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. 2021 Oct 18;11:20607. doi: 10.1038/s41598-021-99747-2

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Comparison of in silico strategies to prioritize 249 variants of uncertain significance with functional investigations performed. (a) Receiver operating characteristic area under the curve (AUC) comparisons for nine in silico prioritization strategies demonstrating that SpliceAI (AUC = 0.95, 95%CI 0.92–0.97) and a consensus approach (AUC = 0.94, 95% CI 0.91–0.97) outperform other strategies for prioritization; (b) AUC comparisons between SpliceAI, a consensus approach and a novel metric, demonstrates that a weighted approach slightly increases accuracy of prioritization over single approaches alone (AUC = 0.96, 95% CI 0.94–0.98); (c, d) Accuracy comparisons of each in silico prioritization approach across 2000 bootstraps utilizing region-specific pre-defined thresholds: (c) Violin plot demonstrating the calculated accuracy of each in silico prioritization approach; (d) frequency that each strategy is the best or joint-best performing.