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Abstract MotivationUnderstanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to learn to predict enhancer–promoter (EP) relationships in a data-driven manner. ResultsWe applied machine learning to one of the largest enhancer perturbation studies integrated with transcription factor (TF) and histone modification ChIP-seq. The results uncovered a discrepancy in the prediction of genome-wide data compared to data from targeted experiments. Relative strength of contact was important for prediction, confirming the basic principle of EP regulation. Novel features such as the density of the enhancers/promoters in the genomic region was found to be important, highlighting our lack of understanding on how other elements in the region contribute to the regulation. Several TF peaks were identified that improved the prediction by identifying the negatives and reducing False Positives. In summary, integrating genomic assays with enhancer perturbation studies increased the accuracy of the model, and provided novel insights into the understanding of enhancer-driven transcription. Availability and implementationThe trained models, data, and the source code are available at http://doi.org/10.5281/zenodo.11290386 and https://github.com/HanLabUNLV/sleps.
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This content will become publicly available on May 30, 2026
The study of plasmodesmal biology using proximity labeling technologies
Abstract Plasmodesmata (PD) are essential cellular structures that facilitate intercellular communication in plants, enabling the transport of nutrients and signaling molecules. Over the past decades, significant strides have been made in unraveling the formation, function, and regulation of PD. Identification and functional characterization of PD-associated proteins have greatly advanced our understanding of PD. This review discusses past efforts in uncovering PD proteomes and highlights recent breakthroughs in applying proximity labeling (PL) assays to map plant protein interactomes. Special attention is given to using PL assays in studying PD biology, emphasizing their potential to drive future advancements and deepen our understanding of PD function and regulation. By integrating PL technologies with established methodologies, researchers can gain comprehensive insights into the dynamic composition and roles of PD.
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- Award ID(s):
- 2339067
- PAR ID:
- 10656851
- Publisher / Repository:
- OXFORD ACADEMIC
- Date Published:
- Journal Name:
- Journal of Experimental Botany
- ISSN:
- 0022-0957
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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