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Published on: March 3, 2015
PTRcombiner: mining combinatorial regulation of gene expression from post-transcriptional interaction maps
Gianluca Corrado, Toma Tebaldi, Giulio Bertamini
1Department of Information Engineering and Computer Science (DISI), University of Trento, 38123 Trento, Italy. gabriella.viero@cnr.it.
This study introduces PTRcombiner, a computational tool to identify clusters of RNA binding proteins and non-coding RNAs involved in gene expression regulation. It analyzes co-regulator binding sites to reveal combinatorial post-transcriptional regulatory patterns.
Area of Science:
- Molecular Biology
- Bioinformatics
- Computational Biology
Background:
- Transcriptome-wide mapping of RNA-protein and RNA-RNA interactions is advancing.
- Understanding post-transcriptional regulation requires deciphering combinatorial patterns.
Purpose of the Study:
- To develop a computational tool for extracting clusters of mRNA trans-acting co-regulators.
- To analyze binding site similarity within co-regulator clusters.
Main Methods:
- Developed an innovative computational tool (PTRcombiner).
- Extracted co-regulator clusters from pairwise interaction data.
- Analyzed co-regulator binding site similarity using positional information.
Main Results:
- Identified modules of co-regulators coordinating functionally related messages in human and yeast.
- Demonstrated the tool's capability to uncover combinatorial post-transcriptional regulatory patterns.
Conclusions:
- PTRcombiner offers an original approach to uncovering combinatorial patterns in post-transcriptional regulation.
- The tool integrates available post-transcriptional interaction data for comprehensive analysis.
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