SLIDR and SLOPPR: flexible identification of spliced leader trans-splicing and prediction of eukaryotic operons from RNA-Seq data

Abstract

Background Spliced leader (SL) trans-splicing replaces the 5′ end of pre-mRNAs with the spliced leader, an exon derived from a specialised non-coding RNA originating from elsewhere in the genome. This process is essential for resolving polycistronic pre-mRNAs produced by eukaryotic operons into monocistronic transcripts. SL trans-splicing and operons may have independently evolved multiple times throughout Eukarya, yet our understanding of these phenomena is limited to only a few well-characterised organisms, most notably C. elegans and trypanosomes. The primary barrier to systematic discovery and characterisation of SL trans-splicing and operons is the lack of computational tools for exploiting the surge of transcriptomic and genomic resources for a wide range of eukaryotes. Results Here we present two novel pipelines that automate the discovery of SLs and the prediction of operons in eukaryotic genomes from RNA-Seq data. SLIDR assembles putative SLs from 5′ read tails present after read alignment to a reference genome or transcriptome, which are then verified by interrogating corresponding SL RNA genes for sequence motifs expected in bona fide SL RNA molecules. SLOPPR identifies RNA-Seq reads that contain a given 5′ SL sequence, quantifies genome-wide SL trans-splicing events and predicts operons via distinct patterns of SL trans-splicing events across adjacent genes. We tested both pipelines with organisms known to carry out SL trans-splicing and organise their genes into operons, and demonstrate that (1) SLIDR correctly detects expected SLs and often discovers novel SL variants; (2) SLOPPR correctly identifies functionally specialised SLs, correctly predicts known operons and detects plausible novel operons. Conclusions SLIDR and SLOPPR are flexible tools that will accelerate research into the evolutionary dynamics of SL trans-splicing and operons throughout Eukarya and improve gene discovery and annotation for a wide range of eukaryotic genomes. Both pipelines are implemented in Bash and R and are built upon readily available software commonly installed on most bioinformatics servers. Biological insight can be gleaned even from sparse, low-coverage datasets, implying that an untapped wealth of information can be retrieved from existing RNA-Seq datasets as well as from novel full-isoform sequencing protocols as they become more widely available.

U6 snRNA m6A modification is required for accurate and efficient cis- and trans-splicing ofC. elegansmRNAs

“…Therefore, the trans-spliceosome needs to bring two separate RNA molecules together. The majority of the trans-spliced RNAs use SL1 non-coding RNA (80-90% of all trans-spliced RNAs), and the SL2 non-coding RNA is mainly used by transcripts in operons (7% of all trans-spliced RNAs) (31)(32)(33). The 5′SS on the SL1 RNA is an invariant AG//GUAAA, and most of the C. elegans 3′ trans-splice sites have the same UUUCAG/R sequence found in cis-spliced 3′SSs.…”

Monodopsis and Vischeria Genomes Shed New Light on the Biology of Eustigmatophyte Algae

“…We identified SLs in the C73, C74, and C141 strains as well as M. gaditana CCMP1894 (RNA-Seq library SRR10431616 from SRA) using SLIDR 1.1.4 with distance-based clustering ( Wenzel et al 2021 ). We relaxed the SL length limit (−x 1.25), required GT/AG splice sites and disabled the Sm binding motif filter.…”

“…We then tested whether genome-wide SL trans -splicing events may indicate the presence of operonic gene organization using SLOPPR 1.1.3 ( Wenzel et al 2021 ). Because SLOPPR requires accurate gene annotations, particularly at the 5′ end, we first predicted 5′ UTRs guided by identified SLs using UTRme ( Radío et al 2018 ), relaxing maximum UTR length to 10,000 bp and maximum UTR ORF length to 400 amino acids.…”

Mutational profile of the regenerative process andde novogenome assembly of the planarianSchmidtea polychroa

“…Among the chimeric transcripts, i.e., those mapping to non-overlapping fragments on two different scaffolds, repeat-overlapping transcripts were also present with a high contribution of 41.3%, while the remaining chimeric mappings were caused either by spliced-leader trans-splicing (12.5%), true fragmentation of bm_Spol_g1 (38.3%), erroneous scaffolding (1.8%) or incorrect GMAP mapping of short transcript fragments to nearby homologous regions (6%). Trans-splicing used a spliced-leader sequence (Fig S6), encoded by 6 spliced-leader genes in several clusters as assessed by SLIDR [31],…”

“…Genes were annotated by remapping the transcripts in the PCFL set onto the final genome assembly by GMAP [30]. Multi-mapper, duplicate and chimeric transcripts were manually curated by their overlaps with repeat elements, by the presence of spliced-leader trans-splicing leader sequences as determines by the SLIDR/SLOPPR pipeline [31], and the lengths and relative positions of the multiple mapping sites. The scaffold containing the mitochondrial genome was manually curated, and mitochondrial genes were annotated with the MITOS webserver [64].…”