Institute of Plant and Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan.
Nat Commun. 2024 Oct 24;15(1):9160. doi: 10.1038/s41467-024-53565-y.
Histone modifications can regulate transcription epigenetically by marking specific genomic loci, which can be mapped using chromatin immunoprecipitation sequencing (ChIP-seq). Here we present QHistone, a predictive database of 1534 ChIP-seqs from 27 histone modifications in Arabidopsis, offering three key functionalities. Firstly, QHistone employs machine learning to predict the epigenomic profile of a query protein, characterized by its most associated histone modifications, and uses these modifications to infer the protein's role in transcriptional regulation. Secondly, it predicts synergistic regulatory activities between two proteins by comparing their profiles. Lastly, it detects previously unexplored co-regulating protein pairs by screening all known proteins. QHistone accurately identifies histone modifications associated with specific known proteins, and allows users to computationally validate their results using gene expression data from various plant tissues. These functions demonstrate an useful approach to utilizing epigenome data for gene regulation analysis, making QHistone a valuable resource for the scientific community ( https://qhistone.paoyang.ipmb.sinica.edu.tw ).
组蛋白修饰可以通过标记特定的基因组位点来在表观遗传学上调控转录，这些位点可以使用染色质免疫沉淀测序（ChIP-seq）进行定位。在这里，我们介绍了 QHistone，这是一个包含来自拟南芥 27 种组蛋白修饰的 1534 个 ChIP-seq 的预测数据库，提供了三个关键功能。首先，QHistone 使用机器学习来预测查询蛋白的表观基因组图谱，其特征是最相关的组蛋白修饰，并使用这些修饰来推断该蛋白在转录调控中的作用。其次，它通过比较两个蛋白的图谱来预测它们之间的协同调控活性。最后，它通过筛选所有已知的蛋白来检测以前未探索的共同调控蛋白对。QHistone 能够准确地识别与特定已知蛋白相关的组蛋白修饰，并允许用户使用来自各种植物组织的基因表达数据来计算验证他们的结果。这些功能展示了一种利用表观基因组数据进行基因调控分析的有效方法，使 QHistone 成为科学界的宝贵资源（https://qhistone.paoyang.ipmb.sinica.edu.tw）。
