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

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

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

Abstract 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.

2018

Kmers, wavelet transform, complex numbers, sequence similarity, frequency domain

DOI: 10.1186/s12859-018-2155-9

BMC Bioinformatics

BMC

Biology (General), Computer applications to medicine. Medical informatics

EN