Benchmarking UMI clustering tools for accurate detection of low-frequency variants from deep sequencing.

ABSTRACT: Accurate detection of low-frequency variants utilizing next-generation sequencing (NGS) is of paramount importance in both biomedical research and clinical diagnosis. However, its analytical sensitivity is impeded by NGS's inherently high error rates. An efficient solution employs unique molecular identifiers (UMIs) to tag individual DNA molecules before amplification to mitigate NGS errors. Nevertheless, UMIs are prone to collisions and amplification- or sequencing-induced errors, leading to the inaccurate clustering of UMI-tagged data. Multiple UMI clustering tools have been proposed to address these challenges; however, a systematic evaluation of their impact on low-frequency variant detection remains lacking. Here, we conducted a comprehensive benchmarking of eight UMI clustering tools

SUBMITTER: Pu D 

PROVIDER: S-EPMC12830995 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature