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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.
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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
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2
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de la Fuente R, Díaz-Villanueva W, Arnau V, Moya A. Genomic Signature in Evolutionary Biology: A Review. BIOLOGY 2023; 12:biology12020322. [PMID: 36829597 PMCID: PMC9953303 DOI: 10.3390/biology12020322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 02/19/2023]
Abstract
Organisms are unique physical entities in which information is stored and continuously processed. The digital nature of DNA sequences enables the construction of a dynamic information reservoir. However, the distinction between the hardware and software components in the information flow is crucial to identify the mechanisms generating specific genomic signatures. In this work, we perform a bibliometric analysis to identify the different purposes of looking for particular patterns in DNA sequences associated with a given phenotype. This study has enabled us to make a conceptual breakdown of the genomic signature and differentiate the leading applications. On the one hand, it refers to gene expression profiling associated with a biological function, which may be shared across taxa. This signature is the focus of study in precision medicine. On the other hand, it also refers to characteristic patterns in species-specific DNA sequences. This interpretation plays a key role in comparative genomics, identifying evolutionary relationships. Looking at the relevant studies in our bibliographic database, we highlight the main factors causing heterogeneities in genome composition and how they can be quantified. All these findings lead us to reformulate some questions relevant to evolutionary biology.
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Affiliation(s)
- Rebeca de la Fuente
- Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Spanish Research Council (CSIC), 46980 Valencia, Spain
- Correspondence:
| | - Wladimiro Díaz-Villanueva
- Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Spanish Research Council (CSIC), 46980 Valencia, Spain
| | - Vicente Arnau
- Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Spanish Research Council (CSIC), 46980 Valencia, Spain
| | - Andrés Moya
- Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Spanish Research Council (CSIC), 46980 Valencia, Spain
- Foundation for the Promotion of Sanitary and Biomedical Research of the Valencian Community (FISABIO), 46020 Valencia, Spain
- CIBER in Epidemiology and Public Health (CIBEResp), 28029 Madrid, Spain
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3
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Bohnsack KS, Kaden M, Abel J, Villmann T. Alignment-Free Sequence Comparison: A Systematic Survey From a Machine Learning Perspective. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:119-135. [PMID: 34990369 DOI: 10.1109/tcbb.2022.3140873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The encounter of large amounts of biological sequence data generated during the last decades and the algorithmic and hardware improvements have offered the possibility to apply machine learning techniques in bioinformatics. While the machine learning community is aware of the necessity to rigorously distinguish data transformation from data comparison and adopt reasonable combinations thereof, this awareness is often lacking in the field of comparative sequence analysis. With realization of the disadvantages of alignments for sequence comparison, some typical applications use more and more so-called alignment-free approaches. In light of this development, we present a conceptual framework for alignment-free sequence comparison, which highlights the delineation of: 1) the sequence data transformation comprising of adequate mathematical sequence coding and feature generation, from 2) the subsequent (dis-)similarity evaluation of the transformed data by means of problem-specific but mathematically consistent proximity measures. We consider coding to be an information-loss free data transformation in order to get an appropriate representation, whereas feature generation is inevitably information-lossy with the intention to extract just the task-relevant information. This distinction sheds light on the plethora of methods available and assists in identifying suitable methods in machine learning and data analysis to compare the sequences under these premises.
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4
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In silico identification of multiple conserved motifs within the control region of Culicidae mitogenomes. Sci Rep 2022; 12:21920. [PMID: 36536037 PMCID: PMC9763401 DOI: 10.1038/s41598-022-26236-5] [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: 08/11/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Mosquitoes are important vectors for human and animal diseases. Genetic markers, like the mitochondrial COI gene, can facilitate the taxonomic classification of disease vectors, vector-borne disease surveillance, and prevention. Within the control region (CR) of the mitochondrial genome, there exists a highly variable and poorly studied non-coding AT-rich area that contains the origin of replication. Although the CR hypervariable region has been used for species differentiation of some animals, few studies have investigated the mosquito CR. In this study, we analyze the mosquito mitogenome CR sequences from 125 species and 17 genera. We discovered four conserved motifs located 80 to 230 bp upstream of the 12S rRNA gene. Two of these motifs were found within all 392 Anopheles (An.) CR sequences while the other two motifs were identified in all 37 Culex (Cx.) CR sequences. However, only 3 of the 304 non-Culicidae Dipteran mitogenome CR sequences contained these motifs. Interestingly, the short motif found in all 37 Culex sequences had poly-A and poly-T stretch of similar length that is predicted to form a stable hairpin. We show that supervised learning using the frequency chaos game representation of the CR can be used to differentiate mosquito genera from their dipteran relatives.
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5
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Sun N, Zhao X, Yau SST. An efficient numerical representation of genome sequence: natural vector with covariance component. PeerJ 2022; 10:e13544. [PMID: 35729905 PMCID: PMC9206847 DOI: 10.7717/peerj.13544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 05/16/2022] [Indexed: 01/17/2023] Open
Abstract
Background The characterization and comparison of microbial sequences, including archaea, bacteria, viruses and fungi, are very important to understand their evolutionary origin and the population relationship. Most methods are limited by the sequence length and lack of generality. The purpose of this study is to propose a general characterization method, and to study the classification and phylogeny of the existing datasets. Methods We present a new alignment-free method to represent and compare biological sequences. By adding the covariance between each two nucleotides, the new 18-dimensional natural vector successfully describes 24,250 genomic sequences and 95,542 DNA barcode sequences. The new numerical representation is used to study the classification and phylogenetic relationship of microbial sequences. Results First, the classification results validate that the six-dimensional covariance vector is necessary to characterize sequences. Then, the 18-dimensional natural vector is further used to conduct the similarity relationship between giant virus and archaea, bacteria, other viruses. The nearest distance calculation results reflect that the giant viruses are closer to bacteria in distribution of four nucleotides. The phylogenetic relationships of the three representative families, Mimiviridae, Pandoraviridae and Marsellieviridae from giant viruses are analyzed. The trees show that ten sequences of Mimiviridae are clustered with Pandoraviridae, and Mimiviridae is closer to the root of the tree than Marsellieviridae. The new developed alignment-free method can be computed very fast, which provides an effective numerical representation for the sequence of microorganisms.
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Affiliation(s)
- Nan Sun
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Xin Zhao
- Beijing Electronic Science and Technology Institute, Beijing, China
| | - 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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Bohnsack KS, Kaden M, Abel J, Saralajew S, Villmann T. The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1357. [PMID: 34682081 PMCID: PMC8534762 DOI: 10.3390/e23101357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 10/11/2021] [Accepted: 10/14/2021] [Indexed: 11/16/2022]
Abstract
In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.
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Affiliation(s)
- Katrin Sophie Bohnsack
- Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (M.K.); (J.A.)
| | - Marika Kaden
- Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (M.K.); (J.A.)
| | - Julia Abel
- Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (M.K.); (J.A.)
| | - Sascha Saralajew
- Bosch Center for Artificial Intelligence, 71272 Renningen, Germany;
| | - Thomas Villmann
- Saxon Institute for Computational Intelligence and Machine Learning, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (M.K.); (J.A.)
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7
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Qian Y, Zhang Y, Zhang J. Alignment-Free Sequence Comparison With Multiple k Values. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1841-1849. [PMID: 31765317 DOI: 10.1109/tcbb.2019.2955081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Alignment-free sequence comparison approaches have become increasingly popular in computational biology, because alignment-based approaches are inefficient to process large-scale datasets. Still, there is no way to determine the optimal value of the critical parameter k for alignment-free approaches in general. In this article, we tried to solve the problem by involving multiple k values simultaneously. The method counts the occurrence of each k-mer with different k values in a sequence. Two weighting schemes, based on maximizing deviation method and genetic algorithm, are then used on these counts. We applied the method to enhance the three common alignment-free approaches D2, D2S, and D2*, and evaluated its performance on similarity search and functionally related regulatory sequences recognition. The enhanced approaches achieve better performance than the original approaches in all cases, and much better performance than some other common measures, such as Pcc, Eu, Ma, Ch, Kld, and Cos.
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8
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Ni H, Mu H, Qi D. Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses. J Mol Graph Model 2021; 107:107942. [PMID: 34058640 DOI: 10.1016/j.jmgm.2021.107942] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/16/2021] [Accepted: 05/10/2021] [Indexed: 11/28/2022]
Abstract
As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis.
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Affiliation(s)
- Haiming Ni
- College of Science, Northeast Forestry University, Hexing Road 26, Harbin, Heilongjiang Province, 150040, PR China.
| | - Hongbo Mu
- College of Science, Northeast Forestry University, Hexing Road 26, Harbin, Heilongjiang Province, 150040, PR China
| | - Dawei Qi
- College of Science, Northeast Forestry University, Hexing Road 26, Harbin, Heilongjiang Province, 150040, PR China.
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Chockalingam SP, Pannu J, Hooshmand S, Thankachan SV, Aluru S. An alignment-free heuristic for fast sequence comparisons with applications to phylogeny reconstruction. BMC Bioinformatics 2020; 21:404. [PMID: 33203364 PMCID: PMC7672814 DOI: 10.1186/s12859-020-03738-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 09/04/2020] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Alignment-free methods for sequence comparisons have become popular in many bioinformatics applications, specifically in the estimation of sequence similarity measures to construct phylogenetic trees. Recently, the average common substring measure, ACS, and its k-mismatch counterpart, ACSk, have been shown to produce results as effective as multiple-sequence alignment based methods for reconstruction of phylogeny trees. Since computing ACSk takes O(n logkn) time and hence impractical for large datasets, multiple heuristics that can approximate ACSk have been introduced.
Results
In this paper, we present a novel linear-time heuristic to approximate ACSk, which is faster than computing the exact ACSk while being closer to the exact ACSk values compared to previously published linear-time greedy heuristics. Using four real datasets, containing both DNA and protein sequences, we evaluate our algorithm in terms of accuracy, runtime and demonstrate its applicability for phylogeny reconstruction. Our algorithm provides better accuracy than previously published heuristic methods, while being comparable in its applications to phylogeny reconstruction.
Conclusions
Our method produces a better approximation for ACSk and is applicable for the alignment-free comparison of biological sequences at highly competitive speed. The algorithm is implemented in Rust programming language and the source code is available at https://github.com/srirampc/adyar-rs.
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10
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Positional Correlation Natural Vector: A Novel Method for Genome Comparison. Int J Mol Sci 2020; 21:ijms21113859. [PMID: 32485813 PMCID: PMC7312176 DOI: 10.3390/ijms21113859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 05/17/2020] [Accepted: 05/26/2020] [Indexed: 12/17/2022] Open
Abstract
Advances in sequencing technology have made large amounts of biological data available. Evolutionary analysis of data such as DNA sequences is highly important in biological studies. As alignment methods are ineffective for analyzing large-scale data due to their inherently high costs, alignment-free methods have recently attracted attention in the field of bioinformatics. In this paper, we introduce a new positional correlation natural vector (PCNV) method that involves converting a DNA sequence into an 18-dimensional numerical feature vector. Using frequency and position correlation to represent the nucleotide distribution, it is possible to obtain a PCNV for a DNA sequence. This new numerical vector design uses six suitable features to characterize the correlation among nucleotide positions in sequences. PCNV is also very easy to compute and can be used for rapid genome comparison. To test our novel method, we performed phylogenetic analysis with several viral and bacterial genome datasets with PCNV. For comparison, an alignment-based method, Bayesian inference, and two alignment-free methods, feature frequency profile and natural vector, were performed using the same datasets. We found that the PCNV technique is fast and accurate when used for phylogenetic analysis and classification of viruses and bacteria.
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11
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Dencker T, Leimeister CA, Gerth M, Bleidorn C, Snir S, Morgenstern B. 'Multi-SpaM': a maximum-likelihood approach to phylogeny reconstruction using multiple spaced-word matches and quartet trees. NAR Genom Bioinform 2020; 2:lqz013. [PMID: 33575565 PMCID: PMC7671388 DOI: 10.1093/nargab/lqz013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/31/2019] [Accepted: 10/13/2019] [Indexed: 02/03/2023] Open
Abstract
Word-based or 'alignment-free' methods for phylogeny inference have become popular in recent years. These methods are much faster than traditional, alignment-based approaches, but they are generally less accurate. Most alignment-free methods calculate 'pairwise' distances between nucleic-acid or protein sequences; these distance values can then be used as input for tree-reconstruction programs such as neighbor-joining. In this paper, we propose the first word-based phylogeny approach that is based on 'multiple' sequence comparison and 'maximum likelihood'. Our algorithm first samples small, gap-free alignments involving four taxa each. For each of these alignments, it then calculates a quartet tree and, finally, the program 'Quartet MaxCut' is used to infer a super tree for the full set of input taxa from the calculated quartet trees. Experimental results show that trees produced with our approach are of high quality.
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Affiliation(s)
- Thomas Dencker
- Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Chris-André Leimeister
- Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Michael Gerth
- Institute for Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, L69 7ZB Liverpool, UK
| | - Christoph Bleidorn
- Department of Animal Evolution and Biodiversity, Universität Göttingen, Untere Karspüle 2, 37073 Göttingen, Germany
- Museo Nacional de Ciencias Naturales, Spanish National Research Council (CSIC), 28006 Madrid, Spain
| | - Sagi Snir
- Institute of Evolution, Department of Evolutionary and Environmental Biology, University of Haifa, 199 Aba Khoushy Ave. Mount Carmel, Haifa, Israel
| | - Burkhard Morgenstern
- Department of Bioinformatics, Institute of Microbiology and Genetics, Universität Göttingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
- Göttingen Center of Molecular Biosciences (GZMB), Justus-von-Liebig-Weg 11, 37077 Göttingen, Germany
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12
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Dohmen E, Klasberg S, Bornberg-Bauer E, Perrey S, Kemena C. The modular nature of protein evolution: domain rearrangement rates across eukaryotic life. BMC Evol Biol 2020; 20:30. [PMID: 32059645 PMCID: PMC7023805 DOI: 10.1186/s12862-020-1591-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 01/31/2020] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Modularity is important for evolutionary innovation. The recombination of existing units to form larger complexes with new functionalities spares the need to create novel elements from scratch. In proteins, this principle can be observed at the level of protein domains, functional subunits which are regularly rearranged to acquire new functions. RESULTS In this study we analyse the mechanisms leading to new domain arrangements in five major eukaryotic clades (vertebrates, insects, fungi, monocots and eudicots) at unprecedented depth and breadth. This allows, for the first time, to directly compare rates of rearrangements between different clades and identify both lineage specific and general patterns of evolution in the context of domain rearrangements. We analyse arrangement changes along phylogenetic trees by reconstructing ancestral domain content in combination with feasible single step events, such as fusion or fission. Using this approach we explain up to 70% of all rearrangements by tracing them back to their precursors. We find that rates in general and the ratio between these rates for a given clade in particular, are highly consistent across all clades. In agreement with previous studies, fusions are the most frequent event leading to new domain arrangements. A lineage specific pattern in fungi reveals exceptionally high loss rates compared to other clades, supporting recent studies highlighting the importance of loss for evolutionary innovation. Furthermore, our methodology allows us to link domain emergences at specific nodes in the phylogenetic tree to important functional developments, such as the origin of hair in mammals. CONCLUSIONS Our results demonstrate that domain rearrangements are based on a canonical set of mutational events with rates which lie within a relatively narrow and consistent range. In addition, gained knowledge about these rates provides a basis for advanced domain-based methodologies for phylogenetics and homology analysis which complement current sequence-based methods.
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Affiliation(s)
- Elias Dohmen
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, Münster, 48149, Germany.,Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, August-Schmidt-Ring 10, Recklinghausen, 45665, Germany
| | - Steffen Klasberg
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, Münster, 48149, Germany
| | - Erich Bornberg-Bauer
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, Münster, 48149, Germany
| | - Sören Perrey
- Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, August-Schmidt-Ring 10, Recklinghausen, 45665, Germany
| | - Carsten Kemena
- Institute for Evolution and Biodiversity, University of Münster, Hüfferstrasse 1, Münster, 48149, Germany.
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13
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Röhling S, Linne A, Schellhorn J, Hosseini M, Dencker T, Morgenstern B. The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances. PLoS One 2020; 15:e0228070. [PMID: 32040534 PMCID: PMC7010260 DOI: 10.1371/journal.pone.0228070] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 01/08/2020] [Indexed: 12/14/2022] Open
Abstract
We study the number Nk of length-k word matches between pairs of evolutionarily related DNA sequences, as a function of k. We show that the Jukes-Cantor distance between two genome sequences-i.e. the number of substitutions per site that occurred since they evolved from their last common ancestor-can be estimated from the slope of a function F that depends on Nk and that is affine-linear within a certain range of k. Integers kmin and kmax can be calculated depending on the length of the input sequences, such that the slope of F in the relevant range can be estimated from the values F(kmin) and F(kmax). This approach can be generalized to so-called Spaced-word Matches (SpaM), where mismatches are allowed at positions specified by a user-defined binary pattern. Based on these theoretical results, we implemented a prototype software program for alignment-free sequence comparison called Slope-SpaM. Test runs on real and simulated sequence data show that Slope-SpaM can accurately estimate phylogenetic distances for distances up to around 0.5 substitutions per position. The statistical stability of our results is improved if spaced words are used instead of contiguous words. Unlike previous alignment-free methods that are based on the number of (spaced) word matches, Slope-SpaM produces accurate results, even if sequences share only local homologies.
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Affiliation(s)
- Sophie Röhling
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | - Alexander Linne
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | - Jendrik Schellhorn
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | | | - Thomas Dencker
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | - Burkhard Morgenstern
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
- Göttingen Center of Molecular Biosciences (GZMB), Göttingen, Germany
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14
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Zielezinski A, Girgis HZ, Bernard G, Leimeister CA, Tang K, Dencker T, Lau AK, Röhling S, Choi JJ, Waterman MS, Comin M, Kim SH, Vinga S, Almeida JS, Chan CX, James BT, Sun F, Morgenstern B, Karlowski WM. Benchmarking of alignment-free sequence comparison methods. Genome Biol 2019; 20:144. [PMID: 31345254 PMCID: PMC6659240 DOI: 10.1186/s13059-019-1755-7] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 07/03/2019] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Alignment-free (AF) sequence comparison is attracting persistent interest driven by data-intensive applications. Hence, many AF procedures have been proposed in recent years, but a lack of a clearly defined benchmarking consensus hampers their performance assessment. RESULTS Here, we present a community resource (http://afproject.org) to establish standards for comparing alignment-free approaches across different areas of sequence-based research. We characterize 74 AF methods available in 24 software tools for five research applications, namely, protein sequence classification, gene tree inference, regulatory element detection, genome-based phylogenetic inference, and reconstruction of species trees under horizontal gene transfer and recombination events. CONCLUSION The interactive web service allows researchers to explore the performance of alignment-free tools relevant to their data types and analytical goals. It also allows method developers to assess their own algorithms and compare them with current state-of-the-art tools, accelerating the development of new, more accurate AF solutions.
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Affiliation(s)
- Andrzej Zielezinski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University Poznan, Uniwersytetu Poznańskiego 6, 61-614, Poznan, Poland
| | - Hani Z Girgis
- Tandy School of Computer Science, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, USA
| | | | - Chris-Andre Leimeister
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Kujin Tang
- Department of Biological Sciences, Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
| | - Thomas Dencker
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Anna Katharina Lau
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Sophie Röhling
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Jae Jin Choi
- Department of Chemistry, University of California, Berkeley, CA, 94720, USA
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Michael S Waterman
- Department of Biological Sciences, Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
- Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, 200433, China
| | - Matteo Comin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Sung-Hou Kim
- Department of Chemistry, University of California, Berkeley, CA, 94720, USA
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
- IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal
| | - Jonas S Almeida
- Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NIH/NCI), Bethesda, USA
| | - Cheong Xin Chan
- Institute for Molecular Bioscience, and School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Benjamin T James
- Tandy School of Computer Science, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, USA
| | - Fengzhu Sun
- Department of Biological Sciences, Quantitative and Computational Biology Program, University of Southern California, Los Angeles, CA, 90089, USA
- Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, 200433, China
| | - Burkhard Morgenstern
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Göttingen, Goldschmidtstr. 1, 37077, Göttingen, Germany
| | - Wojciech M Karlowski
- Department of Computational Biology, Faculty of Biology, Adam Mickiewicz University Poznan, Uniwersytetu Poznańskiego 6, 61-614, Poznan, Poland.
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15
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Criscuolo A. A fast alignment-free bioinformatics procedure to infer accurate distance-based phylogenetic trees from genome assemblies. RESEARCH IDEAS AND OUTCOMES 2019. [DOI: 10.3897/rio.5.e36178] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
This paper describes a novel alignment-free distance-based procedure for inferring phylogenetic trees from genome contig sequences using publicly available bioinformatics tools. For each pair of genomes, a dissimilarity measure is first computed and next transformed to obtain an estimation of the number of substitution events that have occurred during their evolution. These pairwise evolutionary distances are then used to infer a phylogenetic tree and assess a confidence support for each internal branch. Analyses of both simulated and real genome datasets show that this bioinformatics procedure allows accurate phylogenetic trees to be reconstructed with fast running times, especially when launched on multiple threads. Implemented in a publicly available script, named JolyTree, this procedure is a useful approach for quickly inferring species trees without the burden and potential biases of multiple sequence alignments.
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16
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Dong R, He L, He RL, Yau SST. A Novel Approach to Clustering Genome Sequences Using Inter-nucleotide Covariance. Front Genet 2019; 10:234. [PMID: 31024610 PMCID: PMC6465635 DOI: 10.3389/fgene.2019.00234] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 03/04/2019] [Indexed: 11/30/2022] Open
Abstract
Classification of DNA sequences is an important issue in the bioinformatics study, yet most existing methods for phylogenetic analysis including Multiple Sequence Alignment (MSA) are time-consuming and computationally expensive. The alignment-free methods are popular nowadays, whereas the manual intervention in those methods usually decreases the accuracy. Also, the interactions among nucleotides are neglected in most methods. Here we propose a new Accumulated Natural Vector (ANV) method which represents each DNA sequence by a point in ℝ18. By calculating the Accumulated Indicator Functions of nucleotides, we can further find an Accumulated Natural Vector for each sequence. This new Accumulated Natural Vector not only can capture the distribution of each nucleotide, but also provide the covariance among nucleotides. Thus global comparison of DNA sequences or genomes can be done easily in ℝ18. The tests of ANV of datasets of different sizes and types have proved the accuracy and time-efficiency of the new proposed ANV method.
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Affiliation(s)
- Rui Dong
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Lily He
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
| | - Rong Lucy He
- Department of Biological Sciences, Chicago State University, Chicago, IL, United States
| | - Stephen S-T Yau
- Department of Mathematical Sciences, Tsinghua University, Beijing, China
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17
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Saw AK, Raj G, Das M, Talukdar NC, Tripathy BC, Nandi S. Alignment-free method for DNA sequence clustering using Fuzzy integral similarity. Sci Rep 2019; 9:3753. [PMID: 30842590 PMCID: PMC6403383 DOI: 10.1038/s41598-019-40452-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 01/28/2019] [Indexed: 12/28/2022] Open
Abstract
A larger amount of sequence data in private and public databases produced by next-generation sequencing put new challenges due to limitation associated with the alignment-based method for sequence comparison. So, there is a high need for faster sequence analysis algorithms. In this study, we developed an alignment-free algorithm for faster sequence analysis. The novelty of our approach is the inclusion of fuzzy integral with Markov chain for sequence analysis in the alignment-free model. The method estimate the parameters of a Markov chain by considering the frequencies of occurrence of all possible nucleotide pairs from each DNA sequence. These estimated Markov chain parameters were used to calculate similarity among all pairwise combinations of DNA sequences based on a fuzzy integral algorithm. This matrix is used as an input for the neighbor program in the PHYLIP package for phylogenetic tree construction. Our method was tested on eight benchmark datasets and on in-house generated datasets (18 s rDNA sequences from 11 arbuscular mycorrhizal fungi (AMF) and 16 s rDNA sequences of 40 bacterial isolates from plant interior). The results indicate that the fuzzy integral algorithm is an efficient and feasible alignment-free method for sequence analysis on the genomic scale.
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Affiliation(s)
- Ajay Kumar Saw
- Institute of Advanced Study in Science and Technology, Mathematical Sciences Division, Guwahati, 781035, India
| | - Garima Raj
- Institute of Advanced Study in Science and Technology, Life Science Division, Guwahati, 781035, India
| | - Manashi Das
- Institute of Advanced Study in Science and Technology, Life Science Division, Guwahati, 781035, India
| | - Narayan Chandra Talukdar
- Institute of Advanced Study in Science and Technology, Life Science Division, Guwahati, 781035, India
| | | | - Soumyadeep Nandi
- Institute of Advanced Study in Science and Technology, Life Science Division, Guwahati, 781035, India.
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18
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Leimeister CA, Schellhorn J, Dörrer S, Gerth M, Bleidorn C, Morgenstern B. Prot-SpaM: fast alignment-free phylogeny reconstruction based on whole-proteome sequences. Gigascience 2019; 8:giy148. [PMID: 30535314 PMCID: PMC6436989 DOI: 10.1093/gigascience/giy148] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 09/10/2018] [Accepted: 11/20/2018] [Indexed: 11/20/2022] Open
Abstract
Word-based or 'alignment-free' sequence comparison has become an active research area in bioinformatics. While previous word-frequency approaches calculated rough measures of sequence similarity or dissimilarity, some new alignment-free methods are able to accurately estimate phylogenetic distances between genomic sequences. One of these approaches is Filtered Spaced Word Matches. Here, we extend this approach to estimate evolutionary distances between complete or incomplete proteomes; our implementation of this approach is called Prot-SpaM. We compare the performance of Prot-SpaM to other alignment-free methods on simulated sequences and on various groups of eukaryotic and prokaryotic taxa. Prot-SpaM can be used to calculate high-quality phylogenetic trees for dozens of whole-proteome sequences in a matter of seconds or minutes and often outperforms other alignment-free approaches. The source code of our software is available through Github: https://github.com/jschellh/ProtSpaM.
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Affiliation(s)
- Chris-Andre Leimeister
- University of Göttingen, Department of Bioinformatics, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Jendrik Schellhorn
- University of Göttingen, Department of Bioinformatics, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Svenja Dörrer
- University of Göttingen, Department of Bioinformatics, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Michael Gerth
- Institute for Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, L69 7ZB Liverpool, UK
| | - Christoph Bleidorn
- University of Göttingen, Department of Animal Evolution and Biodiversity, Untere Karspüle 2, 37073 Göttingen, Germany
- Museo Nacional de Ciencias Naturales, Spanish National Research Council (CSIC), 28006 Madrid, Spain
| | - Burkhard Morgenstern
- University of Göttingen, Department of Bioinformatics, Goldschmidtstr. 1, 37077 Göttingen, Germany
- Göttingen Center of Molecular Biosciences (GZMB), Justus-von-Liebig-Weg 11, 37077 Göttingen
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19
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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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20
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Abstract
With sharp increasing in biological sequences, the traditional sequence alignment methods become unsuitable and infeasible. It motivates a surge of fast alignment-free techniques for sequence analysis. Among these methods, many sorts of feature vector methods are established and applied to reconstruction of species phylogeny. The vectors basically consist of some typical numerical features for certain biological problems. The features may come from the primary sequences, secondary or three dimensional structures of macromolecules. In this study, we propose a novel numerical vector based on only primary sequences of organism to build their phylogeny. Three chemical and physical properties of primary sequences: purine, pyrimidine and keto are also incorporated to the vector. Using each property, we convert the nucleotide sequence into a new sequence consisting of only two kinds of letters. Therefore, three sequences are constructed according to the three properties. For each letter of each sequence we calculate the number of the letter, the average position of the letter and the variation of the position of the letter appearing in the sequence. Tested on several datasets related to mammals, viruses and bacteria, this new tool is fast in speed and accurate for inferring the phylogeny of organisms.
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21
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Leimeister CA, Sohrabi-Jahromi S, Morgenstern B. Fast and accurate phylogeny reconstruction using filtered spaced-word matches. Bioinformatics 2017; 33:971-979. [PMID: 28073754 PMCID: PMC5409309 DOI: 10.1093/bioinformatics/btw776] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 12/02/2016] [Indexed: 11/13/2022] Open
Abstract
Motivation Word-based or ‘alignment-free’ algorithms are increasingly used for phylogeny reconstruction and genome comparison, since they are much faster than traditional approaches that are based on full sequence alignments. Existing alignment-free programs, however, are less accurate than alignment-based methods. Results We propose Filtered Spaced Word Matches (FSWM), a fast alignment-free approach to estimate phylogenetic distances between large genomic sequences. For a pre-defined binary pattern of match and don’t-care positions, FSWM rapidly identifies spaced word-matches between input sequences, i.e. gap-free local alignments with matching nucleotides at the match positions and with mismatches allowed at the don’t-care positions. We then estimate the number of nucleotide substitutions per site by considering the nucleotides aligned at the don’t-care positions of the identified spaced-word matches. To reduce the noise from spurious random matches, we use a filtering procedure where we discard all spaced-word matches for which the overall similarity between the aligned segments is below a threshold. We show that our approach can accurately estimate substitution frequencies even for distantly related sequences that cannot be analyzed with existing alignment-free methods; phylogenetic trees constructed with FSWM distances are of high quality. A program run on a pair of eukaryotic genomes of a few hundred Mb each takes a few minutes. Availability and Implementation The program source code for FSWM including a documentation, as well as the software that we used to generate artificial genome sequences are freely available at http://fswm.gobics.de/ Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chris-André Leimeister
- Department of Bioinformatics, University of Göttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077?Göttingen, Germany
| | - Salma Sohrabi-Jahromi
- Department of Bioinformatics, University of Göttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077?Göttingen, Germany
| | - Burkhard Morgenstern
- Department of Bioinformatics, University of Göttingen, Institute of Microbiology and Genetics, Goldschmidtstr. 1, 37077 Göttingen, Germany.,University of Göttingen, Center for Computational Sciences, Goldschmidtstr. 1, 37077 Göttingen, Germany
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22
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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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23
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Yang WF, Yu ZG, Anh V. Whole genome/proteome based phylogeny reconstruction for prokaryotes using higher order Markov model and chaos game representation. Mol Phylogenet Evol 2015; 96:102-111. [PMID: 26724405 DOI: 10.1016/j.ympev.2015.12.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Revised: 12/17/2015] [Accepted: 12/18/2015] [Indexed: 01/18/2023]
Abstract
UNLABELLED Traditional methods for sequence comparison and phylogeny reconstruction rely on pair wise and multiple sequence alignments. But alignment could not be directly applied to whole genome/proteome comparison and phylogenomic studies due to their high computational complexity. Hence alignment-free methods became popular in recent years. Here we propose a fast alignment-free method for whole genome/proteome comparison and phylogeny reconstruction using higher order Markov model and chaos game representation. In the present method, we use the transition matrices of higher order Markov models to characterize amino acid or DNA sequences for their comparison. The order of the Markov model is uniquely identified by maximizing the average Shannon entropy of conditional probability distributions. Using one-dimensional chaos game representation and linked list, this method can reduce large memory and time consumption which is due to the large-scale conditional probability distributions. To illustrate the effectiveness of our method, we employ it for fast phylogeny reconstruction based on genome/proteome sequences of two species data sets used in previous published papers. Our results demonstrate that the present method is useful and efficient. AVAILABILITY AND IMPLEMENTATION The source codes for our algorithm to get the distance matrix and genome/proteome sequences can be downloaded from ftp://121.199.20.25/. The software Phylip and EvolView we used to construct phylogenetic trees can be referred from their websites.
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Affiliation(s)
- Wei-Feng Yang
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, PR China; Department of Mathematics and Physics, Hunan Institute of Engineering, Hunan 411104, PR China.
| | - Zu-Guo Yu
- Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, PR China; School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia.
| | - Vo Anh
- School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia.
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24
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A novel k-word relative measure for sequence comparison. Comput Biol Chem 2014; 53PB:331-338. [PMID: 25462340 DOI: 10.1016/j.compbiolchem.2014.10.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2014] [Revised: 08/10/2014] [Accepted: 10/25/2014] [Indexed: 12/28/2022]
Abstract
In order to extract phylogenetic information from DNA sequences, the new normalized k-word average relative distance is proposed in this paper. The proposed measure was tested by discriminate analysis and phylogenetic analysis. The phylogenetic trees based on the Manhattan distance measure are reconstructed with k ranging from 1 to 12. At the same time, a new method is suggested to reduce the matrix dimension, can greatly lessen the amount of calculation and operation time. The experimental assessment demonstrated that our measure was efficient. What's more, comparing with other methods' results shows that our method is feasible and powerful for phylogenetic analysis.
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25
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Kollmar M, Kollmar L, Hammesfahr B, Simm D. diArk--the database for eukaryotic genome and transcriptome assemblies in 2014. Nucleic Acids Res 2014; 43:D1107-12. [PMID: 25378341 PMCID: PMC4384042 DOI: 10.1093/nar/gku990] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Eukaryotic genomes are the basis for understanding the complexity of life from populations to the molecular level. Recent technological innovations have revolutionized the speed of data generation enabling the sequencing of eukaryotic genomes and transcriptomes within days. The database diArk (http://www.diark.org) has been developed with the aim to provide access to all available assembled genomes and transcriptomes. In September 2014, diArk contains about 2600 eukaryotes with 6000 genome and transcriptome assemblies, of which 22% are not available via NCBI/ENA/DDBJ. Several indicators for the quality of the assemblies are provided to facilitate their comparison for selecting the most appropriate dataset for further studies. diArk has a user-friendly web interface with extensive options for filtering and browsing the sequenced eukaryotes. In this new version of the database we have also integrated species, for which transcriptome assemblies are available, and we provide more analyses of assemblies.
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Affiliation(s)
- Martin Kollmar
- Group Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Göttingen, 37085, Germany
| | - Lotte Kollmar
- Group Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Göttingen, 37085, Germany
| | - Björn Hammesfahr
- Group Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Göttingen, 37085, Germany
| | - Dominic Simm
- Group Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Göttingen, 37085, Germany
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26
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Leimeister CA, Boden M, Horwege S, Lindner S, Morgenstern B. Fast alignment-free sequence comparison using spaced-word frequencies. ACTA ACUST UNITED AC 2014; 30:1991-9. [PMID: 24700317 PMCID: PMC4080745 DOI: 10.1093/bioinformatics/btu177] [Citation(s) in RCA: 105] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Motivation: Alignment-free methods for sequence comparison are increasingly used for genome analysis and phylogeny reconstruction; they circumvent various difficulties of traditional alignment-based approaches. In particular, alignment-free methods are much faster than pairwise or multiple alignments. They are, however, less accurate than methods based on sequence alignment. Most alignment-free approaches work by comparing the word composition of sequences. A well-known problem with these methods is that neighbouring word matches are far from independent. Results: To reduce the statistical dependency between adjacent word matches, we propose to use ‘spaced words’, defined by patterns of ‘match’ and ‘don’t care’ positions, for alignment-free sequence comparison. We describe a fast implementation of this approach using recursive hashing and bit operations, and we show that further improvements can be achieved by using multiple patterns instead of single patterns. To evaluate our approach, we use spaced-word frequencies as a basis for fast phylogeny reconstruction. Using real-world and simulated sequence data, we demonstrate that our multiple-pattern approach produces better phylogenies than approaches relying on contiguous words. Availability and implementation: Our program is freely available at http://spaced.gobics.de/. Contact:chris.leimeister@stud.uni-goettingen.de Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chris-Andre Leimeister
- Department of Bioinformatics, University of Göttingen, Institute of Microbiology and Genetics, 37073 Göttingen, Germany and Université d'Évry Val d'Essonne, Laboratoire Statistique et Génome, UMR CNRS 8071, USC INRA, 91037 Évry, France
| | - Marcus Boden
- Department of Bioinformatics, University of Göttingen, Institute of Microbiology and Genetics, 37073 Göttingen, Germany and Université d'Évry Val d'Essonne, Laboratoire Statistique et Génome, UMR CNRS 8071, USC INRA, 91037 Évry, France
| | - Sebastian Horwege
- Department of Bioinformatics, University of Göttingen, Institute of Microbiology and Genetics, 37073 Göttingen, Germany and Université d'Évry Val d'Essonne, Laboratoire Statistique et Génome, UMR CNRS 8071, USC INRA, 91037 Évry, France
| | - Sebastian Lindner
- Department of Bioinformatics, University of Göttingen, Institute of Microbiology and Genetics, 37073 Göttingen, Germany and Université d'Évry Val d'Essonne, Laboratoire Statistique et Génome, UMR CNRS 8071, USC INRA, 91037 Évry, France
| | - Burkhard Morgenstern
- Department of Bioinformatics, University of Göttingen, Institute of Microbiology and Genetics, 37073 Göttingen, Germany and Université d'Évry Val d'Essonne, Laboratoire Statistique et Génome, UMR CNRS 8071, USC INRA, 91037 Évry, FranceDepartment of Bioinformatics, University of Göttingen, Institute of Microbiology and Genetics, 37073 Göttingen, Germany and Université d'Évry Val d'Essonne, Laboratoire Statistique et Génome, UMR CNRS 8071, USC INRA, 91037 Évry, France
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27
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Schwende I, Pham TD. Pattern recognition and probabilistic measures in alignment-free sequence analysis. Brief Bioinform 2013; 15:354-68. [PMID: 24096012 DOI: 10.1093/bib/bbt070] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
With the massive production of genomic and proteomic data, the number of available biological sequences in databases has reached a level that is not feasible anymore for exact alignments even when just a fraction of all sequences is used. To overcome this inevitable time complexity, ultrafast alignment-free methods are studied. Within the past two decades, a broad variety of nonalignment methods have been proposed including dissimilarity measures on classical representations of sequences like k-words or Markov models. Furthermore, articles were published that describe distance measures on alternative representations such as compression complexity, spectral time series or chaos game representation. However, alignments are still the standard method for real world applications in biological sequence analysis, and the time efficient alignment-free approaches are usually applied in cases when the accustomed algorithms turn out to fail or be too inconvenient.
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Affiliation(s)
- Isabel Schwende
- PhD, Aizu Research Cluster for Medical Informatics and Engineering (ARC-Medical), Research Center for Advanced Information Science and Technology (CAIST), The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan.
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28
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Mühlhausen S, Kollmar M. Whole genome duplication events in plant evolution reconstructed and predicted using myosin motor proteins. BMC Evol Biol 2013; 13:202. [PMID: 24053117 PMCID: PMC3850447 DOI: 10.1186/1471-2148-13-202] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2013] [Accepted: 09/16/2013] [Indexed: 01/22/2023] Open
Abstract
Background The evolution of land plants is characterized by whole genome duplications (WGD), which drove species diversification and evolutionary novelties. Detecting these events is especially difficult if they date back to the origin of the plant kingdom. Established methods for reconstructing WGDs include intra- and inter-genome comparisons, KS age distribution analyses, and phylogenetic tree constructions. Results By analysing 67 completely sequenced plant genomes 775 myosins were identified and manually assembled. Phylogenetic trees of the myosin motor domains revealed orthologous and paralogous relationships and were consistent with recent species trees. Based on the myosin inventories and the phylogenetic trees, we have identified duplications of the entire myosin motor protein family at timings consistent with 23 WGDs, that had been reported before. We also predict 6 WGDs based on further protein family duplications. Notably, the myosin data support the two recently reported WGDs in the common ancestor of all extant angiosperms. We predict single WGDs in the Manihot esculenta and Nicotiana benthamiana lineages, two WGDs for Linum usitatissimum and Phoenix dactylifera, and a triplication or two WGDs for Gossypium raimondii. Our data show another myosin duplication in the ancestor of the angiosperms that could be either the result of a single gene duplication or a remnant of a WGD. Conclusions We have shown that the myosin inventories in angiosperms retain evidence of numerous WGDs that happened throughout plant evolution. In contrast to other protein families, many myosins are still present in extant species. They are closely related and have similar domain architectures, and their phylogenetic grouping follows the genome duplications. Because of its broad taxonomic sampling the dataset provides the basis for reliable future identification of further whole genome duplications.
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Affiliation(s)
- Stefanie Mühlhausen
- Group Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for biophysical Chemistry, Göttingen, Germany.
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Abstract
Phylogenetic analysis based on alignment method meets huge challenges when dealing with whole-genome sequences, for example, recombination, shuffling, and rearrangement of sequences. Thus, various alignment-free methods for phylogeny construction have been proposed. However, most of these methods have not been implemented as tools or web servers. Researchers cannot use these methods easily with their data sets. To facilitate the usage of various alignment-free methods, we implemented most of the popular alignment-free methods and constructed a user-friendly web server for alignment-free genome phylogeny (AGP). AGP integrated the phylogenetic tree construction, visualization, and comparison functions together. Both AGP and all source code of the methods are available at http://www.herbbol.org:8000/agp (last accessed February 26, 2013). AGP will facilitate research in the field of whole-genome phylogeny and comparison.
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Yang R, Jarvis DE, Chen H, Beilstein MA, Grimwood J, Jenkins J, Shu S, Prochnik S, Xin M, Ma C, Schmutz J, Wing RA, Mitchell-Olds T, Schumaker KS, Wang X. The Reference Genome of the Halophytic Plant Eutrema salsugineum. FRONTIERS IN PLANT SCIENCE 2013; 4:46. [PMID: 23518688 PMCID: PMC3604812 DOI: 10.3389/fpls.2013.00046] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 02/24/2013] [Indexed: 05/02/2023]
Abstract
Halophytes are plants that can naturally tolerate high concentrations of salt in the soil, and their tolerance to salt stress may occur through various evolutionary and molecular mechanisms. Eutrema salsugineum is a halophytic species in the Brassicaceae that can naturally tolerate multiple types of abiotic stresses that typically limit crop productivity, including extreme salinity and cold. It has been widely used as a laboratorial model for stress biology research in plants. Here, we present the reference genome sequence (241 Mb) of E. salsugineum at 8× coverage sequenced using the traditional Sanger sequencing-based approach with comparison to its close relative Arabidopsis thaliana. The E. salsugineum genome contains 26,531 protein-coding genes and 51.4% of its genome is composed of repetitive sequences that mostly reside in pericentromeric regions. Comparative analyses of the genome structures, protein-coding genes, microRNAs, stress-related pathways, and estimated translation efficiency of proteins between E. salsugineum and A. thaliana suggest that halophyte adaptation to environmental stresses may occur via a global network adjustment of multiple regulatory mechanisms. The E. salsugineum genome provides a resource to identify naturally occurring genetic alterations contributing to the adaptation of halophytic plants to salinity and that might be bioengineered in related crop species.
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Affiliation(s)
- Ruolin Yang
- School of Plant Sciences, University of ArizonaTucson, AZ, USA
| | - David E. Jarvis
- School of Plant Sciences, University of ArizonaTucson, AZ, USA
| | - Hao Chen
- School of Plant Sciences, University of ArizonaTucson, AZ, USA
| | | | - Jane Grimwood
- Department of Energy Joint Genome InstituteWalnut Creek, CA, USA
- HudsonAlpha Institute of BiotechnologyHuntsville, AL, USA
| | - Jerry Jenkins
- Department of Energy Joint Genome InstituteWalnut Creek, CA, USA
- HudsonAlpha Institute of BiotechnologyHuntsville, AL, USA
| | - ShengQiang Shu
- Department of Energy Joint Genome InstituteWalnut Creek, CA, USA
| | - Simon Prochnik
- Department of Energy Joint Genome InstituteWalnut Creek, CA, USA
| | - Mingming Xin
- School of Plant Sciences, University of ArizonaTucson, AZ, USA
| | - Chuang Ma
- School of Plant Sciences, University of ArizonaTucson, AZ, USA
| | - Jeremy Schmutz
- Department of Energy Joint Genome InstituteWalnut Creek, CA, USA
- HudsonAlpha Institute of BiotechnologyHuntsville, AL, USA
| | - Rod A. Wing
- School of Plant Sciences, University of ArizonaTucson, AZ, USA
| | | | - Karen S. Schumaker
- School of Plant Sciences, University of ArizonaTucson, AZ, USA
- *Correspondence: Karen S. Schumaker and Xiangfeng Wang, School of Plant Sciences, University of Arizona, 303 Forbes Hall, 1140 E. South Campus Drive, Tucson, AZ 85721-0036, USA. e-mail: ;
| | - Xiangfeng Wang
- School of Plant Sciences, University of ArizonaTucson, AZ, USA
- *Correspondence: Karen S. Schumaker and Xiangfeng Wang, School of Plant Sciences, University of Arizona, 303 Forbes Hall, 1140 E. South Campus Drive, Tucson, AZ 85721-0036, USA. e-mail: ;
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