PT  - JOURNAL ARTICLE
AU  - Mateusz Chiliński
AU  - Jakub Lipiński
AU  - Abhishek Agarwal
AU  - Yijun Ruan
AU  - Dariusz Plewczynski
TI  - Enhanced performance of gene expression predictive models with protein-mediated spatial chromatin interactions
AID  - 10.1101/2023.04.06.535849
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.04.06.535849
4099  - http://biorxiv.org/content/early/2023/04/06/2023.04.06.535849.short
4100  - http://biorxiv.org/content/early/2023/04/06/2023.04.06.535849.full
AB  - 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 StatementThe authors have declared no competing interest.