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

Sagi Abelson, Andy G.X. Zeng, Ido Nofech-Mozes, Ting Ting Wang, Stanley W.K. Ng, Mark D. Minden, Trevor J. Pugh, Philip Awadalla, Liran I. Shlush, Tracy Murphy, Steven M. Chan, John E. Dick, Scott V. Bratman

Research output: Contribution to journalArticlepeer-review


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) throughout their treatment course. Furthermore, we showed that accurate mutation calling in a small number of informative genomic loci might provide a cost-efficient strategy for pragmatic risk prediction of AML development in healthy individuals. More broadly, we aim for Espresso to aid with accurate mutation detection in many other research and clinical settings.

Original languageEnglish
Article number3722
Number of pages15
JournalScience Advances
Issue number50
StatePublished - 9 Dec 2020

All Science Journal Classification (ASJC) codes

  • General


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