Symphonizing pileup and full-alignment for deep learning-based long-read variant calling

Deep learning-based variant callers are becoming the standard and have achieved superior single nucleotide polymorphisms calling performance using long reads. Here we present Clair3, which leverages two major method categories: pileup calling handles most variant candidates with speed, and full-alignment tackles complicated candidates to maximize precision and recall. Clair3 runs faster than any of the other state-of-the-art variant callers and demonstrates improved performance, especially at lower coverage. Leveraging both the simple pileup input and full-alignment input, small variant calling using noisy long reads has improved speed and accuracy.