{"title":"结构变体集成和可视化:一个全面的R包,用于集成来自多个调用者的体细胞结构变体和可视化","authors":"Lei Yu, Le Zhang, Lili Wang, Zhenyu Jia","doi":"10.36922/td.0894","DOIUrl":null,"url":null,"abstract":"Whole genome sequencing (WGS) emerges as a powerful tool for detecting structural variations (SVs) in genomes. However, different SV callers can produce variable results due to the distinct rationale and sensitivity of pipelines, highlighting the need for effective tools to compare and merge results from multiple callers. Here, we developed an R package, structural variants integration and visualization, to facilitate the integration, classification, and visualization of SV results from multiple callers, allowing for accurate identification of the most reliable SVs. Our package relies on a complex translocation projection and clustering method, enabling the projection of each translation to a point in a Cartesian coordinate system and visualization of SVs at both whole-genome and individual chromosome levels. Thus, our approach provides a valuable framework for analyzing SVs from WGS data, improving the accuracy and efficiency of SV detection, and enhancing the potential of WGS for clinical and research applications.","PeriodicalId":94260,"journal":{"name":"Tumor discovery","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Structural variants integration and visualization: A comprehensive R package for integration of somatic structural variations from multiple callers and visualization\",\"authors\":\"Lei Yu, Le Zhang, Lili Wang, Zhenyu Jia\",\"doi\":\"10.36922/td.0894\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Whole genome sequencing (WGS) emerges as a powerful tool for detecting structural variations (SVs) in genomes. However, different SV callers can produce variable results due to the distinct rationale and sensitivity of pipelines, highlighting the need for effective tools to compare and merge results from multiple callers. Here, we developed an R package, structural variants integration and visualization, to facilitate the integration, classification, and visualization of SV results from multiple callers, allowing for accurate identification of the most reliable SVs. Our package relies on a complex translocation projection and clustering method, enabling the projection of each translation to a point in a Cartesian coordinate system and visualization of SVs at both whole-genome and individual chromosome levels. Thus, our approach provides a valuable framework for analyzing SVs from WGS data, improving the accuracy and efficiency of SV detection, and enhancing the potential of WGS for clinical and research applications.\",\"PeriodicalId\":94260,\"journal\":{\"name\":\"Tumor discovery\",\"volume\":\"10 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tumor discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36922/td.0894\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tumor discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36922/td.0894","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Structural variants integration and visualization: A comprehensive R package for integration of somatic structural variations from multiple callers and visualization
Whole genome sequencing (WGS) emerges as a powerful tool for detecting structural variations (SVs) in genomes. However, different SV callers can produce variable results due to the distinct rationale and sensitivity of pipelines, highlighting the need for effective tools to compare and merge results from multiple callers. Here, we developed an R package, structural variants integration and visualization, to facilitate the integration, classification, and visualization of SV results from multiple callers, allowing for accurate identification of the most reliable SVs. Our package relies on a complex translocation projection and clustering method, enabling the projection of each translation to a point in a Cartesian coordinate system and visualization of SVs at both whole-genome and individual chromosome levels. Thus, our approach provides a valuable framework for analyzing SVs from WGS data, improving the accuracy and efficiency of SV detection, and enhancing the potential of WGS for clinical and research applications.