Predicting Protein Synergistic Effect in Arabidopsis Using Epigenome Profiling

Overview

Journal Nat Commun

Specialty Biology

Date 2024 Oct 25

PMID 39448614

Authors

Chih-Hung Hsieh

Ya-Ting Sabrina Chang

Ming-Ren Yen

Jo-Wei Allison Hsieh

Pao-Yang Chen

Affiliations

Soon will be listed here.

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 ).

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