{"title":"通过扩展参考序列提高基因组压缩性能","authors":"XiangDong Ma, Jianhua Chen","doi":"10.1109/ISBP57705.2023.10061320","DOIUrl":null,"url":null,"abstract":"We propose an efficient referential genome compression algorithm called RCCG. It extends reference genomes by its reverse complementation and uses coprime window sampling to detect the maximum matches (MEMs) between two genome sequences. After the assessment, those selected matches will be united to form mutation-containing matches (MCMs). The average compression ratio of the proposed algorithm is higher than that of the state-of-the-art genome compression algorithms.","PeriodicalId":309634,"journal":{"name":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","volume":"47 46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving Genome Compression Performance by Extending Reference Sequences\",\"authors\":\"XiangDong Ma, Jianhua Chen\",\"doi\":\"10.1109/ISBP57705.2023.10061320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an efficient referential genome compression algorithm called RCCG. It extends reference genomes by its reverse complementation and uses coprime window sampling to detect the maximum matches (MEMs) between two genome sequences. After the assessment, those selected matches will be united to form mutation-containing matches (MCMs). The average compression ratio of the proposed algorithm is higher than that of the state-of-the-art genome compression algorithms.\",\"PeriodicalId\":309634,\"journal\":{\"name\":\"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)\",\"volume\":\"47 46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISBP57705.2023.10061320\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Intelligent Supercomputing and BioPharma (ISBP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBP57705.2023.10061320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving Genome Compression Performance by Extending Reference Sequences
We propose an efficient referential genome compression algorithm called RCCG. It extends reference genomes by its reverse complementation and uses coprime window sampling to detect the maximum matches (MEMs) between two genome sequences. After the assessment, those selected matches will be united to form mutation-containing matches (MCMs). The average compression ratio of the proposed algorithm is higher than that of the state-of-the-art genome compression algorithms.