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DuplexDiscoverer: a computational method for the analysis of experimental duplex RNA-RNA interaction data.

Egor Semenchenko^1,2, Volodymyr Tsybulskyi^1,2, Irmtraud M Meyer^1,2,3

¹Laboratory of bioinformatics of RNA Structure and Transcriptome Regulation, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, 13125 Berlin, Germany.

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|April 12, 2025

Summary

DuplexDiscoverer efficiently analyzes RNA-RNA interaction duplex data, improving computational methods for transcriptomics. This R package offers adjustable analysis and integrates seamlessly with bioinformatics tools.

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Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

High-throughput sequencing generates duplex data for studying cis and trans RNA-RNA interactions.
Existing computational methods for analyzing duplex data often lack efficiency, adaptability, and robust statistical validation.

Purpose of the Study:

To develop an efficient, adjustable, and conceptually coherent computational method for analyzing RNA-RNA interaction duplex data.
To provide an R package, DuplexDiscoverer, that overcomes limitations of current duplex data analysis tools.

Main Methods:

DuplexDiscoverer employs a computational approach for analyzing raw duplex sequencing data.
The method is implemented as an R package, allowing for adjustable processing steps and parameter values.
It ensures interoperability with common R-based bioinformatics tools for transcriptomics.

Main Results:

DuplexDiscoverer provides efficient and adaptable analysis of duplex data from various experimental protocols.
The package seamlessly integrates with standard R bioinformatics workflows.
Predictions generated by DuplexDiscoverer are of superior or comparable quality to existing methods, with significant improvements in time and memory efficiency.

Conclusions:

DuplexDiscoverer offers a superior computational solution for analyzing RNA-RNA interaction duplex data.
The R package enhances the efficiency, adaptability, and statistical rigor of transcriptomic analyses.
It facilitates more reliable identification and interpretation of cis and trans RNA-RNA interactions.