Ramos Jairo, Felty Quentin, Roy Deodutta
Department of Environmental Health Sciences, Florida International University, Miami, FL, USA.
Methods Mol Biol. 2020;2102:35-59. doi: 10.1007/978-1-0716-0223-2_3.
The objective of this chapter is to describe step-by-step bioinformatics and functional genomics solutions for analyzing ChIP-Seq and RNA-Seq data for understanding the regulatory mechanisms of chromatin modifiers and transcription factors that can drive pathogenesis of chronic complex human diseases, such as cancer. Here we have used two transcription regulatory proteins: nuclear respiratory factor 1 (NRF1) and inhibitor of differentiation protein 3 (ID3) for ChIP-Seq and RNA-Seq data as examples for discussing the importance of selecting the appropriate computational analysis methods, software, and parameters for the processing of raw data as well as their integrative regulatory landscape analysis to obtain accurate and reliable results. Both ChIP-Seq and RNA-Seq analytic methodologies are used as instructional examples to identify NRF1 or ID3 binding to the promoters and enhancers in the genome and their effects on the activity as well as to discover target genes that can drive breast cancer.
本章的目的是逐步描述生物信息学和功能基因组学解决方案,用于分析染色质免疫沉淀测序(ChIP-Seq)和RNA测序(RNA-Seq)数据,以了解染色质修饰因子和转录因子的调控机制,这些机制可能驱动慢性复杂人类疾病(如癌症)的发病机制。在这里,我们使用了两种转录调节蛋白:核呼吸因子1(NRF1)和分化抑制蛋白3(ID3)的ChIP-Seq和RNA-Seq数据作为示例,讨论选择合适的计算分析方法、软件和参数处理原始数据的重要性,以及它们的综合调控景观分析,以获得准确可靠的结果。ChIP-Seq和RNA-Seq分析方法均用作指导性示例,以识别NRF1或ID3与基因组中启动子和增强子的结合及其对活性的影响,并发现可驱动乳腺癌的靶基因。