{"title":"关于重复集合索引问题","authors":"Ali Alatabbi, Carl Barton, C. Iliopoulos","doi":"10.1109/BIBMW.2012.6470220","DOIUrl":null,"url":null,"abstract":"In large data sets such as genomes from a single species, large sets of reads, and version control data it is often noted that each entry only differs from another by a very small number of variations. This leads to a large set of data with a great deal of redundancy and repetitiveness. Rapid development in DNA sequencing technologies has caused a drastic growth in the size of publicly available sequence databases with such data. DNA sequencing has become so fast and cost-effective that sequencing individual genomes will soon become a common task [9] making querying and storing such sets of data an important task. In this paper, we propose an indexing structure for highly repetitive collections of sequence data based on a multilevel g-gram model. In particular, the proposed algorithm accommodates variations that may occur in the target sequence with respect to the reference sequence. The paper is organized as follows. Section [1] and [2] introduce the basic concepts and go through the related literature. In Section [3] we present notions and facts. Details of the proposed data structure/algorithm will be given in Section [5] and [4], Section [6] discusses complexity analysis and Section [7] gives conclusions of future work.","PeriodicalId":6392,"journal":{"name":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"On the repetitive collection indexing problem\",\"authors\":\"Ali Alatabbi, Carl Barton, C. Iliopoulos\",\"doi\":\"10.1109/BIBMW.2012.6470220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In large data sets such as genomes from a single species, large sets of reads, and version control data it is often noted that each entry only differs from another by a very small number of variations. This leads to a large set of data with a great deal of redundancy and repetitiveness. Rapid development in DNA sequencing technologies has caused a drastic growth in the size of publicly available sequence databases with such data. DNA sequencing has become so fast and cost-effective that sequencing individual genomes will soon become a common task [9] making querying and storing such sets of data an important task. In this paper, we propose an indexing structure for highly repetitive collections of sequence data based on a multilevel g-gram model. In particular, the proposed algorithm accommodates variations that may occur in the target sequence with respect to the reference sequence. The paper is organized as follows. Section [1] and [2] introduce the basic concepts and go through the related literature. In Section [3] we present notions and facts. Details of the proposed data structure/algorithm will be given in Section [5] and [4], Section [6] discusses complexity analysis and Section [7] gives conclusions of future work.\",\"PeriodicalId\":6392,\"journal\":{\"name\":\"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIBMW.2012.6470220\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBMW.2012.6470220","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In large data sets such as genomes from a single species, large sets of reads, and version control data it is often noted that each entry only differs from another by a very small number of variations. This leads to a large set of data with a great deal of redundancy and repetitiveness. Rapid development in DNA sequencing technologies has caused a drastic growth in the size of publicly available sequence databases with such data. DNA sequencing has become so fast and cost-effective that sequencing individual genomes will soon become a common task [9] making querying and storing such sets of data an important task. In this paper, we propose an indexing structure for highly repetitive collections of sequence data based on a multilevel g-gram model. In particular, the proposed algorithm accommodates variations that may occur in the target sequence with respect to the reference sequence. The paper is organized as follows. Section [1] and [2] introduce the basic concepts and go through the related literature. In Section [3] we present notions and facts. Details of the proposed data structure/algorithm will be given in Section [5] and [4], Section [6] discusses complexity analysis and Section [7] gives conclusions of future work.