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Similarity evaluation of DNA sequences based on frequent patterns and entropy

BACKGROUND: DNA sequence analysis is an important research topic in bioinformatics. Evaluating the similarity between sequences, which is crucial for sequence analysis, has attracted much research effort in the last two decades, and a dozen of algorithms and tools have been developed. These methods...

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Autores principales: Xie, Xiaojing, Guan, Jihong, Zhou, Shuigeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331808/
https://www.ncbi.nlm.nih.gov/pubmed/25707937
http://dx.doi.org/10.1186/1471-2164-16-S3-S5
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author Xie, Xiaojing
Guan, Jihong
Zhou, Shuigeng
author_facet Xie, Xiaojing
Guan, Jihong
Zhou, Shuigeng
author_sort Xie, Xiaojing
collection PubMed
description BACKGROUND: DNA sequence analysis is an important research topic in bioinformatics. Evaluating the similarity between sequences, which is crucial for sequence analysis, has attracted much research effort in the last two decades, and a dozen of algorithms and tools have been developed. These methods are based on alignment, word frequency and geometric representation respectively, each of which has its advantage and disadvantage. RESULTS: In this paper, for effectively computing the similarity between DNA sequences, we introduce a novel method based on frequency patterns and entropy to construct representative vectors of DNA sequences. Experiments are conducted to evaluate the proposed method, which is compared with two recently-developed alignment-free methods and the BLASTN tool. When testing on the β-globin genes of 11 species and using the results from MEGA as the baseline, our method achieves higher correlation coefficients than the two alignment-free methods and the BLASTN tool. CONCLUSIONS: Our method is not only able to capture fine-granularity information (location and ordering) of DNA sequences via sequence blocking, but also insensitive to noise and sequence rearrangement due to considering only the maximal frequent patterns. It outperforms major existing methods or tools.
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spelling pubmed-43318082015-03-19 Similarity evaluation of DNA sequences based on frequent patterns and entropy Xie, Xiaojing Guan, Jihong Zhou, Shuigeng BMC Genomics Proceedings BACKGROUND: DNA sequence analysis is an important research topic in bioinformatics. Evaluating the similarity between sequences, which is crucial for sequence analysis, has attracted much research effort in the last two decades, and a dozen of algorithms and tools have been developed. These methods are based on alignment, word frequency and geometric representation respectively, each of which has its advantage and disadvantage. RESULTS: In this paper, for effectively computing the similarity between DNA sequences, we introduce a novel method based on frequency patterns and entropy to construct representative vectors of DNA sequences. Experiments are conducted to evaluate the proposed method, which is compared with two recently-developed alignment-free methods and the BLASTN tool. When testing on the β-globin genes of 11 species and using the results from MEGA as the baseline, our method achieves higher correlation coefficients than the two alignment-free methods and the BLASTN tool. CONCLUSIONS: Our method is not only able to capture fine-granularity information (location and ordering) of DNA sequences via sequence blocking, but also insensitive to noise and sequence rearrangement due to considering only the maximal frequent patterns. It outperforms major existing methods or tools. BioMed Central 2015-01-29 /pmc/articles/PMC4331808/ /pubmed/25707937 http://dx.doi.org/10.1186/1471-2164-16-S3-S5 Text en Copyright © 2015 Xie et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Proceedings
Xie, Xiaojing
Guan, Jihong
Zhou, Shuigeng
Similarity evaluation of DNA sequences based on frequent patterns and entropy
title Similarity evaluation of DNA sequences based on frequent patterns and entropy
title_full Similarity evaluation of DNA sequences based on frequent patterns and entropy
title_fullStr Similarity evaluation of DNA sequences based on frequent patterns and entropy
title_full_unstemmed Similarity evaluation of DNA sequences based on frequent patterns and entropy
title_short Similarity evaluation of DNA sequences based on frequent patterns and entropy
title_sort similarity evaluation of dna sequences based on frequent patterns and entropy
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331808/
https://www.ncbi.nlm.nih.gov/pubmed/25707937
http://dx.doi.org/10.1186/1471-2164-16-S3-S5
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