Bioinformatics. 2015 Apr 15;31(8):1191-8. doi: 10.1093/bioinformatics/btu804. Epub 2014 Dec 6.

SNPlice: variants that modulate Intron retention from RNA-sequencing data.

Mudvari P1, Movassagh M1, Kowsari K1, Seyfi A1, Kokkinaki M2, Edwards NJ2, Golestaneh N3, Horvath A1.

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Abstract

RATIONALE:

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

RESULTS:

We 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.