Computational Analysis and Modeling of Chromatin Spatial Organization

ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), 2015

September 9-12, 2015
Georgia Tech Global Learning Center, Atlanta, GA.

Abstract

The field of regulatory genomics has recently witnessed significantly increased interest in the three-dimensional structure of DNA in the nucleus, catalyzed by the availability of chromosome conformation capture (3C) data sets that characterize the 3D organization of chromatin at a genome-wide scale. This organization, also referred to as the 3D nucleome, is not only important for packing the genome into the nucleus but also has significant impact on how the genome functions. In this tutorial, we will present recent tools and methodologies developed for analysis of genome-wide 3C data sets generated using high-throughput sequencing (Hi-C). We will cover computational approaches that span: (i) processing basics and normalization of Hi-C data, (ii) identification of genomic domains with high contact intensity, (iii) extraction of significant contacts, and (iv) inference of 3D models of the chromatin organization from contact count data. This tutorial will be beneficial for researchers in the broad fields of computational systems biology, gene regulation and transcription, next generation sequencing data analysis, and biological network modeling and analysis.

Tutorial resources

Relevant literature

• Ay F. and Noble W. Analysis methods for studying the 3D architecture of the genome (Review Article). Genome Biology (2015). Link
• Lajoie B. R., Dekker J, Kaplan N. The Hitchhiker's guide to Hi-C analysis: practical guidelines (Review Article). Methods (2014). Link
• Rosa A and Zimmer C. Computational Models of Large-Scale Genome Architecture (Review Article). International Review of Cell and Molecular Biology (2014). Link
• Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
• Yaffe, E. and Tanay, A. Probabilistic modeling of Hi-C contact maps eliminates systematic biases to characterize global chromosomal architecture. Nat. Genet. 43, 1059–1065 (2011).
• Hu, M. et al. HiCNorm: removing biases in Hi-C data via Poisson regression. Bioinformatics 28, 3131–3133 (2012).
• Cournac, A., Marie-Nelly, H., Marbouty, M., Koszul, R. and Mozziconacci, J. Normalization of a chromosomal contact map. BMC Genomics 13, 436 (2012).
• Imakaev, M. et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nature Methods 9, 999–1003 (2012).
• Rao, S. S. P., Huntley, M. H., Durand, N. C. and Stamenova, E. K. A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell 1–16 (2014). doi:10.1016/j.cell.2014.11.021
• Rousseau, M., Fraser, J., Ferraiuolo, M. A., Dostie, J. and Blanchette, M. Three-dimensional modeling of chromatin structure from interaction frequency data using Markov chain Monte Carlo sampling. BMC Bioinformatics 12, 414 (2011).
• Hu, M. et al. Bayesian inference of spatial organizations of chromosomes. PLoS Comput Biol 9, e1002893 (2013).
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• Lesne, A., Riposo, J., Roger, P., Cournac, A. and Mozziconacci, J. 3D genome reconstruction from chromosomal contacts. Nat. Methods (2014). doi:10.1038/nmeth.3104
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• Ay, F. et al. Three-dimensional modeling of the P. falciparum genome during the erythrocytic cycle reveals a strong connection between genome architecture and gene expression. Genome Res. 24, 974–988 (2014).
• Ay, F., Bailey, T. L. and Noble, W. S. Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome Res. 24, 999–1011 (2014).
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• Sefer, E., Duggal, G. and Kingsford, C. in Research in Computational Molecular Biology 293–308 (Springer International Publishing, 2015).
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Presenters

• Ferhat Ay, PhD, received his B.S. degrees in Computer Engineering and Mathematics both from Middle East Technical University, Turkey in 2007. Ferhat received his PhD degree from the Department of Computer Science at the University of Florida in 2011. He subsequently joined the Department of Genome Sciences at the University of Washington as a Computing Innovation Fellow awarded by Computing Research Association. Ferhat’s primary research areas are bioinformatics, computational biology, epigenomics and regulatory genomics. Ferhat works on understanding the relationship between genome architecture and gene regulation in different human cell lines and organisms. Ferhat is currently Research Assistant Professor in the Department of Preventive Medicine-Health and Biomedical Informatics at Northwestern University.


• Geet Duggal, PhD, received his B.S. degree in Computer Science from Northwestern University and continued to work for the Department of Defense for five years thereafter. He earned his Ph.D. in the Computational Biology Department at the School of Computer Science from Carnegie Mellon University in December, 2014 (Advisor: Carl Kingsford). During his Ph.D. training, he worked on algorithms to identify compact regions of 3D chromatin structure and to associate these structures with gene regulation. He also developed algorithms for network clustering and inferring network growth models. He is currently interested in continuing work on 3D chromatin structure and gene regulation. In addition, to facilitate the functional annotation of long non-coding RNAs, he is developing methods and services to categorize and search terabytes of RNA-seq gene expression data. Geet is currently a Postdoctoral Fellow at Carnegie Mellon University.


• Ming Hu, PhD, is an Assistant Professor of Biostatistics in the Division of Biostatistics. His research interests lie in Bayesian analysis in bioinformatics and statistical genetics, with particular focus on analyzing the next generation sequencing data. His most recent work focuses on Bayesian inference of spatial organizations of chromosomes using Hi-C data. He also has interested in developing and applying statistical and computational methods in analyzing ChIP-Seq and RNA-Seq data. He received his PhD from the Department of Biostatistics at the University of Michigan. He was previously a postdoctoral fellow in the Department of Statistics at Harvard University.


• Emre Sefer, PhD, received his B.S. degree in Computer Engineering from Bogazici University Istanbul, Turkey. He also received a M.S. degree in Computer Science from University of Maryland College Park, and Ph.D. from Carnegie Mellon University School of Computer Science. He is currently a post-doctoral researcher at Computer Science Department, Carnegie Mellon University, Pittsburgh PA. During his Ph.D. training, he worked on algorithms to analyze the present and the past of the social and biological networks from limited uncertain data. His main research interests include systems biology, chromosome conformation capture, and machine learning applications on social and biological networks.

Organizing Committee

• Ferhat Ay, Research Assistant Professor, Northwestern University
• Geet Duggal, Postdoctoral Fellow, Carnegie Mellon University
• Ming Hu, Assistant Professor, New York University
• Emre Sefer, Postdoctoral Fellow, Carnegie Mellon University
• Ramana V. Davuluri, Professor, Northwestern University
• Carl Kingsford, Associate Professor, Carnegie Mellon University
• Jun S. Liu, Professor, Harvard University
• William S. Noble, Professor, University of Washington

Please direct questions to Ferhat Ay (ferhatay@uw.edu).