Mapping the chromosomal targets of STAT1 by Sequence Tag Analysis of Genomic Enrichment (STAGE)
Mapping the chromosomal targets of STAT1 by Sequence Tag Analysis of Genomic Enrichment (STAGE)
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DOI：
10.1101/gr.5574907
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发表时间：
2007-06-01
期刊：
影响因子：
7
通讯作者：
Iyer, Vishwanath R.
中科院分区：
文献类型：
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作者：
Bhinge, Akshay A.;Kim, Jonghwan;Iyer, Vishwanath R.
识别转录因子的全基因组结合位点对于破译转录调控网络是重要的。ChIP芯片（Chromatin immunoprecipitation combined with microarrays，染色质免疫沉淀结合微阵列）已被广泛应用于人类基因组中转录因子结合位点的定位。然而，全基因组ChIP芯片分析在脊椎动物中仍然具有技术挑战性。我们最近开发的阶段作为一个公正的方法，用于确定基因组中的转录因子结合位点。STAGE在概念上基于SAGE，除了输入是ChIP富集的DNA。在这项研究中，我们实施了一个改进的测序策略和分析方法，并应用STAGE定位干扰素治疗后转录因子STAT 1的基因组结合谱。STAT 1主要负责介导细胞对干扰素的应答，如细胞增殖、凋亡、免疫监视和免疫应答。我们提出了新的算法STAGE标签分析，以确定丰富的基因座具有高特异性，通过定量ChIP验证。STAGE鉴定了几个以前未知的STAT 1靶基因，其中许多参与介导对干扰素-γ信号的应答。因此，STAGE是一种可行的方法，用于识别转录因子的染色体靶点，并产生有意义的生物学假设，进一步加深我们对转录调控网络的理解。
Identifying the genome-wide binding sites of transcription factors is important in deciphering transcriptional regulatory networks. ChIP-chip ( Chromatin immunoprecipitation combined with microarrays) has been widely used to map transcription factor binding sites in the human genome. However, whole genome ChIP-chip analysis is still technically challenging in vertebrates. We recently developed STAGE as an unbiased method for identifying transcription factor binding sites in the genome. STAGE is conceptually based on SAGE, except that the input is ChIP-enriched DNA. In this study, we implemented an improved sequencing strategy and analysis methods and applied STAGE to map the genomic binding profile of the transcription factor STAT1 after interferon treatment. STAT1 is mainly responsible for mediating the cellular responses to interferons, such as cell proliferation, apoptosis, immune surveillance, and immune responses. We present novel algorithms for STAGE tag analysis to identify enriched loci with high specificity, as verified by quantitative ChIP. STAGE identified several previously unknown STAT1 target genes, many of which are involved in mediating the response to interferon-gamma signaling. STAGE is thus a viable method for identifying the chromosomal targets of transcription factors and generating meaningful biological hypotheses that further our understanding of transcriptional regulatory networks.