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author:("lublin, Yaniv")
1.  Single cell Hi-C reveals cell-to-cell variability in chromosome structure 
Nature  2013;502(7469):10.1038/nature12593.
Large-scale chromosome structure and spatial nuclear arrangement have been linked to control of gene expression and DNA replication and repair. Genomic techniques based on chromosome conformation capture assess contacts for millions of loci simultaneously, but do so by averaging chromosome conformations from millions of nuclei. Here we introduce single cell Hi-C, combined with genome-wide statistical analysis and structural modeling of single copy X chromosomes, to show that individual chromosomes maintain domain organisation at the megabase scale, but show variable cell-to-cell chromosome territory structures at larger scales. Despite this structural stochasticity, localisation of active gene domains to boundaries of territories is a hallmark of chromosomal conformation. Single cell Hi-C data bridge current gaps between genomics and microscopy studies of chromosomes, demonstrating how modular organisation underlies dynamic chromosome structure, and how this structure is probabilistically linked with genome activity patterns.
PMCID: PMC3869051  PMID: 24067610
2.  An integrated open framework for thermodynamics of reactions that combines accuracy and coverage 
Bioinformatics  2012;28(15):2037-2044.
Motivation: The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations. We present a framework for unifying thermodynamic data from multiple sources and demonstrate two new techniques for extrapolating the Gibbs energies of unmeasured reactions and conditions.
Results: Both methods account for changes in cellular conditions (pH, ionic strength, etc.) by using linear regression over the ΔG○ of pseudoisomers and reactions. The Pseudoisomeric Reactant Contribution method systematically infers compound formation energies using measured K′ and pKa data. The Pseudoisomeric Group Contribution method extends the group contribution method and achieves a high coverage of unmeasured reactions. We define a continuous index that predicts the reversibility of a reaction under a given physiological concentration range. In the characteristic physiological range 3μM–3mM, we find that roughly half of the reactions in Escherichia coli's metabolism are reversible. These new tools can increase the accuracy of thermodynamic-based models, especially in non-standard pH and ionic strengths. The reversibility index can help modelers decide which reactions are reversible in physiological conditions.
Availability: Freely available on the web at: Website implemented in Python, MySQL, Apache and Django, with all major browsers supported. The framework is open-source (, implemented in pure Python and tested mainly on Linux.
Supplementary Information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3400964  PMID: 22645166
3.  Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project 
Gerstein, Mark B. | Lu, Zhi John | Van Nostrand, Eric L. | Cheng, Chao | Arshinoff, Bradley I. | Liu, Tao | Yip, Kevin Y. | Robilotto, Rebecca | Rechtsteiner, Andreas | Ikegami, Kohta | Alves, Pedro | Chateigner, Aurelien | Perry, Marc | Morris, Mitzi | Auerbach, Raymond K. | Feng, Xin | Leng, Jing | Vielle, Anne | Niu, Wei | Rhrissorrakrai, Kahn | Agarwal, Ashish | Alexander, Roger P. | Barber, Galt | Brdlik, Cathleen M. | Brennan, Jennifer | Brouillet, Jeremy Jean | Carr, Adrian | Cheung, Ming-Sin | Clawson, Hiram | Contrino, Sergio | Dannenberg, Luke O. | Dernburg, Abby F. | Desai, Arshad | Dick, Lindsay | Dosé, Andréa C. | Du, Jiang | Egelhofer, Thea | Ercan, Sevinc | Euskirchen, Ghia | Ewing, Brent | Feingold, Elise A. | Gassmann, Reto | Good, Peter J. | Green, Phil | Gullier, Francois | Gutwein, Michelle | Guyer, Mark S. | Habegger, Lukas | Han, Ting | Henikoff, Jorja G. | Henz, Stefan R. | Hinrichs, Angie | Holster, Heather | Hyman, Tony | Iniguez, A. Leo | Janette, Judith | Jensen, Morten | Kato, Masaomi | Kent, W. James | Kephart, Ellen | Khivansara, Vishal | Khurana, Ekta | Kim, John K. | Kolasinska-Zwierz, Paulina | Lai, Eric C. | Latorre, Isabel | Leahey, Amber | Lewis, Suzanna | Lloyd, Paul | Lochovsky, Lucas | Lowdon, Rebecca F. | Lubling, Yaniv | Lyne, Rachel | MacCoss, Michael | Mackowiak, Sebastian D. | Mangone, Marco | McKay, Sheldon | Mecenas, Desirea | Merrihew, Gennifer | Miller, David M. | Muroyama, Andrew | Murray, John I. | Ooi, Siew-Loon | Pham, Hoang | Phippen, Taryn | Preston, Elicia A. | Rajewsky, Nikolaus | Rätsch, Gunnar | Rosenbaum, Heidi | Rozowsky, Joel | Rutherford, Kim | Ruzanov, Peter | Sarov, Mihail | Sasidharan, Rajkumar | Sboner, Andrea | Scheid, Paul | Segal, Eran | Shin, Hyunjin | Shou, Chong | Slack, Frank J. | Slightam, Cindie | Smith, Richard | Spencer, William C. | Stinson, E. O. | Taing, Scott | Takasaki, Teruaki | Vafeados, Dionne | Voronina, Ksenia | Wang, Guilin | Washington, Nicole L. | Whittle, Christina M. | Wu, Beijing | Yan, Koon-Kiu | Zeller, Georg | Zha, Zheng | Zhong, Mei | Zhou, Xingliang | Ahringer, Julie | Strome, Susan | Gunsalus, Kristin C. | Micklem, Gos | Liu, X. Shirley | Reinke, Valerie | Kim, Stuart K. | Hillier, LaDeana W. | Henikoff, Steven | Piano, Fabio | Snyder, Michael | Stein, Lincoln | Lieb, Jason D. | Waterston, Robert H.
Science (New York, N.Y.)  2010;330(6012):1775-1787.
We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor–binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor–binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.
PMCID: PMC3142569  PMID: 21177976
4.  Gene Expression Divergence in Yeast is Coupled to Evolution of DNA-Encoded Nucleosome Organization 
Nature genetics  2009;41(4):438-445.
Eukaryotic transcription occurs within a chromatin environment, whose organization plays an important regulatory role and is partly encoded in cis by the DNA sequence itself1-6. Here, we examine whether evolutionary changes in gene expression are linked to changes in the DNA-encoded nucleosome organization of promoters. We find that in aerobic yeast species, where cellular respiration genes are active under typical growth conditions, the promoter sequences of these genes encode a relatively open (nucleosome-depleted) chromatin organization. This nucleosome-depleted organization requires only DNA sequence information, is independent of any co-factors and of transcription, and is a general property of growth-related genes. In contrast, in anaerobic yeast species, where cellular respiration genes are inactive under typical growth conditions, respiration gene promoters encode relatively closed (nucleosome-occupied) chromatin organizations. Thus, our results suggest a previously unidentified genetic mechanism underlying phenotypic diversity, consisting of DNA sequence changes that directly alter the DNA-encoded nucleosome organization of promoters.
PMCID: PMC2744203  PMID: 19252487
5.  Distinct Modes of Regulation by Chromatin Encoded through Nucleosome Positioning Signals 
PLoS Computational Biology  2008;4(11):e1000216.
The detailed positions of nucleosomes profoundly impact gene regulation and are partly encoded by the genomic DNA sequence. However, less is known about the functional consequences of this encoding. Here, we address this question using a genome-wide map of ∼380,000 yeast nucleosomes that we sequenced in their entirety. Utilizing the high resolution of our map, we refine our understanding of how nucleosome organizations are encoded by the DNA sequence and demonstrate that the genomic sequence is highly predictive of the in vivo nucleosome organization, even across new nucleosome-bound sequences that we isolated from fly and human. We find that Poly(dA:dT) tracts are an important component of these nucleosome positioning signals and that their nucleosome-disfavoring action results in large nucleosome depletion over them and over their flanking regions and enhances the accessibility of transcription factors to their cognate sites. Our results suggest that the yeast genome may utilize these nucleosome positioning signals to regulate gene expression with different transcriptional noise and activation kinetics and DNA replication with different origin efficiency. These distinct functions may be achieved by encoding both relatively closed (nucleosome-covered) chromatin organizations over some factor binding sites, where factors must compete with nucleosomes for DNA access, and relatively open (nucleosome-depleted) organizations over other factor sites, where factors bind without competition.
Author Summary
The detailed positions of nucleosomes along genomes have critical roles in transcriptional regulation. Consequently, it is important to understand the principles that govern the organization of nucleosomes in vivo and the functional consequences of this organization. Here we report on progress in identifying the functional consequences of nucleosome organization, in understanding the way in which nucleosome organization is encoded in the DNA, and in linking the two, by suggesting that distinct transcriptional behaviors are encoded through the genome's intrinsic nucleosome organization. Our results thus provide insight on the broader question of understanding how transcriptional programs are encoded in the DNA sequence. These new insights were enabled by individually sequencing ∼380,000 nucleosomes from yeast in their entirety. Using this map, we refine our previous model for predicting nucleosome positions and demonstrate that our new model predicts nucleosome organizations in yeast with high accuracy and that its nucleosome positioning signals are predictive across eukaryotes. We show that the yeast genome may utilize these nucleosome positioning signals to encode regions with both relatively open (nucleosome-depleted) chromatin organizations and relatively closed (nucleosome-covered) chromatin organizations and that this encoding can partly explain aspects of transcription factor binding, gene expression, transcriptional noise, and DNA replication.
PMCID: PMC2570626  PMID: 18989395

Results 1-5 (5)