Systematic In Silico Off-Target Assessment of siRNAs: Integrated Tissue-Specific Scoring and Cross-Species Preclinical Model Selection with TargetSureR

Abstract

Small interfering RNAs (siRNAs) have become a transformative class of nucleic acid therapeutics for clinical disease treatment, yet sequence-dependent off-target silencing continues to pose a major safety barrier that hinders their preclinical refinement and large-scale translational application. Existing bioinformatics tools only support partial off-target evaluation, either focusing on basic sequence optimization or simple seed-region scanning, and fail to deliver systematic, multi-dimensional and reproducible safety assessment for siRNA lead screening. To fill this gap, we developed TargetSureR, a lightweight, modular and CRAN-compatible open-source R package dedicated to full-process siRNA off-target risk profiling. This tool accommodates dual sequence-based and precomputed position-based inputs, integrates GTEx multi-tissue expression data and curated cancer, adverse-event and immune gene panels, and establishes a seven-dimensional scoring framework to stratify off-target risks into four hierarchical tiers. It further enables tissue-specific safety characterization and quantitative cross-species model selection, with an Ensembl API fallback mechanism ensuring high transcript annotation resolution. Built purely in R with no external shell dependencies, TargetSureR provides a standardized, robust and user-friendly workflow for systematic siRNA preclinical safety evaluation, and is freely available at https://github.com/nishuai/TargetSureR.