Finding a Maximum Density Subgraph
Finding a Maximum Density Subgraph
Bioinformatics
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Recent chromosome conformation capture (3C) experiments produce genome-wide networks of chromatin interactions to help to study how chromosome structures relate to genomic functions. We investigate whether properties of chromatin interaction graphs based on shortest paths, maximum flows, and dense cores correlate with the spatial proximity in a three-dimensional model of the yeast genome. We demonstrate that within automatically-detected dense subgraphs, which correspond to spatially compact cores of interacting chromatin, these properties are well-correlated with spatial volume. We show that all tested methods are able to identify spatially compact sets when the test sets contain fragments from several chromosomes. We use a framework for systematically evaluating whether a method can accurately assess the spatial enrichment of a set of genomic loci for a hypothesized biological function. In such regions, we observe that the sets of fragments contained in the maximum density subgraph overlap highly with the sets of fragments in the spatially compact cores. Further, we observe that all methods agree on the spatial closeness of the yeast genomic annotations. Together, we show that compared to the more computationally complex and expensive three-dimensional embedding approach, the topological features of 3C graphs can be used to directly detect spatial closeness.