NOODL, a context-aware DNA linker generator for synthetic biology

DNA spacers are commonly used in synthetic biology to reduce unintended interactions between adjacent genetic components, but poorly designed spacers can form secondary structures or hybridize with neighboring sequences, compromising construct performance. Existing spacer design approaches do not explicitly account for neighboring genetic sequences. Without a method to design spacers tailored to the functional content of a genetic construct, spacer evaluation is left to expensive experimental evaluation. To address this limitation, we developed NOODL (Novel Optimization Of DNA Linkers), a modular genetic algorithm implemented in Julia that automates the design of DNA spacer sequences. NOODL evaluates candidate spacers using sequence-complementarity and thermodynamic criteria while satisfying user-defined constraints such as spacer length and target AT content. Through iterative selection, crossover, and mutation, the algorithm enables the de novo design of spacer sequences optimized to minimize predicted hybridization with neighboring functional sequences (specifically accounting for the exact primary nucleotide sequence of adjacent flanking regions). We evaluated NOODL against published spacer sequences and found that NOODL-generated sequences showed reduced predicted hybridization potential while maintaining desired sequence characteristics. By replacing manual and extensive experimental testing and validation with computational optimization, NOODL provides a straightforward approach that is likely broadly applicable in synthetic biology.