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

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 framework that estimates sequencing errors using (k, v)-mer sketches. By identifying the consensus through 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 error rates and spectra. It also reliably handles complex datasets containing multiple strains, alleles, and repetitive regions through an outlier filtering strategy. Skiver is computationally efficient and provides a lightweight solution for error profiling in high-throughput sequencing. Availability and ImplementationThe implementation of skiver is available at https://github.com/GZHoffie/skiver, and the dataset and scripts for reproducibility are available at https://github.com/GZHoffie/skiver-test.