Using Partial Correlation Analysis to Identify Regulatory Targets of Cell Cycle Transcription Factors in Yeast

• Main Authors: Julie Shao-MeiChang, 張徐少梅
• Other Authors: Paw-Choo Chung
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/82102367003519526399

碩士 === 國立成功大學 === 電腦與通信工程研究所 === 98 === 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.