{"title":"亚硫酸酯测序数据差异甲基化鉴定的五种统计方法比较","authors":"Xiaoqing Yu, Shuying Sun","doi":"10.1515/sagmb-2015-0078","DOIUrl":null,"url":null,"abstract":"Abstract We are presenting a comprehensive comparative analysis of five differential methylation (DM) identification methods: methylKit, BSmooth, BiSeq, HMM-DM, and HMM-Fisher, which are developed for bisulfite sequencing (BS) data. We summarize the features of these methods from several analytical aspects and compare their performances using both simulated and real BS datasets. Our comparison results are summarized below. First, parameter settings may largely affect the accuracy of DM identification. Different from default settings, modified parameter settings yield higher sensitivity and/or lower false positive rates. Second, all five methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance, probably due to the different approaches they have used for DM identification. Third, HMM-DM and HMM-Fisher yield relatively higher sensitivity and lower false positive rates than others, especially in DM regions with large variation. Finally, we have found that among the three methods that involve methylation estimation (methylKit, BSmooth, and BiSeq), BiSeq can best present raw methylation signals. Therefore, based on these results, we suggest that users select DM identification methods based on the characteristics of their data and the advantages of each method.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"15 1","pages":"173 - 191"},"PeriodicalIF":0.9000,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/sagmb-2015-0078","citationCount":"20","resultStr":"{\"title\":\"Comparing five statistical methods of differential methylation identification using bisulfite sequencing data\",\"authors\":\"Xiaoqing Yu, Shuying Sun\",\"doi\":\"10.1515/sagmb-2015-0078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract We are presenting a comprehensive comparative analysis of five differential methylation (DM) identification methods: methylKit, BSmooth, BiSeq, HMM-DM, and HMM-Fisher, which are developed for bisulfite sequencing (BS) data. We summarize the features of these methods from several analytical aspects and compare their performances using both simulated and real BS datasets. Our comparison results are summarized below. First, parameter settings may largely affect the accuracy of DM identification. Different from default settings, modified parameter settings yield higher sensitivity and/or lower false positive rates. Second, all five methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance, probably due to the different approaches they have used for DM identification. Third, HMM-DM and HMM-Fisher yield relatively higher sensitivity and lower false positive rates than others, especially in DM regions with large variation. Finally, we have found that among the three methods that involve methylation estimation (methylKit, BSmooth, and BiSeq), BiSeq can best present raw methylation signals. Therefore, based on these results, we suggest that users select DM identification methods based on the characteristics of their data and the advantages of each method.\",\"PeriodicalId\":49477,\"journal\":{\"name\":\"Statistical Applications in Genetics and Molecular Biology\",\"volume\":\"15 1\",\"pages\":\"173 - 191\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2016-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1515/sagmb-2015-0078\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Applications in Genetics and Molecular Biology\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1515/sagmb-2015-0078\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Applications in Genetics and Molecular Biology","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1515/sagmb-2015-0078","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
Comparing five statistical methods of differential methylation identification using bisulfite sequencing data
Abstract We are presenting a comprehensive comparative analysis of five differential methylation (DM) identification methods: methylKit, BSmooth, BiSeq, HMM-DM, and HMM-Fisher, which are developed for bisulfite sequencing (BS) data. We summarize the features of these methods from several analytical aspects and compare their performances using both simulated and real BS datasets. Our comparison results are summarized below. First, parameter settings may largely affect the accuracy of DM identification. Different from default settings, modified parameter settings yield higher sensitivity and/or lower false positive rates. Second, all five methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance, probably due to the different approaches they have used for DM identification. Third, HMM-DM and HMM-Fisher yield relatively higher sensitivity and lower false positive rates than others, especially in DM regions with large variation. Finally, we have found that among the three methods that involve methylation estimation (methylKit, BSmooth, and BiSeq), BiSeq can best present raw methylation signals. Therefore, based on these results, we suggest that users select DM identification methods based on the characteristics of their data and the advantages of each method.
期刊介绍:
Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.