Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Selected articles from the 10th International Workshop on Computational Systems Biology (WCSB) 2013: Bioinformatics

Open Access Research

Classification of genomic signals using dynamic time warping

Helena Skutkova1, Martin Vitek12, Petr Babula2, Rene Kizek3 and Ivo Provaznik12*

Author Affiliations

1 Department of Biomedical Engineering, Brno University of Technology, Technicka 12, CZ -616 00 Brno, Czech Republic

2 International Clinical Research Center - Center of Biomedical Engineering, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic

3 Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ - 613 00 Brno, Czech Republic

For all author emails, please log on.

BMC Bioinformatics 2013, 14(Suppl 10):S1  doi:10.1186/1471-2105-14-S10-S1

Published: 12 August 2013

Abstract

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.