Golgi Function and Dysfunction in the First COG4- Deficient CDG Type II Patient
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Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Autism Multiplex Family with 16P11.2P12.2 Microduplication Syndrome in Monozygotic Twins and Distal 16P11.2 Deletion in Their Brother
European Journal of Human Genetics (2012) 20, 540–546 & 2012 Macmillan Publishers Limited All rights reserved 1018-4813/12 www.nature.com/ejhg ARTICLE Autism multiplex family with 16p11.2p12.2 microduplication syndrome in monozygotic twins and distal 16p11.2 deletion in their brother Anne-Claude Tabet1,2,3,4, Marion Pilorge2,3,4, Richard Delorme5,6,Fre´de´rique Amsellem5,6, Jean-Marc Pinard7, Marion Leboyer6,8,9, Alain Verloes10, Brigitte Benzacken1,11,12 and Catalina Betancur*,2,3,4 The pericentromeric region of chromosome 16p is rich in segmental duplications that predispose to rearrangements through non-allelic homologous recombination. Several recurrent copy number variations have been described recently in chromosome 16p. 16p11.2 rearrangements (29.5–30.1 Mb) are associated with autism, intellectual disability (ID) and other neurodevelopmental disorders. Another recognizable but less common microdeletion syndrome in 16p11.2p12.2 (21.4 to 28.5–30.1 Mb) has been described in six individuals with ID, whereas apparently reciprocal duplications, studied by standard cytogenetic and fluorescence in situ hybridization techniques, have been reported in three patients with autism spectrum disorders. Here, we report a multiplex family with three boys affected with autism, including two monozygotic twins carrying a de novo 16p11.2p12.2 duplication of 8.95 Mb (21.28–30.23 Mb) characterized by single-nucleotide polymorphism array, encompassing both the 16p11.2 and 16p11.2p12.2 regions. The twins exhibited autism, severe ID, and dysmorphic features, including a triangular face, deep-set eyes, large and prominent nasal bridge, and tall, slender build. The eldest brother presented with autism, mild ID, early-onset obesity and normal craniofacial features, and carried a smaller, overlapping 16p11.2 microdeletion of 847 kb (28.40–29.25 Mb), inherited from his apparently healthy father. -
Linkage Analysis in 15 Families, Physical And
European Journal of Human Genetics (2002) 11, 145 – 154 ª 2002 Nature Publishing Group All rights reserved 1018 – 4813/02 $25.00 www.nature.com/ejhg ARTICLE Familial juvenile hyperuricaemic nephropathy (FJHN): linkage analysis in 15 families, physical and transcriptional characterisation of the FJHN critical region on chromosome 16p11.2 and the analysis of seven candidate genes Blanka Stibu˚ rkova´1, Jacek Majewski2, Katerˇina Hodanˇova´1, Lenka Ondrova´1, Marke´ta Jerˇa´bkova´1, Marie Zika´nova´1, Petr Vylet’al1, Ivan Sˇebesta3, Anthony Marinaki4, Anne Simmonds4, Gert Matthijs5, Jean-Pierre Fryns5, Rosa Torres6, Juan Garcı´a Puig7, Jurg Ott2 and Stanislav Kmoch*,1 1Center for Integrated Genomics, Institute for Inherited Metabolic Disorders, Charles University 1st School of Medicine and General Faculty Hospital Prague, Czech Republic; 2Laboratory of Statistical Genetics, Rockefeller University, New York, NY, USA; 3Department of Clinical Biochemistry, Charles University 1st School of Medicine and General Faculty Hospital Prague, Czech Republic; 4Purine Research Unit, GKT, Guy’s Hospital, London, UK; 5Center for Human Genetics, University of Leuven, Leuven, Belgium; 6Department of Clinical Biochemistry, La Paz Hospital, Madrid, Spain; 7Division of Internal Medicine, La Paz Hospital, Madrid, Spain Familial juvenile hyperuricaemic nephropathy (FJHN) is an autosomal dominant renal disease characterised by juvenile onset of hyperuricaemia, gouty arthritis, and progressive renal failure at an early age. Recent studies in four kindreds showed linkage of a gene for FJHN to the same genomic interval on chromosome 16p11.2, where the gene for the phenotypically similar medullary cystic disease type 2 (MCKD2) has been localised. In this study we performed linkage analysis in additional 15 FJHN families. -
Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia. -
Congenital Disorders of Glycosylation from a Neurological Perspective
brain sciences Review Congenital Disorders of Glycosylation from a Neurological Perspective Justyna Paprocka 1,* , Aleksandra Jezela-Stanek 2 , Anna Tylki-Szyma´nska 3 and Stephanie Grunewald 4 1 Department of Pediatric Neurology, Faculty of Medical Science in Katowice, Medical University of Silesia, 40-752 Katowice, Poland 2 Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, 01-138 Warsaw, Poland; [email protected] 3 Department of Pediatrics, Nutrition and Metabolic Diseases, The Children’s Memorial Health Institute, W 04-730 Warsaw, Poland; [email protected] 4 NIHR Biomedical Research Center (BRC), Metabolic Unit, Great Ormond Street Hospital and Institute of Child Health, University College London, London SE1 9RT, UK; [email protected] * Correspondence: [email protected]; Tel.: +48-606-415-888 Abstract: Most plasma proteins, cell membrane proteins and other proteins are glycoproteins with sugar chains attached to the polypeptide-glycans. Glycosylation is the main element of the post- translational transformation of most human proteins. Since glycosylation processes are necessary for many different biological processes, patients present a diverse spectrum of phenotypes and severity of symptoms. The most frequently observed neurological symptoms in congenital disorders of glycosylation (CDG) are: epilepsy, intellectual disability, myopathies, neuropathies and stroke-like episodes. Epilepsy is seen in many CDG subtypes and particularly present in the case of mutations -
Large-Scale Rnai Screening Uncovers New Therapeutic Targets in the Human Parasite
bioRxiv preprint doi: https://doi.org/10.1101/2020.02.05.935833; this version posted February 6, 2020. 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. 1 Large-scale RNAi screening uncovers new therapeutic targets in the human parasite 2 Schistosoma mansoni 3 4 Jipeng Wang1*, Carlos Paz1*, Gilda Padalino2, Avril Coghlan3, Zhigang Lu3, Irina Gradinaru1, 5 Julie N.R. Collins1, Matthew Berriman3, Karl F. Hoffmann2, James J. Collins III1†. 6 7 8 1Department of Pharmacology, UT Southwestern Medical Center, Dallas, Texas 75390 9 2Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, 10 Aberystwyth, Wales, UK. 11 3Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK 12 *Equal Contribution 13 14 15 16 17 18 19 20 21 22 23 †To whom correspondence should be addressed 24 [email protected] 25 UT Southwestern Medical Center 26 Department of Pharmacology 27 6001 Forest Park. Rd. 28 Dallas, TX 75390 29 United States of America bioRxiv preprint doi: https://doi.org/10.1101/2020.02.05.935833; this version posted February 6, 2020. 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. 30 ABSTRACT 31 Schistosomes kill 250,000 people every year and are responsible for serious morbidity in 240 32 million of the world’s poorest people. -
Prevalence and Incidence of Rare Diseases: Bibliographic Data
Number 1 | January 2019 Prevalence and incidence of rare diseases: Bibliographic data Prevalence, incidence or number of published cases listed by diseases (in alphabetical order) www.orpha.net www.orphadata.org If a range of national data is available, the average is Methodology calculated to estimate the worldwide or European prevalence or incidence. When a range of data sources is available, the most Orphanet carries out a systematic survey of literature in recent data source that meets a certain number of quality order to estimate the prevalence and incidence of rare criteria is favoured (registries, meta-analyses, diseases. This study aims to collect new data regarding population-based studies, large cohorts studies). point prevalence, birth prevalence and incidence, and to update already published data according to new For congenital diseases, the prevalence is estimated, so scientific studies or other available data. that: Prevalence = birth prevalence x (patient life This data is presented in the following reports published expectancy/general population life expectancy). biannually: When only incidence data is documented, the prevalence is estimated when possible, so that : • Prevalence, incidence or number of published cases listed by diseases (in alphabetical order); Prevalence = incidence x disease mean duration. • Diseases listed by decreasing prevalence, incidence When neither prevalence nor incidence data is available, or number of published cases; which is the case for very rare diseases, the number of cases or families documented in the medical literature is Data collection provided. A number of different sources are used : Limitations of the study • Registries (RARECARE, EUROCAT, etc) ; The prevalence and incidence data presented in this report are only estimations and cannot be considered to • National/international health institutes and agencies be absolutely correct. -
MECHANISMS in ENDOCRINOLOGY: Novel Genetic Causes of Short Stature
J M Wit and others Genetics of short stature 174:4 R145–R173 Review MECHANISMS IN ENDOCRINOLOGY Novel genetic causes of short stature 1 1 2 2 Jan M Wit , Wilma Oostdijk , Monique Losekoot , Hermine A van Duyvenvoorde , Correspondence Claudia A L Ruivenkamp2 and Sarina G Kant2 should be addressed to J M Wit Departments of 1Paediatrics and 2Clinical Genetics, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, Email The Netherlands [email protected] Abstract The fast technological development, particularly single nucleotide polymorphism array, array-comparative genomic hybridization, and whole exome sequencing, has led to the discovery of many novel genetic causes of growth failure. In this review we discuss a selection of these, according to a diagnostic classification centred on the epiphyseal growth plate. We successively discuss disorders in hormone signalling, paracrine factors, matrix molecules, intracellular pathways, and fundamental cellular processes, followed by chromosomal aberrations including copy number variants (CNVs) and imprinting disorders associated with short stature. Many novel causes of GH deficiency (GHD) as part of combined pituitary hormone deficiency have been uncovered. The most frequent genetic causes of isolated GHD are GH1 and GHRHR defects, but several novel causes have recently been found, such as GHSR, RNPC3, and IFT172 mutations. Besides well-defined causes of GH insensitivity (GHR, STAT5B, IGFALS, IGF1 defects), disorders of NFkB signalling, STAT3 and IGF2 have recently been discovered. Heterozygous IGF1R defects are a relatively frequent cause of prenatal and postnatal growth retardation. TRHA mutations cause a syndromic form of short stature with elevated T3/T4 ratio. Disorders of signalling of various paracrine factors (FGFs, BMPs, WNTs, PTHrP/IHH, and CNP/NPR2) or genetic defects affecting cartilage extracellular matrix usually cause disproportionate short stature. -
Psykisk Utviklingshemming Og Forsinket Utvikling
Psykisk utviklingshemming og forsinket utvikling Genpanel, versjon v03 Tabellen er sortert på gennavn (HGNC gensymbol) Navn på gen er iht. HGNC >x10 Andel av genet som har blitt lest med tilfredstillende kvalitet flere enn 10 ganger under sekvensering x10 er forventet dekning; faktisk dekning vil variere. Gen Gen (HGNC Transkript >10x Fenotype (symbol) ID) AAAS 13666 NM_015665.5 100% Achalasia-addisonianism-alacrimia syndrome OMIM AARS 20 NM_001605.2 100% Charcot-Marie-Tooth disease, axonal, type 2N OMIM Epileptic encephalopathy, early infantile, 29 OMIM AASS 17366 NM_005763.3 100% Hyperlysinemia OMIM Saccharopinuria OMIM ABCB11 42 NM_003742.2 100% Cholestasis, benign recurrent intrahepatic, 2 OMIM Cholestasis, progressive familial intrahepatic 2 OMIM ABCB7 48 NM_004299.5 100% Anemia, sideroblastic, with ataxia OMIM ABCC6 57 NM_001171.5 93% Arterial calcification, generalized, of infancy, 2 OMIM Pseudoxanthoma elasticum OMIM Pseudoxanthoma elasticum, forme fruste OMIM ABCC9 60 NM_005691.3 100% Hypertrichotic osteochondrodysplasia OMIM ABCD1 61 NM_000033.3 77% Adrenoleukodystrophy OMIM Adrenomyeloneuropathy, adult OMIM ABCD4 68 NM_005050.3 100% Methylmalonic aciduria and homocystinuria, cblJ type OMIM ABHD5 21396 NM_016006.4 100% Chanarin-Dorfman syndrome OMIM ACAD9 21497 NM_014049.4 99% Mitochondrial complex I deficiency due to ACAD9 deficiency OMIM ACADM 89 NM_000016.5 100% Acyl-CoA dehydrogenase, medium chain, deficiency of OMIM ACADS 90 NM_000017.3 100% Acyl-CoA dehydrogenase, short-chain, deficiency of OMIM ACADVL 92 NM_000018.3 100% VLCAD -
Revostmm Vol 10-4-2018 Ingles Maquetaciûn 1
108 ORIGINALS / Rev Osteoporos Metab Miner. 2018;10(4):108-18 Roca-Ayats N1, Falcó-Mascaró M1, García-Giralt N2, Cozar M1, Abril JF3, Quesada-Gómez JM4, Prieto-Alhambra D5,6, Nogués X2, Mellibovsky L2, Díez-Pérez A2, Grinberg D1, Balcells S1 1 Departamento de Genética, Microbiología y Estadística - Facultad de Biología - Universidad de Barcelona - Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER) - Instituto de Salud Carlos III (ISCIII) - Instituto de Biomedicina de la Universidad de Barcelona (IBUB) - Instituto de Investigación Sant Joan de Déu (IRSJD) - Barcelona (España) 2 Unidad de Investigación en Fisiopatología Ósea y Articular (URFOA); Instituto Hospital del Mar de Investigaciones Médicas (IMIM) - Parque de Salud Mar - Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES); Instituto de Salud Carlos III (ISCIII) - Barcelona (España) 3 Departamento de Genética, Microbiología y Estadística; Facultad de Biología; Universidad de Barcelona - Instituto de Biomedicina de la Universidad de Barcelona (IBUB) - Barcelona (España) 4 Unidad de Metabolismo Mineral; Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC); Hospital Universitario Reina Sofía - Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES); Instituto de Salud Carlos III (ISCIII) - Córdoba (España) 5 Grupo de Investigación en Enfermedades Prevalentes del Aparato Locomotor (GREMPAL) - Instituto de Investigación en Atención Primaria (IDIAP) Jordi Gol - Centro de Investigación -
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. -
ARP55242 P050) Data Sheet
COG4 antibody - middle region (ARP55242_P050) Data Sheet Product Number ARP55242_P050 Product Name COG4 antibody - middle region (ARP55242_P050) Size 50ug Gene Symbol COG4 Alias Symbols COD1; DKFZp586E1519; CDG2J Nucleotide Accession# NM_015386 Protein Size (# AA) 789 amino acids Molecular Weight 89kDa Subunit 4 Product Format Lyophilized powder NCBI Gene Id 25839 Host Rabbit Clonality Polyclonal Official Gene Full Name Component of oligomeric golgi complex 4 Gene Family COG This is a rabbit polyclonal antibody against COG4. It was validated on Western Blot using a cell lysate as a Description positive control. Aviva Systems Biology strives to provide antibodies covering each member of a whole protein family of your interest. We also use our best efforts to provide you antibodies recognize various epitopes of a target protein. For availability of antibody needed for your experiment, please inquire (). Peptide Sequence Synthetic peptide located within the following region: LFSQGIGGEQAQAKFDSCLSDLAAVSNKFRDLLQEGLTELNSTAIKPQVQ Target Reference Suzuki,Y., (2004) Genome Res. 14 (9), 1711-1718 Multiprotein complexes are key determinants of Golgi apparatus structure and its capacity for intracellular transport and glycoprotein modification. Several complexes have been identified, including the Golgi transport complex (GTC), the LDLC complex, which is involved in glycosylation reactions, and the SEC34 complex, which is involved in vesicular transport. These 3 complexes are identical and have been termed the conserved oligomeric Golgi (COG) complex, which includes COG4.Multiprotein complexes are key determinants of Golgi Description of Target apparatus structure and its capacity for intracellular transport and glycoprotein modification. Several complexes have been identified, including the Golgi transport complex (GTC), the LDLC complex, which is involved in glycosylation reactions, and the SEC34 complex, which is involved in vesicular transport.