Statler College of Engineering and Mining Resources
Lane Department of Computer Science and Electrical Engineering
Background: Alignment-free sequence similarity analysis methods often lead to significant savings in computational time over alignment-based counterparts.
Results: Anewalignment-freesequencesimilarityanalysismethod,calledSSAWisproposed.SSAWstandsfor 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: Usingtwodifferenttypesofapplications,namely,clusteringandclassification,wecomparedSSAW 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.
Digital Commons Citation
Lin, Jie; Adjeroh, Donald; Jiang, Bing-Hua; and Jiang, Yue, "SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform" (2018). Faculty & Staff Scholarship. 1609.
Lin, J., Wei, J., Adjeroh, D., Jiang, B.-H., & Jiang, Y. (2018). SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform. BMC Bioinformatics, 19(1). https://doi.org/10.1186/s12859-018-2155-9