PT  - JOURNAL ARTICLE
AU  - Xu, Chang
AU  - Gu, Xiujing
AU  - Padmanabhan, Raghavendra
AU  - Wu, Zhong
AU  - Peng, Quan
AU  - DiCarlo, John
AU  - Wang, Yexun
TI  - smCounter2: an accurate low-frequency variant caller for targeted sequencing data with unique molecular identifiers
AID  - 10.1101/281659
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 281659
4099  - http://biorxiv.org/content/early/2018/03/14/281659.short
4100  - http://biorxiv.org/content/early/2018/03/14/281659.full
AB  - Motivation Low-frequency DNA mutations are often confounded with technical artifacts from sample preparation and sequencing. With unique molecular identifiers (UMIs), most of the sequencing errors can be corrected. However, errors before UMI tagging, such as DNA polymerase errors during end-repair and the first PCR cycle, cannot be corrected with single-strand UMIs and impose fundamental limits to UMI-based variant calling.Results We developed smCounter2, a UMI-based variant caller for targeted sequencing data and an upgrade from the current version of smCounter. Compared to smCounter, smCounter2 features lower detection limit at 0.5%, better overall accuracy (particularly in non-coding regions), a consistent threshold that can be applied to both deep and shallow sequencing runs, and easier use via a Docker image and code for read pre-processing. We benchmarked smCounter2 against several state-of-the-art UMI-based variant calling methods using multiple datasets and demonstrated smCounter2’s superior performance in detecting somatic variants. At the core of smCounter2 is a statistical test to determine whether the allele frequency of the putative variant is significantly above the background error rate, which was carefully modeled using an independent dataset. The improved accuracy in non-coding regions was mainly achieved using novel repetitive region filters that were specifically designed for UMI data.Availability The entire pipeline is available at https://github.com/qiaseq/qiaseq-dna under MIT license.