DIGGER-Bac

DIGGER-Bac predicts optimal seed regions for synthetic bacterial small RNAs and generates primers for Golden Gate assembly to enable high-fidelity construction of sRNA constructs for targeted post-transcriptional regulation of gene expression.

Key Features:

• SEEDling: Predicts optimal seed regions for synthetic sRNAs from user-defined sRNA scaffolds to maximize specificity and regulatory efficacy on specified mRNA targets.
• G-GArden: Designs primers for Golden Gate assembly to support high-fidelity construction of synthetic sRNA constructs and minimize off-target effects.

Scientific Applications:

• Targeted post-transcriptional control: Enables design of sRNAs with optimal seed regions to achieve targeted post-transcriptional regulation of bacterial gene expression.
• Microbial engineering: Supports engineering of bacterial strains by enabling specific modulation of gene expression via synthetic sRNAs.
• Functional genomics: Facilitates perturbation of bacterial gene expression for functional genomics studies using designed sRNAs.
• Therapeutic development: Applicable to therapeutic development contexts that require precise regulation of bacterial gene expression.

Methodology:

SEEDling predicts seed regions from user-defined sRNA scaffolds for specified mRNA targets; G-GArden designs primers for Golden Gate assembly of the synthetic sRNA constructs.