https://socictopen.socict.org/files/original/4d334b19974b12f75134e0c37279c587.pdf d34050e2abcaf9ec5efbd2260775cdf4 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource SSAW: A new sequence similarity analysis method based on the stationary discrete wavelet transform Creator An entity primarily responsible for making the resource Jie Lin, Jing Wei, Donald Adjeroh, Bing-Hua Jiang, Yue Jiang Description An account of the resource 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. Date A point or period of time associated with an event in the lifecycle of the resource 2018 Subject The topic of the resource Kmers, wavelet transform, complex numbers, sequence similarity, frequency domain Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s12859-018-2155-9 Source A related resource from which the described resource is derived BMC Bioinformatics Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General), Computer applications to medicine. Medical informatics Language A language of the resource EN