Abstract
MotivationConventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue.Availability and implementationPUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/).Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
The regulation of gene expression involves a complicated network of interacting elements
We used PUMA to integrate target gene coexpression information for each tissue with an initial regulatory network, which we based on miRNA target predictions from either TargetScan or miRanda
Our analysis provides two alternative miRNA-mediated gene regulatory networks for each of the 38 tissues, one based on the TargetScan prior and the other alternative based on the miRanda prior
Summary
The regulation of gene expression involves a complicated network of interacting elements. The biological process of transcription begins with the binding of transcription factors to specific sequence motifs upstream of a gene’s transcription initiation site. This induces conformational changes in the DNA and initiates the assembly of the RNA polymerase complex, which in turn carries out transcription of the gene to a messenger RNA (mRNA). What emerges is not a single set of interactions, or even a single pathway, but a complex network of interacting genes and gene products Capturing these interactions is critical as we seek to understand how gene expression is regulated in different tissue environments, and how this regulation is disrupted in disease
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