PDF(Pubmed)
Abstract：
BACKGROUND: Splicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights.
RESULTS: Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest.
CONCLUSIONS: In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://bc.imb.sinica.edu.tw/SpliceAPP . Source code can be downloaded at https://github.com/hsinnan75/SpliceAPP .
RESULTS: Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest.
CONCLUSIONS: In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://bc.imb.sinica.edu.tw/SpliceAPP . Source code can be downloaded at https://github.com/hsinnan75/SpliceAPP .
摘要：
背景：剪接变体是一类主要的致病突变，其严重程度相当于无意义突变。然而,冗余和简并剪接信号阻碍内含子内序列变异的功能评估，特别是在分支机构。我们已经建立了大规模平行剪接测定以评估对11,191个疾病相关变体的剪接的影响。根据实验结果,然后，我们应用基于回归的方法来确定决定拼接决策的因素及其各自的权重。
结果：我们的统计模型高度敏感，准确注释近外显子内含子变异体的剪接缺陷，优于最先进的预测工具。我们已经将算法和分支点信息整合到基于网络的工具中，SpliceAPP,以提供交互式应用程序。这个用户友好的网站允许用户上传任何具有基因组坐标的遗传变异(例如，chr1574,687,208AG)，该工具将输出剪接误差分数的预测，并评估对附近剪接位点的影响。此外,用户可以查询感兴趣区域内的分支站点信息。
结论：总之，SpliceAPP代表了筛选致病性内含子变异的开创性方法，促进精准医学的发展。它还有助于拼接基序的注释。使用链接https://bc可以免费访问SpliceAPP。imb.sinica.edu.tw/SpliceAPP.源代码可以在https://github.com/hsinnan75/SpliceAPP下载。
结果：我们的统计模型高度敏感，准确注释近外显子内含子变异体的剪接缺陷，优于最先进的预测工具。我们已经将算法和分支点信息整合到基于网络的工具中，SpliceAPP,以提供交互式应用程序。这个用户友好的网站允许用户上传任何具有基因组坐标的遗传变异(例如，chr1574,687,208AG)，该工具将输出剪接误差分数的预测，并评估对附近剪接位点的影响。此外,用户可以查询感兴趣区域内的分支站点信息。
结论：总之，SpliceAPP代表了筛选致病性内含子变异的开创性方法，促进精准医学的发展。它还有助于拼接基序的注释。使用链接https://bc可以免费访问SpliceAPP。imb.sinica.edu.tw/SpliceAPP.源代码可以在https://github.com/hsinnan75/SpliceAPP下载。
