Predicting protein synergistic effect in Arabidopsis using epigenome profiling
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, of...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-53565-y |
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| author | Chih-Hung Hsieh Ya-Ting Sabrina Chang Ming-Ren Yen Jo-Wei Allison Hsieh Pao-Yang Chen |
| author_facet | Chih-Hung Hsieh Ya-Ting Sabrina Chang Ming-Ren Yen Jo-Wei Allison Hsieh Pao-Yang Chen |
| author_sort | Chih-Hung Hsieh |
| collection | DOAJ |
| description | 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 ). |
| format | Article |
| id | doaj-art-8a0c795616b04ad78cf46f37dd28aa50 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-8a0c795616b04ad78cf46f37dd28aa502025-08-20T02:11:29ZengNature PortfolioNature Communications2041-17232024-10-0115111110.1038/s41467-024-53565-yPredicting protein synergistic effect in Arabidopsis using epigenome profilingChih-Hung Hsieh0Ya-Ting Sabrina Chang1Ming-Ren Yen2Jo-Wei Allison Hsieh3Pao-Yang Chen4Institute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaInstitute of Plant and Microbial Biology, Academia SinicaAbstract 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 ).https://doi.org/10.1038/s41467-024-53565-y |
| spellingShingle | Chih-Hung Hsieh Ya-Ting Sabrina Chang Ming-Ren Yen Jo-Wei Allison Hsieh Pao-Yang Chen Predicting protein synergistic effect in Arabidopsis using epigenome profiling Nature Communications |
| title | Predicting protein synergistic effect in Arabidopsis using epigenome profiling |
| title_full | Predicting protein synergistic effect in Arabidopsis using epigenome profiling |
| title_fullStr | Predicting protein synergistic effect in Arabidopsis using epigenome profiling |
| title_full_unstemmed | Predicting protein synergistic effect in Arabidopsis using epigenome profiling |
| title_short | Predicting protein synergistic effect in Arabidopsis using epigenome profiling |
| title_sort | predicting protein synergistic effect in arabidopsis using epigenome profiling |
| url | https://doi.org/10.1038/s41467-024-53565-y |
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