Repun: an accurate small variant representation unification method for multiple sequencing platforms

Abstract

Ensuring a unified variant representation aligning the sequencing data is critical for downstream analysis as variant representation may differ across platforms and sequencing conditions. Current approaches typically treat variant unification as a post-step following variant calling and are incapable of measuring the correct variant representation from the outset. Aligning variant representations with the alignment before variant calling has benefits like providing reliable training labels for deep learning-based variant caller model training and enabling direct assessment of alignment quality. However, it also poses challenges due to the large number of candidates to handle. Here, we present Repun, a haplotype-aware variant-alignment unification algorithm that harmonizes the variant representation between provided variants and alignments in different sequencing platforms. Repun leverages phasing to facilitate equivalent haplotype matches between variants and alignments. Our approach reduced the comparisons between variant haplotypes and candidate haplotypes by utilizing haplotypes with read evidence to speed up the unification process. Repun achieved >99.99% precision and > 99.5% recall through extensive evaluations of various Genome in a Bottle Consortium samples encompassing three sequencing platforms: Oxford Nanopore Technology, Pacific Biosciences, and Illumina. Repun is open-source and available at (https://github.com/zhengzhenxian/Repun).