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<p>High-throughput clinical genetic testing assays based on next generation sequencing often rely on Sanger sequencing to “rescue” or “backfill” regions with low coverage in order to detect variants with high sensitivity. This can lead to a longer turn-around time, higher cost, and excessive workload burden on the laboratory performing the testing. In response to these challenges, a data-driven computational approach was developed to reduce the total false negative call risk in a sample by designating a subset of high-risk gene segments for rescue testing, instead of performing rescue testing for all the low-coverage segments in a patient sample. This approach maintains the high sensitivity required for clinical testing while significantly reducing the number of targeted Sanger tests needed.</p>

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Risk-based approach to rescue regions of low coverage in targeted NGS sequencing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

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  • Eldar Giladi,
  • Qiandong Zeng,
  • Binbin Huang,
  • Angela Kenyon,
  • Stanley Letovsky

摘要

High-throughput clinical genetic testing assays based on next generation sequencing often rely on Sanger sequencing to “rescue” or “backfill” regions with low coverage in order to detect variants with high sensitivity. This can lead to a longer turn-around time, higher cost, and excessive workload burden on the laboratory performing the testing. In response to these challenges, a data-driven computational approach was developed to reduce the total false negative call risk in a sample by designating a subset of high-risk gene segments for rescue testing, instead of performing rescue testing for all the low-coverage segments in a patient sample. This approach maintains the high sensitivity required for clinical testing while significantly reducing the number of targeted Sanger tests needed.