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A complex network framework for unbiased statistical analyses of DNA-DNA contact maps.

ABSTRACT: Experimental techniques for the investigation of three-dimensional (3D) genome organization are being developed at a fast pace. Currently, the associated computational methods are mostly specific to the individual experimental approach. Here 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, for which a broad number of directly applicable methods already exist. In such a network representation, DNA segments and contacts between them are denoted as nodes and edges, respectively. Furthermore, we 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. By integrating a variety of large-scale genome-wide datasets we demonstrate that meiotic crossover sites display enriched genomic contacts and that cohesin-bound genes are significantly colocalized in the yeast nucleus. We anticipate that the complex network framework in conjunction with the randomization of DNA-DNA contact networks will become a widely used tool in the study of nuclear architecture.

SUBMITTER: Kruse K

PROVIDER: S-EPMC3553935 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Accurate prediction of atomic-level protein structure is important for annotating the biological functions of protein molecules and for designing new compounds to regulate the functions. Template-based modeling (TBM), which aims to construct structural models by copying and refining the structural frameworks of other known proteins, remains the most accurate method for protein structure prediction. Due to the difficulty in recognizing distant-homology templates, however, the accuracy of TBM decreases rapidly when the evolutionary relationship between the query and template vanishes. In this study, we propose a new method, CEthreader, which first predicts residue-residue contacts by coupling evolutionary precision matrices with deep residual convolutional neural-networks. The predicted contact maps are then integrated with sequence profile alignments to recognize structural templates from the PDB. The method was tested on two independent benchmark sets consisting collectively of 1,153 non-homologous protein targets, where CEthreader detected 176% or 36% more correct templates with a TM-score >0.5 than the best state-of-the-art profile- or contact-based threading methods, respectively, for the Hard targets that lacked homologous templates. Moreover, CEthreader was able to identify 114% or 20% more correct templates with the same Fold as the query, after excluding structures from the same SCOPe Superfamily, than the best profile- or contact-based threading methods. Detailed analyses show that the major advantage of CEthreader lies in the efficient coupling of contact maps with profile alignments, which helps recognize global fold of protein structures when the homologous relationship between the query and template is weak. These results demonstrate an efficient new strategy to combine ab initio contact map prediction with profile alignments to significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

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A complex network framework for unbiased statistical analyses of DNA-DNA contact maps.

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