarkers. While features varied significantly between pairs of species and viral genes, several codons were conserved in Swine-to-Human and Avian-to-Human misclassified sequences, and in particular in the NA, HA, and NP genes, suggesting their importance for zoonosis in Humans. Discussion: Analysis of viral sequences using neural network interpretation approaches revealed important genetic differences between zoonotic viruses with pandemic potential, compared to non-zoonotic viral strains or zoonotic viruses incapable of Human-to-Human transmission.
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Affiliation(s)
- Nissrine Hatibi
- Ecole Nationale Supérieure d'Informatique et d'Analyse des Systèmes, Mohammed V University in Rabat, Rabat, Morocco
- Institute of Biological Sciences (ISSB), UM6P Faculty of Medical Sciences, Mohammed VI Polytechnic University, Ben Guerir, Morocco
| | | | - Zakaria Alouani
- Institute of Biological Sciences (ISSB), UM6P Faculty of Medical Sciences, Mohammed VI Polytechnic University, Ben Guerir, Morocco
| | - Rachid El Fatimy
- Institute of Biological Sciences (ISSB), UM6P Faculty of Medical Sciences, Mohammed VI Polytechnic University, Ben Guerir, Morocco
| | - Mounia Abik
- Ecole Nationale Supérieure d'Informatique et d'Analyse des Systèmes, Mohammed V University in Rabat, Rabat, Morocco
| | - Tariq Daouda
- Institute of Biological Sciences (ISSB), UM6P Faculty of Medical Sciences, Mohammed VI Polytechnic University, Ben Guerir, Morocco
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Pan J, You W, Lu X, Wang S, You Z, Sun Y. GSPHI: A novel deep learning model for predicting phage-host interactions via multiple biological information. Comput Struct Biotechnol J 2023; 21:3404-3413. [PMID: 37397626 PMCID: PMC10314231 DOI: 10.1016/j.csbj.2023.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 07/04/2023] Open
Abstract
Emerging evidence suggests that due to the misuse of antibiotics, bacteriophage (phage) therapy has been recognized as one of the most promising strategies for treating human diseases infected by antibiotic-resistant bacteria. Identification of phage-host interactions (PHIs) can help to explore the mechanisms of bacterial response to phages and provide new insights into effective therapeutic approaches. Compared to conventional wet-lab experiments, computational models for predicting PHIs can not only save time and cost, but also be more efficient and economical. In this study, we developed a deep learning predictive framework called GSPHI to identify potential phage and target bacterium pairs through DNA and protein sequence information. More specifically, GSPHI first initialized the node representations of phages and target bacterial hosts via a natural language processing algorithm. Then a graph embedding algorithm structural deep network embedding (SDNE) was utilized to extract local and global information from the interaction network, and finally, a deep neural network (DNN) was applied to accurately detect the interactions between phages and their bacterial hosts. In the drug-resistant bacteria dataset ESKAPE, GSPHI achieved a prediction accuracy of 86.65 % and AUC of 0.9208 under the 5-fold cross-validation technique, significantly better than other methods. In addition, case studies in Gram-positive and negative bacterial species demonstrated that GSPHI is competent in detecting potential Phage-host interactions. Taken together, these results indicate that GSPHI can provide reasonable candidate sensitive bacteria to phages for biological experiments. The webserver of the GSPHI predictor is freely available at http://120.77.11.78/GSPHI/.
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Affiliation(s)
- Jie Pan
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Wencai You
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Xiaoliang Lu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shiwei Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Yanmei Sun
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, The College of Life Sciences, Northwest University, Xi’an 710069, China
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Gonzalez-Isunza G, Jawaid MZ, Liu P, Cox DL, Vazquez M, Arsuaga J. Using machine learning to detect coronaviruses potentially infectious to humans. Sci Rep 2023; 13:9319. [PMID: 37291260 PMCID: PMC10248971 DOI: 10.1038/s41598-023-35861-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/24/2023] [Indexed: 06/10/2023] Open
Abstract
Establishing the host range for novel viruses remains a challenge. Here, we address the challenge of identifying non-human animal coronaviruses that may infect humans by creating an artificial neural network model that learns from spike protein sequences of alpha and beta coronaviruses and their binding annotation to their host receptor. The proposed method produces a human-Binding Potential (h-BiP) score that distinguishes, with high accuracy, the binding potential among coronaviruses. Three viruses, previously unknown to bind human receptors, were identified: Bat coronavirus BtCoV/133/2005 and Pipistrellus abramus bat coronavirus HKU5-related (both MERS related viruses), and Rhinolophus affinis coronavirus isolate LYRa3 (a SARS related virus). We further analyze the binding properties of BtCoV/133/2005 and LYRa3 using molecular dynamics. To test whether this model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-CoV-2 and all viral sequences released after the SARS-CoV-2 was published. The results predict the binding of SARS-CoV-2 with a human receptor, indicating that machine learning methods are an excellent tool for the prediction of host expansion events.
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Affiliation(s)
| | - M Zaki Jawaid
- Department of Physics, University of California, Davis, USA
| | - Pengyu Liu
- Department of Microbiology and Molecular Genetics, University of California, Davis, CA, USA
| | - Daniel L Cox
- Department of Physics, University of California, Davis, USA
| | - Mariel Vazquez
- Department of Microbiology and Molecular Genetics, University of California, Davis, CA, USA
- Department of Mathematics, University of California, Davis, CA, USA
| | - Javier Arsuaga
- Department of Molecular and Cellular Biology, University of California, Davis, CA, USA.
- Department of Mathematics, University of California, Davis, CA, USA.
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8
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Sadad T, Aurangzeb RA, Safran M, Alfarhood S, Kim J. Classification of Highly Divergent Viruses from DNA/RNA Sequence Using Transformer-Based Models. Biomedicines 2023; 11:biomedicines11051323. [PMID: 37238994 DOI: 10.3390/biomedicines11051323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/18/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Viruses infect millions of people worldwide each year, and some can lead to cancer or increase the risk of cancer. As viruses have highly mutable genomes, new viruses may emerge in the future, such as COVID-19 and influenza. Traditional virology relies on predefined rules to identify viruses, but new viruses may be completely or partially divergent from the reference genome, rendering statistical methods and similarity calculations insufficient for all genome sequences. Identifying DNA/RNA-based viral sequences is a crucial step in differentiating different types of lethal pathogens, including their variants and strains. While various tools in bioinformatics can align them, expert biologists are required to interpret the results. Computational virology is a scientific field that studies viruses, their origins, and drug discovery, where machine learning plays a crucial role in extracting domain- and task-specific features to tackle this challenge. This paper proposes a genome analysis system that uses advanced deep learning to identify dozens of viruses. The system uses nucleotide sequences from the NCBI GenBank database and a BERT tokenizer to extract features from the sequences by breaking them down into tokens. We also generated synthetic data for viruses with small sample sizes. The proposed system has two components: a scratch BERT architecture specifically designed for DNA analysis, which is used to learn the next codons unsupervised, and a classifier that identifies important features and understands the relationship between genotype and phenotype. Our system achieved an accuracy of 97.69% in identifying viral sequences.
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Affiliation(s)
- Tariq Sadad
- Department of Computer Science, University of Engineering & Technology, Mardan 23200, Pakistan
| | - Raja Atif Aurangzeb
- Department of Computer Science & Software Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan
| | - Mejdl Safran
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Sultan Alfarhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Jungsuk Kim
- Department of Biomedical Engineering, Gachon University, Seongnam-si 13120, Republic of Korea
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Chaturvedi A, Borkar K, Priyakumar UD, Vinod P. PREHOST: Host prediction of coronaviridae family using machine learning. Heliyon 2023; 9:e13646. [PMID: 36816252 PMCID: PMC9922161 DOI: 10.1016/j.heliyon.2023.e13646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 02/05/2023] [Accepted: 02/06/2023] [Indexed: 02/13/2023] Open
Abstract
Coronavirus, a zoonotic virus capable of transmitting infections from animals to humans, emerged as a pandemic recently. In such circumstances, it is essential to understand the virus's origin. In this study, we present a novel machine-learning pipeline PreHost for host prediction of the family, Coronaviridae. We leverage the complete viral genome and sequences at the protein level (spike protein, membrane protein, and nucleocapsid protein). Compared with the current state-of-the-art approaches, the random forest model attained high accuracy and recall scores of 99.91% and 0.98, respectively, for genome sequences. In addition to the spike protein sequences, our study shows membrane and nucleocapsid protein sequences can be utilized to predict the host of viruses. We also identified important sites in the viral sequences that help distinguish between different host classes. The host prediction pipeline PreHost will cater as a valuable tool to take effective measures to govern the transmission of future viruses.
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10
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Bajiya N, Dhall A, Aggarwal S, Raghava GPS. Advances in the field of phage-based therapy with special emphasis on computational resources. Brief Bioinform 2023; 24:6961791. [PMID: 36575815 DOI: 10.1093/bib/bbac574] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 11/07/2022] [Accepted: 11/25/2022] [Indexed: 12/29/2022] Open
Abstract
In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.
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Affiliation(s)
- Nisha Bajiya
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Suchet Aggarwal
- Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India
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11
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Iuchi H, Kawasaki J, Kubo K, Fukunaga T, Hokao K, Yokoyama G, Ichinose A, Suga K, Hamada M. Bioinformatics approaches for unveiling virus-host interactions. Comput Struct Biotechnol J 2023; 21:1774-1784. [PMID: 36874163 PMCID: PMC9969756 DOI: 10.1016/j.csbj.2023.02.044] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
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.
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Affiliation(s)
- Hitoshi Iuchi
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan
| | - Junna Kawasaki
- Faculty of Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Kento Kubo
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Tsukasa Fukunaga
- Waseda Institute for Advanced Study, Waseda University, Nishi Waseda, Shinjuku-ku, Tokyo 169-0051, Japan
| | - Koki Hokao
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Gentaro Yokoyama
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Akiko Ichinose
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan
| | - Kanta Suga
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Michiaki Hamada
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan.,Graduate School of Medicine, Nippon Medical School, Tokyo 113-8602, Japan
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12
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Roy T, Sharma K, Dhall A, Patiyal S, Raghava GPS. In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences. J Gen Virol 2022; 103. [PMID: 36318663 DOI: 10.1099/jgv.0.001802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023] Open
Abstract
Influenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. In this study, we have developed a computational method for the prediction of human-associated and non-human-associated influenza A virus sequences. The models were trained and validated on proteins and genome sequences of influenza A virus. Firstly, we have developed prediction models for 15 types of influenza A proteins using composition-based and one-hot-encoding features. We have achieved a highest AUC of 0.98 for HA protein on a validation dataset using dipeptide composition-based features. Of note, we obtained a maximum AUC of 0.99 using one-hot-encoding features for protein-based models on a validation dataset. Secondly, we built models using whole genome sequences which achieved an AUC of 0.98 on a validation dataset. In addition, we showed that our method outperforms a similarity-based approach (i.e., blast) on the same validation dataset. Finally, we integrated our best models into a user-friendly web server 'FluSPred' (https://webs.iiitd.edu.in/raghava/fluspred/index.html) and a standalone version (https://github.com/raghavagps/FluSPred) for the prediction of human-associated/non-human-associated influenza A virus strains.
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Affiliation(s)
- Trinita Roy
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
| | - Khushal Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
| | - Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
| | - Gajendra Pal Singh Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi-110020, India
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13
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Bartoszewicz JM, Nasri F, Nowicka M, Renard BY. Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection. Bioinformatics 2022; 38:ii168-ii174. [PMID: 36124807 DOI: 10.1093/bioinformatics/btac495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2022] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone. RESULTS We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats. CONCLUSIONS The neural networks trained using our data collection enable accurate detection of novel fungal pathogens. A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task. AVAILABILITY AND IMPLEMENTATION The data, models and code are hosted at https://zenodo.org/record/5846345, https://zenodo.org/record/5711877 and https://gitlab.com/dacs-hpi/deepac. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jakub M Bartoszewicz
- Hasso Plattner Institute for Digital Engineering, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany.,Department of Mathematics and Computer Science, Free University of Berlin, Berlin 14195, Germany
| | - Ferdous Nasri
- Hasso Plattner Institute for Digital Engineering, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany.,Department of Mathematics and Computer Science, Free University of Berlin, Berlin 14195, Germany
| | - Melania Nowicka
- Hasso Plattner Institute for Digital Engineering, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany.,Department of Mathematics and Computer Science, Free University of Berlin, Berlin 14195, Germany
| | - Bernhard Y Renard
- Hasso Plattner Institute for Digital Engineering, Digital Engineering Faculty, University of Potsdam, Potsdam 14482, Germany
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14
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Xu Y, Wojtczak D. Dive into machine learning algorithms for influenza virus host prediction with hemagglutinin sequences. Biosystems 2022; 220:104740. [DOI: 10.1016/j.biosystems.2022.104740] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 07/02/2022] [Accepted: 07/16/2022] [Indexed: 11/26/2022]
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15
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Câmara GBM, Coutinho MGF, da Silva LMD, Gadelha WVDN, Torquato MF, Barbosa RDM, Fernandes MAC. Convolutional Neural Network Applied to SARS-CoV-2 Sequence Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:5730. [PMID: 35957287 PMCID: PMC9371030 DOI: 10.3390/s22155730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/28/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
COVID-19, the illness caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus belonging to the Coronaviridade family, a single-strand positive-sense RNA genome, has been spreading around the world and has been declared a pandemic by the World Health Organization. On 17 January 2022, there were more than 329 million cases, with more than 5.5 million deaths. Although COVID-19 has a low mortality rate, its high capacities for contamination, spread, and mutation worry the authorities, especially after the emergence of the Omicron variant, which has a high transmission capacity and can more easily contaminate even vaccinated people. Such outbreaks require elucidation of the taxonomic classification and origin of the virus (SARS-CoV-2) from the genomic sequence for strategic planning, containment, and treatment of the disease. Thus, this work proposes a high-accuracy technique to classify viruses and other organisms from a genome sequence using a deep learning convolutional neural network (CNN). Unlike the other literature, the proposed approach does not limit the length of the genome sequence. The results show that the novel proposal accurately distinguishes SARS-CoV-2 from the sequences of other viruses. The results were obtained from 1557 instances of SARS-CoV-2 from the National Center for Biotechnology Information (NCBI) and 14,684 different viruses from the Virus-Host DB. As a CNN has several changeable parameters, the tests were performed with forty-eight different architectures; the best of these had an accuracy of 91.94 ± 2.62% in classifying viruses into their realms correctly, in addition to 100% accuracy in classifying SARS-CoV-2 into its respective realm, Riboviria. For the subsequent classifications (family, genera, and subgenus), this accuracy increased, which shows that the proposed architecture may be viable in the classification of the virus that causes COVID-19.
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Affiliation(s)
- Gabriel B. M. Câmara
- Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil;
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil; (M.G.F.C.); (L.M.D.d.S.); (W.V.d.N.G.); (M.F.T.)
| | - Maria G. F. Coutinho
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil; (M.G.F.C.); (L.M.D.d.S.); (W.V.d.N.G.); (M.F.T.)
| | - Lucileide M. D. da Silva
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil; (M.G.F.C.); (L.M.D.d.S.); (W.V.d.N.G.); (M.F.T.)
- Federal Institute of Education, Science and Technology of Rio Grande do Norte, Paraiso, Santa Cruz 59200-000, RN, Brazil
| | - Walter V. do N. Gadelha
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil; (M.G.F.C.); (L.M.D.d.S.); (W.V.d.N.G.); (M.F.T.)
| | - Matheus F. Torquato
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil; (M.G.F.C.); (L.M.D.d.S.); (W.V.d.N.G.); (M.F.T.)
| | - Raquel de M. Barbosa
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil; (M.G.F.C.); (L.M.D.d.S.); (W.V.d.N.G.); (M.F.T.)
- Department of Pharmacy and Pharmaceutical Technology, University of Granada, 18071 Granada, Spain
| | - Marcelo A. C. Fernandes
- Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil;
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil; (M.G.F.C.); (L.M.D.d.S.); (W.V.d.N.G.); (M.F.T.)
- Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
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16
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Hufsky F, Abecasis A, Agudelo-Romero P, Bletsa M, Brown K, Claus C, Deinhardt-Emmer S, Deng L, Friedel CC, Gismondi MI, Kostaki EG, Kühnert D, Kulkarni-Kale U, Metzner KJ, Meyer IM, Miozzi L, Nishimura L, Paraskevopoulou S, Pérez-Cataluña A, Rahlff J, Thomson E, Tumescheit C, van der Hoek L, Van Espen L, Vandamme AM, Zaheri M, Zuckerman N, Marz M. Women in the European Virus Bioinformatics Center. Viruses 2022; 14:1522. [PMID: 35891501 PMCID: PMC9319252 DOI: 10.3390/v14071522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 02/01/2023] Open
Abstract
Viruses are the cause of a considerable burden to human, animal and plant health, while on the other hand playing an important role in regulating entire ecosystems. The power of new sequencing technologies combined with new tools for processing "Big Data" offers unprecedented opportunities to answer fundamental questions in virology. Virologists have an urgent need for virus-specific bioinformatics tools. These developments have led to the formation of the European Virus Bioinformatics Center, a network of experts in virology and bioinformatics who are joining forces to enable extensive exchange and collaboration between these research areas. The EVBC strives to provide talented researchers with a supportive environment free of gender bias, but the gender gap in science, especially in math-intensive fields such as computer science, persists. To bring more talented women into research and keep them there, we need to highlight role models to spark their interest, and we need to ensure that female scientists are not kept at lower levels but are given the opportunity to lead the field. Here we showcase the work of the EVBC and highlight the achievements of some outstanding women experts in virology and viral bioinformatics.
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Affiliation(s)
- Franziska Hufsky
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany
| | - Ana Abecasis
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, New University of Lisbon, 1349-008 Lisbon, Portugal
| | - Patricia Agudelo-Romero
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Wal-Yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Nedlands, WA 6009, Australia
| | - Magda Bletsa
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
| | - Katherine Brown
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Claudia Claus
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Institute of Medical Microbiology and Virology, Medical Faculty, Leipzig University, 04103 Leipzig, Germany
| | - Stefanie Deinhardt-Emmer
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Institute of Medical Microbiology, Jena University Hospital, 07747 Jena, Germany
| | - Li Deng
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Institute of Virology, Helmholtz Centre Munich-German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Microbial Disease Prevention, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Caroline C. Friedel
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Institute of Informatics, Ludwig-Maximilians-Universität München, 80333 Munich, Germany
| | - María Inés Gismondi
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Institute of Agrobiotechnology and Molecular Biology (IABIMO), National Institute for Agriculture Technology (INTA), National Research Council (CONICET), Hurlingham B1686IGC, Argentina
- Department of Basic Sciences, National University of Luján, Luján B6702MZP, Argentina
| | - Evangelia Georgia Kostaki
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece
| | - Denise Kühnert
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Transmission, Infection, Diversification and Evolution Group, Max Planck Institute for the Science of Human History, 07745 Jena, Germany
| | - Urmila Kulkarni-Kale
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Bioinformatics Centre, Savitribai Phule Pune University, Pune 411007, India
| | - Karin J. Metzner
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Irmtraud M. Meyer
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 10115 Berlin, Germany
- Institute of Chemistry and Biochemistry, Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, 14195 Berlin, Germany
- Faculty of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany
| | - Laura Miozzi
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Institute for Sustainable Plant Protection, National Research Council of Italy, 10135 Torino, Italy
| | - Luca Nishimura
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Department of Genetics, School of Life Science, The Graduate University for Advanced Studies (SOKENDAI), Mishima 411-8540, Japan
- Human Genetics Laboratory, National Institute of Genetics, Mishima 411-8540, Japan
| | - Sofia Paraskevopoulou
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Methods Development and Research Infrastructure, Bioinformatics and Systems Biology, Robert Koch Institute, 13353 Berlin, Germany
| | - Alba Pérez-Cataluña
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- VISAFELab, Department of Preservation and Food Safety Technologies, Institute of Agrochemistry and Food Technology, IATA-CSIC, 46980 Valencia, Spain
| | - Janina Rahlff
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Centre for Ecology and Evolution in Microbial Model Systems (EEMiS), Department of Biology and Environmental Science, Linneaus University, 391 82 Kalmar, Sweden
| | - Emma Thomson
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow G51 4TF, UK
- MRC-University of Glasgow Centre for Virus Research, Glasgow G61 1QH, UK
| | - Charlotte Tumescheit
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- School of Biological Sciences, Seoul National University, Seoul 08826, Korea
| | - Lia van der Hoek
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Laboratory of Experimental Virology, Department of Medical Microbiology and Infection Prevention, Amsterdam UMC, University of Amsterdam, 1012 WX Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, 1100 DD Amsterdam, The Netherlands
| | - Lore Van Espen
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
| | - Anne-Mieke Vandamme
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Department of Microbiology, Immunology and Transplantation, Rega Institute, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
- Global Health and Tropical Medicine, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, 1349-008 Lisbon, Portugal
- Institute for the Future, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium
| | - Maryam Zaheri
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Neta Zuckerman
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- Central Virology Laboratory, Public Health Services, Ministry of Health and Sheba Medical Center, Ramat Gan 52621, Israel
| | - Manja Marz
- European Virus Bioinformatics Center, 07743 Jena, Germany; (A.A.); (P.A.-R.); (M.B.); (K.B.); (C.C.); (S.D.-E.); (L.D.); (C.C.F.); (M.I.G.); (E.G.K.); (D.K.); (U.K.-K.); (K.J.M.); (I.M.M.); (L.M.); (L.N.); (S.P.); (A.P.-C.); (J.R.); (E.T.); (C.T.); (L.v.d.H.); (L.V.E.); (A.-M.V.); (M.Z.); (N.Z.)
- RNA Bioinformatics and High-Throughput Analysis, Friedrich Schiller University Jena, 07743 Jena, Germany
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Li M, Zhang W. PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion. Brief Bioinform 2021; 23:6362109. [PMID: 34472593 DOI: 10.1093/bib/bbab348] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 07/05/2021] [Accepted: 07/18/2021] [Indexed: 01/01/2023] Open
Abstract
Phage therapy has become one of the most promising alternatives to antibiotics in the treatment of bacterial diseases, and identifying phage-host interactions (PHIs) helps to understand the possible mechanism through which a phage infects bacteria to guide the development of phage therapy. Compared with wet experiments, computational methods of identifying PHIs can reduce costs and save time and are more effective and economic. In this paper, we propose a PHI prediction method with a generative adversarial network (GAN)-based data augmentation and sequence-based feature fusion (PHIAF). First, PHIAF applies a GAN-based data augmentation module, which generates pseudo PHIs to alleviate the data scarcity. Second, PHIAF fuses the features originated from DNA and protein sequences for better performance. Third, PHIAF utilizes an attention mechanism to consider different contributions of DNA/protein sequence-derived features, which also provides interpretability of the prediction model. In computational experiments, PHIAF outperforms other state-of-the-art PHI prediction methods when evaluated via 5-fold cross-validation (AUC and AUPR are 0.88 and 0.86, respectively). An ablation study shows that data augmentation, feature fusion and an attention mechanism are all beneficial to improve the prediction performance of PHIAF. Additionally, four new PHIs with the highest PHIAF score in the case study were verified by recent literature. In conclusion, PHIAF is a promising tool to accelerate the exploration of phage therapy.
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Affiliation(s)
- Menglu Li
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China
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Guo Q, Li M, Wang C, Guo J, Jiang X, Tan J, Wu S, Wang P, Xiao T, Zhou M, Fang Z, Xiao Y, Zhu H. Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic. Sci Rep 2021; 11:17422. [PMID: 34465838 PMCID: PMC8408148 DOI: 10.1038/s41598-021-96903-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 08/18/2021] [Indexed: 11/16/2022] Open
Abstract
The SARS-CoV-2 pandemic has raised concerns in the identification of the hosts of the virus since the early stages of the outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting viral genomic features automatically, to predict the host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool, reaching a satisfactory AUC of 0.975 in the five-classification, and could make a reliable prediction for the novel viruses without close neighbors in phylogeny. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existing tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of the COVID-19 pandemic, we inferred that minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, a large-scale genome analysis, based on DeepHoF's computation for the later pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.
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Affiliation(s)
- Qian Guo
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA
| | - Mo Li
- Peking University-Tsinghua University-National Institute of Biological Sciences (PTN) Joint PhD Program, School of Life Sciences, Peking University, Beijing, 100871, China
| | - Chunhui Wang
- Peking University-Tsinghua University-National Institute of Biological Sciences (PTN) Joint PhD Program, School of Life Sciences, Peking University, Beijing, 100871, China
| | - Jinyuan Guo
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA
| | - Xiaoqing Jiang
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
| | - Jie Tan
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
| | - Shufang Wu
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Peihong Wang
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
| | - Tingting Xiao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Man Zhou
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Zhencheng Fang
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China
- Center for Quantitative Biology, Peking University, Beijing, 100871, China
| | - Yonghong Xiao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310006, China.
| | - Huaiqiu Zhu
- State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
- Center for Quantitative Biology, Peking University, Beijing, 100871, China.
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China.
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19
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Yakimovich A. Machine Learning and Artificial Intelligence for the Prediction of Host-Pathogen Interactions: A Viral Case. Infect Drug Resist 2021; 14:3319-3326. [PMID: 34456575 PMCID: PMC8385421 DOI: 10.2147/idr.s292743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 08/03/2021] [Indexed: 01/27/2023] Open
Abstract
The research of interactions between the pathogens and their hosts is key for understanding the biology of infection. Commencing on the level of individual molecules, these interactions define the behavior of infectious agents and the outcomes they elicit. Discovery of host-pathogen interactions (HPIs) conventionally involves a stepwise laborious research process. Yet, amid the global pandemic the urge for rapid discovery acceleration through the novel computational methodologies has become ever so poignant. This review explores the challenges of HPI discovery and investigates the efforts currently undertaken to apply the latest machine learning (ML) and artificial intelligence (AI) methodologies to this field. This includes applications to molecular and genetic data, as well as image and language data. Furthermore, a number of breakthroughs, obstacles, along with prospects of AI for host-pathogen interactions (HPI), are discussed.
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20
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Global overview and major challenges of host prediction methods for uncultivated phages. Curr Opin Virol 2021; 49:117-126. [PMID: 34126465 DOI: 10.1016/j.coviro.2021.05.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/20/2021] [Accepted: 05/22/2021] [Indexed: 12/14/2022]
Abstract
Bacterial communities play critical roles across all of Earth's biomes, affecting human health and global ecosystem functioning. They do so under strong constraints exerted by viruses, that is, bacteriophages or 'phages'. Phages can reshape bacterial communities' structure, influence long-term evolution of bacterial populations, and alter host cell metabolism during infection. Metagenomics approaches, that is, shotgun sequencing of environmental DNA or RNA, recently enabled large-scale exploration of phage genomic diversity, yielding several millions of phage genomes now to be further analyzed and characterized. One major challenge however is the lack of direct host information for these phages. Several methods and tools have been proposed to bioinformatically predict the potential host(s) of uncultivated phages based only on genome sequence information. Here we review these different approaches and highlight their distinct strengths and limitations. We also outline complementary experimental assays which are being proposed to validate and refine these bioinformatic predictions.
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21
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Bartoszewicz JM, Seidel A, Renard BY. Interpretable detection of novel human viruses from genome sequencing data. NAR Genom Bioinform 2021; 3:lqab004. [PMID: 33554119 PMCID: PMC7849996 DOI: 10.1093/nargab/lqab004] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 01/04/2021] [Accepted: 01/15/2021] [Indexed: 01/21/2023] Open
Abstract
Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.
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Affiliation(s)
- Jakub M Bartoszewicz
- Bioinformatics (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany
- Department of Mathematics and Computer Science, Free University of Berlin, 14195 Berlin, Germany
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, 14482 Potsdam, Brandenburg, Germany
- Digital Engineering Faculty, University of Postdam, 14482 Potsdam, Brandenburg, Germany
| | - Anja Seidel
- Bioinformatics (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany
- Department of Mathematics and Computer Science, Free University of Berlin, 14195 Berlin, Germany
| | - Bernhard Y Renard
- Bioinformatics (MF1), Department of Methodology and Research Infrastructure, Robert Koch Institute, 13353 Berlin, Germany
- Data Analytics and Computational Statistics, Hasso Plattner Institute for Digital Engineering, 14482 Potsdam, Brandenburg, Germany
- Digital Engineering Faculty, University of Postdam, 14482 Potsdam, Brandenburg, Germany
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