Dear All It may be useful to provide further explanations about the content of the upcoming CSDM13 - Chromosome structure determination using modelling and Hi-C data - training course. I spoke with several people that questioned me about its usefulness in evolution studies, for example. The following text is an attempt to correct this. IMPORTANT DATES for this Course Deadline for applications: November 20th 2013 Notification of acceptance within 72 hours of application (working days count) Course date: November 27th to November 29th 2013 Thank you Pedro Fernandes -- Pedro Fernandes GTPB Coordinator Instituto Gulbenkian de Ciência Apartado 14 2781-901 OEIRAS PORTUGAL Tel +351 21 4407912 http://gtpb.igc.gulbenkian.pt CSDM13 training Course Chromosome structure determination using modelling and Hi-C data In the analysis of patterns and processes occurred during the evolution of our genome, the detection of evolutionary forces acting in protein coding genes has already proved its relevance in the context of human health and disease [1]⁠. The tools generally used in this field are based in the concepts and methods of evolutionary genomics, phylogenetic analysis, and bioinformatics on the complete genomes of different mammalian species [2]⁠. The main outcome from this kind of analysis is the classification of specific features of our genomes into categories, according to the impact of these differences on changes to evolution. In the context of epigenetics, the relation with disease has been extensively studied, at the level of DNA methylation, histone modification and even chromatin remodeling [3]⁠. Under this last category some work has been done, more specifically on chromatin structure, that is able to highlight structural variations directly linked to human disease [4,5]⁠. Overall it is now widely accepted that the complete elucidation of chromatin three-dimensional structure is the next frontier in epigenetic studies; and some of its possible applications to human health are promising, as markers for gene expression state [6]⁠, in the understanding of the cancer process 7⁠, in the maintenance of cellular memories or in the modulation of phenotypes [8]⁠. However, in the context of epigenetics the relation between selective pressure and the potential impact of observing epigenetic variation, although it has been reported [9,10⁠], has not yet been modeled or measured. The chromatin interaction map, that somehow summarizes the epigenetic state of a given genomic region [11]⁠, stands as a outstanding candidate to study how evolution may shape our epigenomic landscape, and, at present, potentially the only candidate to offer significant clues about the real role of the non-genic 95% of our genomes. The CSDM13 course content In this course we aim to share our experience in analyzing and inferring the structure of chromatin (how it folds in three-dimensions) [12–15]⁠. Using available Hi-C data,course participants will be asked to find topologically associating domains10⁠, infer evolutionary conservation (or cell specificity), calculate the physical distances between a gene and its promoter, infer chromatin accessibility of specific regions, etc. Some computational skills are recommended for this course. However, the tools presented in this course are designed to be used by non computer-scientists. We will provide a basic introduction to the linux operating system and the Python programming language, aiming at quickly creating a working environment where every participant is guaranteed to engage. With this, we will minimize the danger of leaving anyone behind because of healthy differences in background literacy about computing. 1. Thomas, P. D. & Kejariwal, A. Coding single-nucleotide polymorphisms associated with complex vs. Mendelian disease: evolutionary evidence for differences in molecular effects. Proc. Natl. Acad. Sci. U. S. A. 101, 15398 (2004). 2. Sánchez, R. et al. Phylemon 2.0: a suite of web-tools for molecular evolution, phylogenetics, phylogenomics and hypotheses testing. Nucleic Acids Res. 39, W470–4 (2011). 3. Portela, A. & Esteller, M. Epigenetic modifications and human disease. Nat. Biotechnol. 28, 1057–68 (2010). 4. Vavouri, T. & Lehner, B. Chromatin organization in sperm may be the major functional consequence of base composition variation in the human genome. PLoS Genet. 7, e1002036 (2011). 5. Engreitz, J. M., Agarwala, V. & Mirny, L. a. Three-dimensional genome architecture influences partner selection for chromosomal translocations in human disease. PLoS One 7, e44196 (2012). 6. Crutchley, J. L., Wang, X. Q. D., Ferraiuolo, M. a & Dostie, J. Chromatin conformation signatures: ideal human disease biomarkers? Biomark. Med. 4, 611–29 (2010). 7. Göndör, A. Nuclear architecture and chromatin structure on the path to cancer. Semin. Cancer Biol. 23, 63–4 (2013). 8. Göndör, A. Dynamic chromatin loops bridge health and disease in the nuclear landscape. Semin. Cancer Biol. 23, 90–8 (2013). 9. Nagase, H. & Ghosh, S. Epigenetics: differential DNA methylation in mammalian somatic tissues. FEBS J. 275, 1617–23 (2008). 10. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–80 (2012). 11. Tanay, A. & Cavalli, G. Chromosomal domains: epigenetic contexts and functional implications of genomic compartmentalization. Curr. Opin. Genet. Dev. 23, 197–203 (2013). 12. Dekker, J., Marti-Renom, M. a & Mirny, L. a. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 14, 390–403 (2013). 13. Baù, D. et al. The three-dimensional folding of the α-globin gene domain reveals formation of chromatin globules. Nat. Struct. Mol. Biol. 18, 107–14 (2011). 14. Umbarger, M. a et al. The three-dimensional architecture of a bacterial genome and its alteration by genetic perturbation. Mol. Cell 44, 252–64 (2011). 15. Baù, D. & Marti-Renom, M. a. Structure determination of genomic domains by satisfaction of spatial restraints. Chromosome Res. 19, 25–35 (2011).