Dataset Information

HiC-GNN: A generalizable model for 3D chromosome reconstruction using graph convolutional neural networks.

ABSTRACT: Chromosome conformation capture (3 C) is a method of measuring chromosome topology in terms of loci interaction. The Hi-C method is a derivative of 3 C that allows for genome-wide quantification of chromosome interaction. From such interaction data, it is possible to infer the three-dimensional (3D) structure of the underlying chromosome. In this paper, we developed a novel method, HiC-GNN, for predicting the 3D structures of chromosomes from Hi-C data. HiC-GNN is unique from other methods for chromosome structure prediction in that the models learned by HiC-GNN can be generalized to data that is distinct from the training data. This aspect of HiC-GNN allows models that were trained on one Hi-C contact map to be used for inference on entirely different maps. To the authors' knowledge, this generalizing capability is not present in any existing methods. HiC-GNN uses a node embedding algorithm and a graph neural network to predict the 3D coordinates of each genomic loci from the corresponding Hi-C contact data. Unlike other methods, our algorithm allows for the storage of pre-trained parameters, thus enabling prediction on data that is entirely different from the training data. We show that our method can accurately generalize a single model across Hi-C resolutions, multiple restriction enzymes, and multiple cell populations while maintaining reconstruction accuracy across three Hi-C datasets. Our algorithm outperforms the state-of-the-art methods in accuracy of prediction and runtime and introduces a novel method for 3D structure prediction from Hi-C data. All our source codes and data are available at https://github.com/OluwadareLab/HiC-GNN.

SUBMITTER: Hovenga V

PROVIDER: S-EPMC9842867 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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HiC-GNN: A generalizable model for 3D chromosome reconstruction using graph convolutional neural networks.

Hovenga Van V Kalita Jugal J Oluwadare Oluwatosin O

Computational and structural biotechnology journal 20221231

Chromosome conformation capture (3 C) is a method of measuring chromosome topology in terms of loci interaction. The Hi-C method is a derivative of 3 C that allows for genome-wide quantification of chromosome interaction. From such interaction data, it is possible to infer the three-dimensional (3D) structure of the underlying chromosome. In this paper, we developed a novel method, HiC-GNN, for predicting the 3D structures of chromosomes from Hi-C data. HiC-GNN is unique from other methods for c ...[more]

PMID: 36698967

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Project description:BackgroundCancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently.ResultsIn this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9-94.7%) among 34 classes (33 cancer types and a normal group). In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 markers genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific.ConclusionNovel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.

Dataset Information

HiC-GNN: A generalizable model for 3D chromosome reconstruction using graph convolutional neural networks.

Publications

HiC-GNN: A generalizable model for 3D chromosome reconstruction using graph convolutional neural networks.

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