SummaryChromosome conformation capture technologies have revealed important insights into genome folding. Yet, how spatial genome architecture is related to gene expression and cell fate remains unclear. We comprehensively mapped 3D chromatin organization during mouse neural differentiation in vitro and in vivo, generating the highest-resolution Hi-C maps available to date. We found that transcription is correlated with chromatin insulation and long-range interactions, but dCas9-mediated activation is insufficient for creating TAD boundaries de novo. Additionally, we discovered long-range contacts between gene bodies of exon-rich, active genes in all cell types. During neural differentiation, contacts between active TADs become less pronounced while inactive TADs interact more strongly. An extensive Polycomb network in stem cells is disrupted, while dynamic interactions between neural transcription factors appear in vivo. Finally, cell type-specific enhancer-promoter contacts are established concomitant to gene expression. This work shows that multiple factors influence the dynamics of chromatin interactions in development.
Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.
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