MiRfold

MiRfold predicts microRNA (miRNA)-like hairpin folding in plant genomic and small RNA sequences to identify candidate small non-coding RNAs and assess their structural stability.

Key Features:

• Hybrid Methodology: Integrates computational analyses with expressed small RNA sequences to improve identification of novel non-coding RNAs (ncRNAs).
• Pattern-Matching and Folding Prediction: Uses pattern-matching strategies to compare RNA sequences against known genomes (e.g., Arabidopsis thaliana) and plant sequence databases and predicts stable hairpin structures indicative of miRNA-like folding.
• Optimization for Plant miRNAs: Specifically optimized for plant miRNAs and applied to species such as Arabidopsis thaliana and Nicotiana tabacum.
• Experimental Validation Support: Produces predictions that can be validated using Northern blot analyses to confirm the presence and size of RNA candidates.

Scientific Applications:

• Identification of Novel ncRNAs: Analyzes cDNA libraries from small RNAs (20–30 nucleotides) to identify unique clones corresponding to non-coding sequences in plant genomes.
• Understanding RNA Genesis: Predicts stable hairpin structures for 21–24 nucleotide candidate miRNAs to inform on structural stability and potential biogenesis.

Methodology:

Sequence comparison and candidate selection using pattern-matching software (e.g., Patbank) against known genomes and plant sequence databases; prediction of stable folding of selected RNA sequences with their flanking regions to identify hairpin structures characteristic of miRNAs.