Samovar: Single-sample mosaic SNV calling with linked reads

Abstract

We present Samovar, a mosaic single-nucleotide variant (SNV) caller for linked-read wholegenome shotgun sequencing data. Samovar scores candidate sites using a random forest model trained using the input dataset that considers read quality, phasing, and linked-read characteristics. We show Samovar calls mosaic SNVs within a single sample with accuracy comparable to what previously required trios or matched tumor/normal pairs and outperform single-sample mosaic variant callers at MAF 5%-50% with at least 30x coverage. Furthermore, we use Samovar to find somatic variants in whole genome sequencing of both tumor and normal from 13 pediatric cancer cases that can be corroborated with high recall with whole exome sequencing. Samovar is available open-source at https://github.com/cdarby/samovar under the MIT license.

Sequencing Technologies and Analyses: Where Have We Been and Where Are We Going?

“…Similar to read mapping, detection of variants in different applications (e.g., somatic variants in cancer genomes) requires specialized tools. Charlotte Darby presented a clever approach (Darby et al., 2019) to detect mosaic variants using the 10X Genomics linked-read technology (Zheng et al., 2016). Unlike germline variants, mosaic variants are those that are present in only a subset of the cells of an individual and are harder to detect.…”