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  • 1
    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
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  • 2
    Publication Date: 2015-02-13
    Description: Motivation: Translating findings in rodent models to human models has been a cornerstone of modern biology and drug development. However, in many cases, a naive ‘extrapolation’ between the two species has not succeeded. As a result, clinical trials of new drugs sometimes fail even after considerable success in the mouse or rat stage of development. In addition to in vitro studies, inter-species translation requires analytical tools that can predict the enriched gene sets in human cells under various stimuli from corresponding measurements in animals. Such tools can improve our understanding of the underlying biology and optimize the allocation of resources for drug development. Results: We developed an algorithm to predict differential gene set enrichment as part of the sbv IMPROVER (systems biology verification in Industrial Methodology for Process Verification in Research) Species Translation Challenge, which focused on phosphoproteomic and transcriptomic measurements of normal human bronchial epithelial (NHBE) primary cells under various stimuli and corresponding measurements in rat (NRBE) primary cells. We find that gene sets exhibit a higher inter-species correlation compared with individual genes, and are potentially more suited for direct prediction. Furthermore, in contrast to a similar cross-species response in protein phosphorylation states 5 and 25 min after exposure to stimuli, gene set enrichment 6 h after exposure is significantly different in NHBE cells compared with NRBE cells. In spite of this difference, we were able to develop a robust algorithm to predict gene set activation in NHBE with high accuracy using simple analytical methods. Availability and implementation: Implementation of all algorithms is available as source code (in Matlab) at http://bhanot.biomaps.rutgers.edu/wiki/codes_SC3_Predicting_GeneSets.zip , along with the relevant data used in the analysis. Gene sets, gene expression and protein phosphorylation data are available on request. Contact: hormoz@kitp.ucsb.edu
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 3
    Publication Date: 2015-02-13
    Description: Motivation: Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli. Results: We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli. Availability: Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h . Contact: christoph.hafemeister@nyu.edu or atarca@med.wayne.edu . Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 4
    Publication Date: 2016-07-08
    Description: The plasmid vector pGreenII is widely used to produce plant transformants via a process that involves propagation in Escherichia coli . However, we show here that pGreenII-based constructs can be unstable in E. coli as a consequence of them hampering cell division and promoting cell death. In addition, we describe a new version of pGreenII that does not cause these effects, thereby removing the selective pressure for mutation, and a new strain of E. coli that better tolerates existing pGreenII-based constructs without reducing plasmid yield. The adoption of the new derivative of pGreenII and the E. coli strain, which we have named pViridis and MW906, respectively, should help to ensure the integrity of genes destined for study in plants while they are propagated and manipulated in E. coli . The mechanism by which pGreenII perturbs E. coli growth appears to be dysregulation within the ColE1 origin of replication.
    Electronic ISSN: 2160-1836
    Topics: Biology
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