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UMI-VarCal: a new UMI-based variant caller that efficiently improves low-frequency variant detection in paired-end sequencing NGS libraries

By Vincent Sater, Pierre-Julien Viailly, Thierry Lecroq, Elise Prieur-Gaston, Elodie Bohers, Mathieu Viennot, Philippe Ruminy, Helene Dauchel, Pierre Vera, Fabrice Jardin

Posted 19 Sep 2019
bioRxiv DOI: 10.1101/775817

Next Generation Sequencing (NGS) has become the go-to standard method for the detection of Single Nucleotide Variants (SNV) in tumor cells. The use of such technologies requires a PCR amplification step and a sequencing step, steps in which artifacts are introduced at very low frequencies. These artifacts are often confused with true low-frequency variants that can be found in tumor cells and cell-free DNA. The recent use of Unique Molecular Identifiers (UMI) in targeted sequencing protocols has offered a trustworthy approach to filter out artifactual variants and accurately call low frequency variants. However, the integration of UMI analysis in the variant calling process led to developing tools that are significantly slower and more memory consuming than raw-reads-based variant callers. We present UMI-VarCal, a UMI-based variant caller for targeted sequencing data with better sensitivity compared to other variant callers. Being developed with performance in mind, UMI-VarCal stands out from the crowd by being one of the few variant callers that don't rely on SAMtools to do their pileup. Instead, at its core runs an innovative homemade pileup algorithm specifically designed to treat the UMI tags in the reads. After the pileup, a Poisson statistical test is applied at every position to determine if the frequency of the variant is significantly higher than the background error noise. Finally, an analysis of UMI tags is performed, a strand bias and a homopolymer length filter are applied to achieve better accuracy. We illustrate the results obtained using UMI-VarCal through the sequencing of tumor samples and we show how UMI-VarCal is both faster and more sensitive than other publicly available solutions.

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