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RNF2 Antibody Order 021-34695924 [email protected] Support 400-6123-828 50Ul [email protected] 100 Ul √ √ Web
TD7403 RNF2 Antibody Order 021-34695924 [email protected] Support 400-6123-828 50ul [email protected] 100 uL √ √ Web www.ab-mart.com.cn Description: E3 ubiquitin-protein ligase that mediates monoubiquitination of 'Lys-119' of histone H2A (H2AK119Ub), thereby playing a central role in histone code and gene regulation. H2AK119Ub gives a specific tag for epigenetic transcriptional repression and participates in X chromosome inactivation of female mammals. May be involved in the initiation of both imprinted and random X inactivation (By similarity). Essential component of a Polycomb group (PcG) multiprotein PRC1-like complex, a complex class required to maintain the transcriptionally repressive state of many genes, including Hox genes, throughout development. PcG PRC1 complex acts via chromatin remodeling and modification of histones, rendering chromatin heritably changed in its expressibility. E3 ubiquitin-protein ligase activity is enhanced by BMI1/PCGF4. Acts as the main E3 ubiquitin ligase on histone H2A of the PRC1 complex, while RING1 may rather act as a modulator of RNF2/RING2 activity (Probable). Association with the chromosomal DNA is cell-cycle dependent. In resting B- and T-lymphocytes, interaction with AURKB leads to block its activity, thereby maintaining transcription in resting lymphocytes (By similarity). Uniprot:Q99496 Alternative Names: BAP 1; BAP1; DING; DinG protein; E3 ubiquitin protein ligase RING 2; E3 ubiquitin protein ligase RING2; E3 ubiquitin-protein ligase RING2; HIP2 interacting protein 3; HIP2- interacting protein 3; HIPI 3; HIPI3; Huntingtin interacting protein 2 interacting protein 3; Huntingtin-interacting protein 2-interacting protein 3; OTTHUMP00000060668; Polycomb M33 interacting protein Ring 1B; Polycomb M33 interacting protein Ring1B; Protein DinG; RING 1B; RING 2; RING finger protein 1B; RING finger protein 2; RING finger protein BAP 1; RING finger protein BAP-1; RING finger protein BAP1; RING1b; RING2_HUMAN; RNF 2; Rnf2; Specificity: RNF2 Antibody detects endogenous levels of total RNF2. -
Product Data Sheet
For research purposes only, not for human use Product Data Sheet RNF2 siRNA (Human) Catalog # Source Reactivity Applications CRH4092 Synthetic H RNAi Description siRNA to inhibit RNF2 expression using RNA interference Specificity RNF2 siRNA (Human) is a target-specific 19-23 nt siRNA oligo duplexes designed to knock down gene expression. Form Lyophilized powder Gene Symbol RNF2 Alternative Names BAP1; DING; HIPI3; RING1B; E3 ubiquitin-protein ligase RING2; Huntingtin-interacting protein 2-interacting protein 3; HIP2-interacting protein 3; Protein DinG; RING finger protein 1B; RING1b; RING finger protein 2; RING finger protein BAP-1 Entrez Gene 6045 (Human) SwissProt Q99496 (Human) Purity > 97% Quality Control Oligonucleotide synthesis is monitored base by base through trityl analysis to ensure appropriate coupling efficiency. The oligo is subsequently purified by affinity-solid phase extraction. The annealed RNA duplex is further analyzed by mass spectrometry to verify the exact composition of the duplex. Each lot is compared to the previous lot by mass spectrometry to ensure maximum lot-to-lot consistency. Components We offers pre-designed sets of 3 different target-specific siRNA oligo duplexes of human RNF2 gene. Each vial contains 5 nmol of lyophilized siRNA. The duplexes can be transfected individually or pooled together to achieve knockdown of the target gene, which is most commonly assessed by qPCR or western blot. Our siRNA oligos Application key: E- ELISA, WB- Western blot, IH- Immunohistochemistry, IF- Immunofluorescence, FC- -
Association of Gene Ontology Categories with Decay Rate for Hepg2 Experiments These Tables Show Details for All Gene Ontology Categories
Supplementary Table 1: Association of Gene Ontology Categories with Decay Rate for HepG2 Experiments These tables show details for all Gene Ontology categories. Inferences for manual classification scheme shown at the bottom. Those categories used in Figure 1A are highlighted in bold. Standard Deviations are shown in parentheses. P-values less than 1E-20 are indicated with a "0". Rate r (hour^-1) Half-life < 2hr. Decay % GO Number Category Name Probe Sets Group Non-Group Distribution p-value In-Group Non-Group Representation p-value GO:0006350 transcription 1523 0.221 (0.009) 0.127 (0.002) FASTER 0 13.1 (0.4) 4.5 (0.1) OVER 0 GO:0006351 transcription, DNA-dependent 1498 0.220 (0.009) 0.127 (0.002) FASTER 0 13.0 (0.4) 4.5 (0.1) OVER 0 GO:0006355 regulation of transcription, DNA-dependent 1163 0.230 (0.011) 0.128 (0.002) FASTER 5.00E-21 14.2 (0.5) 4.6 (0.1) OVER 0 GO:0006366 transcription from Pol II promoter 845 0.225 (0.012) 0.130 (0.002) FASTER 1.88E-14 13.0 (0.5) 4.8 (0.1) OVER 0 GO:0006139 nucleobase, nucleoside, nucleotide and nucleic acid metabolism3004 0.173 (0.006) 0.127 (0.002) FASTER 1.28E-12 8.4 (0.2) 4.5 (0.1) OVER 0 GO:0006357 regulation of transcription from Pol II promoter 487 0.231 (0.016) 0.132 (0.002) FASTER 6.05E-10 13.5 (0.6) 4.9 (0.1) OVER 0 GO:0008283 cell proliferation 625 0.189 (0.014) 0.132 (0.002) FASTER 1.95E-05 10.1 (0.6) 5.0 (0.1) OVER 1.50E-20 GO:0006513 monoubiquitination 36 0.305 (0.049) 0.134 (0.002) FASTER 2.69E-04 25.4 (4.4) 5.1 (0.1) OVER 2.04E-06 GO:0007050 cell cycle arrest 57 0.311 (0.054) 0.133 (0.002) -
RING1B/RNF2 Rabbit Pab
Leader in Biomolecular Solutions for Life Science [KO Validated] RING1B/RNF2 Rabbit pAb Catalog No.: A18076 KO Validated Basic Information Background Catalog No. Polycomb group (PcG) of proteins form the multiprotein complexes that are important A18076 for the transcription repression of various genes involved in development and cell proliferation. The protein encoded by this gene is one of the PcG proteins. It has been Observed MW shown to interact with, and suppress the activity of, transcription factor CP2 38kDa (TFCP2/CP2). Studies of the mouse counterpart suggested the involvement of this gene in the specification of anterior-posterior axis, as well as in cell proliferation in early Calculated MW development. This protein was also found to interact with huntingtin interacting protein 29kDa/37kDa 2 (HIP2), an ubiquitin-conjugating enzyme, and possess ubiquitin ligase activity. Category Primary antibody Applications WB, IHC, IF, IP, ChIP Cross-Reactivity Human, Mouse, Rat Recommended Dilutions Immunogen Information WB 1:500 - 1:2000 Gene ID Swiss Prot 6045 Q99496 IHC 1:50 - 1:200 Immunogen 1:50 - 1:200 IF Recombinant fusion protein containing a sequence corresponding to amino acids 1-336 of human RING1B/RING1B/RNF2 (NP_009143.1). IP 1:50 - 1:200 Synonyms ChIP 1:50 - 1:200 RNF2;BAP-1;BAP1;DING;HIPI3;RING1B;RING2 Contact Product Information www.abclonal.com Source Isotype Purification Rabbit IgG Affinity purification Storage Store at -20℃. Avoid freeze / thaw cycles. Buffer: PBS with 0.02% sodium azide,50% glycerol,pH7.3. Validation Data Western blot analysis of extracts from normal (control) and RING1B/RNF2 knockout (KO) HeLa cells, using RING1B/RNF2 antibody (A18076) at 1:1000 dilution. -
Annual Scientific Report 2013 on the Cover Structure 3Fof in the Protein Data Bank, Determined by Laponogov, I
EMBL-European Bioinformatics Institute Annual Scientific Report 2013 On the cover Structure 3fof in the Protein Data Bank, determined by Laponogov, I. et al. (2009) Structural insight into the quinolone-DNA cleavage complex of type IIA topoisomerases. Nature Structural & Molecular Biology 16, 667-669. © 2014 European Molecular Biology Laboratory This publication was produced by the External Relations team at the European Bioinformatics Institute (EMBL-EBI) A digital version of the brochure can be found at www.ebi.ac.uk/about/brochures For more information about EMBL-EBI please contact: [email protected] Contents Introduction & overview 3 Services 8 Genes, genomes and variation 8 Molecular atlas 12 Proteins and protein families 14 Molecular and cellular structures 18 Chemical biology 20 Molecular systems 22 Cross-domain tools and resources 24 Research 26 Support 32 ELIXIR 36 Facts and figures 38 Funding & resource allocation 38 Growth of core resources 40 Collaborations 42 Our staff in 2013 44 Scientific advisory committees 46 Major database collaborations 50 Publications 52 Organisation of EMBL-EBI leadership 61 2013 EMBL-EBI Annual Scientific Report 1 Foreword Welcome to EMBL-EBI’s 2013 Annual Scientific Report. Here we look back on our major achievements during the year, reflecting on the delivery of our world-class services, research, training, industry collaboration and European coordination of life-science data. The past year has been one full of exciting changes, both scientifically and organisationally. We unveiled a new website that helps users explore our resources more seamlessly, saw the publication of ground-breaking work in data storage and synthetic biology, joined the global alliance for global health, built important new relationships with our partners in industry and celebrated the launch of ELIXIR. -
Roles of Nickel Binding Proteins in Helicobacter Species
ROLES OF NICKEL BINDING PROTEINS IN HELICOBACTER SPECIES by SUSMITHA SESHADRI (Under the Direction of Robert J. Maier) ABSTRACT Proteins Hpn and Hpn-like of the gastric pathogen H. pylori were hypothesized to bind nickel since histidine residues make up 45% and 27% of their amino acid content, respectively. Characterization of an hpn, an hpn-like and an hpn, hpn-like double mutant revealed novel functions for these gene products in nickel detoxification and storage. Compared to the wild-type parent, mutant strains were more sensitive to elevated concentrations of nickel, cobalt, and cadmium, indicating roles for the two proteins in surviving metal toxicity. Under low nickel conditions, the mutants exhibited higher urease activities and had increased amount of Ni- associated with urease; but similar urease apo-protein levels to wild-type. The parent achieved mutant level urease activities under nickel supplementation and lower pH conditions while growth with a nickel chelator decreased mutant but not wild-type urease activities. These results strongly imply a role for these proteins as nickel reservoirs/storage proteins. H. hepaticus colonizes a non-acidic niche, hence nickel metabolism may be different than in H. pylori. A nikR mutant exhibited higher urease and hydrogenase activities under all supplemental nickel conditions, but there was no change in urease expression, and NikR did not bind pUreA or pHydA. Higher total nickel levels (detected by ICP-MS) in the nikR mutant implied possible higher nickel transporter (NikA) levels, which was later verified by qRT-PCR and binding of NikR to pNikA. Periplasmic nitrate reductase (NapA) was upregulated in the NikR strain. -
Open Data for Differential Network Analysis in Glioma
International Journal of Molecular Sciences Article Open Data for Differential Network Analysis in Glioma , Claire Jean-Quartier * y , Fleur Jeanquartier y and Andreas Holzinger Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, 8036 Graz, Austria; [email protected] (F.J.); [email protected] (A.H.) * Correspondence: [email protected] These authors contributed equally to this work. y Received: 27 October 2019; Accepted: 3 January 2020; Published: 15 January 2020 Abstract: The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. -
A Highly Accurate Model for Screening Prostate Cancer Using Propensity Index Panel of Ten Genes
bioRxiv preprint doi: https://doi.org/10.1101/2021.03.22.436371; this version posted March 22, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. A highly accurate model for screening prostate cancer using propensity index panel of ten genes Shipra Jain#, Kawal Preet Kaur Malhotra#, Sumeet Patiyal#, Gajendra P. S. Raghava* Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi-110020, India. # - Contributed Equally * - Corresponding Author Mailing Address of Authors Shipra Jain: [email protected] ORCID ID: https://orcid.org/0000-0002-7045-5188 Kawal Preet Kaur Malhotra: [email protected] Sumeet Patiyal: [email protected] ORCID ID: https://orcid.org/0000-0003-1358-292X GPS Raghava: [email protected] ORCID ID: https://orcid.org/0000-0002-8902-2876 Corresponding Author Gajendra Pal Singh Raghava Head and Professor, Department of Computational Biology Office: A-302 R&D Block, Indraprastha Institute of Information Technology, Delhi Okhla Industrial Estate, Phase III, (Near Govind Puri Metro Station) New Delhi, India - 110020 Phone: 011-26907444 Email: [email protected] Website: http://webs.iiitd.edu.in/raghava/ Highlights ● Application of Machine learning techniques to identify Biomarkers for PRAD cancer. ● Highly accurate models developed for classifying prostate cancer vs. normal sample. ● Introducing Propensity index concept for enhancing model performance. ● Top 10 genes identified using feature selection techniques. bioRxiv preprint doi: https://doi.org/10.1101/2021.03.22.436371; this version posted March 22, 2021. -
The Human Genome Project
TO KNOW OURSELVES ❖ THE U.S. DEPARTMENT OF ENERGY AND THE HUMAN GENOME PROJECT JULY 1996 TO KNOW OURSELVES ❖ THE U.S. DEPARTMENT OF ENERGY AND THE HUMAN GENOME PROJECT JULY 1996 Contents FOREWORD . 2 THE GENOME PROJECT—WHY THE DOE? . 4 A bold but logical step INTRODUCING THE HUMAN GENOME . 6 The recipe for life Some definitions . 6 A plan of action . 8 EXPLORING THE GENOMIC LANDSCAPE . 10 Mapping the terrain Two giant steps: Chromosomes 16 and 19 . 12 Getting down to details: Sequencing the genome . 16 Shotguns and transposons . 20 How good is good enough? . 26 Sidebar: Tools of the Trade . 17 Sidebar: The Mighty Mouse . 24 BEYOND BIOLOGY . 27 Instrumentation and informatics Smaller is better—And other developments . 27 Dealing with the data . 30 ETHICAL, LEGAL, AND SOCIAL IMPLICATIONS . 32 An essential dimension of genome research Foreword T THE END OF THE ROAD in Little has been rapid, and it is now generally agreed Cottonwood Canyon, near Salt that this international project will produce Lake City, Alta is a place of the complete sequence of the human genome near-mythic renown among by the year 2005. A skiers. In time it may well And what is more important, the value assume similar status among molecular of the project also appears beyond doubt. geneticists. In December 1984, a conference Genome research is revolutionizing biology there, co-sponsored by the U.S. Department and biotechnology, and providing a vital of Energy, pondered a single question: Does thrust to the increasingly broad scope of the modern DNA research offer a way of detect- biological sciences. -
E2-25K/Hip2 Antibody A
Revision 1 C 0 2 - t E2-25K/Hip2 Antibody a e r o t S Orders: 877-616-CELL (2355) [email protected] Support: 877-678-TECH (8324) 7 4 Web: [email protected] 8 www.cellsignal.com 3 # 3 Trask Lane Danvers Massachusetts 01923 USA For Research Use Only. Not For Use In Diagnostic Procedures. Applications: Reactivity: Sensitivity: MW (kDa): Source: UniProt ID: Entrez-Gene Id: WB H M R Mk Endogenous 25 Rabbit P61086 3093 Product Usage Information Application Dilution Western Blotting 1:1000 Storage Supplied in 10 mM sodium HEPES (pH 7.5), 150 mM NaCl, 100 µg/ml BSA and 50% glycerol. Store at –20°C. Do not aliquot the antibody. Specificity / Sensitivity E2-25K/Hip2 Antibody detects endogenous levels of total E2-25K/Hip2 protein. Species Reactivity: Human, Mouse, Rat, Monkey Source / Purification Polyclonal antibodies are produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Arg11 of human E2-25K/Hip2 protein. Antibodies are purified by protein A and peptide affinity chromatography. Background Protein ubiquitination requires the concerted action of the E1, E2, and E3 ubiquitin- conjugating enzymes. Ubiquitin is first activated through ATP-dependent formation of a thiol ester with ubiquitin-activating enzyme E1. The activated ubiquitin is then transferred to a thiol group of ubiquitin-carrier enzyme E2. The final step is the transfer of ubiquitin from E2 to an ε-amino group of the target protein lysine residue, which is mediated by ubiquitin-ligase enzyme E3 (1). E2-25K (Hip2) is a member of the E2 protein family that catalyzes multiubiquitin chain synthesis via Lys48 of ubiquitin (2). -
Complex Rearrangement of Chromosomes 19, 21, and 22 In
Cancer Genetics and Cytogenetics 181 (2008) 81e92 Complex rearrangement of chromosomes 19, 21, and 22 in Ewing sarcoma involving a novel reciprocal inversioneinsertion mechanism of EWSeERG fusion gene formation: a case analysis and literature review Georges Mairea, Christopher W. Brownb,c,d, Jane Bayania, Carlos Pereirae, Denis H. Gravelf, John C. Bellc, Maria Zielenskae,g,h, Jeremy A. Squirea,h* aDivision of Applied Molecular Oncology, Ontario Cancer Institute, Princess Margaret Hospital, University Health Network, 610 University Avenue, Room 9-717, Toronto, Ontario M5G 2M9, Canada bOttawa Health Research Institute, Centre for Cancer Therapeutics, Ottawa, Ontario, Canada cDepartment of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, Canada dDepartment of Orthopaedic Surgery, Ottawa Hospital and University of Ottawa, Ottawa, Ontario, Canada eDepartment of Pediatric Laboratory Medicine and Pathology, The Hospital for Sick Children, Toronto, Ontario, Canada fDepartment of Pathology and Laboratory Medicine, Ottawa Hospital and University of Ottawa, Ottawa, Ontario, Canada gGenetics and Genome Biology, Hospital for Sick Children, Toronto, Ontario, Canada hDepartment of Laboratory Medicine and Pathology, University of Toronto, Toronto, Ontario, Canada Received 9 August 2007; received in revised form 5 November 2007; accepted 7 November 2007 Abstract EWSeERG Ewing sarcoma (ES) gene fusions often result from complex chromosomal rearrange- ments. We report an unusually aggressive case of ES with an EWSeERG fusion gene that appeared to be a result of a simple balanced and reciprocal translocation, t(19;22)(q13.2;q12.2). Subsequent molecular investigation of the primary tumor, the metastasis, and a cell line generated from this ES permitted reconstruction of each genomic step in the evolution of this complex EWSeERG fusion. -
COMPUTATIONAL MODELLING of PROTEIN FIBRILLATION with APPLICATION to GLUCAGON Hamed Tabatabaei Ghomi Purdue University
Purdue University Purdue e-Pubs Open Access Dissertations Theses and Dissertations January 2015 COMPUTATIONAL MODELLING OF PROTEIN FIBRILLATION WITH APPLICATION TO GLUCAGON Hamed Tabatabaei Ghomi Purdue University Follow this and additional works at: https://docs.lib.purdue.edu/open_access_dissertations Recommended Citation Tabatabaei Ghomi, Hamed, "COMPUTATIONAL MODELLING OF PROTEIN FIBRILLATION WITH APPLICATION TO GLUCAGON" (2015). Open Access Dissertations. 1321. https://docs.lib.purdue.edu/open_access_dissertations/1321 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. i COMPUTATIONAL MODELLING OF PROTEIN FIBRILLATION WITH APPLICATION TO GLUCAGON A Dissertation Submitted to the Faculty of Purdue University by Hamed Tabatabaei Ghomi In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy i December 2015 Purdue University West Lafayette, Indiana ii ای الهه ای رتاهن ام آشیاهن ام یس ای ربای شاد ز تن بهاهن ام قهرمان قصه اهی عاشقاهن ام نس با یم انم تو بهار می شود بذر شعراهی اتزه رد میان خاک بی رقار می شود ii iii ACKNOWLEDGEMENTS I am indebted to my advisors, Dr. Lill and Dr. Topp who taught me many things about science, and about life. Also, I thank my advisory committee, Dr. Park, Dr. Post and Dr. Kihara for all their support. I am grateful to my lovely wife, Elaheh. I owe all my success to her support and sacrifice. I am also grateful to my parent for their support and encouragement. And finally, I owe my deepest and greatest gratitude to God, the Most Beneficent, the Most Merciful.