重建轉錄調控網路(Transcriptional Regulatory Network)有助於我們了解細胞是如何因應環境變化而調控其各個基因表現狀態。免疫沉澱實驗晶片資料(ChIP-chip Data)因為提供了轉錄因子(Transcription Factor)會結合到那些基因啟動子(promoter)的資訊而廣泛地被使用在轉錄調控網路重建的問題上。但是轉錄因子有結合到某個基因啟動子上並不代表轉錄因子就能調控此基因的表現，所以必須發展一套演算法從免疫沉澱實驗晶片資料中把轉錄因子真正會調控的基因找出來。本篇論文提出了轉錄因子調控基因的偵測法Regulatory Target Extraction Algorithm (RETEA)，我們使用淨相關分析 (Partial Correlation Analysis)，從免疫沉澱實驗晶片資料提供的轉錄因子會結合到那些基因的資訊，偵測出轉錄因子真正會調控那些基因的表現。我們應用RETEA於細胞週期之基因表現資料(Gene expression data for the cell cycle process)，找出了會受到細胞週期轉錄因子調控的基因。再將找出的這群基因利用所參與共同功能的多寡以及所包含細胞週期基因的個數比例這兩個方式來驗證我們的結果。在評估RETEA功能優異性方面，將它與三個已發表的演算法比較(Garten et al.’s method, MA-Network and TRIA)，我們發現 RETEA的效果最好。總括本篇論文提出的細胞週期轉錄因子調控基因的偵測法，因能從免疫沉澱實驗晶片資料中找出真正被轉錄因子調控的基因，所以在提高免疫沉澱實驗晶片資料的實用性上，是一個很有用的工具。

Reconstructing transcriptional regulatory networks (TRNs) is crucial for understanding how a cell reorganizes its gene expression patterns to respond to environmental and physiological changes. ChIP-chip data, which indicate binding of transcription factors (TFs) to DNA regions in vivo, are widely used to reconstruct TRNs. However, the binding of a TF to a gene does not necessarily imply regulation. Thus, it is important to develop computational methods which can extract a TF’s regulatory targets from its binding targets. The REgulatory Targets Extraction Algorithm (RETEA) is developed in this study, which uses partial correlation analysis on gene expression data to extract a TF’s regulatory targets from its binding targets inferred from the ChIP-chip data. We applied RETEA to yeast cell cycle microarray data and identified the plausible regulatory targets of eleven cell cycle TFs. Our predictions are validated by checking the enrichments for cell cycle genes and shared molecular functions. Moreover, we showed that RETEA performs better than three published methods (Garten et al.’s Method, MA-Network and TRIA). In summary, RETEA is capable of extracting the TF-gene regulatory relationships from the TF-promoter binding relationships (inferred by the ChIP-chip data). Thus, using RETEA to preprocess the ChIP-chip data is crucial to make the ChIP-chip data useful in systems biology studies.

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