incaRNAfbinv

incaRNAfbinv designs RNA sequences by solving the inverse RNA folding problem using a fragment-based approach that incorporates biologically meaningful constraints.

Key Features:

• Fragment-Based Design: Employs a fragment-based strategy to design sequences that preserve the coarse-grained tree graph shape of the target secondary structure.
• Biologically Meaningful Constraints: Integrates biological information by allowing specification of sequence and motif constraints to ensure designed sequences meet biological criteria.
• Weighted Sampling Algorithm: Uses the weighted sampling algorithm from incaRNAtion to generate diverse candidate sequences that satisfy design constraints.
• Customizable Input Parameters: Supports specification of target RNA secondary structure, optional sequence and motif constraints, minimum free energy, neutrality, and GC content.

Scientific Applications:

• Design of Synthetic Regulatory Sequences: Enables design of synthetic regulatory RNA sequences with specified structural and sequence constraints.
• Detection of Novel Naturally Occurring RNAs: Can be used as a preprocessing step in workflows aimed at identifying or characterizing novel naturally occurring RNAs.

Methodology:

Combines the fragment-based inverse folding methodology of RNAfbinv with the weighted sampling algorithm from incaRNAtion.