mtR_find: A Parallel Processing Tool to Identify and Annotate RNAs Derived from the Mitochondrial Genome

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Mitochondrial RNAs are prevalent in sequencing data, but their unique features pose challenges for analysis.
• Existing tools struggle to accurately identify and annotate mitochondrial small RNAs (mt-sRNAs) and long non-coding RNAs (mt-lncRNAs).

Purpose of the Study:

• To develop a novel computational tool, mtR_find, for the detection and annotation of mitochondrial RNAs.
• To establish a standardized nomenclature for accurate annotation of mt-sRNAs.
• To explore the potential of mt-ncRNAs as diagnostic or prognostic markers.

Main Methods:

• Developed mtR_find, a tool utilizing a novel method for counting RNA sequences from adapter-trimmed reads.
• Applied mtR_find to analyze existing transcriptomic datasets.
• Benchmarked mtR_find using simulated datasets for validation.

Main Results:

• Identified mt-sRNAs significantly associated with hepatocellular carcinoma and obesity.
• Discovered novel mt-sRNAs and mt-lncRNAs in mouse early development.
• Demonstrated the tool's ability to extract novel biological insights from existing data.

Conclusions:

• mtR_find provides unprecedented resolution and simplicity for analyzing mt-ncRNA transcriptomes.
• The tool facilitates re-analysis of transcriptomic databases, uncovering disease-associated RNAs.
• mt-ncRNAs show promise as potential diagnostic or prognostic markers in medicine.