Mako: A Graph-based Pattern Growth Approach to Detect Complex Structural Variants

• Published in: Genomics, proteomics & bioinformatics Vol. 20; no. 1; pp. 205 - 218
• Main Authors: Lin, Jiadong, Yang, Xiaofei, Kosters, Walter, Xu, Tun, Jia, Yanyan, Wang, Songbo, Zhu, Qihui, Ryan, Mallory, Guo, Li, Zhang, Chengsheng, Gerstein, Mark B., Sanders, Ashley D., Zody, Micheal C., Talkowski, Michael E., Mills, Ryan E., Korbel, Jan O., Marschall, Tobias, Ebert, Peter, Audano, Peter A., Rodriguez-Martin, Bernardo, Porubsky, David, Jan Bonder, Marc, Sulovari, Arvis, Ebler, Jana, Zhou, Weichen, Serra Mari, Rebecca, Yilmaz, Feyza, Zhao, Xuefang, Hsieh, PingHsun, Lee, Joyce, Kumar, Sushant, Rausch, Tobias, Chen, Yu, Chong, Zechen, Munson, Katherine M., Chaisson, Mark J.P., Chen, Junjie, Shi, Xinghua, Wenger, Aaron M., Harvey, William T., Hansenfeld, Patrick, Regier, Allison, Hall, Ira M., Flicek, Paul, Hastie, Alex R., Fairely, Susan, Lee, Charles, Devine, Scott E., Eichler, Evan E., Ye, Kai
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 01.02.2022; School of Computer Science and Technology,Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China%Leiden Institute of Advanced Computer Science,Faculty of Science,Leiden University,Leiden 2311EZ,Netherland%School of Automation Science and Engineering,Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China%The Jackson Laboratory for Genomic Medicine,Farmington,CT 06032,USA%MOE Key Lab for Intelligent Networks&Networks Security,Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China%The Jackson Laboratory for Genomic Medicine,Farmington,CT 06032,USA; School of Automation Science and Engineering,Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China; Genome Institute,the First Affiliated Hospital of Xi'an Jiaotong University,Xi'an 710061,China; Institute for Genome Sciences,University of Maryland School of Medicine,Baltimore,MD 21201,USA%Department of Genome Sciences,University of Washington School of Medicine,Seattle,WA 98119,USA; Leiden Institute of Advanced Computer Science,Faculty of Science,Leiden University,Leiden 2311EZ,Netherland%MOE Key Lab for Intelligent Networks&Networks Security,Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China; Precision Medicine Center,the First Affiliated Hospital of Xi'an Jiaotong University,Xi'an 710061,China%School of Automation Science and Engineering,Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China; The School of Life Science and Technology,Xi'an Jiaotong University,Xi'an 710049,China; Howard Hughes Medical Institute,University of Washington,Seattle,WA 98195,USA%School of Automation Science and Engineering,Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China; MOE Key Lab for Intelligent Networks&Networks Security,Faculty of Electronic and Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China; Elsevier; Oxford University Press
• Subjects: Algorithms; bioinformatics; Complex structural variant; data collection; Formation mechanism; Genome; Genomics; Graph mining; High-Throughput Nucleotide Sequencing; Method; Mutation; Next-generation sequencing; Pattern growth; proteomics; Sequence Analysis, DNA; sequence homology; Complex structural variant; Pattern growth; Next-generation sequencing; Formation mechanism; Graph mining
• ISSN: 1672-0229; 2210-3244; 2210-3244
• DOI: 10.1016/j.gpb.2021.03.007

Complex structural variants (CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants. However, detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy. As a result, there has been limited progress for CSV discovery compared with simple structural variants. Here, we systematically analyzed the multi-breakpoint connection feature of CSVs, and proposed Mako, utilizing a bottom-up guided model-free strategy, to detect CSVs from paired-end short-read sequencing. Specifically, we implemented a graph-based pattern growth approach, where the graph depicts potential breakpoint connections, and pattern growth enables CSV detection without pre-defined models. Comprehensive evaluations on both simulated and real datasets revealed that Mako outperformed other algorithms. Notably, validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%, where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp, respectively. Moreover, the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types, including two novel types of adjacent segment swap and tandem dispersed duplication. Further analysis of these CSVs also revealed the impact of sequence homology on the formation of CSVs. Mako is publicly available at https://github.com/xjtu-omics/Mako.