Journal Article

SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform

Jiang, Yue, Lin, Jie, Jiang, Bing-Hua, Wei, Jing, Adjeroh, Donald

2018

Overview

Background Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts. Results A new alignment-free sequence similarity analysis method, called SSAW is proposed. SSAW stands for Sequence Similarity Analysis using the Stationary Discrete Wavelet Transform (SDWT). It extracts k -mers from a sequence, then maps each k -mer to a complex number field. Then, the series of complex numbers formed are transformed into feature vectors using the stationary discrete wavelet transform. After these steps, the original sequence is turned into a feature vector with numeric values, which can then be used for clustering and/or classification. Conclusions Using two different types of applications, namely, clustering and classification, we compared SSAW against the the-state-of-the-art alignment free sequence analysis methods. SSAW demonstrates competitive or superior performance in terms of standard indicators, such as accuracy, F-score, precision, and recall. The running time was significantly better in most cases. These make SSAW a suitable method for sequence analysis, especially, given the rapidly increasing volumes of sequence data required by most modern applications.

• Publisher: BioMed Central,BMC
• Subject: Algorithms / Bioinformatics / Biomedical and Life Sciences / Cluster Analysis / Complex numbers / Computational Biology - methods / Computational Biology/Bioinformatics / Computer Appl. in Life Sciences / Frequency domain / Humans / k-mers / Life Sciences / Microarrays / Research Article / Sequence analysis (methods) / Sequence Analysis, DNA - methods / Sequence Analysis, Protein - methods / Sequence Homology / Sequence similarity / Wavelet Analysis / Wavelet transform