N. Kawagashira, Yasuhiro Ohtomo, K. Murakami, K. Matsubara, J. Kawai, Piero Carninci, Y. Hayashizaki, S. Kikuchi
{"title":"小波谱在水稻DNA序列分析中的应用","authors":"N. Kawagashira, Yasuhiro Ohtomo, K. Murakami, K. Matsubara, J. Kawai, Piero Carninci, Y. Hayashizaki, S. Kikuchi","doi":"10.1109/CSB.2002.1039368","DOIUrl":null,"url":null,"abstract":"Here we introduce our application of the wavelet analysis method to DNA sequences. In the signal processing field, Fourier transform is popular for analyzing wave data. However, although this method can process frequency information, it fails to handle locational data. In contrast, the wavelet method accommodates both locational and frequency information for wave analysis. The wavelet method is now increasing in its importance for signal processing. Fast Fourier transform is already applied to biological sequence analysis using correlations. We introduce a new method, called wavelet profile, for biological sequence analysis. Our method is based on multiresolution analysis of wavelet transform, offering data decomposition in several scaling at the same time. We applied our wavelet profile method to identifying gene loci among O. sativa genomic sequences.","PeriodicalId":87204,"journal":{"name":"Proceedings. IEEE Computer Society Bioinformatics Conference","volume":"1 1","pages":"345-346"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CSB.2002.1039368","citationCount":"8","resultStr":"{\"title\":\"Wavelet profiles: their application in Oryza sativa DNA sequence analysis\",\"authors\":\"N. Kawagashira, Yasuhiro Ohtomo, K. Murakami, K. Matsubara, J. Kawai, Piero Carninci, Y. Hayashizaki, S. Kikuchi\",\"doi\":\"10.1109/CSB.2002.1039368\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Here we introduce our application of the wavelet analysis method to DNA sequences. In the signal processing field, Fourier transform is popular for analyzing wave data. However, although this method can process frequency information, it fails to handle locational data. In contrast, the wavelet method accommodates both locational and frequency information for wave analysis. The wavelet method is now increasing in its importance for signal processing. Fast Fourier transform is already applied to biological sequence analysis using correlations. We introduce a new method, called wavelet profile, for biological sequence analysis. Our method is based on multiresolution analysis of wavelet transform, offering data decomposition in several scaling at the same time. We applied our wavelet profile method to identifying gene loci among O. sativa genomic sequences.\",\"PeriodicalId\":87204,\"journal\":{\"name\":\"Proceedings. IEEE Computer Society Bioinformatics Conference\",\"volume\":\"1 1\",\"pages\":\"345-346\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/CSB.2002.1039368\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE Computer Society Bioinformatics Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSB.2002.1039368\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE Computer Society Bioinformatics Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSB.2002.1039368","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Wavelet profiles: their application in Oryza sativa DNA sequence analysis
Here we introduce our application of the wavelet analysis method to DNA sequences. In the signal processing field, Fourier transform is popular for analyzing wave data. However, although this method can process frequency information, it fails to handle locational data. In contrast, the wavelet method accommodates both locational and frequency information for wave analysis. The wavelet method is now increasing in its importance for signal processing. Fast Fourier transform is already applied to biological sequence analysis using correlations. We introduce a new method, called wavelet profile, for biological sequence analysis. Our method is based on multiresolution analysis of wavelet transform, offering data decomposition in several scaling at the same time. We applied our wavelet profile method to identifying gene loci among O. sativa genomic sequences.