SNPlice – identify cis-acting, splice-modulating variants from RNA-seq datasets

The growing recognition of the importance of splicing in eukaryotes, together with rapidly accumulating RNA-sequencing data, demand robust high-throughput approaches, which efficiently analyze experimentally derived whole-transcriptome splice profiles.

Researchers from The George Washington University have developed a computational approach, called SNPlice, for identifying cis-acting, splice-modulating variants from RNA-seq datasets. SNPlice mines RNA-seq datasets to find reads that span single nucleotide variant (SNV) loci and nearby splice junctions, assessing the co-occurrence of variants and molecules that remain unspliced at nearby exon-intron boundaries. Hence, SNPlice highlights variants preferentially occurring on intron-containing molecules, possibly resulting from altered splicing. To illustrate co-occurrence of variant nucleotide and exon-intron boundary, allele-specific sequencing was used. SNPlice results are generally consistent with splice-prediction tools, but also indicate splice-modulating elements missed by other algorithms. SNPlice can be applied to identify variants that correlate with unexpected splicing events, and to measure the splice-modulating potential of canonical splice-site SNVs.


Availability: SNPlice is freely available for download from as a self-contained binary package for 64-bit Linux computers and as python source-code.

Contact: and

Mudvari P, Movassagh M, Kowsari K, Seyfi A, Kokkinaki M, Edwards NJ, Golestaneh N, Horvath A. (2014) SNPlice: Variants that Modulate Intron-retention from RNA-sequencing Data. Bioinformatics. [Epub ahead of print]. [abstract]

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