1
|
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.
Collapse
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
| |
Collapse
|
2
|
Zimnyakov DA, Alonova MV, Lavrukhin MS, Lyapina AM, Feodorova VA. Polarization- and Chaos-Game-Based Fingerprinting of Molecular Targets of Listeria Monocytogenes Vaccine and Fully Virulent Strains. Curr Issues Mol Biol 2023; 45:10056-10078. [PMID: 38132474 PMCID: PMC10742786 DOI: 10.3390/cimb45120628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023] Open
Abstract
Two approaches to the synthesis of 2D binary identifiers ("fingerprints") of DNA-associated symbol sequences are considered in this paper. One of these approaches is based on the simulation of polarization-dependent diffraction patterns formed by reading the modeled DNA-associated 2D phase-modulating structures with a coherent light beam. In this case, 2D binarized distributions of close-to-circular extreme polarization states are applied as fingerprints of analyzed nucleotide sequences. The second approach is based on the transformation of the DNA-associated chaos game representation (CGR) maps into finite-dimensional binary matrices. In both cases, the differences between the structures of the analyzed and reference symbol sequences are quantified by calculating the correlation coefficient of the synthesized binary matrices. A comparison of the approaches under consideration is carried out using symbol sequences corresponding to nucleotide sequences of the hly gene from the vaccine and wild-type strains of Listeria monocytogenes as the analyzed objects. These strains differ in terms of the number of substituted nucleotides in relation to the vaccine strain selected as a reference. The results of the performed analysis allow us to conclude that the identification of structural differences in the DNA-associated symbolic sequences is significantly more efficient when using the binary distributions of close-to-circular extreme polarization states. The approach given can be applicable for genetic differentiation immunized from vaccinated animals (DIVA).
Collapse
Affiliation(s)
- Dmitry A. Zimnyakov
- Physics Department, Yury Gagarin State Technical University of Saratov, 77 Polytechnicheskaya Str., 410054 Saratov, Russia;
- Laboratory for Fundamental and Applied Research, Saratov State University of Genetics, Biotechnology and Engineering Named after N.I. Vavilov, 335 Sokolovaya Str., 410005 Saratov, Russia; (M.S.L.); (A.M.L.); (V.A.F.)
| | - Marina V. Alonova
- Physics Department, Yury Gagarin State Technical University of Saratov, 77 Polytechnicheskaya Str., 410054 Saratov, Russia;
| | - Maxim S. Lavrukhin
- Laboratory for Fundamental and Applied Research, Saratov State University of Genetics, Biotechnology and Engineering Named after N.I. Vavilov, 335 Sokolovaya Str., 410005 Saratov, Russia; (M.S.L.); (A.M.L.); (V.A.F.)
| | - Anna M. Lyapina
- Laboratory for Fundamental and Applied Research, Saratov State University of Genetics, Biotechnology and Engineering Named after N.I. Vavilov, 335 Sokolovaya Str., 410005 Saratov, Russia; (M.S.L.); (A.M.L.); (V.A.F.)
| | - Valentina A. Feodorova
- Laboratory for Fundamental and Applied Research, Saratov State University of Genetics, Biotechnology and Engineering Named after N.I. Vavilov, 335 Sokolovaya Str., 410005 Saratov, Russia; (M.S.L.); (A.M.L.); (V.A.F.)
- Department for Microbiology and Biotechnology, Saratov State University of Genetics, Biotechnology and Engineering Named after N.I. Vavilov, 335 Sokolovaya Str., 410005 Saratov, Russia
| |
Collapse
|
3
|
Löchel HF, Heider D. Chaos game representation and its applications in bioinformatics. Comput Struct Biotechnol J 2021; 19:6263-6271. [PMID: 34900136 PMCID: PMC8636998 DOI: 10.1016/j.csbj.2021.11.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 11/18/2022] Open
Abstract
Chaos game representation (CGR), a milestone in graphical bioinformatics, has become a powerful tool regarding alignment-free sequence comparison and feature encoding for machine learning. The algorithm maps a sequence to 2-dimensional space, while an extension of the CGR, the so-called frequency matrix representation (FCGR), transforms sequences of different lengths into equal-sized images or matrices. The CGR is a generalized Markov chain and includes various properties, which allow a unique representation of a sequence. Therefore, it has a broad spectrum of applications in bioinformatics, such as sequence comparison and phylogenetic analysis and as an encoding of sequences for machine learning. This review introduces the construction of CGRs and FCGRs, their applications on DNA and proteins, and gives an overview of recent applications and progress in bioinformatics.
Collapse
Affiliation(s)
- Hannah Franziska Löchel
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Germany
| | - Dominik Heider
- Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Germany
| |
Collapse
|
4
|
Han GS, Li Q, Li Y. Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms. BMC Bioinformatics 2021; 22:129. [PMID: 34078256 PMCID: PMC8170966 DOI: 10.1186/s12859-021-04006-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 02/08/2021] [Indexed: 12/01/2022] Open
Abstract
Background Nucleosome plays an important role in the process of genome expression, DNA replication, DNA repair and transcription. Therefore, the research of nucleosome positioning has invariably received extensive attention. Considering the diversity of DNA sequence representation methods, we tried to integrate multiple features to analyze its effect in the process of nucleosome positioning analysis. This process can also deepen our understanding of the theoretical analysis of nucleosome positioning. Results Here, we not only used frequency chaos game representation (FCGR) to construct DNA sequence features, but also integrated it with other features and adopted the principal component analysis (PCA) algorithm. Simultaneously, support vector machine (SVM), extreme learning machine (ELM), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and convolutional neural networks (CNN) are used as predictors for nucleosome positioning prediction analysis, respectively. The integrated feature vector prediction quality is significantly superior to a single feature. After using principal component analysis (PCA) to reduce the feature dimension, the prediction quality of H. sapiens dataset has been significantly improved. Conclusions Comparative analysis and prediction on H. sapiens, C. elegans, D. melanogaster and S. cerevisiae datasets, demonstrate that the application of FCGR to nucleosome positioning is feasible, and we also found that integrative feature representation would be better.
Collapse
Affiliation(s)
- Guo-Sheng Han
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, Hunan, China. .,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, Hunan, China.
| | - Qi Li
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, Hunan, China.,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Ying Li
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, Hunan, China.,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, Hunan, China
| |
Collapse
|
5
|
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: 4] [Impact Index Per Article: 1.0] [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.
Collapse
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
| |
Collapse
|
6
|
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: 3.8] [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.
Collapse
|