Modeling nascent transcription from chromatin landscape and structure with CLASTER
Year: 2026
Type: article
Abstract: We present the Chromatin Landscape and Structure to Expression Regressor (CLASTER), an epigenetic-based deep neural network that can integrate different data modalities describing the chromatin landscape and its 3D structure. CLASTER effectively translates them into nascent transcription levels measured at a kilobasepair resolution. The model provides a platform to understand the epigenetic drivers and learned rules of nascent transcription, and to predict the impact of in silico epigenetic perturbations. We conclude that the predominant locality of current machine learning approaches emerges as a signature of genomic organization, having broad implications for future modeling approaches.
Source: Genome biology
Authors: Marc Pielies Avellí, Arnór Ingi Sigurdsson, Joaquim Ollé López, Takeo Narita, Nils Krietenstein, Chunaram Choudhary, Simon Rasmussen
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,490
Awards: G1076064052, G2922943904, G4327227034
OpenAlex ID: W7128904419