In this paper, we present a general statistical framework that is widely applicable to the analysis of genomic contact maps, irrespective of the data acquisition and normalization processes. Within this framework DNA–DNA contact data are represented as a complex network where DNA segments and contacts between them are denoted as nodes and edges, respectively. We also present a robust method for generating randomized contact networks that explicitly take into account the inherent 3D nature of the genome and serve as realistic null-models for unbiased statistical analyses. Our paper was chosen as a featured article by NAR. The paper by Kai Kruse et al can be found here.
Growing evidence suggests that aggregation-prone proteins are both harmful and functional for a cell. How do cellular systems balance the detrimental and beneficial effect of protein aggregation? In this work, we reveal that aggregation-prone proteins are subject to differential transcriptional, translational, and degradation control compared to nonaggregation-prone proteins, which leads to their decreased synthesis, low abundance, and high turnover. Genetic modulators that enhance the aggregation phenotype are enriched in genes that influence expression homeostasis. Moreover, genes encoding aggregation-prone proteins are more likely to be harmful when overexpressed. The trends are evolutionarily conserved and suggest a strategy whereby cellular mechanisms specifically modulate the availability of aggregation-prone proteins to (1) keep concentrations below the critical ones required for aggregation and (2) shift the equilibrium between the monomeric and oligomeric/aggregate form, as explained by Le Chatelier’s principle. This strategy may prevent formation of undesirable aggregates and keep functional assemblies/aggregates under control. The paper can be found here.