Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms

M Hossein Radfar1, Willy Wong, Quaid Morris

  • 1Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada. h.radfar@utoronto.ca

Plos One
|June 23, 2011
PubMed

Insights

A new computational method, InMiR, accurately predicts targets of intronic microRNAs (miRNAs) using host gene expression profiles. This approach identifies more true miRNA targets than previous methods, advancing our understanding of gene regulation.

Area of Science:

  • Genomics
  • Computational Biology
  • Gene Regulation

Background:

  • MicroRNAs (miRNAs) located within introns of protein-coding genes often share expression patterns with their host genes.
  • Predicting miRNA targets using host gene expression is a powerful approach but requires refinement due to complexities in miRNA-mRNA interactions and co-expression.

Purpose of the Study:

  • To introduce InMiR, a novel computational method for predicting intronic miRNA targets based on host gene expression profiles.
  • To improve the accuracy and efficiency of intronic miRNA target prediction compared to existing correlation-based methods.

Main Methods:

  • Developed a linear-Gaussian model within InMiR to analyze expression profiles across numerous datasets.
  • Applied InMiR to 140 Affymetrix datasets from Gene Expression Omnibus to build an interaction network.
  • Evaluated InMiR's ability to predict host genes as surrogates for intronic miRNA expression.

Main Results:

  • InMiR recovered nearly twice as many true positives as a comparable correlation-only method at a fixed false positive rate.
  • A network of 19,926 interactions between 57 intronic miRNAs and 3,864 targets was constructed.
  • Identified host genes that are poor surrogates for intronic miRNA expression harbor more miRNA target sites and potential Pol II/III promoters.

Conclusions:

  • InMiR provides a robust method for predicting intronic miRNA targets and understanding their regulatory relationships.
  • The study classifies intronic miRNAs into three regulatory categories based on their relationship with host genes.
  • A dataset of 1,935 predicted mRNA targets for 22 intronic miRNAs, supported by sequence and expression data, is provided.

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