利用相對性R squared方法辨認酵母菌轉錄因子

Abstract

轉錄因子在調控基因表現上扮演著重要的角色，為了更加瞭解細胞裡的轉錄機制，辨認出相關的轉錄因子是很重要的。本篇研究結合染色質免疫沉澱片和基因表現片兩種資料，並以一種新的統計方法-- relative R squared來辨認細胞週期裡的轉錄因子。研究結果辨認出15個轉錄因子，其中12個為已知和細胞週期有關的轉錄因子，其餘3個(Hap4, Reb1 and Tye7)則是我們新發現的轉錄因子且分別有四項證據支持這些轉錄因子的正確性。此外，在這15個轉錄因子中，我們可以辨認出其中7個運作的時間點位於細胞週期的哪個階段，且這些辨認出來的結果大多有相關的文獻來驗證。由於類似的辨認方法很多，故我們以Jaccard similarity score來評斷各方法的優劣，並發現我們的方法優於現存的其他方法。最後，我們將此方法應用於另一筆有關細胞週期的基因表現晶片資料來證明我們的方法具有穩健性。

Transcription factors (TFs) play critical roles in controlling gene expressions. To understand how the cell cycle-regulated genes can be transcribed just before they are needed, it is essential to identify their transcriptional regulators. We developed a novel relative R squared method to identify cell cycle TFs in yeast by integrating the ChIP-chip and cell cycle gene expression data. Our method identified 15 cell cycle TFs, 12 of which are known cell cycle TFs, while the remaining three (Hap4, Reb1 and Tye7) are putative novel cell cycle TFs. Four lines of evidence are provided to show the biological significance of our prediction. Besides, for seven of the 15 identified cell cycle TFs, we can further assign a specific cell cycle phase in which the TFs function. Most of our predictions are supported by previous experimental or computational studies. Furthermore, we show that our method performs better than five existing methods for identifying yeast cell cycle TFs. Finally, an application of our method to different cell cycle gene expression datasets suggests that our method is robust.