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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

Indonesian

Struktur spader genome yang valid disusun dari data Hi-C memberikan wawasan berharga ke dalam organisasi chromatin, tetapi membandingkannya di seluruh kondisi dan titik waktu tetap menantang karena kebisingan, variabilitas, dan orientasi arbitrase. Pendekatan perbandingan struktural state-of-the-art sering bergantung pada ringkasan berbasis kontak, yang tidak mempertahankan geometri spatial dari struktur chromatin yang disusun kembali dan membuat integrasi dengan ekspresi gen dan aksesibilitas chromatin sulit. Oleh karena itu, ada kebutuhan untuk penyesuaian struktural yang kuat dan metode abstraksi spatial yang mendukung analisis terintegrasi di seluruh kondisi dan titik waktu. Kami menyajikan MultiPASE, pipa komputasi untuk analisis komparatif dari struktur genome 3D yang disusun ulang yang diwakili sebagai awan titik spatial tingkat gen. A

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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