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de Souza LC, Azevedo KS, de Souza JG, Barbosa RDM, Fernandes MAC. New proposal of viral genome representation applied in the classification of SARS-CoV-2 with deep learning. BMC Bioinformatics 2023; 24:92. [PMID: 36906520 PMCID: PMC10007673 DOI: 10.1186/s12859-023-05188-1] [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] [Received: 10/11/2022] [Accepted: 02/15/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. RESULTS In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. CONCLUSIONS The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs.
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Affiliation(s)
- Luísa C. de Souza
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
| | - Karolayne S. Azevedo
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
| | - Jackson G. de Souza
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
| | - Raquel de M. Barbosa
- Department of Pharmacy and Pharmaceutical Technology, University of Granada, Granada, Spain
| | - Marcelo A. C. Fernandes
- Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal, RN 59078-970 Brazil
- Department of Computer Engineering and Automation, 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
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Bagal UR, Phan J, Welsh RM, Misas E, Wagner D, Gade L, Litvintseva AP, Cuomo CA, Chow NA. MycoSNP: A Portable Workflow for Performing Whole-Genome Sequencing Analysis of Candida auris. Methods Mol Biol 2022; 2517:215-228. [PMID: 35674957 DOI: 10.1007/978-1-0716-2417-3_17] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Candida auris is an urgent public health threat characterized by high drug-resistant rates and rapid spread in healthcare settings worldwide. As part of the C. auris response, molecular surveillance has helped public health officials track the global spread and investigate local outbreaks. Here, we describe whole-genome sequencing analysis methods used for routine C. auris molecular surveillance in the United States; methods include reference selection, reference preparation, quality assessment and control of sequencing reads, read alignment, and single-nucleotide polymorphism calling and filtration. We also describe the newly developed pipeline MycoSNP, a portable workflow for performing whole-genome sequencing analysis of fungal organisms including C. auris.
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Affiliation(s)
- Ujwal R Bagal
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - John Phan
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Rory M Welsh
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Elizabeth Misas
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Lalitha Gade
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Christina A Cuomo
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nancy A Chow
- Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, GA, USA.
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Singh OP, Vallejo M, El-Badawy IM, Aysha A, Madhanagopal J, Mohd Faudzi AA. Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms. Comput Biol Med 2021; 136:104650. [PMID: 34329865 PMCID: PMC8294595 DOI: 10.1016/j.compbiomed.2021.104650] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 07/08/2021] [Accepted: 07/13/2021] [Indexed: 11/28/2022]
Abstract
Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing (DSP) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA, which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10 × 10 cross-validation paired t-test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4 %, sensitivity of 96.2 %, and specificity of 98.2 %, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 s to compute the genome biomarkers, outperforming previous studies.
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Affiliation(s)
| | - Marta Vallejo
- School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, UK
| | - Ismail M El-Badawy
- Electronics and Communications Engineering Department, Arab Academy for Science and Technology, Cairo, Egypt
| | - Ali Aysha
- School of Chemistry, University of Edinburgh, Edinburgh, UK
| | - Jagannathan Madhanagopal
- School of Physiotherapy, Faculty of Allied Health Professional, AIMST University, Semeling Campus, Bedong, Kedah, Malaysia
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Microarray Filtering-Based Fuzzy C-Means Clustering and Classification in Genomic Signal Processing. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2019. [DOI: 10.1007/s13369-019-03945-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Randhawa GS, Hill KA, Kari L. ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels. BMC Genomics 2019; 20:267. [PMID: 30943897 PMCID: PMC6448311 DOI: 10.1186/s12864-019-5571-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 02/27/2019] [Indexed: 11/11/2022] Open
Abstract
Background Although software tools abound for the comparison, analysis, identification, and classification of genomic sequences, taxonomic classification remains challenging due to the magnitude of the datasets and the intrinsic problems associated with classification. The need exists for an approach and software tool that addresses the limitations of existing alignment-based methods, as well as the challenges of recently proposed alignment-free methods. Results We propose a novel combination of supervised Machine Learning with Digital Signal Processing, resulting in ML-DSP: an alignment-free software tool for ultrafast, accurate, and scalable genome classification at all taxonomic levels. We test ML-DSP by classifying 7396 full mitochondrial genomes at various taxonomic levels, from kingdom to genus, with an average classification accuracy of >97%. A quantitative comparison with state-of-the-art classification software tools is performed, on two small benchmark datasets and one large 4322 vertebrate mtDNA genomes dataset. Our results show that ML-DSP overwhelmingly outperforms the alignment-based software MEGA7 (alignment with MUSCLE or CLUSTALW) in terms of processing time, while having comparable classification accuracies for small datasets and superior accuracies for the large dataset. Compared with the alignment-free software FFP (Feature Frequency Profile), ML-DSP has significantly better classification accuracy, and is overall faster. We also provide preliminary experiments indicating the potential of ML-DSP to be used for other datasets, by classifying 4271 complete dengue virus genomes into subtypes with 100% accuracy, and 4,710 bacterial genomes into phyla with 95.5% accuracy. Lastly, our analysis shows that the “Purine/Pyrimidine”, “Just-A” and “Real” numerical representations of DNA sequences outperform ten other such numerical representations used in the Digital Signal Processing literature for DNA classification purposes. Conclusions Due to its superior classification accuracy, speed, and scalability to large datasets, ML-DSP is highly relevant in the classification of newly discovered organisms, in distinguishing genomic signatures and identifying their mechanistic determinants, and in evaluating genome integrity.
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Affiliation(s)
- Gurjit S Randhawa
- Department of Computer Science, University of Western Ontario, London, ON, Canada.
| | - Kathleen A Hill
- Department of Biology, University of Western Ontario, London, ON, Canada
| | - Lila Kari
- School of Computer Science, University of Waterloo, Waterloo, ON, Canada
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Skutkova H, Maderankova D, Sedlar K, Jugas R, Vitek M. A degeneration-reducing criterion for optimal digital mapping of genetic codes. Comput Struct Biotechnol J 2019; 17:406-414. [PMID: 30984363 PMCID: PMC6444178 DOI: 10.1016/j.csbj.2019.03.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 02/07/2019] [Accepted: 03/15/2019] [Indexed: 01/08/2023] Open
Abstract
Bioinformatics may seem to be a scientific field processing primarily large string datasets, as nucleotides and amino acids are represented with dedicated characters. On the other hand, many computational tasks that bioinformatics challenges are mathematical problems understandable as operations with digits. In fact, many computational tasks are solved this way in the background. One of the most widely used digital representations is mapping of nucleotides and amino acids with integers 0–3 and 0–20, respectively. The limitation of this mapping occurs when the digital signal of nucleotides has to be translated into a digital signal of amino acids as the genetic code is degenerated. This causes non-monotonies in a mapping function. Although map for reducing this undesirable effect has already been proposed, it is defined theoretically and for standard genetic codes only. In this study, we derived a novel optimal criterion for reducing the influence of degeneration by utilizing a large dataset of real sequences with various genetic codes. As a result, we proposed a new robust global optimal map suitable for any genetic code as well as specialized optimal maps for particular genetic codes. Optimization of 1D numerical representation for DNA to protein translation. Reducing genetic code degeneracy in numerical representation of DNA sequences. More robust numerical conversion used for genomic-proteomic analysis.
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Affiliation(s)
- Helena Skutkova
- Department of Biomedical Engineering, Brno University of Technology, Technicka 12, 616 00 Brno, Czech republic
| | - Denisa Maderankova
- Department of Biomedical Engineering, Brno University of Technology, Technicka 12, 616 00 Brno, Czech republic
| | - Karel Sedlar
- Department of Biomedical Engineering, Brno University of Technology, Technicka 12, 616 00 Brno, Czech republic
| | - Robin Jugas
- Department of Biomedical Engineering, Brno University of Technology, Technicka 12, 616 00 Brno, Czech republic
| | - Martin Vitek
- Department of Biomedical Engineering, Brno University of Technology, Technicka 12, 616 00 Brno, Czech republic
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Maderankova D, Jugas R, Sedlar K, Vitek M, Skutkova H. Rapid Bacterial Species Delineation Based on Parameters Derived From Genome Numerical Representations. Comput Struct Biotechnol J 2019; 17:118-126. [PMID: 30728919 PMCID: PMC6352304 DOI: 10.1016/j.csbj.2018.12.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 12/07/2018] [Accepted: 12/20/2018] [Indexed: 01/29/2023] Open
Abstract
Species delineation based on bacterial genomes is an essential part of the research of prokaryotes. In silico genome-to-genome comparison methods are computationally demanding, but much less tedious and error prone than the wet-lab methods. In this paper, we present a novel method for the delineation of bacterial genomes based on genomic signal processing. The proposed method uses numerical representations of whole bacterial genomes, phase signal and cumulated phase signal, from which four parameters are derived for each genome. The parameters characterize a genome and their calculation is independent of the other genomes comprising a delineation dataset. The delineation itself is processed as a calculation of the parameters' average similarity. The method was statistically verified on 1826 bacterial genomes. A similarity threshold of 96% was set based on the receiver operating characteristic curve that featured sensitivity of 99.78% and specificity of 97.25%. Additionally, comparative analysis on another 33 bacterial genomes was conducted using standard delineation tools as these tools were not able to process the dataset of 1826 genomes using desktop computer. The proposed method achieved comparable or better delineation results in comparison with the standard tools. Besides the excellent delineation results, another great advantage of the method is its small computational demands, which enables the delineation of thousands of genomes on a desktop computer. The calculation of the parameters takes tens of minutes for thousands of genomes. Moreover, they can be calculated in advance by creating a database, meaning the delineation itself is then completed in a matter of seconds.
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Affiliation(s)
- Denisa Maderankova
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600 Brno, Czech Republic
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Gertych A, Pietka E. Foreword to the special issue on Information Technologies in Biomedicine. Comput Biol Med 2015; 69:234-5. [PMID: 26726075 DOI: 10.1016/j.compbiomed.2015.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Arkadiusz Gertych
- Department of Surgery, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Ewa Pietka
- Department of Informatics and Medical Equipment, Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland.
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