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ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA
- Source :
- Bioinformatics
- Publication Year :
- 2020
- Publisher :
- Oxford University Press (OUP), 2020.
-
Abstract
- Motivation The 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. Results We 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.
- Subjects :
- Statistics and Probability
Somatic cell
Computer science
Computational biology
Biochemistry
Tumor heterogeneity
Circulating Tumor DNA
03 medical and health sciences
chemistry.chemical_compound
0302 clinical medicine
Humans
Molecular Biology
030304 developmental biology
0303 health sciences
Plasma samples
Nucleotides
High-Throughput Nucleotide Sequencing
Reproducibility of Results
Variant allele
Original Papers
Computer Science Applications
Computational Mathematics
Computational Theory and Mathematics
chemistry
Circulating tumor DNA
030220 oncology & carcinogenesis
Mutation
Cell-Free Nucleic Acids
DNA
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 36
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....664beedecd584067b6743249adad07db