.png){kind=icon}
.png){kind=icon}
Return
MATCHA: Probing Multi-way Chromatin Interaction with Hypergraph Representation Learning
.png){kind=icon}
OA
AI
DOI：10.1016/j.cels.2020.04.004.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recent advances in ligation-free, genome-wide chromatin interaction mapping, such as split-pool recognition of interactions by tag extension (SPRITE) and ChIA-Drop, have enabled the identification of simultaneous interactions involving multiple genomic loci within the same nuclei, which are informative to delineate higher-order genome organization and gene regulation mechanisms at single-nucleus resolution. Unfortunately, computational methods for analyzing multi-way chromatin interaction data are significantly underexplored. Here, we develop an algorithm called MATCHA, based on hypergraph representation learning, where multiway chromatin interactions are represented as hyperedges. Applications to SPRITE and ChIA-Drop data suggest that MATCHA is effective to denoise the data and make de novo predictions, which greatly enhances the data quality for analyzing the properties of multi-way chromatin interactions. MATCHA provides a promising framework to significantly improve the analysis of multi-way chromatin interaction data and has the potential to offer unique insights into higher-order chromosome organization and function. MATCHA is freely available for download here: https://github.com/ma-compbio/MATCHA.
Keywords:
GENOME
TRANSCRIPTION
PRINCIPLES
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.