Context-aware simulation enables systematic optimization of long-read mapping parameters

Abstract

The performance of long-read mapping is critical yet highly sensitive to parameter choices. We present CycSim, a context-aware simulator that models sequence-context-dependent errors from empirical sequencing data, coupled with a Bayesian optimization framework for systematic parameter tuning. CycSim more accurately reproduces real error profiles than existing simulators, enabling reliable simulation-based optimization. The framework identified parameter configurations that achieved 2.78-fold faster mapping for data from the newly developed Cyclone platform, and consistently improved both mapping efficiency (8.14-32.65% faster) and structural variant calling accuracy (0.75-1.70% higher F1) across ONT, HiFi, and Cyclone datasets, providing a robust and generalizable foundation for analysis-goal-driven parameter refinement.