Assessing the limits of restraint-based 3D modeling of genomes and genomic domains
Author(s) -
Marie Trussart,
François Serra,
Davide Baù,
Ivan Junier,
Luís Serrano,
Marc A. MartíRenom
Publication year - 2015
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkv221
Subject(s) - genome , biology , a priori and a posteriori , computational biology , noise (video) , field (mathematics) , chromosome , chromosome conformation capture , data mining , biological system , computer science , artificial intelligence , genetics , mathematics , gene , image (mathematics) , philosophy , gene expression , enhancer , epistemology , pure mathematics
Restraint-based modeling of genomes has been recently explored with the advent of Chromosome Conformation Capture (3C-based) experiments. We previously developed a reconstruction method to resolve the 3D architecture of both prokaryotic and eukaryotic genomes using 3C-based data. These models were congruent with fluorescent imaging validation. However, the limits of such methods have not systematically been assessed. Here we propose the first evaluation of a mean-field restraint-based reconstruction of genomes by considering diverse chromosome architectures and different levels of data noise and structural variability. The results show that: first, current scoring functions for 3D reconstruction correlate with the accuracy of the models; second, reconstructed models are robust to noise but sensitive to structural variability; third, the local structure organization of genomes, such as Topologically Associating Domains, results in more accurate models; fourth, to a certain extent, the models capture the intrinsic structural variability in the input matrices and fifth, the accuracy of the models can be a priori predicted by analyzing the properties of the interaction matrices. In summary, our work provides a systematic analysis of the limitations of a mean-field restrain-based method, which could be taken into consideration in further development of methods as well as their applications.
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