基因的轉錄調控通常是由多個轉錄因子共同合作來達成的，因此，轉錄因子之間的合作機制是一個相當重要的課題。之前的一些研究採用並整合許多生物實驗資料，包括染色質免疫抗體沉澱微陣列資料(ChIP-chip)、轉錄因子結合位資訊(TF binding site(TFBS) )、基因表現量資訊(gene expression)、轉錄因子剔除資料(TF knockout)以及蛋白質交互作用資訊(protein-protein interaction(PPIs) )來分析並辨識具有合作關係的轉錄因子對，然而，儘管有越來越多的研究顯示核小體的分布和轉錄因子結合位有一定的關聯，卻很少有人利用核小體的分布資訊來研究此主題。
在此研究中，我們提出一個結合了轉錄因子與基因之間的調控證據(TF-gene documented regulation)、轉錄因子結合位資訊以及核小體分布資訊的方法來推斷兩個轉錄因子之間是否有合作的關係。轉錄因子的標的基因是由下列兩個資料決定：轉錄因子與基因之間的調控證據及轉錄因子結合位資訊，而全基因體的核小體分布資訊則是用來分析轉錄因子結合位上的核小體佔據情況。我們的方法是根據以下兩個原則來辨識具有合作關係的轉錄因子對，(1)假設兩個轉錄因子之間有合作的關係，比起兩個沒有合作關係的轉錄因子，前者會傾向於擁有較多相同的標的基因。(2)假設兩個轉錄因子之間有合作的關係，它們共同的標的基因的啟動子上會同時存在它們兩個的轉錄因子結合位並且這兩個結合位上應同時沒被核小體佔據以利此兩個轉錄因子結合到啟動子上。我們的演算法會賦予每一對轉錄因子一個合作可能性分數，此分數越高則表示這兩個轉錄因子越有可能有互相合作的關係，而根據此分數我們得到17205轉錄因子對排序後的結果。並且從我們的預測結果中，取出前30對最有可能有合作關係的轉錄因子對，每一對至少都有以下三種證據之一來佐證這兩個轉錄因子很有可能有合作關係: (1)文獻證據 (2)蛋白質交互作用 (3)被標註在相同的MIPS功能類別。除此之外，我們採用了三種指標分數來評分並比較我們的演算法與之前12個演算法所預測出來的結果，結果顯示我們的方法在預測兩個轉錄因子是否有合作關係的研究中有較好的表現。最後，我們將預測結果的前30個轉錄因子對建構成一個轉錄因子合作關係網絡圖，從圖中可看出我們的預測是合理並具有生物意義的。
我們的方法能夠有效的預測酵母菌轉錄因子間的合作關係，因為我們的預測結果絕大多數都有文獻證據的佐證，而且比起之前的12篇研究也有較好的表現，我們相信此研究能夠幫助人們更加了解真核生物的轉錄調控機制。

Transcriptional regulation of gene expression is usually accomplished by multiple interactive transcription factors (TFs). Therefore, it is crucial to understand the precise cooperative interactions among TFs. Many experimental data including ChIP-chip, TF binding site (TFBS), gene expression, TF knockout and protein-protein interaction data have been adopted and integrated to identify cooperative TF pairs in previous works. However, the nucleosome occupancy data was rarely considered and used for this topic, despite that more and more researches have revealed the association between nucleosomes and TFBSs.
In this study, we developed a method to infer the cooperativity between two TFs by integrating the TF-gene documented regulation, TFBS and nucleosome occupancy data. TF-gene documented regulation and TFBS data were used to determine the target genes of TFs, and a genome-wide nucleosome map was used to assess the nucleosome occupancy on TFBSs. Our method identifies cooperative TF pairs based on the rationale that if two TFs cooperate, they tend to share a significantly larger set of target genes than random expectation and their binding sites should co-occur in the promoters of their common target genes and both be depleted of nucleosomes to make themselves accessible to TF binding simultaneously. A TF pair is given a cooperativity score by our method. The higher the score is, the more likely a TF pair has cooperativity.
A ranked prediction of 17205 TF pairs was reported according to the cooperativity score. Among the top 30 cooperative TF pairs predicted by our method, all of them (100\%) have at least one of the following three lines of evidence: (i) literature support, (ii) protein-protein interactions, and (iii) annotated in the same MIPS functional category. Moreover, we adopted three performance indices to compare our predictions with 12 previous works' predictions. We show that our method performs better than the 12 previous methods in identifying cooperative TF pairs in yeast. Finally, the cooperative TF network constructed from our top 30 predicted TF pairs shows that our predictions have biological meaning.
Our method is effective in identifying cooperative TF pairs in yeast. Many of our predictions are validated by the literature, and our method outperforms 12 existing methods. We believe that our study may help to understand the mechanism of transcriptional regulation in eukaryotic cells.

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