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Classification of genomic signals using dynamic time warping

BACKGROUND: Classification methods of DNA most commonly use comparison of the differences in DNA symbolic records, which requires the global multiple sequence alignment. This solution is often inappropriate, causing a number of imprecisions and requires additional user intervention for exact alignme...

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Autores principales: Skutkova, Helena, Vitek, Martin, Babula, Petr, Kizek, Rene, Provaznik, Ivo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750471/
https://www.ncbi.nlm.nih.gov/pubmed/24267034
http://dx.doi.org/10.1186/1471-2105-14-S10-S1
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author Skutkova, Helena
Vitek, Martin
Babula, Petr
Kizek, Rene
Provaznik, Ivo
author_facet Skutkova, Helena
Vitek, Martin
Babula, Petr
Kizek, Rene
Provaznik, Ivo
author_sort Skutkova, Helena
collection PubMed
description BACKGROUND: Classification methods of DNA most commonly use comparison of the differences in DNA symbolic records, which requires the global multiple sequence alignment. This solution is often inappropriate, causing a number of imprecisions and requires additional user intervention for exact alignment of the similar segments. The similar segments in DNA represented as a signal are characterized by a similar shape of the curve. The DNA alignment in genomic signals may adjust whole sections not only individual symbols. The dynamic time warping (DTW) is suitable for this purpose and can replace the multiple alignment of symbolic sequences in applications, such as phylogenetic analysis. METHODS: The proposed method is composed of three main parts. The first part represent conversion of symbolic representation of DNA sequences in the form of a string of A,C,G,T symbols to signal representation in the form of cumulated phase of complex components defined for each symbol. Next part represents signals size adjustment realized by standard signal preprocessing methods: median filtration, detrendization and resampling. The final part necessary for genomic signals comparison is position and length alignment of genomic signals by dynamic time warping (DTW). RESULTS: The application of the DTW on set of genomic signals was evaluated in dendrogram construction using cluster analysis. The resulting tree was compared with a classical phylogenetic tree reconstructed using multiple alignment. The classification of genomic signals using the DTW is evolutionary closer to phylogeny of organisms. This method is more resistant to errors in the sequences and less dependent on the number of input sequences. CONCLUSIONS: Classification of genomic signals using dynamic time warping is an adequate variant to phylogenetic analysis using the symbolic DNA sequences alignment; in addition, it is robust, quick and more precise technique.
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spelling pubmed-37504712013-08-27 Classification of genomic signals using dynamic time warping Skutkova, Helena Vitek, Martin Babula, Petr Kizek, Rene Provaznik, Ivo BMC Bioinformatics Research BACKGROUND: Classification methods of DNA most commonly use comparison of the differences in DNA symbolic records, which requires the global multiple sequence alignment. This solution is often inappropriate, causing a number of imprecisions and requires additional user intervention for exact alignment of the similar segments. The similar segments in DNA represented as a signal are characterized by a similar shape of the curve. The DNA alignment in genomic signals may adjust whole sections not only individual symbols. The dynamic time warping (DTW) is suitable for this purpose and can replace the multiple alignment of symbolic sequences in applications, such as phylogenetic analysis. METHODS: The proposed method is composed of three main parts. The first part represent conversion of symbolic representation of DNA sequences in the form of a string of A,C,G,T symbols to signal representation in the form of cumulated phase of complex components defined for each symbol. Next part represents signals size adjustment realized by standard signal preprocessing methods: median filtration, detrendization and resampling. The final part necessary for genomic signals comparison is position and length alignment of genomic signals by dynamic time warping (DTW). RESULTS: The application of the DTW on set of genomic signals was evaluated in dendrogram construction using cluster analysis. The resulting tree was compared with a classical phylogenetic tree reconstructed using multiple alignment. The classification of genomic signals using the DTW is evolutionary closer to phylogeny of organisms. This method is more resistant to errors in the sequences and less dependent on the number of input sequences. CONCLUSIONS: Classification of genomic signals using dynamic time warping is an adequate variant to phylogenetic analysis using the symbolic DNA sequences alignment; in addition, it is robust, quick and more precise technique. BioMed Central 2013-08-12 /pmc/articles/PMC3750471/ /pubmed/24267034 http://dx.doi.org/10.1186/1471-2105-14-S10-S1 Text en Copyright © 2013 Skutkova et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Skutkova, Helena
Vitek, Martin
Babula, Petr
Kizek, Rene
Provaznik, Ivo
Classification of genomic signals using dynamic time warping
title Classification of genomic signals using dynamic time warping
title_full Classification of genomic signals using dynamic time warping
title_fullStr Classification of genomic signals using dynamic time warping
title_full_unstemmed Classification of genomic signals using dynamic time warping
title_short Classification of genomic signals using dynamic time warping
title_sort classification of genomic signals using dynamic time warping
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3750471/
https://www.ncbi.nlm.nih.gov/pubmed/24267034
http://dx.doi.org/10.1186/1471-2105-14-S10-S1
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