MMIA

MMIA integrates microRNA (miRNA) and mRNA expression data to identify miRNA–mRNA regulatory relationships and associate them with biological functions, canonical pathways, diseases, and promoter regulation.

Key Features:

• Integration of Expression Data: Integrates miRNA and mRNA expression data to provide a combined view of regulatory relationships.
• Predicted Target Information: Incorporates miRNA target predictions from TargetScan, PITA, and PicTar to identify potential targets.
• Gene Set Analysis: Performs gene set analysis to link miRNA-associated genes to phenotypes and biological functions.
• Promoter Analysis: Examines promoters of miRNA primary transcripts to investigate miRNA gene regulation.
• Exhaustive Genome Coverage: Uses extensive human genome annotation to classify genes with respect to diseases, canonical pathways, and Gene Ontology terms.

Scientific Applications:

• miRNA–mRNA regulatory network analysis: Identify and characterize interactions between miRNAs and mRNA transcripts using integrated expression and prediction data.
• Functional and pathway annotation: Associate miRNA targets with canonical pathways and Gene Ontology terms to infer biological roles.
• Disease-associated gene classification: Classify genes linked to developmental and adult diseases based on genome-wide annotations and miRNA associations.
• Promoter and transcriptional regulation studies: Investigate regulation of miRNA primary transcripts through promoter analysis.
• Phenotype association: Link miRNA expression patterns to phenotypes via gene set analysis.

Methodology:

Integrates miRNA and mRNA expression data; incorporates TargetScan, PITA, and PicTar target predictions; performs gene set analysis and promoter analysis; uses human genome annotations to classify genes by disease, canonical pathways, and Gene Ontology.