Chromosome Modeling on Downsampled Hi‑C Maps Enhances the Compartmentalization Signal

• Published in: The journal of physical chemistry. B 2021-08, Vol.125 (31), p.8757-8767
• Main Authors: Oliveira Junior, Antonio B, Estrada, Cynthia Perez, Aiden, Erez Lieberman, Contessoto, Vinícius G, Onuchic, José N
• Format: Article
• Language: English
• Subjects: B: Biophysical and Biochemical Systems and Processes; Cell Nucleus; chromatin; Chromatin - genetics; Chromosomes - genetics; energy; Genome; heat; Humans; interphase; Molecular Conformation; physical chemistry; prediction; statistics
• ISSN: 1520-6106; 1520-5207; 1520-5207
• DOI: 10.1021/acs.jpcb.1c04174

The human genome is organized within a nucleus where chromosomes fold into an ensemble of different conformations. Chromosome conformation capture techniques such as Hi-C provide information about the genome architecture by creating a 2D heat map. Initially, Hi-C map experiments were performed in human interphase cell lines. Recently, efforts were expanded to several different organisms, cell lines, tissues, and cell cycle phases where obtaining high-quality maps is challenging. Poor sampled Hi-C maps present high sparse matrices where compartments located far from the main diagonal are difficult to observe. Aided by recently developed models for chromatin folding and dynamics investigation, we introduce a framework to enhance the compartments’ information far from the diagonal observed in experimental sparse matrices. The simulations were performed using the Open-MiChroM platform aided by new trained parameters in the minimal chromatin model (MiChroM) energy function. The simulations optimized on a downsampled experimental map (10% of the original data) allow the prediction of a contact frequency similar to that of the complete (100%) experimental Hi-C. The modeling results open a discussion on how simulations and modeling can increase the statistics and help fill in some Hi-C regions not captured by poor sampling experiments. Open-MiChroM simulations allow us to explore the 3D genome organization of different organisms, cell lines, and cell phases that often do not produce high-quality Hi-C maps.