Inference of the three-dimensional chromatin structure and its temporal behavior
This work addresses limitations in computational methods for genome structure analysis, which is crucial for understanding biological processes and diseases, though it appears incremental as it builds on existing manifold learning and autoencoder approaches.
The authors tackled the problem of inferring the 3D chromatin structure from HiC data by developing REACH-3D, a method using recurrent autoencoders that achieved the highest correlation with microscopy measurements on real data and successfully modeled dynamic chromatin conformation.
Understanding the three-dimensional (3D) structure of the genome is essential for elucidating vital biological processes and their links to human disease. To determine how the genome folds within the nucleus, chromosome conformation capture methods such as HiC have recently been employed. However, computational methods that exploit the resulting high-throughput, high-resolution data are still suffering from important limitations. In this work, we explore the idea of manifold learning for the 3D chromatin structure inference and present a novel method, REcurrent Autoencoders for CHromatin 3D structure prediction (REACH-3D). Our framework employs autoencoders with recurrent neural units to reconstruct the chromatin structure. In comparison to existing methods, REACH-3D makes no transfer function assumption and permits dynamic analysis. Evaluating REACH-3D on synthetic data indicated high agreement with the ground truth. When tested on real experimental HiC data, REACH-3D recovered most faithfully the expected biological properties and obtained the highest correlation coefficient with microscopy measurements. Last, REACH-3D was applied to dynamic HiC data, where it successfully modeled chromatin conformation during the cell cycle.