Biotechnology Center, TU Dresden, Dresden, Germany.
PLoS Comput Biol. 2013;9(11):e1003342. doi: 10.1371/journal.pcbi.1003342. Epub 2013 Nov 21.
Chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) has great potential for elucidating transcriptional networks, by measuring genome-wide binding of transcription factors (TFs) at high resolution. Despite the precision of these experiments, identification of genes directly regulated by a TF (target genes) is not trivial. Numerous target gene scoring methods have been used in the past. However, their suitability for the task and their performance remain unclear, because a thorough comparative assessment of these methods is still lacking. Here we present a systematic evaluation of computational methods for defining TF targets based on ChIP-seq data. We validated predictions based on 68 ChIP-seq studies using a wide range of genomic expression data and functional information. We demonstrate that peak-to-gene assignment is the most crucial step for correct target gene prediction and propose a parameter-free method performing most consistently across the evaluation tests.
染色质免疫沉淀结合深度测序（ChIP-seq）通过测量转录因子（TFs）在全基因组范围内的高分辨率结合，具有阐明转录网络的巨大潜力。尽管这些实验具有很高的精度，但直接识别 TF （靶基因）调控的基因并不简单。过去已经使用了许多靶基因评分方法。然而，它们是否适合这项任务以及它们的性能仍不清楚，因为这些方法的全面比较评估仍然缺乏。在这里，我们对基于 ChIP-seq 数据定义 TF 靶基因的计算方法进行了系统评估。我们使用广泛的基因组表达数据和功能信息验证了基于 68 项 ChIP-seq 研究的预测。我们证明峰到基因的分配是正确预测靶基因的最关键步骤，并提出了一种在评估测试中表现最一致的无参数方法。
