Abstract
The recent experimental technique of chromosome conformational capture gives an in-vivo access to pairwise contact frequencies between genomic loci. We present how network analysis can be exploited to extract information from genome-wide contact maps. We recently proposed to use graph distance for deriving a complete distance matrix from sparse contact maps. Completed with multidimensional scaling (MDS), this network-based method provided a fast algorithm, ShRec3D, for reconstructing 3D genome structures. We here develop an extension of this algorithm, by devising a tunable variant of the graph distance and introducing an alternative implementation of multidimensional scaling. This extended algorithm is shown to be more flexible so as to accommodate additional experimental constraints, focus on specific spatial scales, and produce tractable representations of human data. Network representation of genomic contacts offers a path where physical and systemic approaches are joined to unravel the biological role of the 3D genome structure.
Highlights
The recent experimental technique of chromosome conformational capture gives an in-vivo access to pairwise contact frequencies between genomic loci
We propose in Section “Results and discussion: an extension of Shortest-path 3D reconstruction algorithm (ShRec3D) for human genome” an extension of this reconstruction algorithm, involving a tunable graph distance and two different multidimensional scaling (MDS) implementations
In the line of experiments using fluorescence in-situ hybridization (FISH) data, which evidenced a power-law correlation between contact frequencies and measured distances [2], we explore the relationships between the contact frequencies, the graph distances, and the distances within the reconstructed 3D structures
Summary
Experimental data: We used human Hi-C data obtained from lymphoblastoids (cell type GM12878) at a resolution of 1kb [3]. The procedure takes as a starting point the 3D structure provided by classical MDS, in order to reduce the nonconvex optimization problem to a local minimization problem and exploit the efficient dimensional reduction ensured by cMDS. In this way the computational performance remains satisfactory, especially at large sizes for which the duration of the MDS step is anyhow overwhelmed by the computation of the shortest-path distances (see Additional file 1: Figure S1).
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