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Detection of Chimeric RNAs from RNA-Seq Data with ChiTaRS 8.0: Insights for Liquid Biopsy and Drug Target
Dylan D'Souza1, Daniel Sumbatian1, Bar Sever2
1The Azrieli Faculty of Medicine, Bar Ilan University, Safed, Israel.
Abstract:
Chimeric RNAs (chiRNAs), generated via genomic rearrangements or splicing events, are increasingly recognized as biomarkers and therapeutic targets in cancer and neurodegenerative disorders. This chapter introduces an integrative framework for high-confidence chiRNA identification leveraging the ChiTaRS 8.0 database and the ChiTaH pipeline. ChiTaRS 8.0 encompasses 47,445 human chiRNAs, 1,055 Hi-C breakpoints, and 1,598 drug targets, while ChiTaH facilitates disease-specific analysis of RNA-seq data from 250 peripheral blood mononuclear cell (PBMC) samples-including glioblastoma and oral squamous cell carcinoma-and 199 healthy controls. Our approach combines reference-based fusion detection, BLAT validation against GRCh38, gene-pair compatibility checks, and protein domain conservation analysis. Functional annotation and protein-protein interaction modeling uncovered oncogenic chiRNAs absent from existing databases, exhibiting tissue-specific patterns. In Alzheimer's disease, liquid biopsy analyses identified unique chimeras-such as ENO1-MCUR1 and APOE-APOE-in cerebrospinal fluid, linked to neurotransmitter pathways and amyloid processing, and absent in healthy samples, highlighting their potential as early biomarkers. We describe a scalable digital hospital framework integrating AI-driven fusion detection, relational databases, and clinical metadata for real-time diagnostics and patient monitoring. This system supports fusion-targeted drug discovery and patient stratification, bridging translational gaps in oncology and neurodegeneration. By coupling computational pipelines with multiomics data, our approach advances personalized medicine while addressing challenges in artifact filtering and functional validation. Ultimately, the ChiTaRS-ChiTaH platform offers a versatile tool for chiRNA discovery and annotation across diverse disease contexts, providing insights into molecular mechanisms and clinical applications.
Insights
This study introduces a new framework for identifying chimeric RNAs (chiRNAs), which are crucial biomarkers for diseases like cancer and Alzheimer's. The ChiTaRS-ChiTaH platform enables high-confidence discovery and analysis of these important genetic markers.
Area of Science:
- Bioinformatics
- Genomics
- Molecular Biology
Background:
- Chimeric RNAs (chiRNAs) are increasingly recognized as significant biomarkers and therapeutic targets in various diseases, including cancer and neurodegenerative disorders.
- Accurate identification and functional characterization of chiRNAs are crucial for advancing personalized medicine.
Purpose of the Study:
- To introduce an integrative framework, ChiTaRS 8.0 database and ChiTaH pipeline, for high-confidence chiRNA identification and analysis.
- To discover novel oncogenic and disease-specific chiRNAs with potential as early diagnostic biomarkers.
- To develop a scalable digital hospital framework for AI-driven diagnostics and patient monitoring.
Main Methods:
- Leveraging the ChiTaRS 8.0 database (47,445 human chiRNAs, 1,055 Hi-C breakpoints, 1,598 drug targets) and the ChiTaH pipeline for RNA-seq data analysis.
- Integrating reference-based fusion detection, BLAT validation, gene-pair compatibility checks, and protein domain conservation analysis.
- Utilizing AI-driven fusion detection, relational databases, and clinical metadata for a digital hospital framework.
Main Results:
- Identified novel oncogenic chiRNAs with tissue-specific patterns, not previously listed in existing databases.
- Discovered unique chiRNAs (e.g., ENO1-MCUR1, APOE-APOE) in cerebrospinal fluid of Alzheimer's disease patients, absent in healthy controls.
- Demonstrated the potential of chiRNAs as early biomarkers for neurodegenerative diseases and cancer through liquid biopsy analysis.
Conclusions:
- The ChiTaRS-ChiTaH platform provides a versatile and robust tool for chiRNA discovery, annotation, and functional validation across diverse disease contexts.
- The developed framework advances personalized medicine by enabling real-time diagnostics, patient stratification, and fusion-targeted drug discovery.
- This integrative approach bridges translational gaps in oncology and neurodegeneration, offering insights into molecular mechanisms and clinical applications.
