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Fig. 1 | Genome Medicine

Fig. 1

From: CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores

Fig. 1

Benchmarking available splice predictions on the MFASS data set. We use the Multiplexed Functional Assay of Splicing using Sort-seq (MFASS) data set to benchmark different available splice effect predictors. MFASS studied splicing effects of more than 27,000 human exonic and intronic variants by creating a synthetic library of the respective exons (or nearest exon for intronic variants) between two GFP exons. The genome integrated sequences are transcribed and it is observed how much each exon is spliced in or out of the reporter mRNAs through RNA-seq. Changes in the percent spliced-in (psi) between reference and alternative sequence alleles are used to identify splice disrupting variants (sdv). We analyze how well different machine learning models distinguish between sdv and no-sdv variants

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Genome Medicine

ISSN: 1756-994X

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