A Differential Depth Sequencing Method, SPRE‐Seq, for Enhancing Targeted Region Coverage in Hybridization Capture‐Based NGS

Sequencing depth is a crucial parameter for variant calling accuracy and sensitivity. The trade-off between sequencing breadth and depth is a well-known limitation in capture-based targeted next-generation sequencing (NGS). Herein, we propose a differential depth sequencing method, SPRE-Seq, to acquire different sequencing depths for different targeted regions in an NGS panel. The SPRE-Seq performance was evaluated using a panel of reference standards and clinical samples based on our custom-designed homologous recombination deficiency (HRD) assay. By applying SPRE-Seq, the effective sequencing depths of the homologous recombination repair (HRR) and HRD regions of all seven HRD reference standards met the required thresholds with only half the sequencing data volume (reduced from 12 to 6 GB). The results for the HRR genes and HRD showed 100% consistency with the expected results. In clinical samples, the effective sequencing depth of the HRR regions was significantly higher, with a sequencing data volume of 6 GB using the SPRE-Seq approach compared with 6 GB using a regular capture approach. However, there was no significant difference between a data volume of 6 GB using SPRE-Seq and 12 GB using a regular capture method. The SPRE-Seq approach was feasible and reliable for determining the HRD status and HRR somatic variants in reference standards and clinical samples at a low sequencing volume. SPRE-Seq is a reliable, feasible, and cost-effective method that can acquire an adequate sequencing depth of an NGS panel at a low sequencing data volume.

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Publication Details

Journal
Engineering in Life Sciences
Published
2025-10-01
DOI
https://doi.org/10.1002/elsc.70047
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
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article

A Differential Depth Sequencing Method, SPRE‐Seq, for Enhancing Targeted Region Coverage in Hybridization Capture‐Based NGS

Chunyan Yang, Henan Dong, Dongjie Fan, Xiaojun Yao et al.
Engineering in Life Sciences
Cancer Genomics and Diagnostics
article

A Differential Depth Sequencing Method, SPRE‐Seq, for Enhancing Targeted Region Coverage in Hybridization Capture‐Based NGS

Chunyan Yang, Henan Dong, Dongjie Fan, Xiaojun Yao, Lili Cai, Bing Wang, Yuanwei Zhang, Huijuan Chen, Qiming Zhou
article en

Abstract

Sequencing depth is a crucial parameter for variant calling accuracy and sensitivity. The trade-off between sequencing breadth and depth is a well-known limitation in capture-based targeted next-generation sequencing (NGS). Herein, we propose a differential depth sequencing method, SPRE-Seq, to acquire different sequencing depths for different targeted regions in an NGS panel. The SPRE-Seq performance was evaluated using a panel of reference standards and clinical samples based on our custom-designed homologous recombination deficiency (HRD) assay. By applying SPRE-Seq, the effective sequencing depths of the homologous recombination repair (HRR) and HRD regions of all seven HRD reference standards met the required thresholds with only half the sequencing data volume (reduced from 12 to 6 GB). The results for the HRR genes and HRD showed 100% consistency with the expected results. In clinical samples, the effective sequencing depth of the HRR regions was significantly higher, with a sequencing data volume of 6 GB using the SPRE-Seq approach compared with 6 GB using a regular capture approach. However, there was no significant difference between a data volume of 6 GB using SPRE-Seq and 12 GB using a regular capture method. The SPRE-Seq approach was feasible and reliable for determining the HRD status and HRR somatic variants in reference standards and clinical samples at a low sequencing volume. SPRE-Seq is a reliable, feasible, and cost-effective method that can acquire an adequate sequencing depth of an NGS panel at a low sequencing data volume.

Engineering in Life SciencesVol. 25(10)
Harbin Institute of Technology (CN), Beijing VDJBio (China) (CN), Beijing Enterprises (China) (CN), Annoroad Gene Technology (China) (CN), University of Hong Kong - Shenzhen Hospital (CN), National Institute for Communicable Disease Control and Prevention (CN), Beihang University (CN)
Openalex Percentile: Top 62%
Cancer Genomics and Diagnostics
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