ArticleBioinformatics (Oxford, England)2020
ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Comprehensive benchmarking of methods for mutation calling in circulating tumor DNA.Nature communications · 2025Article
- Enabling sensitive and precise detection of ctDNA through somatic copy number aberrations in breast cancer.NPJ breast cancer · 2025Article
- Identification of Somatic Variants in Cancer Genomes from Tissue and Liquid Biopsy Samples.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Precision cancer classification using liquid biopsy and advanced machine learning techniques.Scientific reports · 2024Article
- Review
- Synggen: fast and data-driven generation of synthetic heterogeneous NGS cancer data.Bioinformatics (Oxford, England) · 2023Article
- Allele-informed copy number evaluation of plasma DNA samples from metastatic prostate cancer patients: the PCF_SELECT consortium assay.NAR cancer · 2022Article
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Authors and funding
10 authors.
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Abstract
motivationThe use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs.
resultsWe present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples. AVAILABILITY AND IMPLEMENTATION: ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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