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Azevedo KS, de Souza LC, Coutinho MGF, de M Barbosa R, Fernandes MAC. Deepvirusclassifier: a deep learning tool for classifying SARS-CoV-2 based on viral subtypes within the coronaviridae family. BMC Bioinformatics 2024; 25:231. [PMID: 38969970 PMCID: PMC11225326 DOI: 10.1186/s12859-024-05754-1] [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: 08/23/2023] [Accepted: 03/19/2024] [Indexed: 07/07/2024] Open
Abstract
PURPOSE In this study, we present DeepVirusClassifier, a tool capable of accurately classifying Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral sequences among other subtypes of the coronaviridae family. This classification is achieved through a deep neural network model that relies on convolutional neural networks (CNNs). Since viruses within the same family share similar genetic and structural characteristics, the classification process becomes more challenging, necessitating more robust models. With the rapid evolution of viral genomes and the increasing need for timely classification, we aimed to provide a robust and efficient tool that could increase the accuracy of viral identification and classification processes. Contribute to advancing research in viral genomics and assist in surveilling emerging viral strains. METHODS Based on a one-dimensional deep CNN, the proposed tool is capable of training and testing on the Coronaviridae family, including SARS-CoV-2. Our model's performance was assessed using various metrics, including F1-score and AUROC. Additionally, artificial mutation tests were conducted to evaluate the model's generalization ability across sequence variations. We also used the BLAST algorithm and conducted comprehensive processing time analyses for comparison. RESULTS DeepVirusClassifier demonstrated exceptional performance across several evaluation metrics in the training and testing phases. Indicating its robust learning capacity. Notably, during testing on more than 10,000 viral sequences, the model exhibited a more than 99% sensitivity for sequences with fewer than 2000 mutations. The tool achieves superior accuracy and significantly reduced processing times compared to the Basic Local Alignment Search Tool algorithm. Furthermore, the results appear more reliable than the work discussed in the text, indicating that the tool has great potential to revolutionize viral genomic research. CONCLUSION DeepVirusClassifier is a powerful tool for accurately classifying viral sequences, specifically focusing on SARS-CoV-2 and other subtypes within the Coronaviridae family. The superiority of our model becomes evident through rigorous evaluation and comparison with existing methods. Introducing artificial mutations into the sequences demonstrates the tool's ability to identify variations and significantly contributes to viral classification and genomic research. As viral surveillance becomes increasingly critical, our model holds promise in aiding rapid and accurate identification of emerging viral strains.
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Affiliation(s)
- Karolayne S Azevedo
- InovAI Lab, nPITI/IMD, Federal University of Rio Grande do Norte, Natal, RN, 59078-970, Brazil
| | - Luísa C de Souza
- InovAI Lab, nPITI/IMD, Federal University of Rio Grande do Norte, Natal, RN, 59078-970, Brazil
| | - Maria G F Coutinho
- InovAI Lab, nPITI/IMD, Federal University of Rio Grande do Norte, Natal, RN, 59078-970, Brazil
| | - Raquel de M Barbosa
- InovAI Lab, nPITI/IMD, Federal University of Rio Grande do Norte, Natal, RN, 59078-970, Brazil.
- Department of Pharmacy and Pharmaceutical Technology, University of Seville, 41012, Seville, Spain.
| | - Marcelo A C Fernandes
- InovAI Lab, nPITI/IMD, Federal University of Rio Grande do Norte, Natal, RN, 59078-970, Brazil.
- Bioinformatics Multidisciplinary Environment (BioME), Federal University of Rio Grande do Norte, Natal, RN, 59078-970, Brazil.
- Department of Computer Engineering and Automation (DCA), Federal University of Rio Grande do Norte, Natal, RN, 59078-970, Brazil.
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Cahuantzi R, Lythgoe KA, Hall I, Pellis L, House T. Unsupervised identification of significant lineages of SARS-CoV-2 through scalable machine learning methods. Proc Natl Acad Sci U S A 2024; 121:e2317284121. [PMID: 38478692 PMCID: PMC10962941 DOI: 10.1073/pnas.2317284121] [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: 10/10/2023] [Accepted: 02/05/2024] [Indexed: 03/21/2024] Open
Abstract
Since its emergence in late 2019, SARS-CoV-2 has diversified into a large number of lineages and caused multiple waves of infection globally. Novel lineages have the potential to spread rapidly and internationally if they have higher intrinsic transmissibility and/or can evade host immune responses, as has been seen with the Alpha, Delta, and Omicron variants of concern. They can also cause increased mortality and morbidity if they have increased virulence, as was seen for Alpha and Delta. Phylogenetic methods provide the "gold standard" for representing the global diversity of SARS-CoV-2 and to identify newly emerging lineages. However, these methods are computationally expensive, struggle when datasets get too large, and require manual curation to designate new lineages. These challenges provide a motivation to develop complementary methods that can incorporate all of the genetic data available without down-sampling to extract meaningful information rapidly and with minimal curation. In this paper, we demonstrate the utility of using algorithmic approaches based on word-statistics to represent whole sequences, bringing speed, scalability, and interpretability to the construction of genetic topologies. While not serving as a substitute for current phylogenetic analyses, the proposed methods can be used as a complementary, and fully automatable, approach to identify and confirm new emerging variants.
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Affiliation(s)
- Roberto Cahuantzi
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
- United Kingdom Health Security Agency, University of Oxford, OxfordOX3 7LF, United Kingdom
| | - Katrina A. Lythgoe
- Department of Biology, University of Oxford, OxfordOX1 3SZ, United Kingdom
- Big Data Institute, University of Oxford, OxfordOX3 7LF, United Kingdom
- Pandemic Sciences Institute, University of Oxford, OxfordOX3 7LF, United Kingdom
| | - Ian Hall
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
| | - Lorenzo Pellis
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
| | - Thomas House
- Department of Mathematics, The University of Manchester, ManchesterM13 9PL, United Kingdom
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Lebatteux D, Soudeyns H, Boucoiran I, Gantt S, Diallo AB. Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures. PLoS One 2024; 19:e0296627. [PMID: 38241279 PMCID: PMC10798494 DOI: 10.1371/journal.pone.0296627] [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: 02/21/2022] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Machine learning was shown to be effective at identifying distinctive genomic signatures among viral sequences. These signatures are defined as pervasive motifs in the viral genome that allow discrimination between species or variants. In the context of SARS-CoV-2, the identification of these signatures can assist in taxonomic and phylogenetic studies, improve in the recognition and definition of emerging variants, and aid in the characterization of functional properties of polymorphic gene products. In this paper, we assess KEVOLVE, an approach based on a genetic algorithm with a machine-learning kernel, to identify multiple genomic signatures based on minimal sets of k-mers. In a comparative study, in which we analyzed large SARS-CoV-2 genome dataset, KEVOLVE was more effective at identifying variant-discriminative signatures than several gold-standard statistical tools. Subsequently, these signatures were characterized using a new extension of KEVOLVE (KANALYZER) to highlight variations of the discriminative signatures among different classes of variants, their genomic location, and the mutations involved. The majority of identified signatures were associated with known mutations among the different variants, in terms of functional and pathological impact based on available literature. Here we showed that KEVOLVE is a robust machine learning approach to identify discriminative signatures among SARS-CoV-2 variants, which are frequently also biologically relevant, while bypassing multiple sequence alignments. The source code of the method and additional resources are available at: https://github.com/bioinfoUQAM/KEVOLVE.
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Affiliation(s)
- Dylan Lebatteux
- Department of Computer Science, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Hugo Soudeyns
- CHU Sainte-Justine Research Centre, Montréal, Québec, Canada
- Department of Microbiology, Infectious Diseases and Immunology, Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada
- Department of Pediatrics, Faculty of Medicine, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Isabelle Boucoiran
- Department of Obstetrics and Gynecology, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Soren Gantt
- CHU Sainte-Justine Research Centre, Montréal, Québec, Canada
- Department of Microbiology, Infectious Diseases and Immunology, Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada
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de Andrade AAS, Grivet M, Brustolini O, Vasconcelos ATR. ( m, n)-mer-a simple statistical feature for sequence classification. BIOINFORMATICS ADVANCES 2023; 3:vbad088. [PMID: 37448814 PMCID: PMC10338135 DOI: 10.1093/bioadv/vbad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 06/22/2023] [Accepted: 07/10/2023] [Indexed: 07/15/2023]
Abstract
Summary The (m, n)-mer is a simple alternative classification feature based on conditional probability distributions. In this application note, we compared k-mer and (m, n)-mer frequency features in 11 distinct datasets used for binary, multiclass and clustering classifications. Our findings show that the (m, n)-mer frequency features are related to the highest performance metrics and often statistically outperformed the k-mers. Here, the (m, n)-mer frequencies improved performance for classifying smaller sequence lengths (as short as 300 bp) and yielded higher metrics when using short values of k (ranging from 2 to 4). Therefore, we present the (m, n)-mers frequencies to the scientific community as a feature that seems to be quite effective in identifying complex discriminatory patterns and classifying polyphyletic sequence groups. Availability and implementation The (m, n)-mer algorithm is released as an R package within the CRAN project (https://cran.r-project.org/web/packages/mnmer) and is also available at https://github.com/labinfo-lncc/mnmer. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Amanda Araújo Serrão de Andrade
- Bioinformatics Laboratory (LABINFO), National Laboratory for Scientific Computing, Av. Getulio Vargas, 333—Quitandinha, 25651-076, Rio de Janeiro, Brazil
| | - Marco Grivet
- Pontifícia Universidade Católica do Rio de Janeiro, Rua Marquês de São Vicente 225, Gávea, 22451-900, Rio de Janeiro, Brazil
| | - Otávio Brustolini
- Bioinformatics Laboratory (LABINFO), National Laboratory for Scientific Computing, Av. Getulio Vargas, 333—Quitandinha, 25651-076, Rio de Janeiro, Brazil
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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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Kumar R, Yadav G, Kuddus M, Ashraf GM, Singh R. Unlocking the microbial studies through computational approaches: how far have we reached? ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:48929-48947. [PMID: 36920617 PMCID: PMC10016191 DOI: 10.1007/s11356-023-26220-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 02/24/2023] [Indexed: 04/16/2023]
Abstract
The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
- Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, USA
| | - Garima Yadav
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India
| | - Mohammed Kuddus
- Department of Biochemistry, College of Medicine, University of Hail, Hail, Saudi Arabia
| | - Ghulam Md Ashraf
- Department of Medical Laboratory Sciences, College of Health Sciences, and Sharjah Institute for Medical Research, University of Sharjah, Sharjah , 27272, United Arab Emirates
| | - Rachana Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India.
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Munagala NVTS, Amanchi PK, Balasubramanian K, Panicker A, Nagaraj N. Compression-Complexity Measures for Analysis and Classification of Coronaviruses. ENTROPY (BASEL, SWITZERLAND) 2022; 25:81. [PMID: 36673224 PMCID: PMC9857615 DOI: 10.3390/e25010081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/10/2022] [Accepted: 12/18/2022] [Indexed: 06/17/2023]
Abstract
Finding a vaccine or specific antiviral treatment for a global pandemic of virus diseases (such as the ongoing COVID-19) requires rapid analysis, annotation and evaluation of metagenomic libraries to enable a quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable and there is a need for fast alignment-free techniques for sequence analysis. Information theory and data compression algorithms provide a rich set of mathematical and computational tools to capture essential patterns in biological sequences. In this study, we investigate the use of compression-complexity (Effort-to-Compress or ETC and Lempel-Ziv or LZ complexity) based distance measures for analyzing genomic sequences. The proposed distance measure is used to successfully reproduce the phylogenetic trees for a mammalian dataset consisting of eight species clusters, a set of coronaviruses belonging to group I, group II, group III, and SARS-CoV-1 coronaviruses, and a set of coronaviruses causing COVID-19 (SARS-CoV-2), and those not causing COVID-19. Having demonstrated the usefulness of these compression complexity measures, we employ them for the automatic classification of COVID-19-causing genome sequences using machine learning techniques. Two flavors of SVM (linear and quadratic) along with linear discriminant and fine K Nearest Neighbors classifer are used for classification. Using a data set comprising 1001 coronavirus sequences (causing COVID-19 and those not causing COVID-19), a classification accuracy of 98% is achieved with a sensitivity of 95% and a specificity of 99.8%. This work could be extended further to enable medical practitioners to automatically identify and characterize coronavirus strains and their rapidly growing mutants in a fast and efficient fashion.
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Affiliation(s)
- Naga Venkata Trinath Sai Munagala
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Ettimadai 641112, Tamil Nadu, India
| | - Prem Kumar Amanchi
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Ettimadai 641112, Tamil Nadu, India
| | - Karthi Balasubramanian
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Ettimadai 641112, Tamil Nadu, India
| | - Athira Panicker
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Ettimadai 641112, Tamil Nadu, India
| | - Nithin Nagaraj
- Consciousness Studies Programme, National Institute of Advanced Studies, Bengaluru 560012, Karnataka, India
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Analysis of DNA Sequence Classification Using CNN and Hybrid Models. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:1835056. [PMID: 34306171 PMCID: PMC8285202 DOI: 10.1155/2021/1835056] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 06/25/2021] [Indexed: 12/23/2022]
Abstract
In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.
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