一个 Ensembl Variant Effect Predictor 的插件，它使用 MaxEntScan 来预测变异的剪接体发生。
A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity.
机构信息
Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane QLD, Australia.
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
出版信息
Bioinformatics. 2019 Jul 1;35(13):2315-2317. doi: 10.1093/bioinformatics/bty960.
SUMMARY
Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotation tools do not adequately assess spliceogenicity outside the native splice site and thus the disease-causing potential of variants in other intronic and exonic regions is often overlooked. Here, we present a plugin for the Ensembl Variant Effect Predictor that packages MaxEntScan and extends its functionality to provide splice site predictions using a maximum entropy model. The plugin incorporates a sliding window algorithm to predict splice site loss or gain for any variant that overlaps a transcript feature. We also demonstrate the utility of the plugin by comparing our predictions to two mRNA splicing datasets containing several cancer-susceptibility genes.
AVAILABILITY AND IMPLEMENTATION
Source code is freely available under the Apache License, Version 2.0: https://github.com/Ensembl/VEP_plugins.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
评估遗传变异的致病性可能是一项复杂而具有挑战性的任务。改变 mRNA 剪接的剪接变异可能会产生编码无功能蛋白产物的成熟转录本，这是孟德尔疾病风险的一个重要预测因子。然而，大多数变异注释工具并不能充分评估非天然剪接位点的剪接发生情况，因此，其他内含子和外显子区域的变异的致病潜力经常被忽视。在这里，我们为 Ensembl 变体效应预测器提供了一个插件，该插件打包了 MaxEntScan 并扩展了其功能，以使用最大熵模型提供剪接位点预测。该插件采用滑动窗口算法来预测任何与转录本特征重叠的变体的剪接位点丢失或获得。我们还通过将我们的预测与包含几个癌症易感性基因的两个 mRNA 剪接数据集进行比较，展示了该插件的实用性。
可用性和实现
源代码可在 Apache License, Version 2.0 下免费获得：https://github.com/Ensembl/VEP_plugins。
补充信息
补充数据可在 Bioinformatics 在线获得。
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