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Updated: Jun 25, 2025
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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
Published on: December 1, 2023
643
RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data
Sandeep Singh1, Xinrui Shi1,2, Samuel Haddox2
1Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA 22908, United States.
Briefings in Bioinformatics
|May 26, 2024
Summary
New software, RTCpredictor, identifies read-through chimeric RNAs involved in cancer. This tool accurately predicts these RNAs and their breakpoints, outperforming existing methods with greater speed and lower memory use.
Area of Science:
- Bioinformatics
- Molecular Biology
- Genomics
Background:
- Read-through chimeric RNAs expand the functional transcriptome.
- Mis-regulated chimeric RNAs contribute to cancer tumorigenesis.
- Current prediction tools often fail to identify these RNAs.
Purpose of the Study:
- To develop a novel computational tool for predicting read-through chimeric RNAs.
- To enhance the identification of cancer-related chimeric RNAs.
- To provide breakpoint coordinates for predicted chimeric RNAs.
Main Methods:
- Developed RTCpredictor, a tool using the ripgrep algorithm.
- Incorporated exonic variants and single nucleotide polymorphisms (SNPs) into the search.
- Compared RTCpredictor's performance against 10 other popular prediction tools.
Main Results:
- RTCpredictor demonstrated high sensitivity on simulated and real biological datasets.
- The tool accurately predicted read-through chimeric RNAs and their breakpoint coordinates.
- RTCpredictor exhibited faster execution times and lower memory requirements compared to existing tools.
Conclusions:
- RTCpredictor is the first dedicated tool for read-through chimeric RNA prediction with breakpoint identification.
- Its efficiency makes it suitable for large-scale genomic data analysis.
- RTCpredictor advances the study of chimeric RNAs in cancer research.
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