Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Bioinformatics. 2013 May 1;29(9):1182-9. doi: 10.1093/bioinformatics/btt108. Epub 2013 Mar 1.
Although chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) or tiling array hybridization (ChIP-chip) is increasingly used to map genome-wide-binding sites of transcription factors (TFs), it still remains difficult to generate a quality ChIPx (i.e. ChIP-seq or ChIP-chip) dataset because of the tremendous amount of effort required to develop effective antibodies and efficient protocols. Moreover, most laboratories are unable to easily obtain ChIPx data for one or more TF(s) in more than a handful of biological contexts. Thus, standard ChIPx analyses primarily focus on analyzing data from one experiment, and the discoveries are restricted to a specific biological context.
We propose to enrich this existing data analysis paradigm by developing a novel approach, ChIP-PED, which superimposes ChIPx data on large amounts of publicly available human and mouse gene expression data containing a diverse collection of cell types, tissues and disease conditions to discover new biological contexts with potential TF regulatory activities. We demonstrate ChIP-PED using a number of examples, including a novel discovery that MYC, a human TF, plays an important functional role in pediatric Ewing sarcoma cell lines. These examples show that ChIP-PED increases the value of ChIPx data by allowing one to expand the scope of possible discoveries made from a ChIPx experiment.
尽管染色质免疫沉淀结合高通量测序（ChIP-seq）或平铺阵列杂交（ChIP-chip）越来越多地用于绘制转录因子（TFs）的全基因组结合位点图谱，但由于开发有效抗体和高效方案需要大量的努力，因此仍然难以生成高质量的 ChIPx（即 ChIP-seq 或 ChIP-chip）数据集。此外，大多数实验室无法在多个生物背景下轻松获得一个或多个 TF 的 ChIPx 数据。因此，标准的 ChIPx 分析主要集中在分析一个实验的数据，并且发现仅限于特定的生物背景。
我们建议通过开发一种新方法 ChIP-PED 来丰富现有的数据分析范例，该方法将 ChIPx 数据叠加在大量公开的人类和小鼠基因表达数据上，这些数据包含了各种细胞类型、组织和疾病状况，以发现具有潜在 TF 调节活性的新生物背景。我们使用了一些示例来说明 ChIP-PED，包括一个新的发现，即人类 TF MYC 在小儿尤文肉瘤细胞系中发挥重要的功能作用。这些示例表明，ChIP-PED 通过允许人们从 ChIPx 实验中扩展可能发现的范围，从而增加了 ChIPx 数据的价值。
