Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors
Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors
复制标题
等位基因特异性转录因子结合作为评估变异影响预测因子的基准
DOI：
10.1101/253427
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
B. Frey
中科院分区：
文献类型：
--
作者：
O. Wagih;D. Merico;Andrew Delong;B. Frey
长期以来，遗传变异被认为会改变转录因子结合位点，有时会导致重大的表型后果。虽然当前结合位点预测因子的性能得到了很好的表征，但对这些模型在预测变体影响方面的性能知之甚少。我们收集并策划了101个转录因子（TF）中超过132，000个潜在的等位基因特异性结合（ASB）ChIP-seq变体。然后，我们评估了来自五种不同方法的TF结合模型在这些高置信度测量上的准确性，发现深度学习方法表现最好，但仍有改进的空间。重要的是，机器学习方法始终优于古老的位置权重矩阵（PWM）。最后，对某些TF的预测一直很差，我们的调查支持使用序列以外的特征，如甲基化，DNA形状和翻译后修饰。我们认为，ASB数据是一个有价值的基准变异影响TF结合。
Genetic variation has long been known to alter transcription factor binding sites, resulting in sometimes major phenotypic consequences. While the performance for current binding site predictors is well characterised, little is known on how these models perform at predicting impact of variants. We collected and curated over 132,000 potential allele-specific binding (ASB) ChIP-seq variants across 101 transcription factors (TFs). We then assessed the accuracy of TF binding models from five different methods on these high-confidence measurements, finding that deep learning methods were best performing yet still have room for improvement. Importantly, machine learning methods were consistently better than the venerable position weight matrix (PWM). Finally, predictions for certain TFs were consistently poor, and our investigation supports efforts to use features beyond sequence, such as methylation, DNA shape, and post-translational modifications. We submit that ASB data is a valuable benchmark for variant impact on TF binding.
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