Document Type
Article
Publication Date
2018
College/Unit
Statler College of Engineering and Mining Resources
Department/Program/Center
Lane Department of Computer Science and Electrical Engineering
Abstract
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.
https://researchrepository.wvu.edu/faculty_publications/1609
Source Citation
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
Included in
Electrical and Computer Engineering Commons, Mathematics Commons, Medical Pathology Commons
Comments
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.