A benchmarking framework for the accurate and cost-effective detection of clinically-relevant structural variants for cancer target identification and diagnosis.

ABSTRACT:

Background

Accurate clinical structural variant (SV) calling is essential for cancer target identification and diagnosis but has been historically challenging due to the lack of ground truth for clinical specimens. Meanwhile, reduced clinical-testing cost is the key to the widespread clinical utility.

Methods

We analyzed massive data from tumor samples of 476 patients and developed a computational framework for accurate and cost-effective detection of clinically-relevant SVs. In addition, standard materials and classical experiments including immunohistochemistry and/or fluorescence in situ hybridization were used to validate the developed computational framework.

Results

We systematically evaluated the common algorithms for SV detection and established an expert-revi

SUBMITTER: Zhuang G 

PROVIDER: S-EPMC10792779 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature