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A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory

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Zeitschriftentitel: Evolutionary Bioinformatics
Personen und Körperschaften: Qi, Xingqin, Wu, Qin, Zhang, Yusen, Fuller, Eddie, Zhang, Cun-Quan
In: Evolutionary Bioinformatics, 7, 2011, S. EBO.S7364
Medientyp: E-Article
Sprache: Englisch
veröffentlicht:
SAGE Publications
Schlagwörter:
author_facet Qi, Xingqin
Wu, Qin
Zhang, Yusen
Fuller, Eddie
Zhang, Cun-Quan
Qi, Xingqin
Wu, Qin
Zhang, Yusen
Fuller, Eddie
Zhang, Cun-Quan
author Qi, Xingqin
Wu, Qin
Zhang, Yusen
Fuller, Eddie
Zhang, Cun-Quan
spellingShingle Qi, Xingqin
Wu, Qin
Zhang, Yusen
Fuller, Eddie
Zhang, Cun-Quan
Evolutionary Bioinformatics
A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
Computer Science Applications
Genetics
Ecology, Evolution, Behavior and Systematics
author_sort qi, xingqin
spelling Qi, Xingqin Wu, Qin Zhang, Yusen Fuller, Eddie Zhang, Cun-Quan 1176-9343 1176-9343 SAGE Publications Computer Science Applications Genetics Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.4137/ebo.s7364 <jats:p> Determination of sequence similarity is one of the major steps in computational phylogenetic studies. As we know, during evolutionary history, not only DNA mutations for individual nucleotide but also subsequent rearrangements occurred. It has been one of major tasks of computational biologists to develop novel mathematical descriptors for similarity analysis such that various mutation phenomena information would be involved simultaneously. In this paper, different from traditional methods (eg, nucleotide frequency, geometric representations) as bases for construction of mathematical descriptors, we construct novel mathematical descriptors based on graph theory. In particular, for each DNA sequence, we will set up a weighted directed graph. The adjacency matrix of the directed graph will be used to induce a representative vector for DNA sequence. This new approach measures similarity based on both ordering and frequency of nucleotides so that much more information is involved. As an application, the method is tested on a set of 0.9-kb mtDNA sequences of twelve different primate species. All output phylogenetic trees with various distance estimations have the same topology, and are generally consistent with the reported results from early studies, which proves the new method's efficiency; we also test the new method on a simulated data set, which shows our new method performs better than traditional global alignment method when subsequent rearrangements happen frequently during evolutionary history. </jats:p> A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory Evolutionary Bioinformatics
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title A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_unstemmed A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_full A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_fullStr A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_full_unstemmed A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_short A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_sort a novel model for dna sequence similarity analysis based on graph theory
topic Computer Science Applications
Genetics
Ecology, Evolution, Behavior and Systematics
url http://dx.doi.org/10.4137/ebo.s7364
publishDate 2011
physical EBO.S7364
description <jats:p> Determination of sequence similarity is one of the major steps in computational phylogenetic studies. As we know, during evolutionary history, not only DNA mutations for individual nucleotide but also subsequent rearrangements occurred. It has been one of major tasks of computational biologists to develop novel mathematical descriptors for similarity analysis such that various mutation phenomena information would be involved simultaneously. In this paper, different from traditional methods (eg, nucleotide frequency, geometric representations) as bases for construction of mathematical descriptors, we construct novel mathematical descriptors based on graph theory. In particular, for each DNA sequence, we will set up a weighted directed graph. The adjacency matrix of the directed graph will be used to induce a representative vector for DNA sequence. This new approach measures similarity based on both ordering and frequency of nucleotides so that much more information is involved. As an application, the method is tested on a set of 0.9-kb mtDNA sequences of twelve different primate species. All output phylogenetic trees with various distance estimations have the same topology, and are generally consistent with the reported results from early studies, which proves the new method's efficiency; we also test the new method on a simulated data set, which shows our new method performs better than traditional global alignment method when subsequent rearrangements happen frequently during evolutionary history. </jats:p>
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author Qi, Xingqin, Wu, Qin, Zhang, Yusen, Fuller, Eddie, Zhang, Cun-Quan
author_facet Qi, Xingqin, Wu, Qin, Zhang, Yusen, Fuller, Eddie, Zhang, Cun-Quan, Qi, Xingqin, Wu, Qin, Zhang, Yusen, Fuller, Eddie, Zhang, Cun-Quan
author_sort qi, xingqin
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container_title Evolutionary Bioinformatics
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description <jats:p> Determination of sequence similarity is one of the major steps in computational phylogenetic studies. As we know, during evolutionary history, not only DNA mutations for individual nucleotide but also subsequent rearrangements occurred. It has been one of major tasks of computational biologists to develop novel mathematical descriptors for similarity analysis such that various mutation phenomena information would be involved simultaneously. In this paper, different from traditional methods (eg, nucleotide frequency, geometric representations) as bases for construction of mathematical descriptors, we construct novel mathematical descriptors based on graph theory. In particular, for each DNA sequence, we will set up a weighted directed graph. The adjacency matrix of the directed graph will be used to induce a representative vector for DNA sequence. This new approach measures similarity based on both ordering and frequency of nucleotides so that much more information is involved. As an application, the method is tested on a set of 0.9-kb mtDNA sequences of twelve different primate species. All output phylogenetic trees with various distance estimations have the same topology, and are generally consistent with the reported results from early studies, which proves the new method's efficiency; we also test the new method on a simulated data set, which shows our new method performs better than traditional global alignment method when subsequent rearrangements happen frequently during evolutionary history. </jats:p>
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spelling Qi, Xingqin Wu, Qin Zhang, Yusen Fuller, Eddie Zhang, Cun-Quan 1176-9343 1176-9343 SAGE Publications Computer Science Applications Genetics Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.4137/ebo.s7364 <jats:p> Determination of sequence similarity is one of the major steps in computational phylogenetic studies. As we know, during evolutionary history, not only DNA mutations for individual nucleotide but also subsequent rearrangements occurred. It has been one of major tasks of computational biologists to develop novel mathematical descriptors for similarity analysis such that various mutation phenomena information would be involved simultaneously. In this paper, different from traditional methods (eg, nucleotide frequency, geometric representations) as bases for construction of mathematical descriptors, we construct novel mathematical descriptors based on graph theory. In particular, for each DNA sequence, we will set up a weighted directed graph. The adjacency matrix of the directed graph will be used to induce a representative vector for DNA sequence. This new approach measures similarity based on both ordering and frequency of nucleotides so that much more information is involved. As an application, the method is tested on a set of 0.9-kb mtDNA sequences of twelve different primate species. All output phylogenetic trees with various distance estimations have the same topology, and are generally consistent with the reported results from early studies, which proves the new method's efficiency; we also test the new method on a simulated data set, which shows our new method performs better than traditional global alignment method when subsequent rearrangements happen frequently during evolutionary history. </jats:p> A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory Evolutionary Bioinformatics
spellingShingle Qi, Xingqin, Wu, Qin, Zhang, Yusen, Fuller, Eddie, Zhang, Cun-Quan, Evolutionary Bioinformatics, A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory, Computer Science Applications, Genetics, Ecology, Evolution, Behavior and Systematics
title A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_full A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_fullStr A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_full_unstemmed A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_short A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
title_sort a novel model for dna sequence similarity analysis based on graph theory
title_unstemmed A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
topic Computer Science Applications, Genetics, Ecology, Evolution, Behavior and Systematics
url http://dx.doi.org/10.4137/ebo.s7364