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- Title
SNPlice: variants that modulate Intron retention from RNA-sequencing data.
- Authors
Mudvari, Prakriti; Movassagh, Mercedeh; Kowsari, Kamran; Seyfi, Ali; Kokkinaki, Maria; Edwards, Nathan J.; Golestaneh, Nady; Horvath, Anelia
- 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 cooccurrence 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.
- Subjects
INTRONS; SPLIT genes; EXONS (Genetics); RNA sequencing; RNA analysis
- Publication
Bioinformatics, 2015, Vol 31, Issue 8, p1191
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btu804