Validation of predicted mRNA splicing mutations using high-throughput transcriptome data

F1000Res. 2014 Jan 13;3:8. doi: 10.12688/f1000research.3-8.v2. eCollection 2014.

Abstract

Interpretation of variants present in complete genomes or exomes reveals numerous sequence changes, only a fraction of which are likely to be pathogenic. Mutations have been traditionally inferred from allele frequencies and inheritance patterns in such data. Variants predicted to alter mRNA splicing can be validated by manual inspection of transcriptome sequencing data, however this approach is intractable for large datasets. These abnormal mRNA splicing patterns are characterized by reads demonstrating either exon skipping, cryptic splice site use, and high levels of intron inclusion, or combinations of these properties. We present, Veridical, an in silico method for the automatic validation of DNA sequencing variants that alter mRNA splicing. Veridical performs statistically valid comparisons of the normalized read counts of abnormal RNA species in mutant versus non-mutant tissues. This leverages large numbers of control samples to corroborate the consequences of predicted splicing variants in complete genomes and exomes.

Grant support

PKR is supported by the Canadian Breast Cancer Foundation, Canadian Foundation for Innovation, Canada Research Chairs Secretariat and the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant 371758-2009). SND received fellowships from the Ontario Graduate Scholarship Program, the Pamela Greenaway-Kohlmeier Translational Breast Cancer Research Unit, the CIHR Strategic Training Program in Cancer Research and Technology Transfer, and the University of Western Ontario.