Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.
Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.
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DOI：
10.1093/bioinformatics/btx480
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发表时间：
2017-11-15
期刊：
影响因子：
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通讯作者：
Gao X
中科院分区：
文献类型：
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作者：
Dai H;Umarov R;Kuwahara H;Li Y;Song L;Gao X
转录因子(Tf)-DNA亲和力的准确表征对于定量理解支持内源性基因调控的分子机制至关重要。虽然生物技术的最新进展为建立结合亲和力预测方法带来了机会，但准确表征Tf-DNA结合亲和力仍然是一个具有挑战性的问题。在这里，我们提出了一种新的序列嵌入方法来模拟转录因子结合亲和力景观。我们的方法将DNA结合序列表示为一个隐马尔可夫模型，该模型既能捕捉序列中的位置特定信息，又能捕捉序列中的长程相关性。该方法的基础是一种新颖的消息传递类嵌入算法，称为Sequence2Vec，它将这些隐马尔可夫模型映射到一个共同的非线性特征空间，并使用这些嵌入的特征来构建预测模型。我们的方法结合了概率图形模型、特征空间嵌入和深度学习的优点。我们在90多个大规模的TF-DNA数据集上进行了全面的实验，这些数据集是通过不同的高通量实验技术测量的。Sequence2VEC的性能优于其他机器学习方法以及最先进的结合亲和力预测方法。我们的计划可在https://github.com/ramzan1990/sequence2vec.上免费获得补充数据可在生物信息学在线上获得。
An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods. Our program is freely available at https://github.com/ramzan1990/sequence2vec. Supplementary data are available at Bioinformatics online.