Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia; School of Clinical Medicine, UNSW Medicine & Health, UNSW Sydney, Sydney, NSW, Australia; UNSW Centre for Childhood Cancer Research, UNSW Sydney, Sydney, NSW, Australia.
Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Sydney, NSW, Australia.
Am J Hum Genet. 2024 Oct 3;111(10):2164-2175. doi: 10.1016/j.ajhg.2024.08.002. Epub 2024 Sep 2.
Variants that alter gene splicing are estimated to comprise up to a third of all disease-causing variants, yet they are hard to predict from DNA sequencing data alone. To overcome this, many groups are incorporating RNA-based analyses, which are resource intensive, particularly for diagnostic laboratories. There are thousands of functionally validated variants that induce mis-splicing; however, this information is not consolidated, and they are under-represented in ClinVar, which presents a barrier to variant interpretation and can result in duplication of validation efforts. To address this issue, we developed SpliceVarDB, an online database consolidating over 50,000 variants assayed for their effects on splicing in over 8,000 human genes. We evaluated over 500 published data sources and established a spliceogenicity scale to standardize, harmonize, and consolidate variant validation data generated by a range of experimental protocols. According to the strength of their supporting evidence, variants were classified as "splice-altering" (∼25%), "not splice-altering" (∼25%), and "low-frequency splice-altering" (∼50%), which correspond to weak or indeterminate evidence of spliceogenicity. Importantly, 55% of the splice-altering variants in SpliceVarDB are outside the canonical splice sites (5.6% are deep intronic). These variants can support the variant curation diagnostic pathway and can be used to provide the high-quality data necessary to develop more accurate in silico splicing predictors. The variants are accessible through an online platform, SpliceVarDB, with additional features for visualization, variant information, in silico predictions, and validation metrics. SpliceVarDB is a very large collection of splice-altering variants and is available at https://splicevardb.org.
变体改变基因剪接，据估计占所有致病变体的三分之一，但仅从 DNA 测序数据很难预测。为了克服这一问题，许多团队正在整合基于 RNA 的分析，这需要大量资源，特别是对于诊断实验室而言。有数千种功能验证的变体可诱导剪接错误；然而，这些信息并未整合，且在 ClinVar 中代表性不足，这对变体解释构成了障碍，并可能导致验证工作的重复。为了解决这个问题，我们开发了 SpliceVarDB，这是一个在线数据库，整合了超过 50,000 个变体，这些变体在超过 8,000 个人类基因中的剪接效应进行了检测。我们评估了 500 多个已发表的数据源，并建立了一个剪接发生性评分标准，以标准化、协调和整合由一系列实验方案生成的变体验证数据。根据其支持证据的强度，变体被分类为“剪接改变”（约 25%）、“非剪接改变”（约 25%）和“低频剪接改变”（约 50%），这对应于剪接发生性的弱或不确定证据。重要的是，SpliceVarDB 中的 55%剪接改变变体位于规范剪接位点之外（5.6%是深内含子）。这些变体可以支持变体 curated 诊断途径，并可用于提供开发更准确的计算剪接预测器所需的高质量数据。变体可通过在线平台 SpliceVarDB 访问，该平台具有用于可视化、变体信息、计算预测和验证指标的附加功能。SpliceVarDB 是一个非常大的剪接改变变体集合，可在 https://splicevardb.org 上获得。
