Abstract
Biomolecular condensates play a significant role in chromatin activities, primarily by concentrating and compartmentalizing proteins and/or nucleic acids. However, their genomic landscapes and compositions remain largely unexplored due to a lack of dedicated computational tools for systematic identification in vivo. To address this, we develop CondSigDetector, a computational framework designed to detect condensate-like chromatin-associated protein co-occupancy signatures (CondSigs), to predict genomic loci and component proteins of distinct chromatin-associated biomolecular condensates. Applying this framework to mouse embryonic stem cells (mESC) and human K562 cells enable us to depict the high-resolution genomic landscape of chromatin-associated biomolecular condensates, and uncover both known and potentially unknown biomolecular condensates. Multi-omics analysis and experimental validation further verify the condensation properties of CondSigs. Additionally, our investigation sheds light on the impact of chromatin-associated biomolecular condensates on chromatin activities. Collectively, CondSigDetector provides an approach to decode the genomic landscape of chromatin-associated condensates, facilitating a deeper understanding of their biological functions and underlying mechanisms in cells.