The workflow of the splicing assays is outlined in Figure S1. To identify potential splicing variants, in silico studies were performed using MaxEntScan (MES, http://hollywood.mit.edu/burgelab/maxent/Xmaxentscan_scoreseq.html, accessed on 1 April 2021) (cut-off ≥ 3.0) [18] and NNSplice (https://www.fruitfly.org/seq_tools/splice.html, accessed on 1 April 2021) [19] when MES was not informative. Potential spliceogenic variants were selected according to the following criteria: (i) MES score changes (>15%) [16,20] and (ii) creation of alternative sites (Table S2).

Four in silico approaches were used to predict variant-induced modifications in splicing regulatory elements: (a) HEXplorer (ΔHZEI; cut-off < −5) (https://www2.hhu.de/rna/html/hexplorer_score.php, accessed on 1 April 2021) [21]; (b) Hot-Skip (https://hot-skip.img.cas.cz/, cut-off ≥ 1, accessed on 1 April 2021) or Ex-Skip (https://ex-skip.img.cas.cz/, accessed on 1 April 2021) [22]; (c) calculation of total ESRseq score changes (ΔtESRseq) by quantitative assessment of RNA hexamers (cut-off < −0.75) [23]; and (d) alteration of the ESE/ESS balance by HSF at Genomnis (https://hsf.genomnis.com/home, accessed on 1 April 2021).

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