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Alipour F, Holmes C, Lu YY, Hill KA, Kari L. Leveraging machine learning for taxonomic classification of emerging astroviruses. Front Mol Biosci 2024; 10:1305506. [PMID: 38274100 PMCID: PMC10808839 DOI: 10.3389/fmolb.2023.1305506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 12/12/2023] [Indexed: 01/27/2024] Open
Abstract
Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence k-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method's pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (HAstV) subgenus of the genus Mamastrovirus, and a goose astrovirus (GoAstV) subgenus of the genus Avastrovirus. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.
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Affiliation(s)
- Fatemeh Alipour
- School of Computer Science, University of Waterloo, Waterloo, ON, Canada
| | - Connor Holmes
- Department of Biology, University of Western Ontario, London, ON, Canada
| | - Yang Young Lu
- School of Computer Science, University of Waterloo, Waterloo, 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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2
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A new graph-theoretic approach to determine the similarity of genome sequences based on nucleotide triplets. Genomics 2020; 112:4701-4714. [PMID: 32827671 PMCID: PMC7437474 DOI: 10.1016/j.ygeno.2020.08.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 07/15/2020] [Accepted: 08/17/2020] [Indexed: 11/22/2022]
Abstract
Methods of finding sequence similarity play a significant role in computational biology. Owing to the rapid increase of genome sequences in public databases, the evolutionary relationship of species becomes more challenging. But traditional alignment-based methods are found inappropriate due to their time-consuming nature. Therefore, it is necessary to find a faster method, which applies to species phylogeny. In this paper, a new graph-theory based alignment-free sequence comparison method is proposed. A complete-bipartite graph is used to represent each genome sequence based on its nucleotide triplets. Subsequently, with the help of the weights of edges of the graph, a vector descriptor is formed. Finally, the phylogenetic tree is drawn using the UPGMA algorithm. In the present case, the datasets for comparison are related to mammals, viruses, and bacteria. In most of the cases, the phylogeny in the present case is found to be more satisfactory as compared to earlier methods. A new graph-theory based alignment-free genome sequence comparison. Use of complete bipartite graph to represent genome sequences. Descriptor based on the weights of the edges of the graph. Comparison of the phylogenetic trees of different mammals, viruses, and bacteria. Less time complexity compared to that of earlier methods.
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3
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Koehl P, Delarue M, Orland H. Optimal transport at finite temperature. Phys Rev E 2019; 100:013310. [PMID: 31499816 DOI: 10.1103/physreve.100.013310] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Indexed: 06/10/2023]
Abstract
Optimal transport (OT) has become a discipline by itself that offers solutions to a wide range of theoretical problems in probability and mathematics. Despite its appealing theoretical properties, solving the OT problem involves the resolution of a linear program whose computational cost can quickly become prohibitive whenever the size of the problem exceeds a few hundred points. The recent introduction of entropy regularization, however, has led to the development of fast algorithms for solving an approximate OT problem. The successes of those algorithms have resulted in a popularization of the applications of OT in several applied fields such as imaging sciences and machine learning, and in data sciences in general. Problems remain, however, as to the numerical convergence of those regularized approximations towards the actual OT solution. In addition, the physical meaning of this regularization is unclear. In this paper, we propose an approach to solving the discrete OT problem using techniques adapted from statistical physics. Our first contribution is to fully describe this formalism, including all the proofs of its main claims. In particular we derive a strongly concave effective free energy function that captures the constraints of the optimal transport problem at a finite temperature. Its maximum defines a pseudo distance between the two set of weighted points that are compared, which satisfies the triangular inequalities. The temperature dependent OT pseudo distance decreases monotonically to the standard OT distance, providing a robust framework for temperature annealing. Our second contribution is to show that the implementation of this formalism has the same properties as the regularized OT algorithms in time complexity, making it a competitive approach to solving the OT problem. We illustrate applications of the framework to the problem of protein fold recognition based on sequence information only.
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Affiliation(s)
- Patrice Koehl
- Department of Computer Science and Genome Center, University of California, Davis, California 95616, USA
| | - Marc Delarue
- Unité de Dynamique Structurale des Macromolécules, Department of Structural Biology and Chemistry, UMR 3528 du CNRS, Institut Pasteur, 75015 Paris, France
| | - Henri Orland
- Institut de Physique Théorique, CEA-Saclay, 91191 Gif/Yvette Cedex, France
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Xi B, Tao J, Liu X, Xu X, He P, Dai Q. RaaMLab: A MATLAB toolbox that generates amino acid groups and reduced amino acid modes. Biosystems 2019; 180:38-45. [PMID: 30904554 DOI: 10.1016/j.biosystems.2019.03.002] [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] [Received: 07/25/2018] [Revised: 12/25/2018] [Accepted: 03/06/2019] [Indexed: 01/31/2023]
Abstract
Amino acid (AA) classification and its different biophysical and chemical characteristics have been widely applied to analyze and predict the structural, functional, expression and interaction profiles of proteins and peptides. We present RaaMLab, a free and open-source MATLAB toolbox, to facilitate studies on proteins and peptides, to generate AA groups and to extract the structural and physicochemical features of reduced AAs (RedAA). This toolbox offers 4 kinds of databases, including the physicochemical properties of AAs and their groupings, 49 AA classification methods and 5 types of biophysicochemical features of RedAAs. These factors can be easily computed based on user-defined alphabet size and AA properties of AA groupings. RaaMLab is an open source freely available at https://github.com/bioinfo0706/RaaMLab. This website also contains a tutorial, extensive documentation and examples.
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Affiliation(s)
- Baohang Xi
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Jin Tao
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Xiaoqing Liu
- College of Sciences, Hangzhou Dianzi University, Hangzhou 310018, People's Republic of China
| | - Xinnan Xu
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Pingan He
- College of Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Qi Dai
- College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
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Farkaš T, Sitarčík J, Brejová B, Lucká M. SWSPM: A Novel Alignment-Free DNA Comparison Method Based on Signal Processing Approaches. Evol Bioinform Online 2019; 15:1176934319849071. [PMID: 31210725 PMCID: PMC6545658 DOI: 10.1177/1176934319849071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 04/12/2019] [Indexed: 11/16/2022] Open
Abstract
Computing similarity between 2 nucleotide sequences is one of the fundamental problems in bioinformatics. Current methods are based mainly on 2 major approaches: (1) sequence alignment, which is computationally expensive, and (2) faster, but less accurate, alignment-free methods based on various statistical summaries, for example, short word counts. We propose a new distance measure based on mathematical transforms from the domain of signal processing. To tolerate large-scale rearrangements in the sequences, the transform is computed across sliding windows. We compare our method on several data sets with current state-of-art alignment-free methods. Our method compares favorably in terms of accuracy and outperforms other methods in running time and memory requirements. In addition, it is massively scalable up to dozens of processing units without the loss of performance due to communication overhead. Source files and sample data are available at https://bitbucket.org/fiitstubioinfo/swspm/src.
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Affiliation(s)
- Tomáš Farkaš
- Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Jozef Sitarčík
- Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Broňa Brejová
- Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava, Bratislava, Slovakia
| | - Mária Lucká
- Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava, Bratislava, Slovakia
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6
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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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7
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Daoud M. Insights of Window-Based Mechanism Approach to Visualize Composite BioData Point in Feature Spaces. Genomics Inform 2019; 17:e4. [PMID: 30929405 PMCID: PMC6459167 DOI: 10.5808/gi.2019.17.1.e4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 01/18/2019] [Indexed: 11/20/2022] Open
Abstract
In this paper, we propose a window-based mechanism visualization approach as an alternative way to measure the seriousness of the difference among data-insights extracted from a composite biodata point. The approach is based on two components: undirected graph and Mosaab-metric space. The significant application of this approach is to visualize the segmented genome of a virus. We use Influenza and Ebola viruses as examples to demonstrate the robustness of this approach and to conduct comparisons. This approach can provide researchers with deep insights about information structures extracted from a segmented genome as a composite biodata point, and consequently, to capture the segmented genetic variations and diversity (variants) in composite data points.
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Affiliation(s)
- Mosaab Daoud
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada.,Sanofi Pasteur (Industrial Partner), Toronto, ON M2R 3T4, Canada
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8
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Turjanski P, Ferreiro DU. On the Natural Structure of Amino Acid Patterns in Families of Protein Sequences. J Phys Chem B 2018; 122:11295-11301. [PMID: 30239207 DOI: 10.1021/acs.jpcb.8b07206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
All known terrestrial proteins are coded as continuous strings of ≈20 amino acids. The patterns formed by the repetitions of elements in groups of finite sequences describes the natural architectures of protein families. We present a method to search for patterns and groupings of patterns in protein sequences using a mathematically precise definition for "repetition", an efficient algorithmic implementation and a robust scoring system with no adjustable parameters. We show that the sequence patterns can be well-separated into disjoint classes according to their recurrence in nested structures. The statistics of the occurrences of patterns indicate that short repetitions are sufficient to account for the differences between natural families and randomized groups of sequences by more than 10 standard deviations, while contiguous sequence patterns shorter than 5 residues are effectively random in their occurrences. A small subset of patterns is sufficient to account for a robust "familiarity" definition between arbitrary sets of sequences.
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Affiliation(s)
- Pablo Turjanski
- KAPOW, Departamento de Computación , Facultad de Ciencias Exactas y Naturales, UBA-CONICET-ICC , Buenos Aires , Argentina
| | - Diego U Ferreiro
- Protein Physiology Lab, Departamento de Química Biológica , Facultad de Ciencias Exactas y Naturales, UBA-CONICET-IQUIBICEN , Buenos Aires , Argentina
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Lin J, Wei J, Adjeroh D, Jiang BH, Jiang Y. SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform. BMC Bioinformatics 2018; 19:165. [PMID: 29720081 PMCID: PMC5930706 DOI: 10.1186/s12859-018-2155-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2017] [Accepted: 04/11/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts. RESULTS A new alignment-free sequence similarity analysis method, called SSAW is proposed. SSAW stands for Sequence Similarity Analysis using the Stationary Discrete Wavelet Transform (SDWT). It extracts k-mers from a sequence, then maps each k-mer to a complex number field. Then, the series of complex numbers formed are transformed into feature vectors using the stationary discrete wavelet transform. After these steps, the original sequence is turned into a feature vector with numeric values, which can then be used for clustering and/or classification. CONCLUSIONS Using two different types of applications, namely, clustering and classification, we compared SSAW against the the-state-of-the-art alignment free sequence analysis methods. SSAW demonstrates competitive or superior performance in terms of standard indicators, such as accuracy, F-score, precision, and recall. The running time was significantly better in most cases. These make SSAW a suitable method for sequence analysis, especially, given the rapidly increasing volumes of sequence data required by most modern applications.
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Affiliation(s)
- Jie Lin
- College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, People's Republic of China
| | - Jing Wei
- College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, People's Republic of China
| | - Donald Adjeroh
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, 26506, WV, USA
| | - Bing-Hua Jiang
- Department of Pathology, University of Iowa, Iowa city, 52242, Iowa, USA
| | - Yue Jiang
- College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350108, People's Republic of China.
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10
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Pizzi C, Ornamenti M, Spangaro S, Rombo SE, Parida L. Efficient Algorithms for Sequence Analysis with Entropic Profiles. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:117-128. [PMID: 28113780 DOI: 10.1109/tcbb.2016.2620143] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Entropy, being closely related to repetitiveness and compressibility, is a widely used information-related measure to assess the degree of predictability of a sequence. Entropic profiles are based on information theory principles, and can be used to study the under-/over-representation of subwords, by also providing information about the scale of conserved DNA regions. Here, we focus on the algorithmic aspects related to entropic profiles. In particular, we propose linear time algorithms for their computation that rely on suffix-based data structures, more specifically on the truncated suffix tree (TST) and on the enhanced suffix array (ESA). We performed an extensive experimental campaign showing that our algorithms, beside being faster, make it possible the analysis of longer sequences, even for high degrees of resolution, than state of the art algorithms.
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11
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Abstract
BACKGROUND Building the evolutionary trees for massive unaligned DNA sequences is challenging and crucial. However, reconstructing evolutionary tree for ultra-large sequences is hard. Massive multiple sequence alignment is also challenging and time/space consuming. Hadoop and Spark are developed recently, which bring spring light for the classical computational biology problems. In this paper, we tried to solve the multiple sequence alignment and evolutionary reconstruction in parallel. RESULTS HPTree, which is developed in this paper, can deal with big DNA sequence files quickly. It works well on the >1GB files, and gets better performance than other evolutionary reconstruction tools. Users could use HPTree for reonstructing evolutioanry trees on the computer clusters or cloud platform (eg. Amazon Cloud). HPTree could help on population evolution research and metagenomics analysis. CONCLUSIONS In this paper, we employ the Hadoop and Spark platform and design an evolutionary tree reconstruction software tool for unaligned massive DNA sequences. Clustering and multiple sequence alignment are done in parallel. Neighbour-joining model was employed for the evolutionary tree building. We opened our software together with source codes via http://lab.malab.cn/soft/HPtree/ .
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Affiliation(s)
- Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, People's Republic of China
- Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen University, Shenzhen, China
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Shixiang Wan
- School of Computer Science and Technology, Tianjin University, Tianjin, People's Republic of China
| | - Xiangxiang Zeng
- Department of Computer Science, Xiamen University, Xiamen, China.
| | - Zhanshan Sam Ma
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
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12
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Nojoomi S, Koehl P. A weighted string kernel for protein fold recognition. BMC Bioinformatics 2017; 18:378. [PMID: 28841820 PMCID: PMC5574112 DOI: 10.1186/s12859-017-1795-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Accepted: 08/15/2017] [Indexed: 11/10/2022] Open
Abstract
Background Alignment-free methods for comparing protein sequences have proved to be viable alternatives to approaches that first rely on an alignment of the sequences to be compared. Much work however need to be done before those methods provide reliable fold recognition for proteins whose sequences share little similarity. We have recently proposed an alignment-free method based on the concept of string kernels, SeqKernel (Nojoomi and Koehl, BMC Bioinformatics, 2017, 18:137). In this previous study, we have shown that while Seqkernel performs better than standard alignment-based methods, its applications are potentially limited, because of biases due mostly to sequence length effects. Methods In this study, we propose improvements to SeqKernel that follows two directions. First, we developed a weighted version of the kernel, WSeqKernel. Second, we expand the concept of string kernels into a novel framework for deriving information on amino acids from protein sequences. Results Using a dataset that only contains remote homologs, we have shown that WSeqKernel performs remarkably well in fold recognition experiments. We have shown that with the appropriate weighting scheme, we can remove the length effects on the kernel values. WSeqKernel, just like any alignment-based sequence comparison method, depends on a substitution matrix. We have shown that this matrix can be optimized so that sequence similarity scores correlate well with structure similarity scores. Starting from no information on amino acid similarity, we have shown that we can derive a scoring matrix that echoes the physico-chemical properties of amino acids. Conclusion We have made progress in characterizing and parametrizing string kernels as alignment-based methods for comparing protein sequences, and we have shown that they provide a framework for extracting sequence information from structure. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1795-5) contains supplementary material, which is available to authorized users.
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Zielezinski A, Vinga S, Almeida J, Karlowski WM. Alignment-free sequence comparison: benefits, applications, and tools. Genome Biol 2017; 18:186. [PMID: 28974235 PMCID: PMC5627421 DOI: 10.1186/s13059-017-1319-7] [Citation(s) in RCA: 248] [Impact Index Per Article: 35.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Alignment-free sequence analyses have been applied to problems ranging from whole-genome phylogeny to the classification of protein families, identification of horizontally transferred genes, and detection of recombined sequences. The strength of these methods makes them particularly useful for next-generation sequencing data processing and analysis. However, many researchers are unclear about how these methods work, how they compare to alignment-based methods, and what their potential is for use for their research. We address these questions and provide a guide to the currently available alignment-free sequence analysis tools.
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Affiliation(s)
- Andrzej Zielezinski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University in Poznan, Umultowska 89, 61-614, Poznan, Poland
| | - Susana Vinga
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
| | - Jonas Almeida
- Stony Brook University (SUNY), 101 Nicolls Road, Stony Brook, NY, 11794, USA
| | - Wojciech M Karlowski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University in Poznan, Umultowska 89, 61-614, Poznan, Poland.
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14
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Protein Sequence Comparison Based on Physicochemical Properties and the Position-Feature Energy Matrix. Sci Rep 2017; 7:46237. [PMID: 28393857 PMCID: PMC5385872 DOI: 10.1038/srep46237] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Accepted: 03/14/2017] [Indexed: 11/08/2022] Open
Abstract
We develop a novel position-feature-based model for protein sequences by employing physicochemical properties of 20 amino acids and the measure of graph energy. The method puts the emphasis on sequence order information and describes local dynamic distributions of sequences, from which one can get a characteristic B-vector. Afterwards, we apply the relative entropy to the sequences representing B-vectors to measure their similarity/dissimilarity. The numerical results obtained in this study show that the proposed methods leads to meaningful results compared with competitors such as Clustal W.
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15
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Nojoomi S, Koehl P. String kernels for protein sequence comparisons: improved fold recognition. BMC Bioinformatics 2017; 18:137. [PMID: 28245816 PMCID: PMC5331664 DOI: 10.1186/s12859-017-1560-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 02/23/2017] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND The amino acid sequence of a protein is the blueprint from which its structure and ultimately function can be derived. Therefore, sequence comparison methods remain essential for the determination of similarity between proteins. Traditional approaches for comparing two protein sequences begin with strings of letters (amino acids) that represent the sequences, before generating textual alignments between these strings and providing scores for each alignment. When the similitude between the two protein sequences to be compared is low however, the quality of the corresponding sequence alignment is usually poor, leading to poor performance for the recognition of similarity. RESULTS In this study, we develop an alignment free alternative to these methods that is based on the concept of string kernels. Starting from recently proposed kernels on the discrete space of protein sequences (Shen et al, Found. Comput. Math., 2013,14:951-984), we introduce our own version, SeqKernel. Its implementation depends on two parameters, a coefficient that tunes the substitution matrix and the maximum length of k-mers that it includes. We provide an exhaustive analysis of the impacts of these two parameters on the performance of SeqKernel for fold recognition. We show that with the right choice of parameters, use of the SeqKernel similarity measure improves fold recognition compared to the use of traditional alignment-based methods. We illustrate the application of SeqKernel to inferring phylogeny on RNA polymerases and show that it performs as well as methods based on multiple sequence alignments. CONCLUSION We have presented and characterized a new alignment free method based on a mathematical kernel for scoring the similarity of protein sequences. We discuss possible improvements of this method, as well as an extension of its applications to other modeling methods that rely on sequence comparison.
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Affiliation(s)
- Saghi Nojoomi
- Biotechnology program, University of California, Davis, 1, Shields Avenue, Davis, CA, 95616 USA
| | - Patrice Koehl
- Department of Computer Science and Genome Center, 1, Shields Avenue, Davis, CA, 95616 USA
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16
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Karamichalis R, Kari L, Konstantinidis S, Kopecki S, Solis-Reyes S. Additive methods for genomic signatures. BMC Bioinformatics 2016; 17:313. [PMID: 27549194 PMCID: PMC4994249 DOI: 10.1186/s12859-016-1157-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 07/19/2016] [Indexed: 01/09/2023] Open
Abstract
Background Studies exploring the potential of Chaos Game Representations (CGR) of genomic sequences to act as “genomic signatures” (to be species- and genome-specific) showed that CGR patterns of nuclear and organellar DNA sequences of the same organism can be very different. While the hypothesis that CGRs of mitochondrial DNA sequences can act as genomic signatures was validated for a snapshot of all sequenced mitochondrial genomes available in the NCBI GenBank sequence database, to our knowledge no such extensive analysis of CGRs of nuclear DNA sequences exists to date. Results We analyzed an extensive dataset, totalling 1.45 gigabase pairs, of nuclear/nucleoid genomic sequences (nDNA) from 42 different organisms, spanning all major kingdoms of life. Our computational experiments indicate that CGR signatures of nDNA of two different origins cannot always be differentiated, especially if they originate from closely-related species such as H. sapiens and P. troglodytes or E. coli and E. fergusonii. To address this issue, we propose the general concept of additive DNA signature of a set (collection) of DNA sequences. One particular instance, the composite DNA signature, combines information from nDNA fragments and organellar (mitochondrial, chloroplast, or plasmid) genomes. We demonstrate that, in this dataset, composite DNA signatures originating from two different organisms can be differentiated in all cases, including those where the use of CGR signatures of nDNA failed or was inconclusive. Another instance, the assembled DNA signature, combines information from many short DNA subfragments (e.g., 100 basepairs) of a given DNA fragment, to produce its signature. We show that an assembled DNA signature has the same distinguishing power as a conventionally computed CGR signature, while using shorter contiguous sequences and potentially less sequence information. Conclusions Our results suggest that, while CGR signatures of nDNA cannot always play the role of genomic signatures, composite and assembled DNA signatures (separately or in combination) could potentially be used instead. Such additive signatures could be used, e.g., with raw unassembled next-generation sequencing (NGS) read data, when high-quality sequencing data is not available, or to complement information obtained by other methods of species identification or classification. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1157-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rallis Karamichalis
- Department of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada
| | - Lila Kari
- School of Computing Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada. .,Department of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada.
| | - Stavros Konstantinidis
- Department of Mathematics and Computing Science, Saint Mary's University, Halifax NS, Canada
| | - Steffen Kopecki
- Department of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada.,Department of Mathematics and Computing Science, Saint Mary's University, Halifax NS, Canada
| | - Stephen Solis-Reyes
- Department of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada
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Number of distinct sequence alignments with k-match and match sections. Comput Biol Med 2015; 63:287-92. [DOI: 10.1016/j.compbiomed.2015.02.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2014] [Revised: 02/25/2015] [Accepted: 02/26/2015] [Indexed: 11/22/2022]
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18
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Lee KH, Shin KS, Lim D, Kim WC, Chung BC, Han GB, Roh J, Cho DH, Cho K. Repetitive element signature-based visualization, distance computation, and classification of 1766 microbial genomes. Genomics 2015; 106:30-42. [PMID: 25918033 DOI: 10.1016/j.ygeno.2015.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 03/24/2015] [Accepted: 04/17/2015] [Indexed: 10/23/2022]
Abstract
The genomes of living organisms are populated with pleomorphic repetitive elements (REs) of varying densities. Our hypothesis that genomic RE landscapes are species/strain/individual-specific was implemented into the Genome Signature Imaging system to visualize and compute the RE-based signatures of any genome. Following the occurrence profiling of 5-nucleotide REs/words, the information from top-50 frequency words was transformed into a genome-specific signature and visualized as Genome Signature Images (GSIs), using a CMYK scheme. An algorithm for computing distances among GSIs was formulated using the GSIs' variables (word identity, frequency, and frequency order). The utility of the GSI-distance computation system was demonstrated with control genomes. GSI-based computation of genome-relatedness among 1766 microbes (117 archaea and 1649 bacteria) identified their clustering patterns; although the majority paralleled the established classification, some did not. The Genome Signature Imaging system, with its visualization and distance computation functions, enables genome-scale evolutionary studies involving numerous genomes with varying sizes.
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Affiliation(s)
- Kang-Hoon Lee
- Department of Surgery, University of California, Davis, Sacramento, CA, United States; Shriners Hospitals for Children Northern California, Sacramento, CA, United States
| | - Kyung-Seop Shin
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Debora Lim
- Department of Surgery, University of California, Davis, Sacramento, CA, United States; Shriners Hospitals for Children Northern California, Sacramento, CA, United States
| | - Woo-Chan Kim
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Byung Chang Chung
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Gyu-Bum Han
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Jeongkyu Roh
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Dong-Ho Cho
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Kiho Cho
- Department of Surgery, University of California, Davis, Sacramento, CA, United States; Shriners Hospitals for Children Northern California, Sacramento, CA, United States.
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Gnanavel M, Mehrotra P, Rakshambikai R, Martin J, Srinivasan N, Bhaskara RM. CLAP: a web-server for automatic classification of proteins with special reference to multi-domain proteins. BMC Bioinformatics 2014; 15:343. [PMID: 25282152 PMCID: PMC4287353 DOI: 10.1186/1471-2105-15-343] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2014] [Accepted: 09/30/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better. RESULTS Our web-server, CLAP (Classification of Proteins), is one such alignment-free software for automatic classification of protein sequences. It utilizes a pattern-matching algorithm that assigns local matching scores (LMS) to residues that are a part of the matched patterns between two sequences being compared. CLAP works on full-length sequences and does not require prior domain definitions.Pilot studies undertaken previously on protein kinases and immunoglobulins have shown that CLAP yields clusters, which have high functional and domain architectural similarity. Moreover, parsing at a statistically determined cut-off resulted in clusters that corroborated with the sub-family level classification of that particular domain family. CONCLUSIONS CLAP is a useful protein-clustering tool, independent of domain assignment, domain order, sequence length and domain diversity. Our method can be used for any set of protein sequences, yielding functionally relevant clusters with high domain architectural homogeneity. The CLAP web server is freely available for academic use at http://nslab.mbu.iisc.ernet.in/clap/.
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