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Integrating long-range regulatory interactions to predict gene expression using graph convolutional neural networks

Jeremy Bigness, Xavier Loinaz, Shalin Patel, Erica Larschan, View ORCID ProfileRitambhara Singh
doi: https://doi.org/10.1101/2020.11.23.394478
Jeremy Bigness
1Center for Computational Molecular Biology, Brown University
2Department of Computer Science, Brown University
4Department of Molecular Biology, Cell Biology and Biochemistry, Brown University
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Xavier Loinaz
2Department of Computer Science, Brown University
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Shalin Patel
3Division of Applied Mathematics, Brown University
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Erica Larschan
1Center for Computational Molecular Biology, Brown University
4Department of Molecular Biology, Cell Biology and Biochemistry, Brown University
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Ritambhara Singh
1Center for Computational Molecular Biology, Brown University
2Department of Computer Science, Brown University
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  • ORCID record for Ritambhara Singh
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Abstract

Long-range spatial interactions among genomic regions are critical for regulating gene expression and their disruption has been associated with a host of diseases. However, when modeling the effects of regulatory factors on gene expression, most deep learning models either neglect long-range interactions or fail to capture the inherent 3D structure of the underlying biological system. This prevents the field from obtaining a more comprehensive understanding of gene regulation and from fully leveraging the structural information present in the data sets. Here, we propose a graph convolutional neural network (GCNN) framework to integrate measurements probing spatial genomic organization and measurements of local regulatory factors, specifically histone modifications, to predict gene expression. This formulation enables the model to incorporate crucial information about long-range interactions via a natural encoding of spatial interaction relationships into a graph representation. Furthermore, we show that our model is interpretable in terms of the observed biological regulatory factors, highlighting both the histone modifications and the interacting genomic regions that contribute to a gene’s predicted expression. We apply our GCNN model to datasets for GM12878 (lymphoblastoid) and K562 (myelogenous leukemia) cell lines and demonstrate its state-of-the-art prediction performance. We also obtain importance scores corresponding to the histone mark features and interacting regions for some exemplar genes and validate them with evidence from the literature. Our model presents a novel setup for predicting gene expression by integrating multimodal datasets.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 24, 2020.
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Integrating long-range regulatory interactions to predict gene expression using graph convolutional neural networks
Jeremy Bigness, Xavier Loinaz, Shalin Patel, Erica Larschan, Ritambhara Singh
bioRxiv 2020.11.23.394478; doi: https://doi.org/10.1101/2020.11.23.394478
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Integrating long-range regulatory interactions to predict gene expression using graph convolutional neural networks
Jeremy Bigness, Xavier Loinaz, Shalin Patel, Erica Larschan, Ritambhara Singh
bioRxiv 2020.11.23.394478; doi: https://doi.org/10.1101/2020.11.23.394478

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