Abstract
Challenging tasks are encountered in the field of bioinformatics. The choice of the genomic sequence’s mapping technique is one the most fastidious tasks. It shows that a judicious choice would serve in examining periodic patterns distribution that concord with the underlying structure of genomes. Despite that, searching for a coding technique that can highlight all the information contained in the DNA has not yet attracted the attention it deserves. In this paper, we propose a new mapping technique based on the chaos game theory that we call the frequency chaos game signal (FCGS). The particularity of the FCGS coding resides in exploiting the statistical properties of the genomic sequence itself. This may reflect important structural and organizational features of DNA. To prove the usefulness of the FCGS approach in the detection of different local periodic patterns, we use the wavelet analysis because it provides access to information that can be obscured by other time-frequency methods such as the Fourier analysis. Thus, we apply the continuous wavelet transform (CWT) with the complex Morlet wavelet as a mother wavelet function. Scalograms that relate to the organism Caenorhabditis elegans (C. elegans) exhibit a multitude of periodic organization of specific DNA sequences.Electronic supplementary materialThe online version of this article (doi:10.1186/s13637-014-0016-z) contains supplementary material, which is available to authorized users.
Highlights
The fundamental information for a living being resides essentially in its nucleic material—the DNA
We investigate the role of the continuous wavelet transform (CWT) in displaying the frequency-dependent structure of genomic signals by using the complex Morlet wavelet scalogram
4 Results and discussion In this work, we focus our study on the analysis of DNA sequences within the C. elegans genome
Summary
The fundamental information for a living being resides essentially in its nucleic material—the DNA. The efficiency of our method in detecting different biological events is demonstrated through application of the continuous wavelet transform (CWT) The choice of such analysis method (we mean CWT) is justified by the need of a time-frequency approach that provides local frequency information which is not guaranteed by other transforms such as the Fourier transform. It appears that the short-time Fourier transform (STFT) is better suited to predict sites with biological relevance in the genomic signals This method requires a good choice of the analysis window’s size that must balance the frequency and temporal resolutions. We investigate the role of the CWT in displaying the frequency-dependent structure of genomic signals by using the complex Morlet wavelet scalogram The purpose of this analysis consists in revealing spectral features that might be of biological significance in the Caenorhabditis elegans (C. elegans) genome.
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