Comparison and benchmark of long-read based structural variant detection strategies

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Background Recent advances in long-read callers and assembly methods have greatly facilitated structural variants (SV) detection via read-based and assembly-based detection strategies. However, the lack of comparison studies, especially for SVs at complex genomic regions, complicates the selection of proper detection strategy for ever-increasing demand of SV analysis. Results In this study, we compared the two most widely-used strategies with six long-read datasets of HG002 genome and benchmarked them with well curated SVs at genomic regions of different complexity. First of all, our results suggest that SVs detected by assembly-based strategy are slightly affected by assemblers on HiFi datasets, especially for its breakpoint identity. Comparably, though read-based strategy is more versatile to different sequencing settings, aligners greatly affect SV breakpoints and type. Furthermore, our comparison reveals that 70% of the assembly-based calls are also detectable by read-based strategy and it even reaches 90% for SVs at high confident regions. While 60% of the assembly-based calls that are totally missed by read-based callers is largely due to the challenges of clustering ambiguous SV signature reads. Lastly, benchmarking with SVs at complex genomic regions, our results show that assembly-based approach outperforms read-based calling with at least 20X coverage, while read-based strategy could achieve 90% recall even with 5X coverage. Conclusions Taken together, with sufficient sequencing coverage, assembly-based strategy is able to detect SVs more consistently than read-based strategy under different settings. However, read-based strategy could detect SVs at complex regions with high sensitivity and specificity but low coverage, thereby suggesting its great potential in clinical application.

Authors 4

1. Jiadong Lin Aachen Leiden Institute of Advanced Computer Science Faculty of Science, Leiden University

   Leiden University · First Affiliated Hospital of Xi'an Jiaotong University · Xi'an Jiaotong University

   Affiliation as printed

   Genome Institute, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 China

   Leiden Institute of Advanced Computer Science, Faculty of Science, Leiden University, Leiden, 2311 EZ, The Netherlands

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

   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

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 70049, China

   School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China

2. Peng Jia

   Xi'an Jiaotong University

   Affiliation as printed

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

   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, 70049, China

   School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China

3. Songbo Wang

   Xi'an Jiaotong University

   Affiliation as printed

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

   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, 70049, China

   School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049, China

4. Kai Ye corresponding Aachen Faculty of Science, Leiden University

   Leiden University · First Affiliated Hospital of Xi'an Jiaotong University · Xi'an Jiaotong University

   Affiliation as printed

   Faculty of Science, Leiden University, Leiden, 2311 EZ, The Netherlands

   Genome Institute, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061 China

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, 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

   Genome Institute, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061 China

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, 70049, 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