Determining mesoscale chromatin structure parameters from spatially correlated cleavage data using a coarse-grained oligonucleosome model.
Ariana Brenner ClerkinNicole PaganeDevany W WestAndrew J SpakowitzViviana I RiscaPublished in: bioRxiv : the preprint server for biology (2024)
The three-dimensional structure of chromatin has emerged as an important feature of eukaryotic gene regulation. Recent technological advances in DNA sequencing-based assays have revealed locus- and chromatin state-specific structural patterns at the length scale of a few nucleosomes (~1 kb). However, interpreting these data sets remains challenging. Radiation-induced correlated cleavage of chromatin (RICC-seq) is one such chromatin structure assay that maps DNA-DNA-contacts at base pair resolution by sequencing single-stranded DNA fragments released from irradiated cells. Here, we develop a flexible modeling and simulation framework to enable the interpretation of RICC-seq data in terms of oligonucleosome structure ensembles. Nucleosomes are modeled as rigid bodies with excluded volume and adjustable DNA wrapping, connected by linker DNA modeled as a worm-like chain. We validate the model's parameters against cryo-electron microscopy and sedimentation data. Our results show that RICC-seq is sensitive to nucleosome spacing, nucleosomal DNA wrapping, and the strength of inter-nucleosome interactions. We show that nucleosome repeat lengths consistent with orthogonal assays can be extracted from experimental RICC-seq data using a 1D convolutional neural net trained on RICC-seq signal predicted from simulated ensembles. We thus provide a suite of analysis tools that add quantitative structural interpretability to RICC-seq experiments.
Keyphrases
- genome wide
- circulating tumor
- single cell
- single molecule
- cell free
- rna seq
- electronic health record
- dna damage
- gene expression
- radiation induced
- transcription factor
- dna methylation
- high throughput
- big data
- nucleic acid
- molecular dynamics
- circulating tumor cells
- machine learning
- oxidative stress
- radiation therapy
- mass spectrometry
- molecular dynamics simulations
- dna binding
- binding protein
- endoplasmic reticulum stress