The Detection and Bioinformatic Analysis of Alternative 3 UTR
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20874 SYNGR1 Antibody
Revision 1 C 0 2 - t SYNGR1 Antibody a e r o t S Orders: 877-616-CELL (2355) [email protected] 4 Support: 877-678-TECH (8324) 7 8 Web: [email protected] 0 www.cellsignal.com 2 # 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 Endogenous 28 Rabbit O43759 9145 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 SYNGR1 Antibody recognizes endogenous levels of total synaptogyrin-1 protein. Species Reactivity: Human, Mouse, Rat Source / Purification Polyclonal antibodies are produced by immunizing animals with a synthetic peptide corresponding to residues surrounding Tyr214 of human synaptogyrin-1 protein. Antibodies are purified by peptide affinity chromatography. Background Synaptogyrin, or SYNGR, are a family of tyrosine-phosphorylated proteins, including neuronal SYNGR1 and SYNGR3 that are found in synaptic vesicles and contribute to the proper synapse function. Synaptogyrin-2 (SYNGR2) expresses ubiquitously and it is not only associated with synaptic vesicles, but also plays an important role in exocytosis processes (1,3). In addition, it has been shown that SYNGRs modulate calcium currents in excitable cells during potassium chloride-dependent exocytosis (3). SYNGR3 and SYNGR1 specifically localize in synaptic vesicles. SYNGR1 modulates synaptic vesicle function similar to SYNGR3 (2,3). SYNGR1 and SYNGR3 contribute to the neurotransmitter release in neurons by interactions with the GABA and VGLUT transporters in primary neurons and in C. -
Expert Curation of the Human and Mouse Olfactory Receptor Gene Repertoires Identifies Conserved Coding Regions Split Across Two Exons
bioRxiv preprint doi: https://doi.org/10.1101/774612; this version posted October 30, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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-ND 4.0 International license. Expert Curation of the Human and Mouse Olfactory Receptor Gene Repertoires Identifies Conserved Coding Regions Split Across Two Exons 5 If H. A. Barnes1†#, Ximena Ibarra-Soria2,3†#, Stephen Fitzgerald3, Jose M. Gonzalez1, Claire Davidson1, Matthew P. Hardy1, Deepa Manthravadi4, Laura Van Gerven5, Mark Jorissen5, Zhen Zeng6, Mona Khan6, Peter Mombaerts6, Jennifer Harrow7, Darren W. Logan3,8,9 and Adam Frankish1#. 10 1. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. 2. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK. 3. Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK. 15 4. Brandeis University, 415 South Street, Waltham, MA 02453, USA. 5. Department of ENT-HNS, UZ Leuven, Herestraat 49, 3000 Leuven, Belgium. 6. Max Planck Research Unit for Neurogenetics, Max von-Laue-Strasse 4, 60438 Frankfurt, Germany. 7. ELIXIR, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. 8. Monell Chemical Senses Center, Philadelphia, PA 19104, USA. 20 9. Waltham Centre for Pet Nutrition, Leicestershire, LE14 4RT, UK. † These authors contributed equally to this work. # To whom correspondence should be addressed. Email: [email protected], [email protected] and [email protected]. -
Repetitive Elements in Humans
International Journal of Molecular Sciences Review Repetitive Elements in Humans Thomas Liehr Institute of Human Genetics, Jena University Hospital, Friedrich Schiller University, Am Klinikum 1, D-07747 Jena, Germany; [email protected] Abstract: Repetitive DNA in humans is still widely considered to be meaningless, and variations within this part of the genome are generally considered to be harmless to the carrier. In contrast, for euchromatic variation, one becomes more careful in classifying inter-individual differences as meaningless and rather tends to see them as possible influencers of the so-called ‘genetic background’, being able to at least potentially influence disease susceptibilities. Here, the known ‘bad boys’ among repetitive DNAs are reviewed. Variable numbers of tandem repeats (VNTRs = micro- and minisatellites), small-scale repetitive elements (SSREs) and even chromosomal heteromorphisms (CHs) may therefore have direct or indirect influences on human diseases and susceptibilities. Summarizing this specific aspect here for the first time should contribute to stimulating more research on human repetitive DNA. It should also become clear that these kinds of studies must be done at all available levels of resolution, i.e., from the base pair to chromosomal level and, importantly, the epigenetic level, as well. Keywords: variable numbers of tandem repeats (VNTRs); microsatellites; minisatellites; small-scale repetitive elements (SSREs); chromosomal heteromorphisms (CHs); higher-order repeat (HOR); retroviral DNA 1. Introduction Citation: Liehr, T. Repetitive In humans, like in other higher species, the genome of one individual never looks 100% Elements in Humans. Int. J. Mol. Sci. alike to another one [1], even among those of the same gender or between monozygotic 2021, 22, 2072. -
Identification of Potential Key Genes and Pathway Linked with Sporadic Creutzfeldt-Jakob Disease Based on Integrated Bioinformatics Analyses
medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 , Iranna Kotturshetti 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. 3. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.12.21.20248688; this version posted December 24, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Abstract Sporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. -
GENCODE: the Reference Human Genome Annotation for the ENCODE Project
Downloaded from genome.cshlp.org on September 26, 2012 - Published by Cold Spring Harbor Laboratory Press Resource GENCODE: The reference human genome annotation for The ENCODE Project Jennifer Harrow,1,9 Adam Frankish,1 Jose M. Gonzalez,1 Electra Tapanari,1 Mark Diekhans,2 Felix Kokocinski,1 Bronwen L. Aken,1 Daniel Barrell,1 Amonida Zadissa,1 Stephen Searle,1 If Barnes,1 Alexandra Bignell,1 Veronika Boychenko,1 Toby Hunt,1 Mike Kay,1 Gaurab Mukherjee,1 Jeena Rajan,1 Gloria Despacio-Reyes,1 Gary Saunders,1 Charles Steward,1 Rachel Harte,2 Michael Lin,3 Ce´dric Howald,4 Andrea Tanzer,5 Thomas Derrien,4 Jacqueline Chrast,4 Nathalie Walters,4 Suganthi Balasubramanian,6 Baikang Pei,6 Michael Tress,7 Jose Manuel Rodriguez,7 Iakes Ezkurdia,7 Jeltje van Baren,8 Michael Brent,8 David Haussler,2 Manolis Kellis,3 Alfonso Valencia,7 Alexandre Reymond,4 Mark Gerstein,6 Roderic Guigo´,5 and Tim J. Hubbard1,9 1Wellcome Trust Sanger Institute, Wellcome Trust Campus, Hinxton, Cambridge CB10 1SA, United Kingdom; 2University of California, Santa Cruz, California 95064, USA; 3Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA; 4Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland; 5Centre for Genomic Regulation (CRG) and UPF, 08003 Barcelona, Catalonia, Spain; 6Yale University, New Haven, Connecticut 06520-8047, USA; 7Spanish National Cancer Research Centre (CNIO), E-28029 Madrid, Spain; 8Center for Genome Sciences & Systems Biology, St. Louis, Missouri 63130, USA The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computa- tional analysis, manual annotation, and experimental validation. -
Genetics of Gene Expression in Primary Immune Cells Identifies Cell
ARTICLES Genetics of gene expression in primary immune cells identifies cell type–specific master regulators and roles of HLA alleles Benjamin P Fairfax1, Seiko Makino1, Jayachandran Radhakrishnan1, Katharine Plant1, Stephen Leslie2, Alexander Dilthey3, Peter Ellis4, Cordelia Langford4, Fredrik O Vannberg1,5 & Julian C Knight1 Trans-acting genetic variants have a substantial, albeit poorly characterized, role in the heritable determination of gene expression. Using paired purified primary monocytes and B cells, we identify new predominantly cell type–specific cis and trans expression quantitative trait loci (eQTLs), including multi-locus trans associations to LYZ and KLF4 in monocytes and B cells, respectively. Additionally, we observe a B cell–specific trans association of rs11171739 at 12q13.2, a known autoimmune disease locus, with IP6K2 (P = 5.8 × 10−15), PRIC285 (P = 3.0 × 10−10) and an upstream region of CDKN1A (P = 2 × 10−52), suggesting roles for cell cycle regulation and peroxisome proliferator-activated receptor γ (PPARγ) signaling in autoimmune pathogenesis. We also find that specific human leukocyte antigen (HLA) alleles form trans associations with the expression of AOAH and ARHGAP24 in monocytes but not in B cells. In summary, we show that mapping gene expression in defined primary cell populations identifies new cell type–specific trans-regulated networks and provides insights into the genetic basis of disease susceptibility. Defining the genetic determinants of gene expression is crucial to especially pertinent in the elucidation of trans-acting eQTLs, where understanding the biological and medical significance of genetic var- context specificity may be of greater relevance12. iation. This is particularly relevant in the drive to identify functional Here we sought to determine cell type–specific eQTLs relevant to variants underlying observed disease associations from genome- immunity and inflammation in paired samples of primary monocytes wide association studies (GWAS)1. -
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. -
The Genetics of Bipolar Disorder
Molecular Psychiatry (2008) 13, 742–771 & 2008 Nature Publishing Group All rights reserved 1359-4184/08 $30.00 www.nature.com/mp FEATURE REVIEW The genetics of bipolar disorder: genome ‘hot regions,’ genes, new potential candidates and future directions A Serretti and L Mandelli Institute of Psychiatry, University of Bologna, Bologna, Italy Bipolar disorder (BP) is a complex disorder caused by a number of liability genes interacting with the environment. In recent years, a large number of linkage and association studies have been conducted producing an extremely large number of findings often not replicated or partially replicated. Further, results from linkage and association studies are not always easily comparable. Unfortunately, at present a comprehensive coverage of available evidence is still lacking. In the present paper, we summarized results obtained from both linkage and association studies in BP. Further, we indicated new potential interesting genes, located in genome ‘hot regions’ for BP and being expressed in the brain. We reviewed published studies on the subject till December 2007. We precisely localized regions where positive linkage has been found, by the NCBI Map viewer (http://www.ncbi.nlm.nih.gov/mapview/); further, we identified genes located in interesting areas and expressed in the brain, by the Entrez gene, Unigene databases (http://www.ncbi.nlm.nih.gov/entrez/) and Human Protein Reference Database (http://www.hprd.org); these genes could be of interest in future investigations. The review of association studies gave interesting results, as a number of genes seem to be definitively involved in BP, such as SLC6A4, TPH2, DRD4, SLC6A3, DAOA, DTNBP1, NRG1, DISC1 and BDNF. -
GENCODE Reference Annotation for the Human and Mouse Genomes
D766–D773 Nucleic Acids Research, 2019, Vol. 47, Database issue Published online 24 October 2018 doi: 10.1093/nar/gky955 GENCODE reference annotation for the human and mouse genomes Adam Frankish1, Mark Diekhans2, Anne-Maud Ferreira3, Rory Johnson4,5, Irwin Jungreis 6,7, Jane Loveland 1, Jonathan M. Mudge1, Cristina Sisu8,9, Downloaded from https://academic.oup.com/nar/article-abstract/47/D1/D766/5144133 by Universite and EPFL Lausanne user on 11 April 2019 James Wright10, Joel Armstrong2, If Barnes1, Andrew Berry1, Alexandra Bignell1, Silvia Carbonell Sala11, Jacqueline Chrast3, Fiona Cunningham 1,Tomas´ Di Domenico 12, Sarah Donaldson1, Ian T. Fiddes2, Carlos Garc´ıa Giron´ 1, Jose Manuel Gonzalez1, Tiago Grego1, Matthew Hardy1, Thibaut Hourlier 1, Toby Hunt1, Osagie G. Izuogu1, Julien Lagarde11, Fergal J. Martin 1, Laura Mart´ınez12, Shamika Mohanan1, Paul Muir13,14, Fabio C.P. Navarro8, Anne Parker1, Baikang Pei8, Fernando Pozo12, Magali Ruffier 1, Bianca M. Schmitt1, Eloise Stapleton1, Marie-Marthe Suner 1, Irina Sycheva1, Barbara Uszczynska-Ratajczak15,JinuriXu8, Andrew Yates1, Daniel Zerbino 1, Yan Zhang8,16, Bronwen Aken1, Jyoti S. Choudhary10, Mark Gerstein8,17,18, Roderic Guigo´ 11,19, Tim J.P. Hubbard20, Manolis Kellis6,7, Benedict Paten2, Alexandre Reymond3, Michael L. Tress12 and Paul Flicek 1,* 1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK, 2UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95064, USA, -
Downloaded from the Tranche Distributed File System (Tranche.Proteomecommons.Org) and Ftp://Ftp.Thegpm.Org/Data/Msms
Research Article Title: The shrinking human protein coding complement: are there now fewer than 20,000 genes? Authors: Iakes Ezkurdia1*, David Juan2*, Jose Manuel Rodriguez3, Adam Frankish4, Mark Diekhans5, Jennifer Harrow4, Jesus Vazquez 6, Alfonso Valencia2,3, Michael L. Tress2,*. Affiliations: 1. Unidad de Proteómica, Centro Nacional de Investigaciones Cardiovasculares, CNIC, Melchor Fernández Almagro, 3, rid, 28029, MadSpain 2. Structural Biology and Bioinformatics Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain 3. National Bioinformatics Institute (INB), Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain 4. Wellcome Trust Sanger Institute, Wellcome Trust Campus, Hinxton, Cambridge CB10 1SA, UK 5. Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), 1156 High Street, Santa Cruz, CA 95064, USA 6. Laboratorio de Proteómica Cardiovascular, Centro Nacional de Investigaciones Cardiovasculares, CNIC, Melchor Fernández Almagro, 3, 28029, Madrid, Spain *: these two authors wish to be considered as joint first authors of the paper. Corresponding author: Michael Tress, [email protected], Tel: +34 91 732 80 00 Fax: +34 91 224 69 76 Running title: Are there fewer than 20,000 protein-coding genes? Keywords: Protein coding genes, proteomics, evolution, genome annotation Abstract Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein coding potential is the detection of cellular protein expression through peptide mass spectrometry experiments. Here we map the peptides detected in 7 large-scale proteomics studies to almost 60% of the protein coding genes in the GENCODE annotation the human genome. -
Connecting Myelin-Related and Synaptic Dysfunction In
www.nature.com/scientificreports OPEN Connecting myelin-related and synaptic dysfunction in schizophrenia with SNP-rich Received: 24 October 2016 Accepted: 27 February 2017 gene expression hubs Published: 07 April 2017 Hedi Hegyi Combining genome-wide mapping of SNP-rich regions in schizophrenics and gene expression data in all brain compartments across the human life span revealed that genes with promoters most frequently mutated in schizophrenia are expression hubs interacting with far more genes than the rest of the genome. We summed up the differentially methylated “expression neighbors” of genes that fall into one of 108 distinct schizophrenia-associated loci with high number of SNPs. Surprisingly, the number of expression neighbors of the genes in these loci were 35 times higher for the positively correlating genes (32 times higher for the negatively correlating ones) than for the rest of the ~16000 genes. While the genes in the 108 loci have little known impact in schizophrenia, we identified many more known schizophrenia-related important genes with a high degree of connectedness (e.g. MOBP, SYNGR1 and DGCR6), validating our approach. Both the most connected positive and negative hubs affected synapse-related genes the most, supporting the synaptic origin of schizophrenia. At least half of the top genes in both the correlating and anti-correlating categories are cancer-related, including oncogenes (RRAS and ALDOA), providing further insight into the observed inverse relationship between the two diseases. Gene expression correlation, protein-protein interaction and other high-throughput experiments in the post-genomic era have revealed that genes tend to form complex, scale-free networks where most genes have a few connections with others and a few have a high number of interactions, commonly referred to as “hubs”, estab- lishing them as important central genes in these gene networks1. -
The GENCODE Consortium the GENCODE Update Trackhub
1/25/2019 The status of GENCODE gene annotation GENCODE Manual Genome Annotation in Reference genebuild ‘First pass’ systematic chr annotation Ensembl • Analysis of cDNA, ESTs, build genes Jane Loveland PhD Mouse complete Annotation Project Leader Ensembl-HAVANA Maturing genebuild Targeted improvement of models • Identification of additional gene, transcripts, exons • ‘Completion’ of models th PAG XXVII, 13 January 2019 • Correct functional annotation Major focus of human work TAGENE MANE project The HAVANA team Manual Annotation: Biotypes Annotation: Biotypes based on transcriptional evidence Whole Genome Targeted regions GENCODE Community projects Protein Coding or chromosome or genes Known_CDS Novel_CDS Putative_CDS Nonsense_mediated_decay Sequences from Transcript retained intron databases putative Non-coding lincRNA Antisense Sense_intronic Sense_overlapping 3’_overlapping_ncRNA Pseudogene Processed Unprocessed Transcribed Translated Unitary Polymorphic Immunoglobulin IG_pseudogene IG_Gene Structural and functional TR_Gene The GENCODE consortium The GENCODE update trackhub HAVANA Genebuild Manual annotation Computational annotation GENCODE gene set 1 1/25/2019 Ensembl gene view: ENO1 updated annotation (more transcripts) Walking across the mouse genome Updated annotation Walking across the mouse genome Walking across the mouse genome Walking across the mouse genome Walking across the mouse genome 2 1/25/2019 Walking across the mouse genome The GENCODE consortium current gene counts Human Mouse Total No of Transcripts 206694 141283