Risk-based approach to rescue regions of low coverage in targeted NGS sequencing
- 1Center of Excellence for Data Sciences, AI and Bioinformatics, Labcorp
Abstract
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.
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BibTeX
@article{giladi2026riskbased,
title = {{Risk-based approach to rescue regions of low coverage in targeted NGS sequencing}},
author = {Eldar Giladi and Qiandong Zeng and Binbin Huang and Angela Kenyon and Stanley Letovsky},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-63567-z},
url = {https://doi.org/10.1038/s41598-026-63567-z},
note = {CC BY},
}
RIS
TY - JOUR
TI - Risk-based approach to rescue regions of low coverage in targeted NGS sequencing
AU - Eldar Giladi
AU - Qiandong Zeng
AU - Binbin Huang
AU - Angela Kenyon
AU - Stanley Letovsky
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-63567-z
UR - https://doi.org/10.1038/s41598-026-63567-z
AB - 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.
SN - 2045-2322
ER -