Cargando…

Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies

The importance of structural variants (SVs) for human phenotypes and diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performance...

Descripción completa

Detalles Bibliográficos
Autores principales: Du, Xiao, Li, Lili, Liang, Fan, Liu, Sanyang, Zhang, Wenxin, Sun, Shuai, Sun, Yuhui, Fan, Fei, Wang, Linying, Liang, Xinming, Qiu, Weijin, Fan, Guangyi, Wang, Ou, Yang, Weifei, Zhang, Jiezhong, Xiao, Yuhui, Wang, Yang, Wang, Depeng, Qu, Shoufang, Chen, Fang, Huang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510867/
https://www.ncbi.nlm.nih.gov/pubmed/33662625
http://dx.doi.org/10.1016/j.gpb.2020.10.006
_version_ 1784797536754597888
author Du, Xiao
Li, Lili
Liang, Fan
Liu, Sanyang
Zhang, Wenxin
Sun, Shuai
Sun, Yuhui
Fan, Fei
Wang, Linying
Liang, Xinming
Qiu, Weijin
Fan, Guangyi
Wang, Ou
Yang, Weifei
Zhang, Jiezhong
Xiao, Yuhui
Wang, Yang
Wang, Depeng
Qu, Shoufang
Chen, Fang
Huang, Jie
author_facet Du, Xiao
Li, Lili
Liang, Fan
Liu, Sanyang
Zhang, Wenxin
Sun, Shuai
Sun, Yuhui
Fan, Fei
Wang, Linying
Liang, Xinming
Qiu, Weijin
Fan, Guangyi
Wang, Ou
Yang, Weifei
Zhang, Jiezhong
Xiao, Yuhui
Wang, Yang
Wang, Depeng
Qu, Shoufang
Chen, Fang
Huang, Jie
author_sort Du, Xiao
collection PubMed
description The importance of structural variants (SVs) for human phenotypes and diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performances in biological and clinical research. To facilitate the validation and application of these SV detection approaches, we established an Asian reference material by characterizing the genome of an Epstein-Barr virus (EBV)-immortalized B lymphocyte line along with identified benchmark regions and high-confidence SV calls. We established a high-confidence SV callset with 8938 SVs by integrating four alignment-based SV callers, including 109× Pacific Biosciences (PacBio) continuous long reads (CLRs), 22× PacBio circular consensus sequencing (CCS) reads, 104× Oxford Nanopore Technologies (ONT) long reads, and 114× Bionano optical mapping platform, and one de novo assembly-based SV caller using CCS reads. A total of 544 randomly selected SVs were validated by PCR amplification and Sanger sequencing, demonstrating the robustness of our SV calls. Combining trio-binning-based haplotype assemblies, we established an SV benchmark for identifying false negatives and false positives by constructing the continuous high-confidence regions (CHCRs), which covered 1.46 gigabase pairs (Gb) and 6882 SVs supported by at least one diploid haplotype assembly. Establishing high-confidence SV calls for a benchmark sample that has been characterized by multiple technologies provides a valuable resource for investigating SVs in human biology, disease, and clinical research.
format Online
Article
Text
id pubmed-9510867
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-95108672022-09-27 Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies Du, Xiao Li, Lili Liang, Fan Liu, Sanyang Zhang, Wenxin Sun, Shuai Sun, Yuhui Fan, Fei Wang, Linying Liang, Xinming Qiu, Weijin Fan, Guangyi Wang, Ou Yang, Weifei Zhang, Jiezhong Xiao, Yuhui Wang, Yang Wang, Depeng Qu, Shoufang Chen, Fang Huang, Jie Genomics Proteomics Bioinformatics Original Research The importance of structural variants (SVs) for human phenotypes and diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performances in biological and clinical research. To facilitate the validation and application of these SV detection approaches, we established an Asian reference material by characterizing the genome of an Epstein-Barr virus (EBV)-immortalized B lymphocyte line along with identified benchmark regions and high-confidence SV calls. We established a high-confidence SV callset with 8938 SVs by integrating four alignment-based SV callers, including 109× Pacific Biosciences (PacBio) continuous long reads (CLRs), 22× PacBio circular consensus sequencing (CCS) reads, 104× Oxford Nanopore Technologies (ONT) long reads, and 114× Bionano optical mapping platform, and one de novo assembly-based SV caller using CCS reads. A total of 544 randomly selected SVs were validated by PCR amplification and Sanger sequencing, demonstrating the robustness of our SV calls. Combining trio-binning-based haplotype assemblies, we established an SV benchmark for identifying false negatives and false positives by constructing the continuous high-confidence regions (CHCRs), which covered 1.46 gigabase pairs (Gb) and 6882 SVs supported by at least one diploid haplotype assembly. Establishing high-confidence SV calls for a benchmark sample that has been characterized by multiple technologies provides a valuable resource for investigating SVs in human biology, disease, and clinical research. Elsevier 2022-02 2021-03-02 /pmc/articles/PMC9510867/ /pubmed/33662625 http://dx.doi.org/10.1016/j.gpb.2020.10.006 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Original Research
Du, Xiao
Li, Lili
Liang, Fan
Liu, Sanyang
Zhang, Wenxin
Sun, Shuai
Sun, Yuhui
Fan, Fei
Wang, Linying
Liang, Xinming
Qiu, Weijin
Fan, Guangyi
Wang, Ou
Yang, Weifei
Zhang, Jiezhong
Xiao, Yuhui
Wang, Yang
Wang, Depeng
Qu, Shoufang
Chen, Fang
Huang, Jie
Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies
title Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies
title_full Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies
title_fullStr Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies
title_full_unstemmed Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies
title_short Robust Benchmark Structural Variant Calls of An Asian Using State-of-the-art Long-read Sequencing Technologies
title_sort robust benchmark structural variant calls of an asian using state-of-the-art long-read sequencing technologies
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9510867/
https://www.ncbi.nlm.nih.gov/pubmed/33662625
http://dx.doi.org/10.1016/j.gpb.2020.10.006
work_keys_str_mv AT duxiao robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT lilili robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT liangfan robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT liusanyang robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT zhangwenxin robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT sunshuai robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT sunyuhui robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT fanfei robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT wanglinying robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT liangxinming robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT qiuweijin robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT fanguangyi robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT wangou robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT yangweifei robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT zhangjiezhong robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT xiaoyuhui robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT wangyang robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT wangdepeng robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT qushoufang robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT chenfang robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies
AT huangjie robustbenchmarkstructuralvariantcallsofanasianusingstateoftheartlongreadsequencingtechnologies