PUMA: PANDA Using MicroRNA Associations
Marieke L Kuijjer1, Maud Fagny2, Alessandro Marin3
1Centre for Molecular Medicine Norway, University of Oslo, Oslo 0318, Norway.
Motivation:
Conventional 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 Implementation:
PUMA 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.
Insights
We developed PUMA, a computational tool to analyze genomic data by integrating microRNA (miRNA) and messenger RNA (mRNA) interactions. PUMA consistently identifies tissue-specific miRNA regulatory processes, aiding in understanding disease development.
Area of Science:
- Genomics
- Computational Biology
- Molecular Biology
Background:
- Conventional genomic analysis methods often overlook the complex interplay between regulatory molecules like microRNAs (miRNAs) and messenger RNAs (mRNAs).
- This limitation hinders the identification of unique cellular processes specific to different tissues.
- Existing approaches frequently fail to capture the full scope of gene regulation.
Purpose of the Study:
- To develop a novel computational tool, PUMA (PANDA Using MicroRNA Associations), for modeling genome-wide gene regulation by miRNAs.
- To integrate miRNA target predictions with gene co-expression data to uncover tissue-specific regulatory networks.
- To identify miRNAs involved in tissue-specific processes and their potential roles in disease.
Main Methods:
- PUMA utilizes a message-passing algorithm to integrate prior miRNA target predictions with co-expression data.
- The tool was applied to RNA-Seq data from 38 tissues in the Genotype-Tissue Expression (GTEx) project.
- Two distinct miRNA target prediction resources (TargetScan and miRanda) were used to build network models.
Main Results:
- Despite differences in initial target predictions, PUMA consistently identified similar tissue-specific miRNA-target regulatory interactions across both network models.
- The identified tissue-specific miRNA functions were highly consistent between networks built on TargetScan and miRanda predictions.
- PUMA successfully identified miRNAs regulating critical tissue-specific processes that, upon mutation, may contribute to disease development within those tissues.
Conclusions:
- PUMA effectively models genome-wide miRNA regulatory networks, capturing crucial tissue-specific interactions.
- The tool demonstrates robustness by yielding consistent results regardless of the miRNA target prediction resource used.
- PUMA provides a valuable platform for discovering miRNAs involved in tissue-specific functions and diseases.
Related Concept Videos
01:22MicroRNAs
01:22MicroRNAs
02:57piRNA - Piwi-interacting RNAs
01:23RNA Interference
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...