A comprehensive model of the ESC's epigenetic network.
My group is interested in the reconstruction of protein networks using computational methods to disentangle direct and indirect interactions and in particular those classified under the general category of co-evolution based approaches (Juan et al, 2013).
In this case we have collected the heterogeneous high-throughput epigenomic datasets available in public repositories for mouse embryonic stem cells (mESCs) including 139 experiments from 30 datasets with a total of 77 epigenomic features, various cytosine modifications (5mC, 5hmC and 5fC), histone marks and Chromatin related Proteins (CrPs).
We applied a set of newly developed statistical analysis methods (see Lasserre et al., 2013) with the goal of understanding the associations between chromatin states, detecting significant co-occurrence between CrPs and epigenetic modifications in different chromatin regions.
The resulting networks reveal the complex relations between cytosine modifications, protein complexes and their dependence on ESC chromatin contexts.
Furthermore, we have applied a newly developed method based on ( Juan et al, 2008) to evaluate the co-evolution between families of "Chromatin related Proteins". The co-evolutionary approach brings orthogonal information that completes the epigenetic network and makes possible a new level of biological interpretation.
I will present the initial network model together with the methodology developed for this study.
This work corresponds to the paper in preparation by Carrillo de Santa Pau, Perner, Juan et al., (2014) and it was developed in collaboration with Martin Vingron's lab (MPIMG, Berlin) in the context Blueprint EU consortium (www.blueprintepigenome.eu)
- Juan, D., Pazos, F., and Valencia, A. (2008). High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. Proc. Natl. Acad. Sci. U.S.a. 105, 934-939.
- Juan, D., Pazos, F., and Valencia, A. (2013). Emerging methods in protein co-evolution. Nat. Rev. Genet. 14, 249-261.
- Lasserre, J., Chung, H.-R., and Vingron, M. (2013). Finding associations among histone modifications using sparse partial correlation networks. PLoS Comp. Biol. 9, e1003168.