Determining mesoscale chromatin structure parameters from spatially correlated cleavage data using a coarse-grained...

Area of Science:

• Molecular Biology
• Genomics
• Structural Biology

Background:

• Chromatin's 3D structure is crucial for eukaryotic gene regulation.
• DNA sequencing assays reveal nucleosome-level structural patterns, but interpretation is challenging.
• Radiation-induced Correlated Cleavage of Chromatin sequencing (RICC-seq) maps DNA contacts at base-pair resolution.

Purpose of the Study:

• To develop a flexible modeling and simulation framework for interpreting RICC-seq data.
• To enable the analysis of RICC-seq data in terms of oligonucleosome structure ensembles.
• To provide quantitative structural interpretability for RICC-seq experiments.

Main Methods:

• Modeling nucleosomes as rigid bodies with adjustable DNA wrapping, connected by worm-like chain linker DNA.
• Validating model parameters against cryo-electron microscopy and sedimentation data.
• Utilizing a 1D convolutional neural network trained on simulated RICC-seq data to extract nucleosome repeat lengths.

Main Results:

• The developed model is sensitive to nucleosome spacing, DNA wrapping, and inter-nucleosome interactions.
• Nucleosome repeat lengths consistent with orthogonal assays were successfully extracted from experimental RICC-seq data.
• The framework provides a suite of analysis tools for RICC-seq data.

Conclusions:

• The modeling framework significantly enhances the quantitative structural interpretability of RICC-seq data.
• This approach facilitates a deeper understanding of chromatin organization and its role in gene regulation.
• The study provides valuable tools for researchers studying chromatin structure using RICC-seq.