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Comparison of the copy-neutral loss of heterozygosity identified from whole-exome sequencing data using three different tools

  • Lee, Gang-Taik (Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea) ;
  • Chung, Yeun-Jun (Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea)
  • Received : 2021.11.04
  • Accepted : 2001.12.31
  • Published : 2022.03.31

Abstract

Loss of heterozygosity (LOH) is a genomic aberration. In some cases, LOH can be generated without changing the copy number, which is called copy-neutral LOH (CN-LOH). CN-LOH frequently occurs in various human diseases, including cancer. However, the biological and clinical implications of CN-LOH for human diseases have not been well studied. In this study, we compared the performance of CN-LOH determination using three commonly used tools. For an objective comparison, we analyzed CN-LOH profiles from single-nucleotide polymorphism array data from 10 colon adenocarcinoma patients, which were used as the reference for comparison with the CN-LOHs obtained through whole-exome sequencing (WES) data of the same patients using three different analysis tools (FACETS, Nexus, and Sequenza). The majority of the CN-LOHs identified from the WES data were consistent with the reference data. However, some of the CN-LOHs identified from the WES data were not consistent between the three tools, and the consistency with the reference CN-LOH profile was also different. The Jaccard index of the CN-LOHs using FACETS (0.84 ± 0.29; mean value, 0.73) was significantly higher than that of Nexus (0.55 ± 0.29; mean value, 0.50; p = 0.02) or Sequenza (0 ± 0.41; mean value, 0.34; p = 0.04). FACETS showed the highest area under the curve value. Taken together, of the three CN-LOH analysis tools, FACETS showed the best performance in identifying CN-LOHs from The Cancer Genome Atlas colon adenocarcinoma WES data. Our results will be helpful in exploring the biological or clinical implications of CN-LOH for human diseases.

Keywords

Acknowledgement

This work was supported by a grant from the National Research Foundation of Korea (2017M3C9A6047615 and 2019R1A5A2027588). We thank KREONET (Korea Research Environment Open NETwork) and KISTI (Korea Institute of Science and Technology Information) for allowing us to use their network infrastructure.

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