Weighted enrichment method for prediction of transcription regulators from transcriptome and global chromatin immunoprecipitation data.
Weighted enrichment method for prediction of transcription regulators from transcriptome and global chromatin immunoprecipitation data.
复制标题
DOI：
10.1093/nar/gkw355
复制
发表时间：
2016-06-20
影响因子：
14.9
通讯作者：
Kitano H
中科院分区：
文献类型：
--
作者：
Kawakami E;Nakaoka S;Ohta T;Kitano H
基于转录组数据预测负责的转录调控因子是理解细胞过程和特征的最有前途的计算方法之一。在这里，我们提出了一种采用大量染色质免疫沉淀（ChIP）实验数据的新方法来解决这个问题。收集全球高通量ChIP数据，构建一个包含454个转录调控因子的8 578 738个结合相互作用的综合数据库。为了整合转录因子（TF）结合事件的异质性频率信息，我们开发了一个灵活的框架，采用加权t检验程序进行基因集分析，即加权参数基因集分析（wPGSA）。使用转录组数据作为输入，wPGSA预测负责观察到的基因表达的转录调控因子的活性。利用已发表的转录组数据（包括来自过表达tf的转录组数据）验证wPGSA，结果表明该方法可以预测各种tf的活性，而不考虑细胞类型和条件，结果与生物学观察结果完全一致。我们还将wPGSA应用于其他已发表的转录组数据，并确定了细胞重编程和流感病毒发病机制的潜在关键调节因子，对潜在的调节机制产生了令人信服的假设。这种灵活的框架将有助于揭示生物调控的动态和稳健的架构，通过以权重的形式合并高通量实验数据。
Predicting responsible transcription regulators on the basis of transcriptome data is one of the most promising computational approaches to understanding cellular processes and characteristics. Here, we present a novel method employing vast amounts of chromatin immunoprecipitation (ChIP) experimental data to address this issue. Global high-throughput ChIP data was collected to construct a comprehensive database, containing 8 578 738 binding interactions of 454 transcription regulators. To incorporate information about heterogeneous frequencies of transcription factor (TF)-binding events, we developed a flexible framework for gene set analysis employing the weighted t-test procedure, namely weighted parametric gene set analysis (wPGSA). Using transcriptome data as an input, wPGSA predicts the activities of transcription regulators responsible for observed gene expression. Validation of wPGSA with published transcriptome data, including that from over-expressed TFs, showed that the method can predict activities of various TFs, regardless of cell type and conditions, with results totally consistent with biological observations. We also applied wPGSA to other published transcriptome data and identified potential key regulators of cell reprogramming and influenza virus pathogenesis, generating compelling hypotheses regarding underlying regulatory mechanisms. This flexible framework will contribute to uncovering the dynamic and robust architectures of biological regulation, by incorporating high-throughput experimental data in the form of weights.