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Ssaw: A New Sequence Similarity Analysis Method Based On The Stationary Discrete Wavelet Transform, Jie Lin, Donald Adjeroh, Bing-Hua Jiang, Yue Jiang

Faculty & Staff Scholarship

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 …