Indeed, the DS provided by the genuine SpliceAI account for the maximal differences between the predictions of the variant and the reference allele, for the 4 predicted categories being acceptor gain (AG), acceptor loss (AL), donor gain (DG), and donor loss (DL). Second, the results are the delta scores (DS) between the raw scores (RS) of the reference allele and the variant allele, which can be difficult to interpret and in some cases misleading, in particular when the reference value is comprised within the intermediate range of interpretation (i.e., ). First, predictions and relative positions of the altered splice sites are displayed as numerical values, which can be confusing when estimating which exact sites are altered, or when dealing with long-distance effects. Still, the standard version of SpliceAI (currently v1.3.1) has some limitations. In a recent improvement, the SpliceAI neural network has been retrained with a curated and manually validated isoforms dataset. This ability to focus not only on the nearby site (destruction or creation) but at the whole transcript level is a unique feature of these deep-learning-based next-generation splicing predictors, such as SpliceAI or Pangolin. Furthermore, thanks to its neural network, SpliceAI is able to make predictions about the global splicing outcome (e.g., exon skipping, splicing rescue by cryptic site activation, pseudo-exon creation, etc.). The efficiency of SpliceAI to predict a variant’s splicing alteration has been attested by multiple studies. Therefore, there is a strong need for in silico tools that can facilitate the precise interpretation of candidate variants to (1) correctly prioritize the best candidates to be investigated, and (2) choose the optimal functional validation test according to the expected alteration. These validations are often time-consuming and expensive. However, the functional validation of a variant predicted to alter splicing requires in vitro tests or additional and sometimes invasive biological samples. A significant proportion (up to 60% ) of the pathogenic variants identified are likely to alter the correct splicing of the transcript. Graphical abstractĮxome and genome sequencing currently identify on a daily basis many novel or uncharacterized variants worldwide. SpliceAI-visual is available as a Google Colab notebook and has also been fully integrated in a free online variant interpretation tool, MobiDetails ( ). We also show how SpliceAI-visual can elucidate several complex splicing defects taken from the literature but also from unpublished cases. We report here the benefits of using SpliceAI-visual and demonstrate its relevance in the assessment/modulation of the PVS1 classification criteria. ![]() ![]() ![]() Third, SpliceAI-visual is currently one of the only SpliceAI-derived implementations able to annotate complex variants (e.g., complex delins). Second, the outcome of SpliceAI-visual is user-friendly thanks to the graphical presentation. First, SpliceAI-visual manipulates raw scores and not delta scores, as the latter can be misleading in certain circumstances. We present here SpliceAI-visual, a free online tool based on the SpliceAI algorithm, and show how it complements the traditional SpliceAI analysis. However, its outputs present several drawbacks: (1) although the numerical values are very convenient for batch filtering, their precise interpretation can be difficult, (2) the outputs are delta scores which can sometimes mask a severe consequence, and (3) complex delins are most often not handled. SpliceAI is an open-source deep learning splicing prediction algorithm that has demonstrated in the past few years its high ability to predict splicing defects caused by DNA variations.
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