Systematic Computational Identification of Variants That Activate Exonic and Intronic Cryptic Splice Sites.

ABSTRACT: We developed a variant-annotation method that combines sequence-based machine-learning classification with a context-dependent algorithm for selecting splice variants. Our approach is distinctive in that it compares the splice potential of a sequence bearing a variant with the splice potential of the reference sequence. After training, classification accurately identified 168 of 180 (93.3%) canonical splice sites of five genes. The combined method, CryptSplice, identified and correctly predicted the effect of 18 of 21 (86%) known splice-altering variants in CFTR, a well-studied gene whose loss-of-function variants cause cystic fibrosis (CF). Among 1,423 unannotated CFTR disease-associated variants, the method identified 32 potential exonic cryptic splice variants, two of which were experim

SUBMITTER: Lee M 

PROVIDER: S-EPMC5420354 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature