Département de Génétique Médicale, Groupe Hospitalier Universitaire de la Pitié Salpêtrière, AP-HP.Sorbonne Université, Laboratoire de Médecine Génomique Sorbonne Université, Paris, France.
Laboratoire de Biologie Médicale Multi-Sites SeqOIA (laboratoire-seqoia.fr/), Paris, France.
Hum Genomics. 2023 Feb 10;17(1):7. doi: 10.1186/s40246-023-00451-1.
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. 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. We present here SpliceAI-visual, a free online tool based on the SpliceAI algorithm, and show how it complements the traditional SpliceAI analysis. First, SpliceAI-visual manipulates raw scores and not delta scores, as the latter can be misleading in certain circumstances. Second, the outcome of SpliceAI-visual is user-friendly thanks to the graphical presentation. Third, SpliceAI-visual is currently one of the only SpliceAI-derived implementations able to annotate complex variants (e.g., complex delins). We report here the benefits of using SpliceAI-visual and demonstrate its relevance in the assessment/modulation of the PVS1 classification criteria. We also show how SpliceAI-visual can elucidate several complex splicing defects taken from the literature but also from unpublished cases. SpliceAI-visual is available as a Google Colab notebook and has also been fully integrated in a free online variant interpretation tool, MobiDetails ( https://mobidetails.iurc.montp.inserm.fr/MD ).
SpliceAI 是一个开源的深度学习剪接预测算法,在过去几年中,它已经证明了其预测由 DNA 变异引起的剪接缺陷的高能力。然而,它的输出有几个缺点:(1)虽然数值对于批量筛选非常方便,但它们的精确解释可能很困难,(2)输出是 delta 分数,有时可能会掩盖严重的后果,(3)复杂的 delins 通常无法处理。我们在这里介绍 SpliceAI-visual,这是一个基于 SpliceAI 算法的免费在线工具,并展示它如何补充传统的 SpliceAI 分析。首先,SpliceAI-visual 操作原始分数而不是 delta 分数,因为在某些情况下后者可能会产生误导。其次,由于图形化的呈现,SpliceAI-visual 的结果易于使用。第三,SpliceAI-visual 是目前为数不多的能够注释复杂变体(例如复杂的 delins)的 SpliceAI 衍生实现之一。我们在这里报告使用 SpliceAI-visual 的好处,并展示其在评估/调节 PVS1 分类标准中的相关性。我们还展示了 SpliceAI-visual 如何阐明从文献中以及未发表的案例中提取的几个复杂剪接缺陷。SpliceAI-visual 可作为 Google Colab 笔记本使用,并且已经完全集成在免费的在线变体解释工具 MobiDetails(https://mobidetails.iurc.montp.inserm.fr/MD)中。