Prediction of regulatory modules comprising microRNAs and target genes

• Published in: Bioinformatics Vol. 21; no. suppl-2; pp. ii93 - ii100
• Main Authors: Yoon, Sungroh, De Micheli, Giovanni
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.09.2005; Oxford Publishing Limited (England)
• Subjects: Algorithms; Base Sequence; Gene Expression Regulation - genetics; Gene Targeting - methods; MicroRNAs - genetics; Molecular Sequence Data; Proteins - genetics; Regulatory Elements, Transcriptional - genetics; RNA Processing, Post-Transcriptional - genetics; Sequence Analysis, RNA - methods
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/bti1116

Motivation: MicroRNAs (miRNAs) are small endogenous RNAs that can play important regulatory roles via the RNA-interference pathway by targeting mRNAs for cleavage or translational repression. We propose a computational method to predict miRNA regulatory modules (MRMs) or groups of miRNAs and target genes that are believed to participate cooperatively in post-transcriptional gene regulation. Results: We tested our method with the human genes and miRNAs, predicting 431 MRMs. We analyze a module with genes: BTG2, WT1, PPM1D, PAK7 and RAB9B, and miRNAs: miR-15a and miR-16. Review of the literature and annotation with Gene Ontology terms reveal that the roles of these genes can indeed be closely related in specific biological processes, such as gene regulation involved in breast, renal and prostate cancers. Furthermore, it has been reported that miR-15a and miR-16 are deleted together in certain types of cancer, suggesting a possible connection between these miRNAs and cancers. Given that most known functionalities of miRNAs are related to negative gene regulation, extending our approach and exploiting the insight thus obtained may provide clues to achieving practical accuracy in the reverse-engineering of gene regulatory networks. Availability: A list of predicted modules is available from the authors upon request. Contact: sryoon@stanford.edu