{"title":"一种用于长读长DNA序列分析的概率方法","authors":"C. G. Molina, J. Mullikin","doi":"10.1109/NNSP.2002.1030016","DOIUrl":null,"url":null,"abstract":"This paper introduces a new algorithm for DNA sequence analysis, based on the use of a reference DNA sequence for the estimation of base positions, and a probabilistic modelling of trace peaks. The new algorithm has been applied to long read-length DNA sequences and its performance has been compared to the base-calling program Phred. The results reported in this paper, after cross-matching with a finished consensus, show a significant improvement by the new algorithm in the final sequence read-length and in the number of correct bases extracted from DNA traces.","PeriodicalId":117945,"journal":{"name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A probabilistic approach for long read-length DNA sequence analysis\",\"authors\":\"C. G. Molina, J. Mullikin\",\"doi\":\"10.1109/NNSP.2002.1030016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces a new algorithm for DNA sequence analysis, based on the use of a reference DNA sequence for the estimation of base positions, and a probabilistic modelling of trace peaks. The new algorithm has been applied to long read-length DNA sequences and its performance has been compared to the base-calling program Phred. The results reported in this paper, after cross-matching with a finished consensus, show a significant improvement by the new algorithm in the final sequence read-length and in the number of correct bases extracted from DNA traces.\",\"PeriodicalId\":117945,\"journal\":{\"name\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.2002.1030016\",\"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 of the 12th IEEE Workshop on Neural Networks for Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.2002.1030016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A probabilistic approach for long read-length DNA sequence analysis
This paper introduces a new algorithm for DNA sequence analysis, based on the use of a reference DNA sequence for the estimation of base positions, and a probabilistic modelling of trace peaks. The new algorithm has been applied to long read-length DNA sequences and its performance has been compared to the base-calling program Phred. The results reported in this paper, after cross-matching with a finished consensus, show a significant improvement by the new algorithm in the final sequence read-length and in the number of correct bases extracted from DNA traces.
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