{"title":"Translocator","authors":"Ye Wu, Ruibang Luo, T. Lam, H. Ting, Junwen Wang","doi":"10.1145/3388440.3412457","DOIUrl":null,"url":null,"abstract":"Translocation is an important class of structural variants known to be associated with cancer formation and treatment. The recent development in single-molecule sequencing technologies that produce long reads has promised an advance in detecting translocations accurately. However, existing tools struggled with the high base error-rate of the long reads. Figuring out the correct translocation breakpoints is especially challenging due to suboptimally aligned reads. To address the problem, we developed Translocator, a robust and accurate translocation detection method that implements an effective realignment algorithm to recover the correct alignments. For benchmarking, we analyzed using NA12878 long reads against a modified GRCh38 reference genome embedded with translocations at known locations. Our results show that Translocator significantly outperformed other state-of-the-art methods, including Sniffles and PBSV. On Oxford Nanopore data, the recall improved from 48.2% to 87.5% and the precision from 88.7% to 92.7%. Translocator is available open-source at https://github.com/HKU-BAL/Translocator.","PeriodicalId":411338,"journal":{"name":"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3388440.3412457","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

Translocation is an important class of structural variants known to be associated with cancer formation and treatment. The recent development in single-molecule sequencing technologies that produce long reads has promised an advance in detecting translocations accurately. However, existing tools struggled with the high base error-rate of the long reads. Figuring out the correct translocation breakpoints is especially challenging due to suboptimally aligned reads. To address the problem, we developed Translocator, a robust and accurate translocation detection method that implements an effective realignment algorithm to recover the correct alignments. For benchmarking, we analyzed using NA12878 long reads against a modified GRCh38 reference genome embedded with translocations at known locations. Our results show that Translocator significantly outperformed other state-of-the-art methods, including Sniffles and PBSV. On Oxford Nanopore data, the recall improved from 48.2% to 87.5% and the precision from 88.7% to 92.7%. Translocator is available open-source at https://github.com/HKU-BAL/Translocator.
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