EPIXplorer

What is E-P interactions?

Enhancers are gene-regulatory elements that bind transcription factors and can activate transcription promoter over long distance⁽¹⁾. With the development of chromosome conformation capture technologies (e.g. Hi-C, ChIA-PET, HiChIP, etc.), more and more enhancer-promoter (E-P) interactions were detected experimentally ^(2-4).

Image from Andrey’s work ⁽¹⁷⁾

Why E-P interactions are important?

Genome-wide association studies (GWAS) have identified thousands of noncoding loci that are associated with human diseases and complex traits, each of which could reveal insights into the mechanisms of disease ⁽⁵⁾. Many of the underlying causal variants may affect enhancers, but we lack accurate maps of enhancers and their target genes to interpret such variants ^(6-7). Thus, the emerging computational methods facilitate the identification of E-P interactions, as well as the interpretation of pathological variants.

How to predict E-P interactions?

EPIXplorer is a web server working on the prediction of E-P interactions, which integrates 9 prevailing computational methods to meet different requirements: Ernst et al. ⁽⁸⁾, Thurman et al. ⁽⁹⁾, PreSTIGE ⁽¹⁰⁾, IM-PET ⁽¹¹⁾, EpiTensor ⁽¹²⁾, TargetFinder ⁽¹³⁾, JEME ⁽¹⁴⁾, 3DPredictor ⁽¹⁵⁾, LoopPredictor ⁽¹⁶⁾, which covers different types of algorithm models and supports different types of input. EPIXplorer provides practical guidance for users to select method (By Model Type, By Input Type, and By Biosample), and integrates Downstream analysis, Visualization module for users to explore the molecular and cellular functions of loops.

Schema of EPIXplorer

12 Apr 2022 iTAD Predictor was renamed EPIXplorer for publication. 15 Dec 2021 iTAD Predictor released the latest version with 9 predictive algorithms integrated and visualization upgraded. 10 Dec 2020 iTAD Predictor released the first version.

References:

1. Furlong EEM, Levine M. 2018. Developmental enhancers and chromosome topology. Science 361: 1341–1345.

2. Lieberman-Aiden E, Berkum NL van, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, et al. 2009. Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome.Science 326: 289–293.

3. Fullwood MJ, Ruan Y. 2009. ChIP‐based methods for the identification of long‐range chromatin interactions. J Cell Biochem 107: 30–39.

4. Mumbach MR, Rubin AJ, Flynn RA, Dai C, Khavari PA, Greenleaf WJ, Chang HY. 2016. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods 13: 919–922.

5. Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., ... & McCarthy, M. I. (2020). A brief history of human disease genetics. Nature, 577(7789), 179-189.

6. Pennacchio, L. A., Ahituv, N., Moses, A. M., Prabhakar, S., Nobrega, M. A., Shoukry, M., ... & Rubin, E. M. (2006). In vivo enhancer analysis of human conserved non-coding sequences. Nature, 444(7118), 499-502.

7. Nasser, J., Bergman, D. T., Fulco, C. P., Guckelberger, P., Doughty, B. R., Patwardhan, T. A., ... & Engreitz, J. M. (2021). Genome-wide enhancer maps link risk variants to disease genes. Nature, 593(7858), 238-243.

8. Ernst, J., Kheradpour, P., Mikkelsen, T. S., Shoresh, N., Ward, L. D., Epstein, C. B., ... & Bernstein, B. E. (2011). Mapping and analysis of chromatin state dynamics in nine human cell types. Nature, 473(7345), 43-49.

9. Thurman, R. E., Rynes, E., Humbert, R., Vierstra, J., Maurano, M. T., Haugen, E., ... & Stamatoyannopoulos, J. A. (2012). The accessible chromatin landscape of the human genome. Nature, 489(7414), 75-82.

10. Corradin, O., Saiakhova, A., Akhtar-Zaidi, B., Myeroff, L., Willis, J., Cowper-Sal, R., ... & Scacheri, P. C. (2014). Combinatorial effects of multiple enhancer variants in linkage disequilibrium dictate levels of gene expression to confer susceptibility to common traits. Genome research, 24(1), 1-13.

11. He, B., Chen, C., Teng, L., & Tan, K. (2014). Global view of enhancer–promoter interactome in human cells. Proceedings of the National Academy of Sciences, 111(21), E2191-E2199.

12. Zhu, Y., Chen, Z., Zhang, K., Wang, M., Medovoy, D., Whitaker, J. W., ... & Wang, W. (2016). Constructing 3D interaction maps from 1D epigenomes. Nature communications, 7(1), 1-11.

13. Whalen, S., Truty, R. M., & Pollard, K. S. (2016). Enhancer–promoter interactions are encoded by complex genomic signatures on looping chromatin. Nature genetics, 48(5), 488-496.

14. Cao, Q., Anyansi, C., Hu, X., Xu, L., Xiong, L., Tang, W., ... & Yip, K. Y. (2017). Reconstruction of enhancer–target networks in 935 samples of human primary cells, tissues and cell lines. Nature genetics, 49(10), 1428-1436.

15. Belokopytova, P. S., Nuriddinov, M. A., Mozheiko, E. A., Fishman, D., & Fishman, V. (2020). Quantitative prediction of enhancer–promoter interactions. Genome research, 30(1), 72-84.

16. Tang, L., Hill, M. C., Wang, J., Wang, J., Martin, J. F., & Li, M. (2020). Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model. Genome research, 30(12), 1835-1845.

17. Andrey, G., & Mundlos, S. (2017). The three-dimensional genome: regulating gene expression during pluripotency and development. Development, 144(20), 3646-3658.