GeneRHi-C: 3D GENomE Reconstruction from Hi-C data

Background: Many computational methods have been developed that leverage the results from biological experiments (such as Hi-C) to infer the 3D organization of the genome. Formally, this is referred to as the 3D genome reconstruction problem (3D-GRP). Hi-C data is now being generated at increasingly high resolutions. As this resolution increases, it has become computationally infeasible to predict a 3D genome organization with the majority of existing methods. None of the existing solution methods have utilized a non-procedural programming approach (such as integer programming) despite the established advantages and successful applications of such approaches for predicting high-resolution 3D structures of other biomolecules. Our objective was to develop a new solution to the 3D-GRP that utilizes non-procedural programming to realize the same advantages. Results: In this paper, we present a three-step consensus method (called GeneRHi-C; pronounced "generic") for solving the 3D-GRP which utilizes both new and existing techniques. Briefly, (1) the dimensionality of the 3D-GRP is reduced by identifying a biologically plausible, ploidy-dependent subset of interactions from the Hi-C data. This is performed by modelling the task as an optimization problem and solving it efficiently with an implementation in a non-procedural programming language. The second step (2) generates a biological network (graph) that represents the subset of interactions identified in the previous step. Briefly, genomic bins are represented as nodes in the network with weighted-edges representing known and detected interactions. Finally, the third step (3) uses the ForceAtlas 3D network layout algorithm to calculate (x, y, z) coordinates for each genomic region in the contact map. The resultant predicted genome organization represents the interactions of a population-averaged consensus structure. The overall workflow was tested with Hi-C data from Schizosaccharomyces pombe (fission yeast). The resulting 3D structure clearly recapitulated previously established features of fission yeast 3D genome organization. Conclusion: Overall, GeneRHi-C demonstrates the power of non-procedural programming and graph theoretic techniques for providing an efficient, generalizable solution to the 3D-GRP. Project Homepage: https://github.com/kimmackay/GeneRHi-C