SL-quant: A fast and flexible pipeline to quantify spliced leader trans-splicing events from RNA-seq data
The spliceosomal transfer of a short spliced leader (SL) RNA to an independent pre-mRNA molecule is called SL trans-splicing and is widespread in the nematode C. elegans. While RNA-seq data contain information on such events, properly documented methods to extract them are lacking. To address this,...
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Veröffentlicht in: | Gigascience 2018-07, Vol.7 (7) |
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Sprache: | eng |
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Zusammenfassung: | The spliceosomal transfer of a short spliced leader (SL) RNA to an independent pre-mRNA molecule is called SL trans-splicing and is widespread in the nematode C. elegans. While RNA-seq data contain information on such events, properly documented methods to extract them are lacking.
To address this, we developed SL-quant, a fast and flexible pipeline that adapts to paired-end and single-end RNA-seq data and accurately quantifies SL trans-splicing events. It is designed to work downstream of read mapping and uses the reads left unmapped as primary input. Briefly, the SL-sequences are identified with high specificity and are trimmed from the input reads, which are then re-mapped on the reference genome and quantified at the nucleotide position level (SL trans-splice sites) or at the gene level.
SL-quant completes within 10 minutes on a basic desktop computer for typical C.elegans RNA-seq datasets, and can be applied to other species as well. Validating the method, the SL trans-splice sites identified display the expected consensus sequence and the results of the gene-level quantification are predictive of the gene position within operons. We also compared SL-quant to a recently published SL-containing read identification strategy which revealed being more sensitive, but less specific than SL-quant. Both methods are implemented as a bash script available under the MIT licence at https://github.com/cyaguesa/SL-quant. Full instructions for its installation, usage, and adaptation to other organisms are provided. |
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ISSN: | 2047-217X 2047-217X |
DOI: | 10.1093/gigascience/giy084 |