DIGGER-Bac: prediction of seed regions for high-fidelity construction of synthetic small RNAs in bacteria

Abstract

Synthetic small RNAs (sRNAs) are gaining increasing attention in the field of synthetic biology and bioengineering for efficient post-transcriptional regulation of gene expression. However, the optimal design of synthetic sRNAs is challenging because alterations may impair functions or off-target effects can arise. Here, we introduce DIGGER-Bac, a toolbox for Design and Identification of seed regions for Golden Gate assembly and Expression of synthetic sRNAs in Bacteria. The SEEDling tool predicts optimal sRNA seed regions in combination with user-defined sRNA scaffolds for efficient regulation of specified mRNA targets. Results are passed on to the G-GArden tool, which assists with primer design for high-fidelity Golden Gate assembly of the desired synthetic sRNA constructs.