Large-scale comparative evaluation of user-friendly tools for predicting variant-induced alterations of splicing regulatory elements.

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 were 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 stand-alone tools and in multiple combined ways based on two independent benchmark datasets adding up to >1300 exonic variants studied at the mRNA level and mapping to 89 different disease-causing genes. These methods display good predictive power, based on decision thresholds derived from ROC curve analyses, with QUEPASA and HAL having the best accuracies either as stand-alone 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. This article is protected by copyright. All rights reserved.