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ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

Authors :
Francesco Orlando
Jenny Xiang
Yari Ciani
Gerhardt Attard
Olivier Elemento
Himisha Beltran
Andrea Sboner
Nicola Casiraghi
Francesca Demichelis
Alessandro Romanel
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.

Details

ISSN :
13674811 and 13674803
Volume :
36
Database :
OpenAIRE
Journal :
Bioinformatics
Accession number :
edsair.doi.dedup.....664beedecd584067b6743249adad07db