Abstract
Perturbation of the binding pattern of one or more DNA-binding proteins, called transcription factors, plays a role in many diseases including, but not limited to, cancer. This has prompted efforts to characterise transcription cofactors i.e., transcription factors that work together to regulate gene expression. The Overlap Correlation Value (OCV), ranging from 0 (no correlation) to 1 (highly correlated), has been previously reported as a measure of the statistical significance in the overlap of binding sites of two transcription factors and thus a measure of the extent to which they may act as cofactors. In this study, we examined the variation in the OCV due to the peak caller employed to identify transcription factor binding sites. We identified that the significance of correlation between two transcription factors was unaffected by the peak-caller employed to identify transcription factor binding sites (Spearman R = 0.98). Furthermore, we used OCV measurements to develop a novel network map to study the correlation between twelve breast cancer cell-line datasets. Our proposed novel map revealed that transcription factor FOXA1 influenced the binding of six other transcription factors: JUND, P300, estrogen receptor alpha (ERα), GATA3, progesterone receptor (PR), and XBP1. Our model identified that binding sites that were targeted by PR were different under progesterone agonist (R5020 or ORG2058) or antagonist (RU486) treatment. Interestingly ERα had a significant OCV with PR when stimulated by anti-progestin, while it showed no significant overlap with PR when simulated with progestin. Our proposed network map drawn using OCV measurements is feature rich, more meaningful, and is better interpretable then using Venn diagram. The network map can be used in all scientific domains.
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Khushi, M., Choudhury, N., Arthur, J.W., Clarke, C.L., Graham, J.D. (2018). Predicting Functional Interactions Among DNA-Binding Proteins. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_7
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