CRII:III:Development of deep learning methods for high-resolution 3D genome structure spatial reconstruction

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An increase in the amount and resolution of data from different individuals and cell types, from multiple cells, single cells, or subcellular localizations, has consequently increased the complexity of these datasets. This increase means we need more sophisticated and automated approaches to infer patterns and structures from these datasets. The cell's three-dimensional (3D) chromosome and genome organization structure is one of the vital structures deducible in our data-rich society. Reconstructing the 3D organization of the genome is a complicated and challenging task. Nevertheless, it is necessary to understand many cellular activities, such as gene expression, gene stability, and regulation. To improve the understanding of chromosome organization within a cell, genomic technologies based on chromosome conformation capture techniques, particularly Hi-C, were developed. This development greatly improved cellular study and led to the development of multiple 3D chromosome structure reconstruction methods over the years. Although many 3D chromosome structure reconstruction methods have been proposed, detailed insight into the structural architecture of the chromosome and genome at a high resolution (=5kb) is lacking. This project aims to develop an advanced and scalable algorithm for high-resolution 3D genome reconstruction from Hi-C data that provides enough detail to explain biological activities such as gene-gene interaction at a refined scale. This project will be developed as open-source tools and software. Also, the multidisciplinary nature of this project provides a mechanism for training and providing hands-on learning and mentoring opportunities in teaching and research to students at both undergraduate and graduate levels.Specifically, the ultimate objective of this project is to develop computational and machine learning-based frameworks to elucidate the interplay between the hierarchical organization within the genome and its functions through high-resolution(=5kb) 3D structure reconstruction. This project is expected to develop algorithms and computational tools to advance 3D genome organization research in the following aspects. First, a robust, reliable, and flexible high-resolution 3D chromosome and genome structure reconstruction algorithm will be developed. This algorithm will use graph convolutional neural networks (GCNNs) as the core method for spatial structure reconstruction. Second, it will develop a novel noninstance-based approach for 3D structure reconstruction capable of high-resolution 3D genome structure model prediction using a generalization approach, where a trained model can be used to reconstruct multiple chromosomes or used for prediction across resolutions. The proposed noninstance-based approach for 3D structure utilizes a node embedding algorithm for the graph node feature representation corresponding to each chromosomal locus. These features are trained with a GCNN to generate predictions for chromosome and genome 3D structure coordinates corresponding to each chromosomal locus. Overall, this project will investigate the GCNN complexity and model depth questions to provide a new approach for chromosome and genome 3D structure reconstruction at a high resolution. The successful implementation of this project will give a unique insight into the 3D organization of chromosomes and genomes and motivate the future development of noninstance-based methods for chromosome 3D structure prediction.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.