SLRanger: an integrated approach for spliced leader detection and operon prediction using long RNA reads

Spliced leader (SL) trans-splicing occurs in a wide range of eukaryotes and plays a critical role in processing mRNAs derived from operon structures. However, current research on this mechanism remains limited, partly due to the difficulty in accurately identifying genuine SL trans-splicing events. The advent of long-read RNA sequencing technologies, such as direct RNA sequencing by Oxford Nanopore Technologies, offers a more promising avenue for detecting these events with greater resolution. Here, we present SLRanger, an integrated tool to detect SL sequences and predict operon structures in eukaryotic transcriptomes. SLRanger improves upon the traditional Smith-Waterman (SW) alignment framework by incorporating an optimized scoring scheme tailored to SL detection in native long RNA reads. We primarily validated our method using direct RNA sequencing data from Caenorhabditis elegans, a well-established model organism for studying trans-splicing. Through a dynamic cutoff strategy, SLRanger robustly identified high-confidence SL-carrying reads. Leveraging the SL information, SLRanger achieved over 80% accuracy in operon gene prediction, recovering more than 70% of known operon genes in C. elegans. SLRanger was also applied to detect SL from cDNA long RNA reads and another trans-spliced species. Our results demonstrate that SLRanger not only provides a reliable approach for characterizing SL trans-splicing events but also serves as an effective framework for operon discovery, enabling transcriptomic analysis for operons and facilitating downstream data-mining applications.

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Publication Details

Journal
Briefings in Bioinformatics
Published
2025-08-15
DOI
https://doi.org/10.1093/bib/bbaf437
Primary Topic
RNA and protein synthesis mechanisms
Type
article
Field-Weighted Citation Impact
0.00

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article

SLRanger: an integrated approach for spliced leader detection and operon prediction using long RNA reads

Runsheng Li, Zhihao Guo, J.R. Chen, Yanwen Shao
Briefings in Bioinformatics
RNA and protein synthesis mechanisms
article

SLRanger: an integrated approach for spliced leader detection and operon prediction using long RNA reads

Runsheng Li, Zhihao Guo, J.R. Chen, Yanwen Shao
article en

Abstract

Spliced leader (SL) trans-splicing occurs in a wide range of eukaryotes and plays a critical role in processing mRNAs derived from operon structures. However, current research on this mechanism remains limited, partly due to the difficulty in accurately identifying genuine SL trans-splicing events. The advent of long-read RNA sequencing technologies, such as direct RNA sequencing by Oxford Nanopore Technologies, offers a more promising avenue for detecting these events with greater resolution. Here, we present SLRanger, an integrated tool to detect SL sequences and predict operon structures in eukaryotic transcriptomes. SLRanger improves upon the traditional Smith-Waterman (SW) alignment framework by incorporating an optimized scoring scheme tailored to SL detection in native long RNA reads. We primarily validated our method using direct RNA sequencing data from Caenorhabditis elegans, a well-established model organism for studying trans-splicing. Through a dynamic cutoff strategy, SLRanger robustly identified high-confidence SL-carrying reads. Leveraging the SL information, SLRanger achieved over 80% accuracy in operon gene prediction, recovering more than 70% of known operon genes in C. elegans. SLRanger was also applied to detect SL from cDNA long RNA reads and another trans-spliced species. Our results demonstrate that SLRanger not only provides a reliable approach for characterizing SL trans-splicing events but also serves as an effective framework for operon discovery, enabling transcriptomic analysis for operons and facilitating downstream data-mining applications.

Briefings in BioinformaticsVol. 26(5)
City University of Hong Kong (HK), City University of Hong Kong, Shenzhen Research Institute (CN)
City University of Hong Kong, Health and Medical Research Fund
Openalex Percentile: Top 79%
RNA and protein synthesis mechanisms
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