An integrated Asian human SNV and indel benchmark established using multiple sequencing methods

Huang, Chuanfeng; Shao, Libin; Qu, Su; Rao, Junhua; Cheng, Tao; Cao, Zhisheng; Liu, Sanyang; Hu, Jie; Liang, Xinming; Shang, Ling; Chen, Yangyi; Liang, Zhikun; Zhang, Jiezhong; Chen, Peipei; Luo, Donghong; Zhu, Anna; Yu, Ting; Zhang, Wenxin; Fan, Guangyi; Chen, Fang; Huang, Jie

doi:10.1038/s41598-020-66605-6

Cited by 4 publications

(3 citation statements)

References 42 publications

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“…The deeply sequenced data in this study can be used in future work to extend our understanding of complex SVs. As mentioned above, in another parallel project [33] , we generated about 4.16 Tb clean data of the same cell line using seven sequencing strategies in different laboratories, including two BGI regular NGS platforms, three Illumina regular NGS platforms, single tube long fragment read (stLFR) sequencing, and 10X Genomics Chromium linked-read sequencing. These large datasets will provide comprehensive variant information, serving as valuable genomic resources to facilitate future genomic or medical research.…”

Section: Discussionmentioning

confidence: 99%

Robust Benchmark Structural Variant Calls of an Asian Using State-of-the-Art Long-Read Sequencing Technologies

Du¹,

Liang

et al. 2021

Genomics, Proteomics &Amp; Bioinformatics

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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.

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Section: Discussionmentioning

confidence: 99%

Robust Benchmark Structural Variant Calls of an Asian Using State-of-the-Art Long-Read Sequencing Technologies

Du¹,

Liang

et al. 2021

Genomics, Proteomics &Amp; Bioinformatics

Self Cite

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“…Genome in a Bottle (GIAB) and other efforts have established various whole-genome reference materials and defined benchmark calls and regions to benchmark germline small variants (SNVs and indels) [5][6][7][8] and structural variants (SVs) [9][10][11] . However, all these efforts on genomic reference materials only evaluated variants identified inside the benchmark regions.…”

Section: Introductionmentioning

confidence: 99%

Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance

Ren

Duan

et al. 2022

Preprint

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Current methods for evaluating the accuracy of germline variant calls are restricted to easy-to-detect high-confidence regions, thus ignoring a substantial portion of difficult variants beyond the benchmark regions. We established four DNA reference materials from immortalized cell lines derived from a Chinese Quartet including parents and monozygotic twins. We integrated benchmark calls of 4.2 million small variants and 15,000 structural variants from multiple platforms and bioinformatic pipelines for evaluating the reliability of germline variant calls inside the benchmark regions. The genetic built-in-truth of the Quartet family design not only improved sensitivity of benchmark calls by removing additional false positive variants with apparently high quality, but also enabled estimation of the precision of variants calls outside the benchmark regions. Batch effects of variant calling in large-scale DNA sequencing efforts can be effectively identified with the concurrent use of the Quartet DNA reference materials along with study samples, and can be alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Matched RNA and protein reference materials were also established in the Quartet project, thereby enabling benchmark calls constructed from DNA reference materials for evaluation of variants calling performance on RNA and protein data. The Quartet DNA reference materials from this study are a resource for objective and comprehensive assessment of the accuracy of germline variant calls throughout the whole-genome regions.

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“…With these technologies, DNBSEQ ™ sequencing platforms generate data with high sequencing accuracy, low duplication rates and reduced index hopping [18]. Previously, we explored the performances of single nucleotide variant (SNV) and small insertion and deletion (indel) detection on DNBSEQ ™ -based WGS data [19,20], while the performances of CNV detection remained unexplored. Several benchmarking analyses of CNV detection on WGS data by Illumina platforms have been reported [6,21]; thus, it is important to understand the performance of DNBSEQ ™ with respect to CNV detection in comparison with Illumina platforms with various CNV tools, so users of DNBSEQ ™ can choose the correct tools according to their needs.…”

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confidence: 99%

Performance of copy number variants detection based on whole-genome sequencing by DNBSEQ platforms

et al. 2020

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Background DNBSEQ™ platforms are new massively parallel sequencing (MPS) platforms that use DNA nanoball technology. Use of data generated from DNBSEQ™ platforms to detect single nucleotide variants (SNVs) and small insertions and deletions (indels) has proven to be quite effective, while the feasibility of copy number variants (CNVs) detection is unclear. Results Here, we first benchmarked different CNV detection tools based on Illumina whole-genome sequencing (WGS) data of NA12878 and then assessed these tools in CNV detection based on DNBSEQ™ sequencing data from the same sample. When the same tool was used, the CNVs detected based on DNBSEQ™ and Illumina data were similar in quantity, length and distribution, while great differences existed within results from different tools and even based on data from a single platform. We further estimated the CNV detection power based on available CNV benchmarks of NA12878 and found similar precision and sensitivity between the DNBSEQ™ and Illumina platforms. We also found higher precision of CNVs shorter than 1 kbp based on DNBSEQ™ platforms than those based on Illumina platforms by using Pindel, DELLY and LUMPY. We carefully compared these two available benchmarks and found a large proportion of specific CNVs between them. Thus, we constructed a more complete CNV benchmark of NA12878 containing 3512 CNV regions. Conclusions We assessed and benchmarked CNV detections based on WGS with DNBSEQ™ platforms and provide guidelines for future studies.

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An integrated Asian human SNV and indel benchmark established using multiple sequencing methods

Cited by 4 publications

References 42 publications

Robust Benchmark Structural Variant Calls of an Asian Using State-of-the-Art Long-Read Sequencing Technologies

Robust Benchmark Structural Variant Calls of an Asian Using State-of-the-Art Long-Read Sequencing Technologies

Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance

Performance of copy number variants detection based on whole-genome sequencing by DNBSEQ platforms

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