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Enhanced performance of gene expression predictive models with protein-mediated spatial chromatin interactions

View ORCID ProfileMateusz Chiliński, Jakub Lipiński, View ORCID ProfileAbhishek Agarwal, View ORCID ProfileYijun Ruan, View ORCID ProfileDariusz Plewczynski
doi: https://doi.org/10.1101/2023.04.06.535849
Mateusz Chiliński
1Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw 00-662, Poland
2Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw 02-097, Poland
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  • ORCID record for Mateusz Chiliński
Jakub Lipiński
3Cellular Genomics, Warsaw, Poland
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Abhishek Agarwal
2Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw 02-097, Poland
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Yijun Ruan
4The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06030, USA
5Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang Province, P. R. China
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Dariusz Plewczynski
1Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw 00-662, Poland
2Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw 02-097, Poland
3Cellular Genomics, Warsaw, Poland
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  • For correspondence: Dariusz.Plewczynski@pw.edu.pl
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Abstract

There have been multiple attempts to predict the expression of the genes based on the sequence, epigenetics, and various other factors. To improve those predictions, we have decided to investigate adding protein-specific 3D interactions that play a major role in the compensation of the chromatin structure in the cell nucleus. To achieve this, we have used the architecture of one of the state-of-the-art algorithms, ExPecto (J. Zhou et al., 2018), and investigated the changes in the model metrics upon adding the spatially relevant data. We have used ChIA-PET interactions that are mediated by cohesin (24 cell lines), CTCF (4 cell lines), and RNAPOL2 (4 cell lines). As the output of the study, we have developed the Spatial Gene Expression (SpEx) algorithm that shows statistically significant improvements in most cell lines.

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 4.0 International license.
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Posted April 06, 2023.
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Enhanced performance of gene expression predictive models with protein-mediated spatial chromatin interactions
Mateusz Chiliński, Jakub Lipiński, Abhishek Agarwal, Yijun Ruan, Dariusz Plewczynski
bioRxiv 2023.04.06.535849; doi: https://doi.org/10.1101/2023.04.06.535849
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Enhanced performance of gene expression predictive models with protein-mediated spatial chromatin interactions
Mateusz Chiliński, Jakub Lipiński, Abhishek Agarwal, Yijun Ruan, Dariusz Plewczynski
bioRxiv 2023.04.06.535849; doi: https://doi.org/10.1101/2023.04.06.535849

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