A graph-based comparative analysis of three-dimensional organization of chromosomes in yeast and mammals.
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Genome-wide maps of chromosomal interactions are becoming increasingly common. Computational tools to analyze such maps, and more importantly, comparing such maps across multiple contexts, and organisms are scarce. We here develop a novel graph-based clustering approach to detect sets of interacting genomic loci and compare them across multiple cellular and organismal contexts. We used a hybrid approach to detect an interaction between two loci to enable our approach to be applicable to 3C data from both simple eukaryotes as well as higher eukaryotes such as mouse and humans. To determine the number of clusters we used a penalized cluster quality criteria and developed numerous statistics to systematically examine properties of chromosomal organization. Application of our approach identified several principles: (a) the proportion of inter-chromosomal interactions is much higher in yeast, compared to mammalian species, (b) in addition to replication forks, distally interacting regions in yeast also exhibited a tendency to be co-regulated (based on gene expression, targets of knockouts and targets of ChIP), (c) most of the chromosomal organization is the same between two mammalian tissues, but there are some regions that exhibit a tissue-specific interaction pattern, (d) comparison of interaction maps between human and mouse identified significant conservation of clusters of regions, but, we did not find a similar conservation between yeasts, and yeast and mammals. Our graph-based clustering approach enabled us to perform a systematic comparison of multiple chromosomal region interaction maps, corroborated known findings of such interaction maps, and also identified novel aspects of such maps.