Instructors:
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Marc A. Marti-Renom obtained a Ph.D. in Biophysics from the Universidad Autonoma de Barcelona where he worked on protein folding under the supervision
of B. Oliva, F.X. Aviles and M. Karplus. After that, he went to the US for a postdoctoral training on protein structure modeling at the Sali Lab (Rockefeller
University) as the recipient of the Burroughs Wellcome Fund fellowship. Later on, Marc was appointed Assistant Adjunct Professor at UCSF. Between 2006 and 2011,
he headed of the Structural Genomics Group at the CIPF in Valencia (Spain). Currently, Marc is an ICREA research professor and leads the Structural Genomics Group
at the National Center for Genomic Analysis - Centre for Genomic Regulation (CNAG-CRG) in Barcelona. His group is broadly interested on how RNA, proteins and genomes
organize and regulate cell fate. Finally, Marc is an Associate Editor of the PLoS Computational Biology journal and has published over 75 articles in international peer-reviewed journals.
Affiliation: Centro Nacional de Análisis Genómico (CNAG) and Center for Genomic Regulation (CRG), Barcelona, ES
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François Serra obtained his Degree in Biology, specialized in Physiology and Neurophysiology,
his Master's Degree in Structural genomics and bioinformatics (Strasbourg I Universty, France) and it's PhD
in Evolutionary Genomics in the Department of Bioinformatics at the CIPF (Valencia). He is now part of the Structural Genomic team of
Marc Marti-Renom at CNAG and at CRG (Barcelona). His main research interests are grounded on comparative genomics and evolution with a special
focus on the effect of evolution in the structural arrangement of genomes. He has taught MEPA and 3DMOG for GTPB, and also in similar courses at
CIPF (Valencia, ES) and the Department of Genetics of the University of Cambridge (UK).
Affiliation: Centro Nacional de Análisis Genómico (CNAG) and Center for Genomic Regulation (CRG), Barcelona, ES
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Marco di Stefano obtained his Ph.D. in Biophysics from SISSA in Trieste (Italy) working under the supervision of C. Micheletti and A. Rosa. He currently works as a post-doctoral researcher in the structural genomics group of Marc Marti-Renom at CNAG-CRG (Barcelona). His main research interest is to integrate biopolymer physics and experimental techniques, such as imaging and 3C, to highlight constitutive mechanisms of chromosome folding. He is involved in the YRM initiative (http://www.yrmr.it/drupal/) for young Physicists. He has taught "Multiscale modeling of biomolecules" at the "Spring College on the Physics of Complex Systems" in ICTP (Trieste, IT).
Affiliation: Centro Nacional de Análisis Genómico (CNAG) and Center for Genomic Regulation (CRG), Barcelona, ES
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Course description
3C-based methods, such as Hi-C, produce a huge amount of raw data as pairs of DNA reads that are in close spatial proximity in the cell nucleus.
Overall, those interaction matrices have been used to study how the genome folds within the nucleus, which is one of the most fascinating problems
in modern biology. The rigorous analysis of those paired-reads using computational tools has been essential to fully exploit the experimental technique,
and to study how the genome is folded in the space. Currently, there is a clear expansion on the wealth of data on genome structure with the availability
of many datasets of Hi-C experiments down to 1Kb resolution (see for example: http://hic.umassmed.edu/welcome/welcome.php ; http://promoter.bx.psu.edu/hi-c/view.php
or http://www.aidenlab.org/data.html ).
In this course, participants will learn to use TADbit, a software designed and developed to manage all dimensionalities of the Hi-C data:
- 1D - Map paired-end sequences to generate Hi-C interaction matrices
- 2D - Normalize matrices and identify constitutive domains (TADs, compartments)
- 3D - Generate populations of structures which satisfy the Hi-C interaction matrices
- 4D - Compare samples at different time points
Participants can bring- specific biological questions and/or their own 3C-based data to analyze during the course. At the end of the course, participants will be
familiar with the TADbit software and will be able to fully analyze Hi-C data.
Note: Although the TADbit software is central in this course, alternative software will be discussed for each part of the analysis.
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Target Audience
The course design is oriented towards experimental researchers and bioinformaticians at the graduate and post-graduate levels. The last edition of this course was attended by people
with different backgrounds and interested in the genome organization.
Moreover, Hi-C data have recently been used in metagenomics studies to accurately cluster metagenome assembly contigs into groups that contain nearly complete genomes of each species.
It is likely that the participants to this course aim at getting involved in generating Hi-C data for chromosome structure determination or that they just want to be able to correctly
interpret and analyse publicly available data.
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Course Pre-requisites
Recommended Linux and basic Python programming skills, graduate level in Life Sciences.
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