Predicting protein synergistic effect in Arabidopsis using epigenome profiling

Chih-Hung Hsieh, Ya-Ting Sabrina Chang, Ming-Ren Yen, Jo-Wei Allison Hsieh and Pao-Yang Chen ()
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Chih-Hung Hsieh: Academia Sinica
Ya-Ting Sabrina Chang: Academia Sinica
Ming-Ren Yen: Academia Sinica
Jo-Wei Allison Hsieh: Academia Sinica
Pao-Yang Chen: Academia Sinica

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract 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 ).

Date: 2024
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DOI: 10.1038/s41467-024-53565-y

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