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Stübinger J, Walter D. Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships. SENSORS (BASEL, SWITZERLAND) 2022; 22:6884. [PMID: 36146233 PMCID: PMC9501639 DOI: 10.3390/s22186884] [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: 06/19/2022] [Revised: 09/04/2022] [Accepted: 09/07/2022] [Indexed: 06/16/2023]
Abstract
This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.
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2
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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: 1.3] [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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3
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Ranjard L, Wong TKF, Rodrigo AG. Effective machine-learning assembly for next-generation amplicon sequencing with very low coverage. BMC Bioinformatics 2019; 20:654. [PMID: 31829137 PMCID: PMC6907241 DOI: 10.1186/s12859-019-3287-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/20/2019] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND In short-read DNA sequencing experiments, the read coverage is a key parameter to successfully assemble the reads and reconstruct the sequence of the input DNA. When coverage is very low, the original sequence reconstruction from the reads can be difficult because of the occurrence of uncovered gaps. Reference guided assembly can then improve these assemblies. However, when the available reference is phylogenetically distant from the sequencing reads, the mapping rate of the reads can be extremely low. Some recent improvements in read mapping approaches aim at modifying the reference according to the reads dynamically. Such approaches can significantly improve the alignment rate of the reads onto distant references but the processing of insertions and deletions remains challenging. RESULTS Here, we introduce a new algorithm to update the reference sequence according to previously aligned reads. Substitutions, insertions and deletions are performed in the reference sequence dynamically. We evaluate this approach to assemble a western-grey kangaroo mitochondrial amplicon. Our results show that more reads can be aligned and that this method produces assemblies of length comparable to the truth while limiting error rate when classic approaches fail to recover the correct length. Finally, we discuss how the core algorithm of this method could be improved and combined with other approaches to analyse larger genomic sequences. CONCLUSIONS We introduced an algorithm to perform dynamic alignment of reads on a distant reference. We showed that such approach can improve the reconstruction of an amplicon compared to classically used bioinformatic pipelines. Although not portable to genomic scale in the current form, we suggested several improvements to be investigated to make this method more flexible and allow dynamic alignment to be used for large genome assemblies.
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Affiliation(s)
- Louis Ranjard
- The Research School of Biology, The Australian National University, Canberra, Australia
| | - Thomas K. F. Wong
- The Research School of Biology, The Australian National University, Canberra, Australia
| | - Allen G. Rodrigo
- The Research School of Biology, The Australian National University, Canberra, Australia
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4
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Li J, Zhang L, Li H, Ping Y, Xu Q, Wang R, Tan R, Wang Z, Liu B, Wang Y. Integrated entropy-based approach for analyzing exons and introns in DNA sequences. BMC Bioinformatics 2019; 20:283. [PMID: 31182012 PMCID: PMC6557737 DOI: 10.1186/s12859-019-2772-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Numerous essential algorithms and methods, including entropy-based quantitative methods, have been developed to analyze complex DNA sequences since the last decade. Exons and introns are the most notable components of DNA and their identification and prediction are always the focus of state-of-the-art research. RESULTS In this study, we designed an integrated entropy-based analysis approach, which involves modified topological entropy calculation, genomic signal processing (GSP) method and singular value decomposition (SVD), to investigate exons and introns in DNA sequences. We optimized and implemented the topological entropy and the generalized topological entropy to calculate the complexity of DNA sequences, highlighting the characteristics of repetition sequences. By comparing digitalizing entropy values of exons and introns, we observed that they are significantly different. After we converted DNA data to numerical topological entropy value, we applied SVD method to effectively investigate exon and intron regions on a single gene sequence. Additionally, several genes across five species are used for exon predictions. CONCLUSIONS Our approach not only helps to explore the complexity of DNA sequence and its functional elements, but also provides an entropy-based GSP method to analyze exon and intron regions. Our work is feasible across different species and extendable to analyze other components in both coding and noncoding region of DNA sequences.
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Affiliation(s)
- Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Li Zhang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Huinian Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Yuan Ping
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Qingzhe Xu
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
| | - Rongjie Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001 China
| | - Renjie Tan
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001 China
| | - Zhen Wang
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
| | - Bo Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001 China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055 China
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001 China
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5
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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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6
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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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7
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Skutkova H, Vitek M, Bezdicek M, Brhelova E, Lengerova M. Advanced DNA fingerprint genotyping based on a model developed from real chip electrophoresis data. J Adv Res 2019; 18:9-18. [PMID: 30788173 PMCID: PMC6369143 DOI: 10.1016/j.jare.2019.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 01/06/2019] [Accepted: 01/10/2019] [Indexed: 11/25/2022] Open
Abstract
Mapping chip electrophoresis distortion based on real data measurement. Determining the transformation function for the adaptive correction of band size deviation. Improving the ability to distinguish closely related DNA fingerprints. Using hierarchical clustering to adjust the global band position. Genotyping all DNA fingerprints from multiple runs at once.
Large-scale comparative studies of DNA fingerprints prefer automated chip capillary electrophoresis over conventional gel planar electrophoresis due to the higher precision of the digitalization process. However, the determination of band sizes is still limited by the device resolution and sizing accuracy. Band matching, therefore, remains the key step in DNA fingerprint analysis. Most current methods evaluate only the pairwise similarity of the samples, using heuristically determined constant thresholds to evaluate the maximum allowed band size deviation; unfortunately, that approach significantly reduces the ability to distinguish between closely related samples. This study presents a new approach based on global multiple alignments of bands of all samples, with an adaptive threshold derived from the detailed migration analysis of a large number of real samples. The proposed approach allows the accurate automated analysis of DNA fingerprint similarities for extensive epidemiological studies of bacterial strains, thereby helping to prevent the spread of dangerous microbial infections.
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Key Words
- Automated chip capillary electrophoresis
- Band matching
- DBSCAN, density-based spatial clustering of applications with noise
- DNA fingerprinting
- DTW, dynamic time warping
- ESBL, extended spectrum beta-lactamases
- Gel sample distortion
- Genotyping
- KLPN, Klebsiella pneumonia
- MALDI-TOF, matrix assisted laser desorption ionization – time of flight
- Pattern recognition
- R-square, ratio of the sum of squares
- RMSE, root mean squared error
- SD, standard deviation
- SLINK, single linkage
- SSE, sum of squares due to error
- UPGMA, unweighted pair group method with arithmetic mean
- rep-PCR, repetitive element palindromic polymerase chain reaction
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Affiliation(s)
- Helena Skutkova
- Department of Biomedical Engineering, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic
- Corresponding author.
| | - Martin Vitek
- Department of Biomedical Engineering, Brno University of Technology, Technicka 12, 616 00 Brno, Czech Republic
| | - Matej Bezdicek
- Department of Internal Medicine, Hematology and Oncology, Masaryk University and University Hospital Brno, Cernopolni 212/9, 662 63 Brno, Czech Republic
| | - Eva Brhelova
- Department of Internal Medicine, Hematology and Oncology, Masaryk University and University Hospital Brno, Cernopolni 212/9, 662 63 Brno, Czech Republic
| | - Martina Lengerova
- Department of Internal Medicine, Hematology and Oncology, Masaryk University and University Hospital Brno, Cernopolni 212/9, 662 63 Brno, Czech Republic
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8
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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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9
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Han R, Li Y, Gao X, Wang S. An accurate and rapid continuous wavelet dynamic time warping algorithm for end-to-end mapping in ultra-long nanopore sequencing. Bioinformatics 2018; 34:i722-i731. [DOI: 10.1093/bioinformatics/bty555] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Renmin Han
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia
| | - Yu Li
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia
| | - Sheng Wang
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, Saudi Arabia
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10
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Mendizabal-Ruiz G, Román-Godínez I, Torres-Ramos S, Salido-Ruiz RA, Vélez-Pérez H, Morales JA. Genomic signal processing for DNA sequence clustering. PeerJ 2018; 6:e4264. [PMID: 29379686 PMCID: PMC5786891 DOI: 10.7717/peerj.4264] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 12/24/2017] [Indexed: 11/20/2022] Open
Abstract
Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.
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Affiliation(s)
| | - Israel Román-Godínez
- Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico
| | - Sulema Torres-Ramos
- Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico
| | - Ricardo A Salido-Ruiz
- Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico
| | - Hugo Vélez-Pérez
- Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico
| | - J Alejandro Morales
- Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico
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11
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Mendizabal-Ruiz G, Román-Godínez I, Torres-Ramos S, Salido-Ruiz RA, Morales JA. On DNA numerical representations for genomic similarity computation. PLoS One 2017; 12:e0173288. [PMID: 28323839 PMCID: PMC5360225 DOI: 10.1371/journal.pone.0173288] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Accepted: 02/17/2017] [Indexed: 11/18/2022] Open
Abstract
Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.
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Affiliation(s)
- Gerardo Mendizabal-Ruiz
- Departamento de Ciencias Computacionales, División de Electrónica y Computación, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Israel Román-Godínez
- Departamento de Ciencias Computacionales, División de Electrónica y Computación, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Sulema Torres-Ramos
- Departamento de Ciencias Computacionales, División de Electrónica y Computación, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Ricardo A. Salido-Ruiz
- Departamento de Ciencias Computacionales, División de Electrónica y Computación, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - J. Alejandro Morales
- Departamento de Ciencias Computacionales, División de Electrónica y Computación, Universidad de Guadalajara, Guadalajara, Jalisco, México
- * E-mail:
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12
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Hou W, Pan Q, Peng Q, He M. A new method to analyze protein sequence similarity using Dynamic Time Warping. Genomics 2016; 109:123-130. [PMID: 27974244 PMCID: PMC7125777 DOI: 10.1016/j.ygeno.2016.12.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Revised: 12/06/2016] [Accepted: 12/10/2016] [Indexed: 12/05/2022]
Abstract
Sequences similarity analysis is one of the major topics in bioinformatics. It helps researchers to reveal evolution relationships of different species. In this paper, we outline a new method to analyze the similarity of proteins by Discrete Fourier Transform (DFT) and Dynamic Time Warping (DTW). The original symbol sequences are converted to numerical sequences according to their physico-chemical properties. We obtain the power spectra of sequences from DFT and extend the spectra to the same length to calculate the distance between different sequences by DTW. Our method is tested in different datasets and the results are compared with that of other software algorithms. In the comparison we find our scheme could amend some wrong classifications appear in other software. The comparison shows our approach is reasonable and effective. We propose a novel method to extract the features of the sequences based on physicochemical property of proteins. We apply the Discrete Fourier Transform (DFT) and Dynamic Time Warping (DTW) to analyze the similarity of proteins. Different datasets are used to prove our model's effectiveness.
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Affiliation(s)
- Wenbing Hou
- School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, PR China
| | - Qiuhui Pan
- School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian 116024, PR China; School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, PR China
| | - Qianying Peng
- Department of Academics, Dalian Naval Academy, Dalian 116001, PR China
| | - Mingfeng He
- School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, PR China.
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13
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Loose M, Malla S, Stout M. Real-time selective sequencing using nanopore technology. Nat Methods 2016; 13:751-4. [PMID: 27454285 PMCID: PMC5008457 DOI: 10.1038/nmeth.3930] [Citation(s) in RCA: 201] [Impact Index Per Article: 25.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 06/22/2016] [Indexed: 01/01/2023]
Abstract
The Oxford Nanopore MinION sequences DNA by sensing changes in electrical current flow in real-time as molecules traverse nanopores. Optionally, the voltage across specific nanopores can be reversed, ejecting the DNA molecule. This enables “Read Until”, the selection of specific DNA molecules for sequencing. We use dynamic time warping to match reads to reference, selecting regions of small genomes, individual amplicons, or normalization of the amplicon set. This first demonstration of direct selection of specific DNA molecules in real-time enables many novel future applications.
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Affiliation(s)
- Matthew Loose
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Sunir Malla
- School of Life Sciences, University of Nottingham, Nottingham, UK
| | - Michael Stout
- School of Life Sciences, University of Nottingham, Nottingham, UK
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