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Samovar: Single-Sample Mosaic Single-Nucleotide Variant Calling with Linked Reads

Charlotte A. Darby,James R. Fitch,P. Brennan,B. Kelly,Natalie Bir,V. Magrini,J. Leonard,C. Cottrell,J. Gastier-Foster,R. Wilson,E. Mardis,P. White,Ben Langmead,M. Schatz

Published 2019 in iScience

ABSTRACT

Summary Linked-read sequencing enables greatly improves haplotype assembly over standard paired-end analysis. The detection of mosaic single-nucleotide variants benefits from haplotype assembly when the model is informed by the mapping between constituent reads and linked reads. Samovar evaluates haplotype-discordant reads identified through linked-read sequencing, thus enabling phasing and mosaic variant detection across the entire genome. Samovar trains a random forest model to score candidate sites using a dataset that considers read quality, phasing, and linked-read characteristics. Samovar calls mosaic single-nucleotide variants (SNVs) within a single sample with accuracy comparable with what previously required trios or matched tumor/normal pairs and outperforms single-sample mosaic variant callers at minor allele frequency 5%–50% with at least 30X coverage. Samovar finds somatic variants in both tumor and normal whole-genome sequencing 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.

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