Center for Molecular Systems Biology, University of Turin, 10043, Orbassano, Turin, Italy.
Dept. of Computer Science, University of Turin, 10149, Turin, Italy.
Sci Rep. 2017 Aug 17;7(1):8564. doi: 10.1038/s41598-017-08754-9.
In the study of genomic regulation, strategies to integrate the data produced by Next Generation Sequencing (NGS)-based technologies in a meaningful ensemble are eagerly awaited and must continuously evolve. Here, we describe an integrative strategy for the analysis of data generated by chromatin immunoprecipitation followed by NGS which combines algorithms for data overlap, normalization and epigenetic state analysis. The performance of our strategy is illustrated by presenting the analysis of data relative to the transcriptional regulator Estrogen Receptor alpha (ERα) in MCF-7 breast cancer cells and of Glucocorticoid Receptor (GR) in A549 lung cancer cells. We went through the definition of reference cistromes for different experimental contexts, the integration of data relative to co-regulators and the overlay of chromatin states as defined by epigenetic marks in MCF-7 cells. With our strategy, we identified novel features of estrogen-independent ERα activity, including FoxM1 interaction, eRNAs transcription and a peculiar ontology of connected genes.
在基因组调控研究中，人们迫切期待能够将基于下一代测序（NGS）技术产生的数据整合到有意义的整体中，并且这种策略必须不断发展。在这里，我们描述了一种整合分析染色质免疫沉淀后 NGS 产生的数据的策略，该策略结合了用于数据重叠、归一化和表观遗传状态分析的算法。通过呈现与 MCF-7 乳腺癌细胞中的转录调节剂雌激素受体 alpha（ERα）和 A549 肺癌细胞中的糖皮质激素受体（GR）相关的数据的分析，展示了我们策略的性能。我们经历了为不同实验环境定义参考顺式作用元件、整合与共调节剂相关的数据以及在 MCF-7 细胞中通过表观遗传标记定义的染色质状态叠加的过程。通过我们的策略，我们确定了雌激素非依赖性 ERα 活性的新特征，包括 FoxM1 相互作用、eRNAs 转录和连接基因的特殊本体论。
