miROrtho

miROrtho predicts precursor microRNA (miRNA) genes in metazoan genomes using orthology and machine-learning approaches to identify and validate conserved miRNA loci.

Key Features:

• Three-tier analysis pipeline: Combines an SVM-based ab initio screening that detects potential hairpin structures and homologs of known miRNAs, orthology delineation to establish conserved relationships among predicted miRNA genes, and an SVM-based classifier applied to ortholog multiple sequence alignments to refine candidates.
• Comprehensive computational survey: Performs large-scale analysis across sequenced metazoan genomes to identify both known and novel miRNA gene candidates.
• Homology-extended alignments: Produces extended alignments for existing miRBase families and newly predicted miRNA families to support comparative analyses and RNA secondary-structure conservation assessment.

Scientific Applications:

• miRNA Gene Discovery: Facilitates identification of novel precursor miRNA genes with evolutionary support complementary to experimentally verified sequences in miRBase.
• Comparative Genomics: Enables cross-species comparison of miRNA genes and their evolutionary trajectories via orthologous multiple sequence alignments.
• Post-Transcriptional Regulation Studies: Supports research into miRNA-mediated regulation of gene expression by providing candidate precursor sequences and conserved RNA secondary-structure information for ~22-nucleotide miRNAs processed from stem-loop precursors.

Methodology:

Applies Support Vector Machine (SVM)-based ab initio screening to detect hairpin structures and homologs, delineates orthologous miRNA relationships, classifies ortholog multiple sequence alignments with an SVM-based classifier, generates homology-extended alignments for miRBase and novel families, and conducts computational surveys across sequenced metazoan genomes incorporating RNA secondary-structure conservation.