Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing.

ABSTRACT: Sensitive mutation detection by next-generation sequencing is critical for early cancer detection, monitoring minimal/measurable residual disease (MRD), and guiding precision oncology. Nevertheless, because of artifacts introduced during library preparation and sequencing, the detection of low-frequency variants at high specificity is problematic. Here, we present Espresso, an error suppression method that considers local sequence features to accurately detect single-nucleotide variants (SNVs). Compared to other advanced error suppression techniques, Espresso consistently demonstrated lower numbers of false-positive mutation calls and greater sensitivity. We demonstrated Espresso's superior performance in detecting MRD in the peripheral blood of patients with acute myeloid leukemia (AML) t

SUBMITTER: Abelson S 

PROVIDER: S-EPMC7725472 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature