Large‐scale comparative evaluation of user‐friendly tools for predicting variant‐induced alterations of splicing regulatory elements. Issue 10 (16th August 2020) - Tubeuf, Hélène; Charbonnier, Camille; Soukarieh, Omar; Blavier, André; Lefebvre, Arnaud; Dauchel, Hélène; Frebourg, Thierry; Gaildrat, Pascaline; Martins, Alexandra
Abstract: Discriminating which nucleotide variants cause disease or contribute to phenotypic traits remains a major challenge in human genetics. In theory, any intragenic variant can potentially affect RNA splicing by altering splicing regulatory elements (SREs). However, these alterations are often ignored mainly because pioneer SRE predictors have proved inefficient. Here, we report the first large‐scale comparative evaluation of four user‐friendly SRE‐dedicated algorithms (QUEPASA, HEXplorer, SPANR, and HAL) tested both as standalone tools and in multiple combined ways based on two independent benchmark datasets adding up to >1, 300 exonic variants studied at the messenger RNA level and mapping to 89 different disease‐causing genes. These methods display good predictive power, based on decision thresholds derived from the receiver operating characteristics curve analyses, with QUEPASA and HAL having the best accuracies either as standalone or in combination. Still, overall there was a tight race between the four predictors, suggesting that all methods may be of use. Additionally, QUEPASA and HEXplorer may be beneficial as well for predicting variant‐induced creation of pseudoexons deep within introns. Our study highlights the potential of SRE predictors as filtering tools for identifying disease‐causing candidates among the plethora of variants detected by high‐throughput DNA sequencing and provides guidance for their use in genomic medicine settings. Abstract : Pinpointing which nucleotide changes located outside acceptor and donor splice sites may disrupt RNA splicing by altering splicing regulatory elements (SREs) remains a major challenge in medical genetics. Our large‐scale comparative evaluation of four recently developed state‐of‐the‐art algorithms (QUEPASA, HEXplorer, SPANR and HAL) revealed that even though QUEPASA and HAL had slightly better accuracies, all four methods displayed good performances in predicting variant‐induced SRE alterations, including abnormally increased exon skipping or inclusion. These methods can thus become useful tools in molecular diagnostics settings for stratifying variants of unknown significance (VUS) for experimental RNA analyses and hence contribute to the identification of potentially pathogenic splicing mutations.