miRNAFold

miRNAFold predicts miRNA precursor hairpin secondary structures in genomic sequences to identify novel microRNA (miRNA) precursors for studies of miRNA biogenesis and post-transcriptional gene regulation.

Key Features:

• Ab initio prediction: miRNAFold performs ab initio prediction of miRNA precursors without relying on existing miRNA databases.
• High sensitivity and speed: The algorithm emphasizes high sensitivity and computational speed suitable for large-scale genomic analyses.

Scientific Applications:

• miRNA research: Facilitates identification of miRNA precursors to support studies of miRNA biogenesis and function.
• Genomic studies: Enables comprehensive analysis of genomic sequences to discover novel miRNAs and investigate gene regulation mechanisms.

Methodology:

miRNAFold is based on the miRNAFold algorithm, which predicts the secondary structures characteristic of miRNA precursors and distinguishes potential miRNA hairpins from other RNA structures within genomic sequences.