Evidence map›Paper›PMID 39448614›Full record

ArticleNature communications2024

Predicting protein synergistic effect in Arabidopsis using epigenome profiling.

Chih-Hung Hsieh, Ya-Ting Sabrina Chang, Ming-Ren Yen, Jo-Wei Allison Hsieh, Pao-Yang Chen

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Chih-Hung HsiehInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan.ORCID 0000-0002-0935-3617
Ya-Ting Sabrina ChangInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan.
Ming-Ren YenInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan.
Jo-Wei Allison HsiehInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan.
Pao-Yang ChenInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, 115201, Taiwan. paoyang@gate.sinica.edu.tw.ORCID 0000-0002-7402-3075

Funding

Academia Sinica AS-NTU-112-12Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan) 109-2927-I-001-504
6 · The paper itself

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 ).

Indexed as

ArabidopsisArabidopsis ProteinsEpigenomeGene Expression Regulation, PlantHistone CodeChromatin Immunoprecipitation SequencingEpigenesis, GeneticEpigenomicsHistonesMachine LearningArabidopsis ProteinsHistones

Identifiers

PMID39448614
PMCPMC11502919

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.