iMIRAGE: an R package to impute microRNA expression using protein-coding genes.

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• MicroRNAs (miRNAs) are key regulators of gene expression at the post-transcriptional level.
• Accurate miRNA profiling is challenging, leading to incomplete miRNA data in many transcriptome datasets.
• Protein-coding gene (PCG) expression reflects miRNA activity, offering an alternative analysis avenue.

Purpose of the Study:

• To develop a computational tool for predicting miRNA expression profiles from PCG expression data.
• To provide a solution for analyzing existing transcriptome datasets lacking direct miRNA measurements.
• To enable the imputation of miRNA activity using machine learning on PCG expression.

Main Methods:

• Development of the iMIRAGE (imputed miRNA activity from gene expression) R package.
• Implementation of an integrated workflow for normalizing and transforming miRNA and PCG expression data.
• Utilizing predicted miRNA targets to impute miRNA activity from independent PCG expression datasets.

Main Results:

• iMIRAGE offers a convenient method to predict miRNA expression using PCG expression data.
• The tool facilitates the analysis of biological consequences of miRNA activity in datasets with missing miRNA profiles.
• Provides a robust workflow for inferring miRNA activity from gene expression data.

Conclusions:

• iMIRAGE effectively imputes miRNA activity from PCG expression, overcoming limitations of direct small RNA profiling.
• This tool enhances the utility of public transcriptome datasets for miRNA-related research.
• Enables deeper insights into gene regulation by inferring miRNA function from readily available gene expression data.