RabbitVar: ultra-fast and accurate somatic small-variant calling on multi-core architectures

Zhang, H.; Song, H.; Yin, Z.; Chang, Q.; Wei, Y.; Niu, B.; Schmidt, B.; Liu, W.

doi:10.1101/2023.01.06.522980

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bioRxiv · 10.1101/2023.01.06.522980

RabbitVar: ultra-fast and accurate somatic small-variant calling on multi-core architectures

Zhang, H.·Song, H.·Yin, Z.·Chang, Q.·Wei, Y.·Niu, B.·Schmidt, B.·Liu, W.

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Abstract

The continuous development of next-generation sequencing (NGS) technology has led to extensive and frequent use of genomic analysis in cancer research. The associated production of large-scale NGS datasets establishes the need for high-precision somatic variant calling methods that are highly optimized on commonly used hardware platforms. We present RabbitVar (https://github.com/LeiHaoa/RabbitVar), a scalable variant caller that can detect small somatic variants from paired tumor/normal NGS data on modern multi-core CPUs. Our approach combines candidate-finding and machine-learning-based filtering strategies with optimized data structures and multi-threading to achieve both high accuracy and efficiency. We have compared the performance of RabbitVar to leading state-of-the-art callers (Strelka2, Mutect2, NeuSomatic, VarDict, VarScan2) on real-world HCC1395 breast cancer datasets under different sequencing conditions and contamination rates. The evaluation results demonstrate that RabbitVar achieves highly competitive F1-scores when calling SNVs. Moreover, when calling the more challenging indel variants, it consistently achieves the highest F1-scores. RabbitVar is able to process a paired tumor and normal whole human genome sequencing datasets with 80x depth in less than 20 minutes on a 48-core workstation outperforming all other tested variant callers in terms of efficiency.

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Zhang, H., Song, H., Yin, Z., Chang, Q., Wei, Y., Niu, B., Schmidt, B., Liu, W.. 2023-01-06. RabbitVar: ultra-fast and accurate somatic small-variant calling on multi-core architectures. https://doi.org/10.1101/2023.01.06.522980

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