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A Novel Model for DNA Sequence Similarity Analysis Based on Graph Theory
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Zeitschriftentitel: | Evolutionary Bioinformatics |
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Personen und Körperschaften: | , , , , |
In: | Evolutionary Bioinformatics, 7, 2011, S. EBO.S7364 |
Medientyp: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
SAGE Publications
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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 |
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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 |
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EBO.S7364 |
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<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 |
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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 |