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 multi-way 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.

无连接、全基因组染色质相互作用作图方面的最新进展，例如通过标签延伸进行相互作用的分割池识别 (SPRITE) 和 ChIA-Drop，已经能够识别涉及同一细胞核内多个基因组位点的同时相互作用，这为在单核分辨率下描绘高阶基因组组织和基因调控机制提供了信息。不幸的是，用于分析多路染色质相互作用数据的计算方法尚未得到充分探索。在这里，我们开发了一种名为 MATCHA 的算法，该算法基于超图表示学习，其中多路染色质相互作用表示为超边。 SPRITE 和 ChIA-Drop 数据的应用表明，MATCHA 可以有效地对数据进行去噪并进行从头预测，从而大大提高了分析多路染色质相互作用特性的数据质量。 MATCHA 提供了一个有前途的框架，可以显着改进多路染色质相互作用数据的分析，并有可能为高阶染色体组织和功能提供独特的见解。 MATCHA 可以在这里免费下载：https://github.com/ma-compbio/MATTCHA。