[Pao-Yang Chen] Predicting Protein Synergistic Effect in Arabidopsis using Epigenome Profiling
POST:
QHistone adapts machine learning to predict the epigenomic profiles of proteins, allowing for the inference of synergistic or antagonistic interactions between proteins. Additionally, QHistone has uncovered numerous unknown synergistic or antagonistic proteins, providing a valuable resource for plant epigenetics research.
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 1,534 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 identified 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 a novel 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).
The first author, Chih-Hung Hsieh, from Dr. Pao-Yang Chen's lab, led the data collection, performed the analyses, and developed the web application. The author thanks Zheng-Zhong Huang and Jimmy Lin from the IPMB IT/Network service for the IT support. The study is published in Nature Communications, and the research was generously supported by both Academia Sinica and the National Science and Technology Council in Taiwan.