In many organisms, including simple animals like worms, genes are often arranged in clusters called operons. When these genes are copied into RNA, they need an extra step of processing called spliced leader (SL) trans-splicing. This process attaches a short piece of RNA, known as the spliced leader, to the beginning of the message. SL trans-splicing is important because it helps make sure that the RNA messages are complete and can be properly translated into proteins.

Until now, it has been difficult for researchers to study SL trans-splicing in detail because traditional methods often miss these events. With the arrival of new long-read RNA sequencing technologies, scientists now have the ability to capture longer stretches of RNA molecules and identify trans-splicing events more accurately.

Researchers at the City University of Hong Kong have developed a tool called SLRanger to tackle this challenge. SLRanger can detect SL sequences and predict operon structures in eukaryotic transcriptomes more reliably than older methods. Using direct RNA sequencing data from the model organism Caenorhabditis elegans, the team showed that SLRanger could detect SL-carrying reads with high confidence and predict operon genes with over 80 percent accuracy.

Workflow of SLRanger

(a) The direct RNA sequencing workflow for species exhibiting trans-splicing. In such species, spliced leader (SL) sequences are located at the 5′ ends of RNA reads due to the trans-splicing mechanism. Mature mRNAs containing SL sequences are sequenced using Oxford Nanopore direct RNA sequencing, and the resulting long reads are subsequently aligned to the reference genome. (b) The SL detection module of SLRanger. After aligning long RNA reads to the reference genome, the unaligned 5′-end fragments are extracted and aligned against a reference set of SL (including SL1 sequence and all SL2 variant sequences) and random sequences (as controls). Using SLRanger’s scoring scheme, each candidate read is assigned an “SLRanger score.” Reads will be assigned to the corresponding SL-type according to the highest “SLRanger score.” Reads that can’t be classified as SL1 will be considered as the SL2 type. If SL1 and SL2 can’t be distinguished, the read is labeled SL_unknown. If variants of SL2 can’t be distinguished, the read is labeled SL2_unknown. A dynamic cutoff is then applied to identify “high-confidence SL reads.” (c) the principle underlying operon prediction by SLRanger. Based on the presence of high-confidence SL sequences in each read and their genomic mapping positions relative to gene annotations, operon structures are inferred. Genes with a high proportion of SL1-type reads are predicted to be upstream operon genes, whereas genes with a high proportion of SL2-type reads or supported by multiple SL2-type reads are predicted to be downstream operon genes.

The tool was also tested on cDNA long RNA reads and in another species that uses trans-splicing, confirming its broad applicability. By making it easier to identify these special RNA events, SLRanger provides scientists with a new way to study how genes are organized and regulated, which may also help uncover new biological insights across diverse organisms.

Availability – SLRanger is available on Github (https://github.com/lrslab/SLRanger).