ArticleScience advances2020
Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Tumor-naïve ctDNA detection with deep learning-enhanced error suppression for sensitive mutation calling.Genome medicine · 2026Article
- Blood-based epigenetic instability linked to human aging and disease.Nature communications · 2026Article
- Genomic and fragmentomic landscapes of cell-free DNA for early cancer detection.Nature reviews. Cancer · 2025Review
- Article
- Implications of immunometabolism for smouldering MS pathology and therapy.Nature reviews. Neurology · 2023Review
- [MinerVa: A high performance bioinformatic algorithm for the detection of minimal residual disease in solid tumors].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Pan-cancer analyses reveal IGSF10 as an immunological and prognostic biomarker.Frontiers in genetics · 2022Article
- Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA.Cell reports methods · 2021Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 3 countries.
Funding
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) 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.
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Registered trials
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