A wavelet-based feature vector model for DNA clustering

Abstract

ABSTRACT. DNA data are important in the bioinformatic domain. To extract useful information from the enormous collection of DNA sequences, DNA clustering is often adopted to efficiently deal with DNA data. The alignmentfree method is a very popular way of creating feature vectors from DNA sequences, which are then used to compare DNA similarities. This paper proposes a wavelet-based feature vector (WFV) model, which is also an alignment-free method. From the perspective of signal processing, a DNA sequence is a sequence of digital signals. However, most traditional alignment-free models only extract features in the time domain. The WFV model uses discrete wavelet transform to adaptively yield feature vectors with a fixed dimension based on the features in both the time and frequency domains. The level of wavelet transform is adjusted according to the length of the DNA sequence rather than a fixed manually set value. The WFV model prefers a 32-dimension feature vector, which greatly promotes system performance. We compared the WFV model with the other five alignment-free models, i.e., k-tuple, DMK, TSM, AMI, and CV, on several large-scale DNA datasets on the DNA clustering application by means of the K-means algorithm. The experimental results showed that the WFV 19163-19172 (2015) model outperformed the other models in terms of both the clustering results and the running time.

Evaluation of genome similarities using the non-decimated wavelet transform

“…The clustering analysis is often adopted to deal with DNA sequences efficiently. A wavelet-based feature vector model was proposed by Bao and Yuan (2015) for clustering of DNA sequences.…”

The Bioinformatics Tools for Discovery of Genetic Diversity by Means of Elastic Net and Hurst Exponent

“…The clustering analysis is often assumed to deal with DNA sequences proficiently. A wavelet-based element vector model was anticipated for grouping of DNA sequences [10].…”

Evaluation of Genome Similarities Using Independent Components

“…The clustering analysis is often adopted to deal with DNA sequences efficiently. A wavelet-based feature vector model was proposed by Bao and Yuan (2015) for clustering of DNA sequences. Saini and Dewan (2016) based on the calculation of the energy of wavelet decomposition coefficients of complete genomic sequences showed that the genomic sequences of MTB could be grouped only into two groups.…”