MultiPASE aligns and analyzes 3D genome shapes across conditions
MultiPASE: Shape-Aware Comparative 3D Genome Analysis
Human-Computer Interaction
Summary
3D genome structures help scientists understand how DNA is arranged in cells, but comparing these structures over time or between conditions is tricky because they can be noisy and oriented differently. The authors developed MultiPASE, a tool that carefully lines up these 3D structures and breaks them down into simpler shapes to compare them better. This method helps reveal changes in genome organization due to viral infection that are hard to see otherwise. It also lets users link the 3D structure with gene activity and DNA accessibility for a fuller picture.
What this means in practice
- •For genomic data analysts: Compare 3D genome structures across experiments using aligned spatial models integrated with gene activity data for biological insights.
- •For biomedical software developers: Build interactive tools that visualize and analyze genome spatial reorganization with MultiPASE’s alignment and shape abstraction methods.
Authors
N. Nipu, G. Johnson, C. Roth, D. Rogers, S. R. Starkenburg, C. R. Steadman, G. E. Marai
Abstract
3D genome structures reconstructed from Hi-C data provide valuable insights into chromatin organization, but comparing them across conditions and time points remains challenging because of noise, variability, and arbitrary orientation. State-of-the-art comparison approaches often rely on contact-matrix-based summaries, which do not preserve the spatial geometry of the reconstructed structures and make integration with gene expression and chromatin accessibility difficult. There is therefore a need for robust structural alignment and spatial abstraction methods that support integrated analysis across conditions and time points. We present MultiPASE, a computational pipeline for comparative analysis of reconstructed 3D genome structures represented as gene-level spatial point clouds. MultiPASE aligns point clouds into a shared coordinate frame using coarse-to-fine rigid registration with PCA-based axis disambiguation and robust ICP refinement, then summarizes spatial organization using two complementary shape abstraction methods that produce stable, nested, multi-scale representations from dense core regions to broader structural extent. Using Hi-C-derived reconstructions from viral infection experiments, we show how the approach reveals condition-dependent structural changes that are difficult to interpret from raw 3D structures alone. By integrating spatial structure with gene expression and chromatin accessibility, MultiPASE supports interactive analysis of spatial organization and gene relocation patterns. A quantitative validation, reported in the Supplementary Materials, shows that the proposed shape abstractions preserve non-convex features, remain stable across samples, and that the alignment pipeline is necessary for meaningful comparison. A usage case and a usability evaluation with eight participants demonstrate the pipeline's effectiveness for comparative structural analysis.