Chrombus-XMBD: A Graph Generative Model Predicting 3D-Genome,ab initiofrom Chromatin Features

Abstract

AbstractsThe landscape of 3D-genome is crucial for transcription regulation. But capturing the dynamics of chromatin conformation is costly and technically challenging. Here we described “Chrombus-XMBD”, a graph generative model capable of predicting chromatin interactionsab initobased on available chromatin features. Chrombus employes dynamic edge convolution with QKV attention setup, which maps the relevant chromatin features to a learnable embedding space thereby generate genomewide 3D-contactmap. We validated Chrombus predictions with published databases of topological associated domains (TAD), eQTLs and gene-enhancer interactions. Chrombus outperforms existing algorithms in efficiently predicting long-range chromatin interactions. Chrombus also exhibits strong generalizability across different cell lineage and species. Additionally, the parameter sets of Chrombus inform the biological processes underlying 3D-genome. Our model provides a new perspective towards interpretable AI-modeling of the dynamics of chromatin interactions and better understanding ofcis-regulation of gene expression.