SIREN: suite for intelligent RNAi design and evaluation of nucleotide sequences

RNA interference (RNAi) is a powerful tool for gene silencing across research, therapeutics, and agriculture. However, designing long double-stranded RNAs (dsRNAs) remains challenging because each dsRNA produces many small interfering RNAs (siRNAs), which can collectively introduce substantial off-target effects. Existing tools often lack the ability to account for cumulative off-target interactions, to incorporate thermodynamic modeling, or to accept custom transcriptome inputs, limiting their applicability and accuracy.

Here, we present SIREN, an open-source pipeline designed to streamline RNAi construct design. SIREN integrates siRNA generation, thermodynamically informed off-target prediction, scoring of dsRNA candidates based on cumulative off-target effects, and primer design for in vitro synthesis. It accepts user-defined transcriptomes for context-specific analysis and provides adjustable sensitivity settings balancing accuracy and computational demands. Benchmarking across plant, oomycete, and human transcriptomes demonstrates predictable scaling with target length and shows that optional speed modes can reduce runtime while preserving a substantial fraction of high-sensitivity designs and high-risk off-target rankings in many cases. Qualitative validation in Phytophthora capsici confirms that SIREN effectively identifies highly specific RNAi constructs with no detectable off-target phenotypes in host plants.

SIREN is implemented in Python and available under an open-source license at https://github.com/pablovargasmejia/SIREN.