Utility of splicing prediction tools in variant interpretation
Multiple in silico tools have been developed to predict the
impact of spliceogenic variants (Table 1), and such prediction is an
important component of variant curation and interpretation processes for
Mendelian disease genes. Several studies have assessed the utility of
prediction tools in interpretation of variants in hereditary breast and
ovarian cancer genes. These include: clinical calibration of the
MaxEntScan (MES) tool to estimate the prior probability of pathogenicity
of genetic variation in BRCA1 and BRCA2 due to impact on
native donor and acceptor motifs, or the creation of exonic de
novo donor sites (Vallée et al., 2016); assessment of the sensitivity
and specificity of different MES thresholds to predict aberrant splicing
using experimentally validated spliceogenic variants in BRCA1 ,BRCA2 , MLH1, MSH2, MSH6 and PMS2 (Shamsani et al.,
2018); and the combined use of MES and Splice Site Finder-like, trained
and validated using in vitro mRNA data to improve in
silico prediction of spliceogenic variants in donor and acceptor splice
site motifs (Leman et al., 2018). The combined MES and Splice Site
Finder-like analysis pipeline has been previously proposed as a
prioritization method for splicing analysis of BRCA1 andBRCA2 variants of uncertain significance (VUSs) (Houdayer et al.,
2012). These studies have shown the reliability of bioinformatic tools
in predicting spliceogenic variants in the donor and acceptor splice
site motifs, especially those that disrupt the highly conserved
dinucleotides at the 3’ (AG) and 5’ splice sites (GT).
In contrast, predictors of variant effects on exonic SREs or BP sites
currently perform poorly (see below), which limits their utility to
inform variant classification in routine diagnostics. There is currently
no prediction tool specifically designed for pseudoexon-activating
variants. In the following sections, we discuss: i) the spliceogenic
variants outside of the donor and acceptor splice site motifs; ii) the
current tools used to predict their effects on splicing and their
predictive performance; and iii) combined strategies using functional
studies and in silico tools to prioritize variants for
confirmatory splicing assays.