Abstract 3278: Performance assessment of de novo assembly-based structural variation detection in the human genome

Abstract

Abstract Structural variations (SVs) contribute to genetic diversity of human populations, affect biologic functions, and cause various human disorders. However, accurately identifying SVs with correct sizes and locations in the human genome remains challenging due to the complexity of the human genome, limitations of sequencing technologies, and drawbacks of analysis methods. The advancement of next-generation sequencing technologies has dramatically decreased the sequencing cost, while substantially increased the lengths of the sequencing reads. Thus, using de novo assembly-based approaches for discovering a full spectrum of SVs in human genome becomes appealing. While various assembly methods have been developed and proposed for general use by the community, the relative efficiency and predictive accuracy of SVs calling based on these assembly methods have not been fully evaluated. In this study, we applied several popular de novo assembly tools to the sequencing read data that were generated using multiple sequencing technologies with technical replicates for NA12878/HG001, a well-studied individual from NIST-led Genome-in-a-Bottle (GIAB) project: a HapMap Caucasian trio and a Chinese Quartet from FDA-led Sequencing Quality Control Phase II (SEQC2) project. Assemblies and SVs callsets were generated for each of the eight samples, and repeatability in the SVs of the technical replicates and reproducibility across sequencing sites were evaluated. The assembly-based SVs callsets have been compared with alignment-based SVs callsets. These results allow better understanding of the impacts of de novo assembly methods on SVs calling, thus providing a better insight to precision medicine. Citation Format: Chunlin Xiao, SEQC2 Working Group #3. Performance assessment of de novo assembly-based structural variation detection in the human genome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3278.