Abstract
We describe Strelka2 ( https://github.com/Illumina/strelka ), an open-source small-variant-calling method for research and clinical germline and somatic sequencing applications. Strelka2 introduces a novel mixture-model-based estimation of insertion/deletion error parameters from each sample, an efficient tiered haplotype-modeling strategy, and a normal sample contamination model to improve liquid tumor analysis. For both germline and somatic calling, Strelka2 substantially outperformed the current leading tools in terms of both variant-calling accuracy and computing cost.
Highlights
Whole-genome sequencing is rapidly transitioning into a tool for clinical research and diagnosis, a shift which brings new challenges for sequence analysis methods
We demonstrate that Strelka2 is both more accurate and substantially faster when compared to current best-in-class small variant calling methods
The improvement of generative sequencing error models to more closely represent the sample data should sharpen the effectiveness of downstream machine-learning approaches by reducing confounding error terms, a circumstance we have already leveraged to improve the accuracy of Strelka2
Summary
Whole-genome sequencing is rapidly transitioning into a tool for clinical research and diagnosis, a shift which brings new challenges for sequence analysis methods. Strelka2 germline and somatic analyses share a common series of high-level stages, including parameter estimation from sample data, candidate variant discovery, realignment, variant probability inference, and empirical re-scoring/filtration. Strelka2’s germline analysis introduces a novel step to adaptively estimate indel error rates from preliminary allele counts in each sample, using a mixture model to estimate both indel variant mutation rates and indel noise rates from a set of error processes (Supplementary Fig. 2).
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