Börlin Christoph S, Bergenholm David, Holland Petter, Nielsen Jens
Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-41296, Sweden.
Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, SE-41296, Sweden.
Biol Methods Protoc. 2019 Aug 6;4(1):bpz011. doi: 10.1093/biomethods/bpz011. eCollection 2019.
The decrease of sequencing cost in the recent years has made genome-wide studies of transcription factor (TF) binding through chromatin immunoprecipitation methods like ChIP-seq and chromatin immunoprecipitation with lambda exonuclease (ChIP-exo) more accessible to a broader group of users. Especially with ChIP-exo, it is now possible to map TF binding sites in more detail and with less noise than previously possible. These improvements came at the cost of making the analysis of the data more challenging, which is further complicated by the fact that to this date no complete pipeline is publicly available. Here we present a workflow developed specifically for ChIP-exo data and demonstrate its capabilities for data analysis. The pipeline, which is completely publicly available on GitHub, includes all necessary analytical steps to obtain a high confidence list of TF targets starting from raw sequencing reads. During the pipeline development, we emphasized the inclusion of different quality control measurements and we show how to use these so users can have confidence in their obtained results.
近年来测序成本的降低使得通过染色质免疫沉淀方法（如ChIP-seq和λ外切核酸酶染色质免疫沉淀法（ChIP-exo））对转录因子（TF）结合进行全基因组研究，能为更广泛的用户群体所采用。特别是使用ChIP-exo，现在能够比以前更详细且噪声更少地绘制TF结合位点。这些改进是以增加数据分析的挑战性为代价的，而至今没有完整的流程可供公开使用这一事实使情况更加复杂。在此，我们展示了专门为ChIP-exo数据开发的工作流程，并演示了其数据分析能力。该流程在GitHub上完全公开，包括从原始测序读数开始获得高可信度TF靶标列表所需的所有分析步骤。在流程开发过程中，我们强调纳入不同的质量控制措施，并展示如何使用这些措施，以便用户对所获得的结果有信心。
