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Shaukat MA, Nguyen TT, Hsu EB, Yang S, Bhatti A. Comparative study of encoded and alignment-based methods for virus taxonomy classification. Sci Rep 2023; 13:18662. [PMID: 37907535 PMCID: PMC10618506 DOI: 10.1038/s41598-023-45461-0] [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: 07/04/2023] [Accepted: 10/19/2023] [Indexed: 11/02/2023] Open
Abstract
The emergence of viruses and their variants has made virus taxonomy more important than ever before in controlling the spread of diseases. The creation of efficient treatments and cures that target particular virus properties can be aided by understanding virus taxonomy. Alignment-based methods are commonly used for this task, but are computationally expensive and time-consuming, especially when dealing with large datasets or when detecting new virus variants is time sensitive. An alternative approach, the encoded method, has been developed that does not require prior sequence alignment and provides faster results. However, each encoded method has its own claimed accuracy. Therefore, careful evaluation and comparison of the performance of different encoded methods are essential to identify the most accurate and reliable approach for virus taxonomy classification. This study aims to address this issue by providing a comprehensive and comparative analysis of the potential of encoded methods for virus classification and phylogenetics. We compared the vectors generated for each encoded method using distance metrics to determine their similarity to alignment-based methods. The results and their validation show that K-merNV followed by CgrDft encoded methods, perform similarly to state-of-the-art multi-sequence alignment methods. This is the first study to incorporate and compare encoded methods that will facilitate future research in making more informed decisions regarding selection of a suitable method for virus taxonomy.
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Affiliation(s)
- Muhammad Arslan Shaukat
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia.
| | - Thanh Thi Nguyen
- Faculty of Information Technology, Monash University, Victoria, Australia
| | - Edbert B Hsu
- Department of Emergency Medicine, Johns Hopkins University, Maryland, USA
| | - Samuel Yang
- Department of Emergency Medicine, Stanford University, California, USA
| | - Asim Bhatti
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
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2
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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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3
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Grønbæk C, Liang Y, Elliott D, Krogh A. Context dependent prediction in DNA sequence using neural networks. PeerJ 2022; 10:e13666. [PMID: 36157058 PMCID: PMC9504454 DOI: 10.7717/peerj.13666] [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: 01/04/2022] [Accepted: 06/10/2022] [Indexed: 01/17/2023] Open
Abstract
One way to better understand the structure in DNA is by learning to predict the sequence. Here, we trained a model to predict the missing base at any given position, given its left and right flanking contexts. Our best-performing model was a neural network that obtained an accuracy close to 54% on the human genome, which is 2% points better than modelling the data using a Markov model. In likelihood-ratio tests, the neural network performed significantly better than any of the alternative models by a large margin. We report on where the accuracy was obtained, first observing that the performance appeared to be uniform over the chromosomes. The models performed best in repetitive sequences, as expected, although their performance far from random in the more difficult coding sections, the proportions being ~70:40%. We further explored the sources of the accuracy, Fourier transforming the predictions revealed weak but clear periodic signals. In the human genome the characteristic periods hinted at connections to nucleosome positioning. We found similar periodic signals in GC/AT content in the human genome, which to the best of our knowledge have not been reported before. On other large genomes similarly high accuracy was found, while lower predictive accuracy was observed on smaller genomes. Only in the mouse genome did we see periodic signals in the same range as in the human genome, though weaker and of a different type. This indicates that the sources of these signals are other or more than nucleosome arrangement. Interestingly, applying a model trained on the mouse genome to the human genome resulted in a performance far below that of the human model, except in the difficult coding regions. Despite the clear outcomes of the likelihood-ratio tests, there is currently a limited superiority of the neural network methods over the Markov model. We expect, however, that there is great potential for better modelling DNA using different neural network architectures.
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Affiliation(s)
- Christian Grønbæk
- Department of Biology, University of Copenhagen, Copenhagen, Denmark,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Yuhu Liang
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Desmond Elliott
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Anders Krogh
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark,Center for Health Data Science, University of Copenhagen, Copenhagen, Denmark
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4
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Li W, Yang L, Qiu Y, Yuan Y, Li X, Meng Z. FFP: joint Fast Fourier transform and fractal dimension in amino acid property-aware phylogenetic analysis. BMC Bioinformatics 2022; 23:347. [PMID: 35986255 PMCID: PMC9392226 DOI: 10.1186/s12859-022-04889-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/11/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Amino acid property-aware phylogenetic analysis (APPA) refers to the phylogenetic analysis method based on amino acid property encoding, which is used for understanding and inferring evolutionary relationships between species from the molecular perspective. Fast Fourier transform (FFT) and Higuchi’s fractal dimension (HFD) have excellent performance in describing sequences’ structural and complexity information for APPA. However, with the exponential growth of protein sequence data, it is very important to develop a reliable APPA method for protein sequence analysis.
Results
Consequently, we propose a new method named FFP, it joints FFT and HFD. Firstly, FFP is used to encode protein sequences on the basis of the important physicochemical properties of amino acids, the dissociation constant, which determines acidity and basicity of protein molecules. Secondly, FFT and HFD are used to generate the feature vectors of encoded sequences, whereafter, the distance matrix is calculated from the cosine function, which describes the degree of similarity between species. The smaller the distance between them, the more similar they are. Finally, the phylogenetic tree is constructed. When FFP is tested for phylogenetic analysis on four groups of protein sequences, the results are obviously better than other comparisons, with the highest accuracy up to more than 97%.
Conclusion
FFP has higher accuracy in APPA and multi-sequence alignment. It also can measure the protein sequence similarity effectively. And it is hoped to play a role in APPA’s related research.
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5
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Dong R, Pei S, Guan M, Yau SC, Yin C, He RL, Yau SST. Full Chromosomal Relationships Between Populations and the Origin of Humans. Front Genet 2022; 12:828805. [PMID: 35186019 PMCID: PMC8847220 DOI: 10.3389/fgene.2021.828805] [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: 12/04/2021] [Accepted: 12/22/2021] [Indexed: 11/23/2022] Open
Abstract
A comprehensive description of human genomes is essential for understanding human evolution and relationships between modern populations. However, most published literature focuses on local alignment comparison of several genes rather than the complete evolutionary record of individual genomes. Combining with data from the 1,000 Genomes Project, we successfully reconstructed 2,504 individual genomes and propose Divided Natural Vector method to analyze the distribution of nucleotides in the genomes. Comparisons based on autosomes, sex chromosomes and mitochondrial genomes reveal the genetic relationships between populations, and different inheritance pattern leads to different phylogenetic results. Results based on mitochondrial genomes confirm the “out-of-Africa” hypothesis and assert that humans, at least females, most likely originated in eastern Africa. The reconstructed genomes are stored on our server and can be further used for any genome-scale analysis of humans (http://yaulab.math.tsinghua.edu.cn/2022_1000genomesprojectdata/). This project provides the complete genomes of thousands of individuals and lays the groundwork for genome-level analyses of the genetic relationships between populations and the origin of humans.
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Affiliation(s)
- Rui Dong
- Yau Mathematical Sciences Center, Tsinghua University, Beijing, China.,Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, China
| | - Shaojun Pei
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Mengcen Guan
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Shek-Chung Yau
- Information Technology Services Center, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
| | - Changchuan Yin
- Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Rong L He
- Department of Biological Sciences, Chicago State University, Chicago, IL, United States
| | - Stephen S-T Yau
- Department of Mathematical Sciences, Tsinghua University, Beijing, China.,Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing, China
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6
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Identification of HIV Rapid Mutations Using Differences in Nucleotide Distribution over Time. Genes (Basel) 2022; 13:genes13020170. [PMID: 35205215 PMCID: PMC8872422 DOI: 10.3390/genes13020170] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/08/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023] Open
Abstract
Mutation is the driving force of species evolution, which may change the genetic information of organisms and obtain selective competitive advantages to adapt to environmental changes. It may change the structure or function of translated proteins, and cause abnormal cell operation, a variety of diseases and even cancer. Therefore, it is particularly important to identify gene regions with high mutations. Mutations will cause changes in nucleotide distribution, which can be characterized by natural vectors globally. Based on natural vectors, we propose a mathematical formula for measuring the difference in nucleotide distribution over time to investigate the mutations of human immunodeficiency virus. The studied dataset is from public databases and includes gene sequences from twenty HIV-infected patients. The results show that the mutation rate of the nine major genes or gene segment regions in the genome exhibits discrepancy during the infected period, and the Env gene has the fastest mutation rate. We deduce that the peak of virus mutation has a close temporal relationship with viral divergence and diversity. The mutation study of HIV is of great significance to clinical diagnosis and drug design.
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7
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Kania A, Sarapata K. The robustness of the chaos game representation to mutations and its application in free-alignment methods. Genomics 2021; 113:1428-1437. [PMID: 33713823 DOI: 10.1016/j.ygeno.2021.03.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 01/22/2021] [Accepted: 03/05/2021] [Indexed: 02/06/2023]
Abstract
Numerical representation of biological sequences plays an important role in bioinformatics and has many practical applications. One of the most popular approaches is the chaos game representation. In this paper, the authors propose a novel look into chaos game construction - an analytical description of this procedure. This type enables to build more general number sequences using different weight functions. The authors suggest three conditions that these functions should hold. Additionally, they present some criteria to compare them and check whether they provide a unique representation. One of the most important advantages of our approach is the possibility to construct such a description that is less sensitive to mutations and as a result, give more reliable values for free-alignment phylogenetic trees constructions. Finally, the authors applied the DFT method using four types of functions and compared the obtained results using the BLAST tool.
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Affiliation(s)
- Adrian Kania
- Department of Computational Biophysics and Bioinformatics, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, 30-387 Cracow, Poland.
| | - Krzysztof Sarapata
- Department of Computational Biophysics and Bioinformatics, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Gronostajowa 7, 30-387 Cracow, Poland
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8
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Li M, Sun X, Chen W. Patient-specific seizure detection method using nonlinear mode decomposition for long-term EEG signals. Med Biol Eng Comput 2020; 58:3075-3088. [DOI: 10.1007/s11517-020-02279-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 10/20/2020] [Indexed: 11/30/2022]
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9
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Sun Z, Pei S, He RL, Yau SST. A novel numerical representation for proteins: Three-dimensional Chaos Game Representation and its Extended Natural Vector. Comput Struct Biotechnol J 2020; 18:1904-1913. [PMID: 32774785 PMCID: PMC7390779 DOI: 10.1016/j.csbj.2020.07.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/04/2020] [Accepted: 07/05/2020] [Indexed: 12/16/2022] Open
Abstract
Chaos Game Representation (CGR) was first proposed to be an image representation method of DNA and have been extended to the case of other biological macromolecules. Compared with the CGR images of DNA, where DNA sequences are converted into a series of points in the unit square, the existing CGR images of protein are not so elegant in geometry and the implications of the distribution of points in the CGR image are not so obvious. In this study, by naturally distributing the twenty amino acids on the vertices of a regular dodecahedron, we introduce a novel three-dimensional image representation of protein sequences with CGR method. We also associate each CGR image with a vector in high dimensional Euclidean space, called the extended natural vector (ENV), in order to analyze the information contained in the CGR images. Based on the results of protein classification and phylogenetic analysis, our method could serve as a precise method to discover biological relationships between proteins.
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Affiliation(s)
- Zeju Sun
- Department of Mathematical Sciences, Tsinghua University, Beijing, PR China
| | - Shaojun Pei
- Department of Mathematical Sciences, Tsinghua University, Beijing, PR China
| | - Rong Lucy He
- Department of Biological Sciences, Chicago State University, Chicago, IL 60628, USA
| | - Stephen S-T Yau
- Department of Mathematical Sciences, Tsinghua University, Beijing, PR China
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10
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Dong R, Pei S, Yin C, He RL, Yau SST. Analysis of the Hosts and Transmission Paths of SARS-CoV-2 in the COVID-19 Outbreak. Genes (Basel) 2020; 11:E637. [PMID: 32526937 PMCID: PMC7349679 DOI: 10.3390/genes11060637] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 05/30/2020] [Accepted: 06/03/2020] [Indexed: 12/11/2022] Open
Abstract
The severe respiratory disease COVID-19 was initially reported in Wuhan, China, in December 2019, and spread into many provinces from Wuhan. The corresponding pathogen was soon identified as a novel coronavirus named SARS-CoV-2 (formerly, 2019-nCoV). As of 2 May, 2020, over 3 million COVID-19 cases had been confirmed, and 235,290 deaths had been reported globally, and the numbers are still increasing. It is important to understand the phylogenetic relationship between SARS-CoV-2 and known coronaviruses, and to identify its hosts for preventing the next round of emergency outbreak. In this study, we employ an effective alignment-free approach, the Natural Vector method, to analyze the phylogeny and classify the coronaviruses based on genomic and protein data. Our results show that SARS-CoV-2 is closely related to, but distinct from the SARS-CoV branch. By analyzing the genetic distances from the SARS-CoV-2 strain to the coronaviruses residing in animal hosts, we establish that the most possible transmission path originates from bats to pangolins to humans.
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Affiliation(s)
- Rui Dong
- Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China; (R.D.); (S.P.)
| | - Shaojun Pei
- Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China; (R.D.); (S.P.)
| | - Changchuan Yin
- Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, Chicago, IL 60607, USA;
| | - Rong Lucy He
- Department of Biological Sciences, Chicago State University, Chicago, IL 60628, USA;
| | - Stephen S.-T. Yau
- Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China; (R.D.); (S.P.)
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11
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Sun N, Dong R, Pei S, Yin C, Yau SST. A New Method Based on Coding Sequence Density to Cluster Bacteria. J Comput Biol 2020; 27:1688-1698. [PMID: 32392428 DOI: 10.1089/cmb.2019.0509] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Bacterial evolution is an important study field, biological sequences are often used to construct phylogenetic relationships. Multiple sequence alignment is very time-consuming and cannot deal with large scales of bacterial genome sequences in a reasonable time. Hence, a new mathematical method, joining density vector method, is proposed to cluster bacteria, which characterizes the features of coding sequence (CDS) in a DNA sequence. Coding sequences carry genetic information that can synthesize proteins. The correspondence between a genomic sequence and its joining density vector (JDV) is one-to-one. JDV reflects the statistical characteristics of genomic sequence and large amounts of data can be analyzed using this new approach. We apply the novel method to do phylogenetic analysis on four bacterial data sets at hierarchies of genus and species. The phylogenetic trees prove that our new method accurately describes the evolutionary relationships of bacterial coding sequences, and is faster than ClustalW and the existing alignment-free methods.
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Affiliation(s)
- Nan Sun
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Rui Dong
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Shaojun Pei
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Changchuan Yin
- Department of Mathematics, Statistics, and Computer Science, The University of Illinois at Chicago, Chicago, Illinois, USA
| | - Stephen S-T Yau
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
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12
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Lichtblau D. Alignment-free genomic sequence comparison using FCGR and signal processing. BMC Bioinformatics 2019; 20:742. [PMID: 31888438 PMCID: PMC6937637 DOI: 10.1186/s12859-019-3330-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 12/17/2019] [Indexed: 01/14/2023] Open
Abstract
Background Alignment-free methods of genomic comparison offer the possibility of scaling to large data sets of nucleotide sequences comprised of several thousand or more base pairs. Such methods can be used for purposes of deducing “nearby” species in a reference data set, or for constructing phylogenetic trees. Results We describe one such method that gives quite strong results. We use the Frequency Chaos Game Representation (FCGR) to create images from such sequences, We then reduce dimension, first using a Fourier trig transform, followed by a Singular Values Decomposition (SVD). This gives vectors of modest length. These in turn are used for fast sequence lookup, construction of phylogenetic trees, and classification of virus genomic data. We illustrate the accuracy and scalability of this approach on several benchmark test sets. Conclusions The tandem of FCGR and dimension reductions using Fourier-type transforms and SVD provides a powerful approach for alignment-free genomic comparison. Results compare favorably and often surpass best results reported in prior literature. Good scalability is also observed.
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Pei S, Dong W, Chen X, He RL, Yau SST. Fast and accurate genome comparison using genome images: The Extended Natural Vector Method. Mol Phylogenet Evol 2019; 141:106633. [PMID: 31563612 DOI: 10.1016/j.ympev.2019.106633] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 09/25/2019] [Accepted: 09/25/2019] [Indexed: 11/25/2022]
Abstract
Using numerical methods for genome comparison has always been of importance in bioinformatics. The Chaos Game Representation (CGR) is an effective genome sequence mapping technology, which converts genome sequences to CGR images. To each CGR image, we associate a vector called an Extended Natural Vector (ENV). The ENV is based on the distribution of intensity values. This mapping produces a one-to-one correspondence between CGR images and their ENVs. We define the distance between two DNA sequences as the distance between their associated ENVs. We cluster and classify several datasets including Influenza A viruses, Bacillus genomes, and Conoidea mitochondrial genomes to build their phylogenetic trees. Results show that our ENV combining CGR method (CGR-ENV) compares favorably in classification accuracy and efficiency against the multiple sequence alignment (MSA) method and other alignment-free methods. The research provides significant insights into the study of phylogeny, evolution, and efficient DNA comparison algorithms for large genomes.
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Affiliation(s)
- Shaojun Pei
- Department of Mathematical Sciences, Tsinghua University, Beijing 100084, PR China
| | - Wenhui Dong
- Department of Mathematical Sciences, Tsinghua University, Beijing 100084, PR China
| | - Xiuqiong Chen
- Department of Mathematical Sciences, Tsinghua University, Beijing 100084, PR China
| | - Rong Lucy He
- Department of Biological Sciences, Chicago State University, Chicago, IL 60628, USA
| | - Stephen S-T Yau
- Department of Mathematical Sciences, Tsinghua University, Beijing 100084, PR China.
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