Karczewski Konrad J, Snyder Michael, Altman Russ B, Tatonetti Nicholas P
Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, California, United States of America ; Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
Department of Genetics, Stanford University School of Medicine, Stanford, California, United States of America.
PLoS Genet. 2014 Feb 6;10(2):e1004122. doi: 10.1371/journal.pgen.1004122. eCollection 2014 Feb.
Transcription factors (TFs) are fundamental controllers of cellular regulation that function in a complex and combinatorial manner. Accurate identification of a transcription factor's targets is essential to understanding the role that factors play in disease biology. However, due to a high false positive rate, identifying coherent functional target sets is difficult. We have created an improved mapping of targets by integrating ChIP-Seq data with 423 functional modules derived from 9,395 human expression experiments. We identified 5,002 TF-module relationships, significantly improved TF target prediction, and found 30 high-confidence TF-TF associations, of which 14 are known. Importantly, we also connected TFs to diseases through these functional modules and identified 3,859 significant TF-disease relationships. As an example, we found a link between MEF2A and Crohn's disease, which we validated in an independent expression dataset. These results show the power of combining expression data and ChIP-Seq data to remove noise and better extract the associations between TFs, functional modules, and disease.
转录因子（TFs）是细胞调控的基本控制器，以复杂且组合的方式发挥作用。准确识别转录因子的靶标对于理解这些因子在疾病生物学中所起的作用至关重要。然而，由于假阳性率高，识别连贯的功能靶标集很困难。我们通过将染色质免疫沉淀测序（ChIP-Seq）数据与从9395个人类表达实验中获得的423个功能模块相结合，创建了一个改进的靶标图谱。我们确定了5002个转录因子-模块关系，显著改善了转录因子靶标预测，并发现了30个高可信度的转录因子-转录因子关联，其中14个是已知的。重要的是，我们还通过这些功能模块将转录因子与疾病联系起来，确定了3859个显著的转录因子-疾病关系。例如，我们发现MEF2A与克罗恩病之间存在联系，并在一个独立的表达数据集中进行了验证。这些结果显示了结合表达数据和ChIP-Seq数据以去除噪声并更好地提取转录因子、功能模块和疾病之间关联的能力。
