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  • 1
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    Identification of functional elements and regulatory circuits by Drosophila modENCODE (2010)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2010-12-24
    Description: To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3192495/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3192495/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉modENCODE Consortium -- Roy, Sushmita -- Ernst, Jason -- Kharchenko, Peter V -- Kheradpour, Pouya -- Negre, Nicolas -- Eaton, Matthew L -- Landolin, Jane M -- Bristow, Christopher A -- Ma, Lijia -- Lin, Michael F -- Washietl, Stefan -- Arshinoff, Bradley I -- Ay, Ferhat -- Meyer, Patrick E -- Robine, Nicolas -- Washington, Nicole L -- Di Stefano, Luisa -- Berezikov, Eugene -- Brown, Christopher D -- Candeias, Rogerio -- Carlson, Joseph W -- Carr, Adrian -- Jungreis, Irwin -- Marbach, Daniel -- Sealfon, Rachel -- Tolstorukov, Michael Y -- Will, Sebastian -- Alekseyenko, Artyom A -- Artieri, Carlo -- Booth, Benjamin W -- Brooks, Angela N -- Dai, Qi -- Davis, Carrie A -- Duff, Michael O -- Feng, Xin -- Gorchakov, Andrey A -- Gu, Tingting -- Henikoff, Jorja G -- Kapranov, Philipp -- Li, Renhua -- MacAlpine, Heather K -- Malone, John -- Minoda, Aki -- Nordman, Jared -- Okamura, Katsutomo -- Perry, Marc -- Powell, Sara K -- Riddle, Nicole C -- Sakai, Akiko -- Samsonova, Anastasia -- Sandler, Jeremy E -- Schwartz, Yuri B -- Sher, Noa -- Spokony, Rebecca -- Sturgill, David -- van Baren, Marijke -- Wan, Kenneth H -- Yang, Li -- Yu, Charles -- Feingold, Elise -- Good, Peter -- Guyer, Mark -- Lowdon, Rebecca -- Ahmad, Kami -- Andrews, Justen -- Berger, Bonnie -- Brenner, Steven E -- Brent, Michael R -- Cherbas, Lucy -- Elgin, Sarah C R -- Gingeras, Thomas R -- Grossman, Robert -- Hoskins, Roger A -- Kaufman, Thomas C -- Kent, William -- Kuroda, Mitzi I -- Orr-Weaver, Terry -- Perrimon, Norbert -- Pirrotta, Vincenzo -- Posakony, James W -- Ren, Bing -- Russell, Steven -- Cherbas, Peter -- Graveley, Brenton R -- Lewis, Suzanna -- Micklem, Gos -- Oliver, Brian -- Park, Peter J -- Celniker, Susan E -- Henikoff, Steven -- Karpen, Gary H -- Lai, Eric C -- MacAlpine, David M -- Stein, Lincoln D -- White, Kevin P -- Kellis, Manolis -- R01 HG004037/HG/NHGRI NIH HHS/ -- R01HG004037/HG/NHGRI NIH HHS/ -- RC2HG005639/HG/NHGRI NIH HHS/ -- U01 HG004258/HG/NHGRI NIH HHS/ -- U01 HG004271/HG/NHGRI NIH HHS/ -- U01 HG004279/HG/NHGRI NIH HHS/ -- U01HG004258/HG/NHGRI NIH HHS/ -- U01HG004261/HG/NHGRI NIH HHS/ -- U01HG004264/HG/NHGRI NIH HHS/ -- U01HG004271/HG/NHGRI NIH HHS/ -- U01HG004274/HG/NHGRI NIH HHS/ -- U01HG004279/HG/NHGRI NIH HHS/ -- U41HG004269/HG/NHGRI NIH HHS/ -- ZIA DK015600-14/Intramural NIH HHS/ -- Howard Hughes Medical Institute/ -- New York, N.Y. -- Science. 2010 Dec 24;330(6012):1787-97. doi: 10.1126/science.1198374. Epub 2010 Dec 22.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/21177974" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; Binding Sites ; *Chromatin/genetics/metabolism ; Computational Biology/methods ; Drosophila Proteins/genetics/metabolism ; Drosophila melanogaster/*genetics/growth & development/metabolism ; Epigenesis, Genetic ; Gene Expression Regulation ; *Gene Regulatory Networks ; Genes, Insect ; *Genome, Insect ; Genomics/methods ; Histones/metabolism ; *Molecular Sequence Annotation ; Nucleosomes/genetics/metabolism ; Promoter Regions, Genetic ; RNA, Small Untranslated/genetics/metabolism ; Transcription Factors/metabolism ; Transcription, Genetic
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
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  • 2
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    A predictive modeling approach for cell line-specific long-range regulatory interactions (2015)
    Oxford University Press
    Details
    Publication Date: 2015-10-15
    Description: Long range regulatory interactions among distal enhancers and target genes are important for tissue-specific gene expression. Genome-scale identification of these interactions in a cell line-specific manner, especially using the fewest possible datasets, is a significant challenge. We develop a novel computational approach, Regulatory Interaction Prediction for Promoters and Long-range Enhancers (RIPPLE), that integrates published Chromosome Conformation Capture (3C) data sets with a minimal set of regulatory genomic data sets to predict enhancer-promoter interactions in a cell line-specific manner. Our results suggest that CTCF, RAD21, a general transcription factor (TBP) and activating chromatin marks are important determinants of enhancer-promoter interactions. To predict interactions in a new cell line and to generate genome-wide interaction maps, we develop an ensemble version of RIPPLE and apply it to generate interactions in five human cell lines. Computational validation of these predictions using existing ChIA-PET and Hi-C data sets showed that RIPPLE accurately predicts interactions among enhancers and promoters. Enhancer-promoter interactions tend to be organized into subnetworks representing coordinately regulated sets of genes that are enriched for specific biological processes and cis -regulatory elements. Overall, our work provides a systematic approach to predict and interpret enhancer-promoter interactions in a genome-wide cell-type specific manner using a few experimentally tractable measurements.
    Keywords: Computational Methods, Genomics, Transcriptome Mapping - Monitoring Gene Expression
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 3
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    Accurate identification of centromere locations in yeast genomes using Hi-C (2015)
    Oxford University Press
    Details
    Publication Date: 2015-06-24
    Description: Centromeres are essential for proper chromosome segregation. Despite extensive research, centromere locations in yeast genomes remain difficult to infer, and in most species they are still unknown. Recently, the chromatin conformation capture assay, Hi-C, has been re-purposed for diverse applications, including de novo genome assembly, deconvolution of metagenomic samples and inference of centromere locations. We describe a method, Centurion, that jointly infers the locations of all centromeres in a single genome from Hi-C data by exploiting the centromeres’ tendency to cluster in three-dimensional space. We first demonstrate the accuracy of Centurion in identifying known centromere locations from high coverage Hi-C data of budding yeast and a human malaria parasite. We then use Centurion to infer centromere locations in 14 yeast species. Across all microbes that we consider, Centurion predicts 89% of centromeres within 5 kb of their known locations. We also demonstrate the robustness of the approach in datasets with low sequencing depth. Finally, we predict centromere coordinates for six yeast species that currently lack centromere annotations. These results show that Centurion can be used for centromere identification for diverse species of yeast and possibly other microorganisms.
    Keywords: Computational Methods, Chromatin and Epigenetics, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 4
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    A predictive modeling approach for cell line-specific long-range regulatory interactions (2016)
    Oxford University Press
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    Publication Date: 2016-03-01
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 5
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    Identification of copy number variations and translocations in cancer cells from Hi-C data (2018)
    Oxford University Press
    Details
    Publication Date: 2018-03-06
    Description: Motivation Eukaryotic chromosomes adapt a complex and highly dynamic three-dimensional (3D) structure, which profoundly affects different cellular functions and outcomes including changes in epigenetic landscape and in gene expression. Making the scenario even more complex, cancer cells harbor chromosomal abnormalities [e.g. copy number variations (CNVs) and translocations] altering their genomes both at the sequence level and at the level of 3D organization. High-throughput chromosome conformation capture techniques (e.g. Hi-C), which are originally developed for decoding the 3D structure of the chromatin, provide a great opportunity to simultaneously identify the locations of genomic rearrangements and to investigate the 3D genome organization in cancer cells. Even though Hi-C data has been used for validating known rearrangements, computational methods that can distinguish rearrangement signals from the inherent biases of Hi-C data and from the actual 3D conformation of chromatin, and can precisely detect rearrangement locations de novo have been missing. Results In this work, we characterize how intra and inter-chromosomal Hi-C contacts are distributed for normal and rearranged chromosomes to devise a new set of algorithms (i) to identify genomic segments that correspond to CNV regions such as amplifications and deletions ( HiCnv ), (ii) to call inter-chromosomal translocations and their boundaries ( HiCtrans ) from Hi-C experiments and (iii) to simulate Hi-C data from genomes with desired rearrangements and abnormalities ( AveSim ) in order to select optimal parameters for and to benchmark the accuracy of our methods. Our results on 10 different cancer cell lines with Hi-C data show that we identify a total number of 105 amplifications and 45 deletions together with 90 translocations, whereas we identify virtually no such events for two karyotypically normal cell lines. Our CNV predictions correlate very well with whole genome sequencing data among chromosomes with CNV events for a breast cancer cell line ( r  = 0.89) and capture most of the CNVs we simulate using Avesim. For HiCtrans predictions, we report evidence from the literature for 30 out of 90 translocations for eight of our cancer cell lines. Furthermore, we show that our tools identify and correctly classify relatively understudied rearrangements such as double minutes and homogeneously staining regions. Considering the inherent limitations of existing techniques for karyotyping (i.e. missing balanced rearrangements and those near repetitive regions), the accurate identification of CNVs and translocations in a cost-effective and high-throughput setting is still a challenge. Our results show that the set of tools we develop effectively utilize moderately sequenced Hi-C libraries (100–300 million reads) to identify known and de novo chromosomal rearrangements/abnormalities in well-established cancer cell lines. With the decrease in required number of cells and the increase in attainable resolution, we believe that our framework will pave the way towards comprehensive mapping of genomic rearrangements in primary cells from cancer patients using Hi-C. Availability and implementation CNV calling: https://github.com/ay-lab/HiCnv , Translocation calling: https://github.com/ay-lab/HiCtrans and Hi-C simulation: https://github.com/ay-lab/AveSim . Contact ferhatay@lji.org Supplementary information Supplementary dataSupplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 6
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    The Influence of Hydraulic Fracturing on the Estimated Ultimate Recovery (2018)
    Institute of Physics (IOP)
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    Publication Date: 2018-11-16
    Description: The paper presents the numerical reservoir model constructed to study the influence of hydraulic fracturing on the effective oil recovery. The work of neighboring development wells is also addressed. The paper seeks to examine the way hydraulic fracturing in a development well affects the degree of recovery from the whole pool of the field. To solve this problem, the oil pool model developed by waterflooding is being considered. The paper examines the efficiency of hydraulic fracturing employed in wells at different locations. The study found that well stimulation technique affects the performance of near wells where there is an increase in the water cut of the production and a decrease in the oil rate.
    Print ISSN: 1755-1307
    Electronic ISSN: 1755-1315
    Topics: Geography , Geosciences , Physics
    Published by Institute of Physics (IOP)
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  • 7
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    A statistical approach for inferring the 3D structure of the genome (2014)
    Oxford University Press
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    Publication Date: 2014-06-17
    Description: Motivation: Recent technological advances allow the measurement, in a single Hi-C experiment, of the frequencies of physical contacts among pairs of genomic loci at a genome-wide scale. The next challenge is to infer, from the resulting DNA–DNA contact maps, accurate 3D models of how chromosomes fold and fit into the nucleus. Many existing inference methods rely on multidimensional scaling (MDS), in which the pairwise distances of the inferred model are optimized to resemble pairwise distances derived directly from the contact counts. These approaches, however, often optimize a heuristic objective function and require strong assumptions about the biophysics of DNA to transform interaction frequencies to spatial distance, and thereby may lead to incorrect structure reconstruction. Methods: We propose a novel approach to infer a consensus 3D structure of a genome from Hi-C data. The method incorporates a statistical model of the contact counts, assuming that the counts between two loci follow a Poisson distribution whose intensity decreases with the physical distances between the loci. The method can automatically adjust the transfer function relating the spatial distance to the Poisson intensity and infer a genome structure that best explains the observed data. Results: We compare two variants of our Poisson method, with or without optimization of the transfer function, to four different MDS-based algorithms—two metric MDS methods using different stress functions, a non-metric version of MDS and ChromSDE, a recently described, advanced MDS method—on a wide range of simulated datasets. We demonstrate that the Poisson models reconstruct better structures than all MDS-based methods, particularly at low coverage and high resolution, and we highlight the importance of optimizing the transfer function. On publicly available Hi-C data from mouse embryonic stem cells, we show that the Poisson methods lead to more reproducible structures than MDS-based methods when we use data generated using different restriction enzymes, and when we reconstruct structures at different resolutions. Availability and implementation: A Python implementation of the proposed method is available at http://cbio.ensmp.fr/pastis . Contact: william-noble@uw.edu or jean-philippe.vert@mines.org
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 8
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    High-gain Al2O3:Nd3+ channel waveguide amplifiers at 880 nm, 1060 nm, and 1330 nm (2010)
    Springer
    Details
    Publication Date: 2010-07-14
    Print ISSN: 0946-2171
    Electronic ISSN: 1432-0649
    Topics: Physics
    Published by Springer
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  • 9
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    Stress effects in prism coupling measurements of thin polymer films (2005)
    Springer
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    Publication Date: 2005-02-01
    Print ISSN: 0947-8396
    Electronic ISSN: 1432-0630
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Published by Springer
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  • 10
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    Current density measurements in direct methanol fuel cells (2007)
    Elsevier
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    Publication Date: 2007-05-01
    Print ISSN: 0378-7753
    Electronic ISSN: 1873-2755
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Elsevier
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