A novel technique for finding pathogenicity islands in genome data with independent component analyses(ICA) is present. First denoise the genomic signal sequences with ICA and detect G+C patterns in genomes by compari...A novel technique for finding pathogenicity islands in genome data with independent component analyses(ICA) is present. First denoise the genomic signal sequences with ICA and detect G+C patterns in genomes by comparing the result sequence with original sequences. The results on G+C patterns analysis of Dradiodurans chromosome I and N.serogroup A strain Z2491 are present. A set of loci that have very different G+C content and have not previously described are detected. The findings show that ICA is a powerful tool to detect differences within and between genomes and to separate small (gene level) and large (putative pathogenicity islands) genomic regions that have different composition characteristics.展开更多
基金Supported by the Electronic Science Foundation of China (No.51415010101DZ02)
文摘A novel technique for finding pathogenicity islands in genome data with independent component analyses(ICA) is present. First denoise the genomic signal sequences with ICA and detect G+C patterns in genomes by comparing the result sequence with original sequences. The results on G+C patterns analysis of Dradiodurans chromosome I and N.serogroup A strain Z2491 are present. A set of loci that have very different G+C content and have not previously described are detected. The findings show that ICA is a powerful tool to detect differences within and between genomes and to separate small (gene level) and large (putative pathogenicity islands) genomic regions that have different composition characteristics.
