Novel computational analysis of protein binding array data identifies direct targets of Nkx2.2 in the pancreas.
Novel computational analysis of protein binding array data identifies direct targets of Nkx2.2 in the pancreas.
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蛋白质结合阵列数据的新计算分析确定了胰腺中NKX2.2的直接靶标。
DOI：
10.1186/1471-2105-12-62
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发表时间：
2011-02-25
影响因子：
3
通讯作者：
Sussel L
中科院分区：
文献类型：
--
作者：
Hill JT;Anderson KR;Mastracci TL;Kaestner KH;Sussel L
建立完整的转录因子结合位点全基因组图谱对于了解体内基因调控网络至关重要。然而，目前的预测方法通常依赖于统计模型，不能很好地模拟转录因子结合。基于蛋白质结合数据而不依赖于这些模型的新预测方法的产生可能会提高预测的敏感性和特异性。我们提出了一种预测基因组中转录因子结合位点的方法，该方法通过将蛋白质结合微阵列（PBM）产生的数据直接映射到基因组，并计算几个重叠八聚体的移动平均值。利用这种独特的算法，我们预测了小鼠基因组中必要的胰岛转录因子Nkx2.2的结合位点，并通过EMSA和ChIP确认了90%的测试位点。与基于PWM的方法相比，该方法生成的分数更准确地预测了相对绑定亲和力。我们还发现了另一个被Nkx2.2同域识别的核心序列。此外，我们已经证明，这种方法正确地识别了传统方法无法预测的两个关键胰岛β细胞基因NeuroD1和insulin2启动子中的结合位点。最后，我们证明了该算法也可以用于预测核受体Hnf4α的结合位点。pbm定位是预测Nkx2.2结合位点的一种准确方法，可广泛应用于转录因子结合位点全基因组图谱的创建。
The creation of a complete genome-wide map of transcription factor binding sites is essential for understanding gene regulatory networks in vivo. However, current prediction methods generally rely on statistical models that imperfectly model transcription factor binding. Generation of new prediction methods that are based on protein binding data, but do not rely on these models may improve prediction sensitivity and specificity. We propose a method for predicting transcription factor binding sites in the genome by directly mapping data generated from protein binding microarrays (PBM) to the genome and calculating a moving average of several overlapping octamers. Using this unique algorithm, we predicted binding sites for the essential pancreatic islet transcription factor Nkx2.2 in the mouse genome and confirmed >90% of the tested sites by EMSA and ChIP. Scores generated from this method more accurately predicted relative binding affinity than PWM based methods. We have also identified an alternative core sequence recognized by the Nkx2.2 homeodomain. Furthermore, we have shown that this method correctly identified binding sites in the promoters of two critical pancreatic islet β-cell genes, NeuroD1 and insulin2, that were not predicted by traditional methods. Finally, we show evidence that the algorithm can also be applied to predict binding sites for the nuclear receptor Hnf4α. PBM-mapping is an accurate method for predicting Nkx2.2 binding sites and may be widely applicable for the creation of genome-wide maps of transcription factor binding sites.
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影响因子：
12.3
作者：
Kel, Alexander E.;Niehof, Monika;Matys, Volker;Zemlin, Ruediger;Borlak, Juergen
通讯作者：
Borlak, Juergen
DOI：
10.1073/pnas.0308604100
发表时间：
2004-03-02
影响因子：
11.1
作者：
Prado, CL;Pugh-Bernard, AE;Sussel, L
通讯作者：
Sussel, L
影响因子：
46.9
作者：
Berger, Michael F.;Philippakis, Anthony A.;Bulyk, Martha L.
通讯作者：
Bulyk, Martha L.
影响因子：
14.9
作者：
Bulyk, ML;Johnson, PLF;Church, GM
通讯作者：
Church, GM
影响因子：
4.8
作者：
Cissell, MA;Zhao, L;Stein, R
通讯作者：
Stein, R