Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals
Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals
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DOI：
10.1089/1066527041410418
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发表时间：
2004-01-01
影响因子：
1.7
通讯作者：
Burge, CB
中科院分区：
文献类型：
--
作者：
Yeo, G;Burge, CB
提出了一个基于最大熵原理(MEP)的序列模体建模框架。我们建议用最大熵分布(MED)近似短序列基序分布，该分布与从现有数据估计的低阶边缘约束一致，其中可能包括不相邻位置之间的相关性以及相邻位置之间的相关性。许多最大熵模型(MEM)是通过简单地改变约束集来指定的。这样的模型可以用来区分信号和诱饵。使用不同MEMS的分类性能可以洞察不同位置之间相关性的相对重要性。我们将我们的框架应用于RNA剪接信号的大数据集。我们最好的模型在区分人类5‘(供体)和3’(受体)剪接位点和诱饵方面优于以前的概率模型。最后，我们讨论了比较模型的机械驱动方式。
We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum entropy distribution (MED) consistent with low-order marginal constraints estimated from available data, which may include dependencies between nonadjacent as well as adjacent positions. Many maximum entropy models (MEMs) are specified by simply changing the set of constraints. Such models can be utilized to discriminate between signals and decoys. Classification performance using different MEMs gives insight into the relative importance of dependencies between different positions. We apply our framework to large datasets of RNA splicing signals. Our best models out-perform previous probabilistic models in the discrimination of human 5' (donor) and 3' (acceptor) splice sites from decoys. Finally, we discuss mechanistically motivated ways of comparing models.