Skiver: Alignment-free Estimation of Sequencing Error Rates and Spectra using (k, v)-mer Sketches

Abstract

BackgroundQuality control of sequencing datasets is an important first step in numerous bioinformatics pipelines such as mapping, variant calling, and assembly. Existing methods typically rely on alignment results or quality scores. However, the reference genome is not always available for mapping, and uncalibrated quality scores may yield biased estimates of error rates. ResultsWe present skiver, a reference-free and alignment-free algorithm that estimates sequencing error rates and calibrates Phred quality scores using (k, v)-mer sketches. By identifying the consensus from the sketched (k, v)-mers, skiver estimates survival and hazard rates that capture positional information of sequencing errors. Across simulated and real datasets from various sequencing platforms, skiver accurately recovers sequencing error rates and the proportion of different error types. We further demonstrate its ability to calibrate Phred scores. It also reliably handles complex datasets containing multiple strains, alleles, and repetitive regions through an iterative outlier filtering strategy. Skiver is computationally efficient and supports tools that need accurate sequencing error rate estimates or quality scores as prior knowledge. Availability and ImplementationAn implementation of skiver is available at https://github.com/GZHoffie/skiver, and dataset and scripts for reproducibility are available at https://github.com/GZHoffie/skiver-test.