Figure 2. Flowchart of BRCA1 variant selection for exonic SRE mapping and evaluation of SRE predictor performance.
BRCA1 variants were from saturation genome editing experiments (SGE) of Findlay et al. (2018) covering exons 2, 3, 5, 6 and 16-24. Cut-off scores selected for determining function class and mRNA depletion are drawn from their Supplementary Table 1. For our selection of negative controls, we set a conservative RNA score cut-off ≥ 0 for variants not depleted in mRNA, as some variants experimentally confirmed to have weak to moderate splicing effects (from literature and unpublished splicing assay data) were observed to have RNA scores between -0.5 and -2 (data not shown).
ǂ Selection of variants outside donor and acceptor splice site motifs and prediction of de novo splice site gain were done through MES-based Variant Effect Predictor plugin using the thresholds and decision flowchart described in Shamsani et al. (2018).
Figure 3. Map of putative exonic SREs (blue) in BRCA1 as inferred from analysis of published functional data of Findlay et al. (2018) and Human Splicing Finder SRE predictions. Findlay et al. (2018) assessed exons 2, 3, 5, 6 and 16-24. Exons 2, 3, and 19-22 did not harbor variants that passed the selection criteria described in Figure 2. The cDNA positions are indicated in the map, and the locations of 33 putative SRE-disrupting variants are bolded and underlined. Longer blue regions reflect several overlapping SRE sequences that extend across the exon. The grey area at the 5’ end of exon 16 represents a region without functional data from Findlay et al. (2018).
Figure 4. Prioritization model to identify SRE-disrupting variants for splicing assay. For exonic variants outside of the donor and acceptor splice site motifs, the first step is to filter out variants that are predicted to lead to de novo splice site gain using the MES algorithm. Then MES is used to determine the native splice site scores, to identify exons most likely to harbor ESEs. Variants in exons with low to moderate donor score (< 8.5), or with high donor MES score (≥ 8.5) and low acceptor score (< 6.2), are then selected as eligible for two-step SRE analysis. Positive calls in HSF are further analyzed using ΔHZEI to minimize the number of false positives.
Supplementary Figure 1. Map of putative exonic SREs inBRCA1 exons 5, 6, 16-18, 23 and 24. The map is based on the combined analysis of results from the functional assay of Findlay et al. (2018), and SRE predictions from the algorithms in Human Splicing Finder v3.1. Variants that passed the selection criteria (see main text and Figure 2) are shown. Putative SRE locations are highlighted in blue. cDNA positions in green font indicate locations of negative control variants outside of putative SREs with false positive HSF SRE predictions. cDNA positions in red font indicate locations of negative control variants within putative SREs but with false positive HSF SRE predictions.
Supplementary Figure 2. Snapshot of Human Splicing Finder results webpage for MLH1 LRG_216t1:c.1559-1732A>T.The Information Theory model of Caminsky et al. (2016) predicted that LRG_216t1:c.1559-1732A>T creates a new acceptor and activates a pseudoexon due to the presence of a downstream pre-existing cryptic donor, but our HSF analysis revealed a s trong putative exonic splicing silencer (PESS) cluster within 30 nucleotides upstream of the donor site of the pseudoexon (encircled in red), which potentially inactivates this cryptic donor.