Format

Send to

Choose Destination
Version 2. F1000Res. 2014 Jan 13 [revised 2014 Apr 7];3:8. doi: 10.12688/f1000research.3-8.v2. eCollection 2014.

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

Author information

1
Department of Computer Science, University of Western Ontario, London, Ontario, N6A 5B7, Canada.
2
Department of Biochemistry, University of Western Ontario, London, Ontario, N6A 5C1, Canada.
3
Cytognomix, Inc., London, Ontario, N6G 4X8, Canada.
4
Department of Computer Science, University of Western Ontario, London, Ontario, N6A 5B7, Canada ; Department of Biochemistry, University of Western Ontario, London, Ontario, N6A 5C1, Canada ; Cytognomix, Inc., London, Ontario, N6G 4X8, Canada.

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.

Supplemental Content

Full text links

Icon for F1000 Research Ltd Icon for PubMed Central
Loading ...
Support Center