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Milton A, Muhanguzi D, Male A, Kajubi A, Buah S, Kubiriba J, Tumuhimbise R. Analysis of Genetic Diversity of Banana Weevils (Cosmopolites sordidus) (Coleoptera: Curculionidae) Using Transcriptome-Derived Simple Sequence Repeat Markers. JOURNAL OF ECONOMIC ENTOMOLOGY 2022; 115:637-646. [PMID: 35021224 DOI: 10.1093/jee/toab213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Indexed: 06/14/2023]
Abstract
The banana weevil, Cosmopolites sordidus (Germar) (Coleoptera: Curculionidae) is an economically important insect pest of bananas. It causes up to 100% yield losses and substantial lifespan reduction in bananas. Advances in genomics, proteomics, and sequencing technologies have provided powerful pathways to genotyping disastrous pests such as C. sordidus. However, such technologies are often not available to the majority of rural subtropical African banana growers and pest control managers. This study was therefore motivated by the need to create cheap and easily accessible C. sordidus genotyping methods that could be deployed by banana pest control managers to the benefit of C. sordidus control programs in the tropics where such advanced technologies are not readily accessible. We used an in-house C. sordidus transcriptome from the an-ongoing study from which we mined an array of simple sequence repeat (SSR) markers. Of these, six highly polymorphic transcriptome-derived SSR markers were used to successfully genotype within and among banana weevil population genetic diversity of 12 C. sordidus populations collected from four banana-growing agro-ecological zones (AEZs) in Uganda. The developed transcriptome-derived SSR markers can be used by researchers in population genetics for characterization of the C. sordidus and identification of new genes that are linked to traits of particular interest. The significant genetic diversity revealed in C. sordidus provides pertinent information for integrated pest management strategies.
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Affiliation(s)
- Ali Milton
- National Agricultural Research Laboratories-Kawanda, Kampala, Uganda
- College of Veterinary Medicine Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Dennis Muhanguzi
- College of Veterinary Medicine Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Allan Male
- International Center for Tropical Agriculture, Kampala, Uganda
| | - Ali Kajubi
- National Agricultural Research Laboratories-Kawanda, Kampala, Uganda
| | - Stephen Buah
- National Agricultural Research Laboratories-Kawanda, Kampala, Uganda
| | - Jerome Kubiriba
- National Agricultural Research Laboratories-Kawanda, Kampala, Uganda
| | - Robooni Tumuhimbise
- Rwebitaba Zonal Agricultural Research and Development Institute, Fort-Portal, Uganda
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2
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Miryeganeh M, Marlétaz F, Gavriouchkina D, Saze H. De novo genome assembly and in natura epigenomics reveal salinity-induced DNA methylation in the mangrove tree Bruguiera gymnorhiza. THE NEW PHYTOLOGIST 2022; 233:2094-2110. [PMID: 34532854 PMCID: PMC9293310 DOI: 10.1111/nph.17738] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 09/02/2021] [Indexed: 05/27/2023]
Abstract
Mangroves are adapted to harsh environments, such as high ultraviolet (UV) light, low nutrition, and fluctuating salinity in coastal zones. However, little is known about the transcriptomic and epigenomic basis of the resilience of mangroves due to limited available genome resources. We performed a de novo genome assembly and in natura epigenome analyses of the mangrove Bruguiera gymnorhiza, one of the dominant mangrove species. We also performed the first genome-guided transcriptome assembly for mangrove species. The 309 Mb of the genome is predicted to encode 34 403 genes and has a repeat content of 48%. Depending on its growing environment, the natural B. gymnorhiza population showed drastic morphological changes associated with expression changes in thousands of genes. Moreover, high-salinity environments induced genome-wide DNA hypermethylation of transposable elements (TEs) in the B. gymnorhiza. DNA hypermethylation was concurrent with the transcriptional regulation of chromatin modifier genes, suggesting robust epigenome regulation of TEs in the B. gymnorhiza genome under high-salinity environments. The genome and epigenome data in this study provide novel insights into the epigenome regulation of mangroves and a better understanding of the adaptation of plants to fluctuating, harsh natural environments.
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Affiliation(s)
- Matin Miryeganeh
- Plant Epigenetics UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawa904‐0495Japan
| | - Ferdinand Marlétaz
- Department of Genetics, Evolution and Environment (GEE)University College LondonDarwin Building, Gower StreetLondonWC1E 6BTUK
| | - Daria Gavriouchkina
- Molecular Genetics UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawa904‐0495Japan
| | - Hidetoshi Saze
- Plant Epigenetics UnitOkinawa Institute of Science and Technology Graduate UniversityOkinawa904‐0495Japan
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3
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Wu YQ, Yu ZG, Tang RB, Han GS, Anh VV. An Information-Entropy Position-Weighted K-Mer Relative Measure for Whole Genome Phylogeny Reconstruction. Front Genet 2021; 12:766496. [PMID: 34745231 PMCID: PMC8568955 DOI: 10.3389/fgene.2021.766496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 09/29/2021] [Indexed: 11/30/2022] Open
Abstract
Alignment methods have faced disadvantages in sequence comparison and phylogeny reconstruction due to their high computational costs in handling time and space complexity. On the other hand, alignment-free methods incur low computational costs and have recently gained popularity in the field of bioinformatics. Here we propose a new alignment-free method for phylogenetic tree reconstruction based on whole genome sequences. A key component is a measure called information-entropy position-weighted k-mer relative measure (IEPWRMkmer), which combines the position-weighted measure of k-mers proposed by our group and the information entropy of frequency of k-mers. The Manhattan distance is used to calculate the pairwise distance between species. Finally, we use the Neighbor-Joining method to construct the phylogenetic tree. To evaluate the performance of this method, we perform phylogenetic analysis on two datasets used by other researchers. The results demonstrate that the IEPWRMkmer method is efficient and reliable. The source codes of our method are provided at https://github.com/ wuyaoqun37/IEPWRMkmer.
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Affiliation(s)
- Yao-Qun Wu
- 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, China.,Provincial Key Laboratory of Informational Service for Rural Area of Southwestern Hunan, Shaoyang University, Shaoyang, 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, China
| | - Run-Bin Tang
- 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, China
| | - Guo-Sheng Han
- 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, China
| | - Vo V Anh
- Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC, Australia
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Allen DR, Thankachan SV, Xu B. An Ultra-Fast and Parallelizable Algorithm for Finding k-Mismatch Shortest Unique Substrings. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:138-148. [PMID: 31985439 DOI: 10.1109/tcbb.2020.2968531] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This paper revisits the k-mismatch shortest unique substring finding problem and demonstrates that a technique recently presented in the context of solving the k-mismatch average common substring problem can be adapted and combined with parts of the existing solution, resulting in a new algorithm which has expected time complexity of O(n logk n), while maintaining a practical space complexity at O(kn), where n is the string length. When , which is the hard case, our new proposal significantly improves the any-case O(n2) time complexity of the prior best method for k-mismatch shortest unique substring finding. Experimental study shows that our new algorithm is practical to implement and demonstrates significant improvements in processing time compared to the prior best solution's implementation when k is small relative to n. For example, our method processes a 200 KB sample DNA sequence with k=1 in just 0.18 seconds compared to 174.37 seconds with the prior best solution. Further, it is observed that significant portions of the adapted technique can be executed in parallel, using two different simple concurrency models, resulting in further significant practical performance improvement. As an example, when using 8 cores, the parallel implementations both achieved processing times that are less than 1/4 of the serial implementation's time cost, when processing a 10 MB sample DNA sequence with k=2. In an age where instances with thousands of gigabytes of RAM are readily available for use through Cloud infrastructure providers, it is likely that the trade-off of additional memory usage for significantly improved processing times will be desirable and needed by many users. For example, the best prior solution may spend years to finish a DNA sample of 200MB for any , while this new proposal, using 24 cores, can finish processing a sample of this size with k=1 in 206.376 seconds with a peak memory usage of 46 GB, which is both easily available and affordable on Cloud. It is expected that this new efficient and practical algorithm for k-mismatch shortest unique substring finding will prove useful to those using the measure on long sequences in fields such as computational biology. We also give a theoretical bound that the k-mismatch shortest unique substring finding problem can be solved using O(n logk n) time and O(n) space, asymptotically much better than the one we implemented, serving as a new discovery of interest.
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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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Abstract
The shortest unique substring (SUS) problem is an active line of research in the field of string algorithms and has several applications in bioinformatics and information retrieval. The initial version of the problem was proposed by Pei et al. [ICDE’13]. Over the years, many variants and extensions have been pursued, which include positional-SUS, interval-SUS, approximate-SUS, palindromic-SUS, range-SUS, etc. In this article, we highlight some of the key results and summarize the recent developments in this area.
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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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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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9
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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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Morgenstern B, Schöbel S, Leimeister CA. Phylogeny reconstruction based on the length distribution of k-mismatch common substrings. Algorithms Mol Biol 2017; 12:27. [PMID: 29238399 PMCID: PMC5724348 DOI: 10.1186/s13015-017-0118-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 11/28/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Various approaches to alignment-free sequence comparison are based on the length of exact or inexact word matches between pairs of input sequences. Haubold et al. (J Comput Biol 16:1487-1500, 2009) showed how the average number of substitutions per position between two DNA sequences can be estimated based on the average length of exact common substrings. RESULTS In this paper, we study the length distribution of k-mismatch common substrings between two sequences. We show that the number of substitutions per position can be accurately estimated from the position of a local maximum in the length distribution of their k-mismatch common substrings.
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Affiliation(s)
- Burkhard Morgenstern
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Goettingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Svenja Schöbel
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Goettingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
| | - Chris-André Leimeister
- Department of Bioinformatics, Institute of Microbiology and Genetics, University of Goettingen, Goldschmidtstr. 1, 37077 Göttingen, Germany
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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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Thankachan SV, Chockalingam SP, Liu Y, Krishnan A, Aluru S. A greedy alignment-free distance estimator for phylogenetic inference. BMC Bioinformatics 2017; 18:238. [PMID: 28617225 PMCID: PMC5471951 DOI: 10.1186/s12859-017-1658-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Alignment-free sequence comparison approaches have been garnering increasing interest in various data- and compute-intensive applications such as phylogenetic inference for large-scale sequences. While k-mer based methods are predominantly used in real applications, the average common substring (ACS) approach is emerging as one of the prominent alignment-free approaches. This ACS approach has been further generalized by some recent work, either greedily or exactly, by allowing a bounded number of mismatches in the common substrings. RESULTS We present ALFRED-G, a greedy alignment-free distance estimator for phylogenetic tree reconstruction based on the concept of the generalized ACS approach. In this algorithm, we have investigated a new heuristic to efficiently compute the lengths of common strings with mismatches allowed, and have further applied this heuristic to phylogeny reconstruction. Performance evaluation using real sequence datasets shows that our heuristic is able to reconstruct comparable, or even more accurate, phylogenetic tree topologies than the kmacs heuristic algorithm at highly competitive speed. CONCLUSIONS ALFRED-G is an alignment-free heuristic for evolutionary distance estimation between two biological sequences. This algorithm is implemented in C++ and has been incorporated into our open-source ALFRED software package ( http://alurulab.cc.gatech.edu/phylo ).
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Affiliation(s)
- Sharma V. Thankachan
- Department of Computer Science, University of Central Florida, Orlando, 32816 FL USA
| | - Sriram P. Chockalingam
- Institute for Data Engineering and Science, Georgia Institute of Technology, Atlanta, 30332 GA USA
| | - Yongchao Liu
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332 GA USA
| | - Ambujam Krishnan
- School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, 70703 LA USA
| | - Srinivas Aluru
- Institute for Data Engineering and Science, Georgia Institute of Technology, Atlanta, 30332 GA USA
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, 30332 GA USA
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13
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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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Zhou J, Zhong P, Zhang T. A Novel Method for Alignment-free DNA Sequence Similarity Analysis Based on the Characterization of Complex Networks. Evol Bioinform Online 2016; 12:229-235. [PMID: 27746676 PMCID: PMC5054945 DOI: 10.4137/ebo.s40474] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Revised: 09/04/2016] [Accepted: 09/06/2016] [Indexed: 11/05/2022] Open
Abstract
Determination of sequence similarity is one of the major steps in computational phylogenetic studies. One of the major tasks of computational biologists is to develop novel mathematical descriptors for similarity analysis. DNA clustering is an important technology that automatically identifies inherent relationships among large-scale DNA sequences. The comparison between the DNA sequences of different species helps determine phylogenetic relationships among species. Alignment-free approaches have continuously gained interest in various sequence analysis applications such as phylogenetic inference and metagenomic classification/clustering, particularly for large-scale sequence datasets. Here, we construct a novel and simple mathematical descriptor based on the characterization of cis sequence complex DNA networks. This new approach is based on a code of three cis nucleotides in a gene that could code for an amino acid. In particular, for each DNA sequence, we will set up a cis sequence complex network that will be used to develop a characterization vector for the analysis of mitochondrial DNA sequence phylogenetic relationships among nine species. The resulting phylogenetic relationships among the nine species were determined to be in agreement with the actual situation.
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Affiliation(s)
- Jie Zhou
- Guangdong Key Laboratory of Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Pianyu Zhong
- Guangdong Key Laboratory of Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
| | - Tinghui Zhang
- Guangdong Key Laboratory of Computer Network, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China
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15
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Hahn L, Leimeister CA, Ounit R, Lonardi S, Morgenstern B. rasbhari: Optimizing Spaced Seeds for Database Searching, Read Mapping and Alignment-Free Sequence Comparison. PLoS Comput Biol 2016; 12:e1005107. [PMID: 27760124 PMCID: PMC5070788 DOI: 10.1371/journal.pcbi.1005107] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 08/11/2016] [Indexed: 12/05/2022] Open
Abstract
Many algorithms for sequence analysis rely on word matching or word statistics. Often, these approaches can be improved if binary patterns representing match and don't-care positions are used as a filter, such that only those positions of words are considered that correspond to the match positions of the patterns. The performance of these approaches, however, depends on the underlying patterns. Herein, we show that the overlap complexity of a pattern set that was introduced by Ilie and Ilie is closely related to the variance of the number of matches between two evolutionarily related sequences with respect to this pattern set. We propose a modified hill-climbing algorithm to optimize pattern sets for database searching, read mapping and alignment-free sequence comparison of nucleic-acid sequences; our implementation of this algorithm is called rasbhari. Depending on the application at hand, rasbhari can either minimize the overlap complexity of pattern sets, maximize their sensitivity in database searching or minimize the variance of the number of pattern-based matches in alignment-free sequence comparison. We show that, for database searching, rasbhari generates pattern sets with slightly higher sensitivity than existing approaches. In our Spaced Words approach to alignment-free sequence comparison, pattern sets calculated with rasbhari led to more accurate estimates of phylogenetic distances than the randomly generated pattern sets that we previously used. Finally, we used rasbhari to generate patterns for short read classification with CLARK-S. Here too, the sensitivity of the results could be improved, compared to the default patterns of the program. We integrated rasbhari into Spaced Words; the source code of rasbhari is freely available at http://rasbhari.gobics.de/.
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Affiliation(s)
- Lars Hahn
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
| | | | - Rachid Ounit
- University of California, Riverside, Department of Computer Science and Engineering, Riverside, California, United States of America
| | - Stefano Lonardi
- University of California, Riverside, Department of Computer Science and Engineering, Riverside, California, United States of America
| | - Burkhard Morgenstern
- University of Göttingen, Department of Bioinformatics, Göttingen, Germany
- University of Göttingen, Center for Computational Sciences, Göttingen, Germany
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