Inference of genomic spatial organization from a whole genome bisulfite sequencing sample.
Common approaches to characterize the structure of the DNA in the nucleus, such as the different Chromosome Conformation Capture methods, have not currently been widely applied to different tissue types due to several practical difficulties including the requirement for intact cells to start the sample preparation. In contrast, techniques based on sodium bisulfite conversion of DNA to assay DNA methylation, have been widely applied to many different tissue types in a variety of organisms. Recent work has shown the possibility of inferring some aspects of the three dimensional DNA structure from DNA methylation data, raising the possibility of three dimensional DNA structure prediction using the large collection of already generated DNA methylation datasets. We propose a simple method to predict the values of the first eigenvector of the Hi-C matrix of a sample (and hence the positions of the A and B compartments) using only the GC content of the sequence and a single whole genome bisulfite sequencing (WGBS) experiment which yields information on the methylation levels and their variability along the genome. We train and test our model on 10 samples for which we have data from both bisulfite sequencing and chromosome conformation experiments and our most relevant finding is that the variability of DNA methylation along the sequence is often a better predictor than methylation itself. We then run a prediction on 206 DNA methylation profiles produced by the Blueprint project and use ChIP-Seq and RNA-Seq data to confirm that the forecasted eigenvector delineates correctly the physical chromatin compartments observed with the Hi-C experiment.