从结合靶标中识别转录因子的调控靶标。

Fred Lai, Julie S Chang, Wei-Sheng Wu
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引用次数: 4

摘要

ChIP-chip数据显示了转录因子(tf)在体内与启动子区域的结合,被生物学家广泛用于鉴定tf的调控靶点。然而,TF与基因的结合并不一定意味着调控。因此,开发能够从结合靶标中提取TF调控靶标的计算方法是很重要的。我们开发了一种称为调控靶标提取算法(RETEA)的方法,该方法使用基因表达数据的偏相关分析从ChIP-chip数据推断的TF结合靶标中提取出TF的调控靶标。我们将RETEA应用于酵母细胞周期微阵列数据,并确定了11个已知细胞周期tf的合理调控靶点。我们通过检查细胞周期调节基因、共同细胞过程和共同分子功能的富集程度来验证我们的预测。最后,我们发现RETEA的性能优于三种已发表的方法(MA-Network, TRIA和Garten等人的方法)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identifying a Transcription Factor's Regulatory Targets from its Binding Targets.

Identifying a Transcription Factor's Regulatory Targets from its Binding Targets.

Identifying a Transcription Factor's Regulatory Targets from its Binding Targets.

Identifying a Transcription Factor's Regulatory Targets from its Binding Targets.

ChIP-chip data, which shows binding of transcription factors (TFs) to promoter regions in vivo, are widely used by biologists to identify the regulatory targets of TFs. 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. We developed a method, called REgulatory Targets Extraction Algorithm (RETEA), which uses partial correlation analysis on gene expression data to extract a TF's regulatory targets from its binding targets inferred from ChIP-chip data. We applied RETEA to yeast cell cycle microarray data and identified the plausible regulatory targets of eleven known cell cycle TFs. We validated our predictions by checking the enrichments for cell cycle-regulated genes, common cellular processes and common molecular functions. Finally, we showed that RETEA performs better than three published methods (MA-Network, TRIA and Garten et al's method).

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