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MultiPASE: Shape-Aware Comparative 3D Genome Analysis

N. Nipu, G. Johnson, C. Roth, D. Rogers, S. R. Starkenburg, C. R. Steadman, G. E. Marai · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.22786 · cited by 0 works (OpenAlex) · open-access PDF

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Las estructuras interconectadas del genoma de 3D son reconstruidas a partir de los datos de Hi-C para proporcionar insights valiosos en la organización de la cromatina, pero compararlas a través de condiciones y puntos de tiempo sigue siendo un reto debido al ruido, la variabilidad y la orientación arbitraria del núcleo. Los enfoques de comparación estructural de última generación a menudo se basan en resúmenes basados en matriz de contacto, que no conservan la geometría espacial de las estructuras reconstruidas y dificultan la integración con la expresión de genes y la accesibilidad de la cromatina. Por lo tanto, hay una necesidad de un alineamiento estructural robusto y métodos de abstracción espacial que apoyan el análisis integrado de las condiciones y puntos de tiempo. Presentamos MultiPASE, un

Machine translation (Meta M2M100); the English original follows.

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.

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Topics: Computer science, Chromatin, Abstraction, Pipeline (software), Point cloud, Computational biology

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