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International audience; In recent years, significant technical advances have been made to better observe the structure and the organization of chromatin. Many Hi-C data are now available in public databases, at different accuracy and resolution and for various species. Generally, the final results of Hi-C data analyses are summarized with the representation of “contact maps”. Using a color scale, these maps (heat-maps) show the frequency of contacts observed between different portions of a genome, both at short-rangeand long-range distances.Even if they are widely used, our observation is that the biological interpretation of contact maps is not trivial. It requires training and significant experience to be useful to validate or invalidate hypotheses concerning the overall organization of the studied genome. In this context, alternative tools have been developed. Some of them produce 3D models ofcontact networks, as a substitute for visualization of Hi-C outputs. By 3D models, we mean representations in a 3D space of a genome, in which euclidean distances are derived from the contact frequency matrices. The information contained in the Hi-C data is thus directly translated into distances in a 3D space. Although care must be taken in interpreting these models (they are not “pictures” of the interior of a cell), we observed they are very helpful to highlight specific structural patterns in chromatin organization, originally hidden in contact maps.Developing new strategies to visualize and integrate large scale multi-omics data is the major objective of Thibault Poinsignon’s thesis (ANR MinOmics). These led us to start building 3D models of yeasts andfilamentous fungi genomes, from Hi-C raw sequencing data. We present here our evaluations of the biological significance and the interest to use these models for projecting heterogeneous epigenomics data and hence better support their biological interpretations.

Additional file 2. Text file describing the method that was applied to associate the 9185 S. cerevisiae chromosomal features with the spatial coordinates of the 3D model.

International audience; Nowadays, generating omics data is a common activity for laboratories in biology. Experimental protocols to prepare biological samples are well described, and technical platforms to generate omics data from these samples are available in most research institutes. Furthermore, manufacturers constantly propose technical improvements, simultaneously decreasing the cost of experiments and increasing the amount of omics data obtained in a single experiment. In this context, biologists are facing the challenge of dealing with large omics datasets, also called "big data" or "data deluge". Working with omics data raises issues usually handled by computer scientists and thus cooperation between biologists and computer scientists has become essential to efficiently study cellular mechanisms in their entirety, as omics data promise. In this chapter, we define omics data, explain how they are produced, and finally, present some of their applications in fundamental and medical research.

Additional file 6. New insights into the transcriptional module related to Upc2 transcription factor.