Large-scale efforts like the ENCODE Project have made tremendous progress in cataloging the genomic binding patterns of DNA-associated proteins (DAPs), such as transcription factors (TFs). However, most chromatin immunoprecipitation-sequencing (ChIP-seq) analyses have focused on a few immortalized cell lines whose activities and physiology differ in important ways from endogenous cells and tissues. Consequently, binding data from primary human tissue are essential to improving our understanding of in vivo gene regulation. Here, we identify and analyze more than 440,000 binding sites using ChIP-seq data for 20 DAPs in two human liver tissue samples. We integrated binding data with transcriptome and phased WGS data to investigate allelic DAP interactions and the impact of heterozygous sequence variation on the expression of neighboring genes. Our tissue-based data set exhibits binding patterns more consistent with liver biology than cell lines, and we describe uses of these data to better prioritize impactful noncoding variation. Collectively, our rich data set offers novel insights into genome function in human liver tissue and provides a valuable resource for assessing disease-related disruptions.

像 ENCODE 项目这样的大规模努力在对 DNA 相关蛋白 (DAP)（例如转录因子 (TF)）的基因组结合模式进行编目方面取得了巨大进展。然而，大多数染色质免疫沉淀测序 (ChIP-seq) 分析都集中在一些永生化细胞系上，这些细胞系的活性和生理学在重要方面与内源细胞和组织不同。因此，来自原代人体组织的结合数据对于提高我们对体内基因调控的理解至关重要。在这里，我们使用 ChIP-seq 数据识别并分析了两个人类肝脏组织样本中 20 个 DAP 的超过 440,000 个结合位点。我们将结合数据与转录组和定相 WGS 数据相结合，以研究等位基因 DAP 相互作用以及杂合序列变异对邻近基因表达的影响。我们基于组织的数据集表现出比细胞系更符合肝脏生物学的结合模式，并且我们描述了这些数据的用途，以更好地优先考虑有影响的非编码变异。总的来说，我们丰富的数据集为人类肝脏组织中的基因组功能提供了新颖的见解，并为评估与疾病相关的破坏提供了宝贵的资源。