From promoter sequence to expression: a probabilistic framework
From promoter sequence to expression: a probabilistic framework
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DOI：
10.1145/565196.565231
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发表时间：
2002-04
期刊：
影响因子：
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通讯作者：
E. Segal;Yoseph Barash;I. Simon;N. Friedman;D. Koller
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文献类型：
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作者：
E. Segal;Yoseph Barash;I. Simon;N. Friedman;D. Koller
我们提出了一个概率框架来模拟转录结合解释不同基因 mRNA 表达的过程。我们的联合概率模型统一了该过程的两个关键组成部分：根据基因启动子区域的序列基序预测基因调控事件，以及根据不同环境下基因调控事件的组合预测 mRNA 表达。我们的方法有几个优点。通过学习直接预测表达数据的启动子序列基序，可以改进结合位点模式的识别。它还能够通过不同转录因子的相互作用来识别组合调节。最后，总体框架允许我们集成其他数据源，包括来自最近的结合定位测定的数据。我们结合 Simon 等人的结合定位信息，展示了我们对 Spellman 等人的细胞周期数据的方法。我们表明，学习的模型可以根据序列预测表达，并且它可以识别具有显着转录因子基序的连贯共同调节组。它还通过这些共同调节的“模块”和控制其行为的组合调节效应，提供了对该领域有价值的生物学见解。
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifies the two key components of this process: the prediction of gene regulation events from sequence motifs in the gene's promoter region, and the prediction of mRNA expression from combinations of gene regulation events in different settings. Our approach has several advantages. By learning promoter sequence motifs that are directly predictive of expression data, it can improve the identification of binding site patterns. It is also able to identify combinatorial regulation via interactions of different transcription factors. Finally, the general framework allows us to integrate additional data sources, including data from the recent binding localization assays. We demonstrate our approach on the cell cycle data of Spellman et al., combined with the binding localization information of Simon et al. We show that the learned model predicts expression from sequence, and that it identifies coherent co-regulated groups with significant transcription factor motifs. It also provides valuable biological insight into the domain via these co-regulated "modules" and the combinatorial regulation effects that govern their behavior.