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Biobin User Guide Current Version: Biobin 2.0
___________________________________ BioBin User Guide Current version: BioBin 2.0 October 2012 Last modified: October 2012 Ritchie Lab, Center for Systems Genomics Pennsylvania State University, University Park, PA 16802 URL: https://ritchielab.psu.edu/ritchielab/software/ Email: [email protected] Table of Contents Overview ............................................................................................................................................................................... 3 BioBin ................................................................................................................................................................................................................ 3 Library of Knowledge Integration (LOKI) Database .................................................................................................................... 3 Quick Reference ............................................................................................................................................................................................ 5 Installation ............................................................................................................................................................................ 6 Prerequisites .................................................................................................................................................................................................. 6 Unpacking ...................................................................................................................................................................................................... -
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
Noelia Díaz Blanco
Effects of environmental factors on the gonadal transcriptome of European sea bass (Dicentrarchus labrax), juvenile growth and sex ratios Noelia Díaz Blanco Ph.D. thesis 2014 Submitted in partial fulfillment of the requirements for the Ph.D. degree from the Universitat Pompeu Fabra (UPF). This work has been carried out at the Group of Biology of Reproduction (GBR), at the Department of Renewable Marine Resources of the Institute of Marine Sciences (ICM-CSIC). Thesis supervisor: Dr. Francesc Piferrer Professor d’Investigació Institut de Ciències del Mar (ICM-CSIC) i ii A mis padres A Xavi iii iv Acknowledgements This thesis has been made possible by the support of many people who in one way or another, many times unknowingly, gave me the strength to overcome this "long and winding road". First of all, I would like to thank my supervisor, Dr. Francesc Piferrer, for his patience, guidance and wise advice throughout all this Ph.D. experience. But above all, for the trust he placed on me almost seven years ago when he offered me the opportunity to be part of his team. Thanks also for teaching me how to question always everything, for sharing with me your enthusiasm for science and for giving me the opportunity of learning from you by participating in many projects, collaborations and scientific meetings. I am also thankful to my colleagues (former and present Group of Biology of Reproduction members) for your support and encouragement throughout this journey. To the “exGBRs”, thanks for helping me with my first steps into this world. Working as an undergrad with you Dr. -
Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. -
The Landscape of Human Mutually Exclusive Splicing
bioRxiv preprint doi: https://doi.org/10.1101/133215; this version posted May 2, 2017. 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. The landscape of human mutually exclusive splicing Klas Hatje1,2,#,*, Ramon O. Vidal2,*, Raza-Ur Rahman2, Dominic Simm1,3, Björn Hammesfahr1,$, Orr Shomroni2, Stefan Bonn2§ & Martin Kollmar1§ 1 Group of Systems Biology of Motor Proteins, Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Göttingen, Germany 2 Group of Computational Systems Biology, German Center for Neurodegenerative Diseases, Göttingen, Germany 3 Theoretical Computer Science and Algorithmic Methods, Institute of Computer Science, Georg-August-University Göttingen, Germany § Corresponding authors # Current address: Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland $ Current address: Research and Development - Data Management (RD-DM), KWS SAAT SE, Einbeck, Germany * These authors contributed equally E-mail addresses: KH: [email protected], RV: [email protected], RR: [email protected], DS: [email protected], BH: [email protected], OS: [email protected], SB: [email protected], MK: [email protected] - 1 - bioRxiv preprint doi: https://doi.org/10.1101/133215; this version posted May 2, 2017. 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. -
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. -
Development of Novel Analysis and Data Integration Systems to Understand Human Gene Regulation
Development of novel analysis and data integration systems to understand human gene regulation Dissertation zur Erlangung des Doktorgrades Dr. rer. nat. der Fakult¨atf¨urMathematik und Informatik der Georg-August-Universit¨atG¨ottingen im PhD Programme in Computer Science (PCS) der Georg-August University School of Science (GAUSS) vorgelegt von Raza-Ur Rahman aus Pakistan G¨ottingen,April 2018 Prof. Dr. Stefan Bonn, Zentrum f¨urMolekulare Neurobiologie (ZMNH), Betreuungsausschuss: Institut f¨urMedizinische Systembiologie, Hamburg Prof. Dr. Tim Beißbarth, Institut f¨urMedizinische Statistik, Universit¨atsmedizin, Georg-August Universit¨at,G¨ottingen Prof. Dr. Burkhard Morgenstern, Institut f¨urMikrobiologie und Genetik Abtl. Bioinformatik, Georg-August Universit¨at,G¨ottingen Pr¨ufungskommission: Prof. Dr. Stefan Bonn, Zentrum f¨urMolekulare Neurobiologie (ZMNH), Referent: Institut f¨urMedizinische Systembiologie, Hamburg Prof. Dr. Tim Beißbarth, Institut f¨urMedizinische Statistik, Universit¨atsmedizin, Korreferent: Georg-August Universit¨at,G¨ottingen Prof. Dr. Burkhard Morgenstern, Weitere Mitglieder Institut f¨urMikrobiologie und Genetik Abtl. Bioinformatik, der Pr¨ufungskommission: Georg-August Universit¨at,G¨ottingen Prof. Dr. Carsten Damm, Institut f¨urInformatik, Georg-August Universit¨at,G¨ottingen Prof. Dr. Florentin W¨org¨otter, Physikalisches Institut Biophysik, Georg-August-Universit¨at,G¨ottingen Prof. Dr. Stephan Waack, Institut f¨urInformatik, Georg-August Universit¨at,G¨ottingen Tag der m¨undlichen Pr¨ufung: der 30. M¨arz2018 -
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BASIC RESEARCH www.jasn.org Phosphoproteomic Analysis Reveals Regulatory Mechanisms at the Kidney Filtration Barrier †‡ †| Markus M. Rinschen,* Xiongwu Wu,§ Tim König, Trairak Pisitkun,¶ Henning Hagmann,* † † † Caroline Pahmeyer,* Tobias Lamkemeyer, Priyanka Kohli,* Nicole Schnell, †‡ †† ‡‡ Bernhard Schermer,* Stuart Dryer,** Bernard R. Brooks,§ Pedro Beltrao, †‡ Marcus Krueger,§§ Paul T. Brinkkoetter,* and Thomas Benzing* *Department of Internal Medicine II, Center for Molecular Medicine, †Cologne Excellence Cluster on Cellular Stress | Responses in Aging-Associated Diseases, ‡Systems Biology of Ageing Cologne, Institute for Genetics, University of Cologne, Cologne, Germany; §Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland; ¶Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; **Department of Biology and Biochemistry, University of Houston, Houston, Texas; ††Division of Nephrology, Baylor College of Medicine, Houston, Texas; ‡‡European Molecular Biology Laboratory–European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom; and §§Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany ABSTRACT Diseases of the kidney filtration barrier are a leading cause of ESRD. Most disorders affect the podocytes, polarized cells with a limited capacity for self-renewal that require tightly controlled signaling to maintain their integrity, viability, and function. Here, we provide an atlas of in vivo phosphorylated, glomerulus- expressed -
A Missense KCNQ1 Mutation Impairs Insulin Secretion in Neonatal Diabetes
bioRxiv preprint doi: https://doi.org/10.1101/2021.08.24.457485; this version posted August 26, 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-NC-ND 4.0 International license. A missense KCNQ1 Mutation Impairs Insulin Secretion in Neonatal Diabetes Zhimin Zhou1#, Maolian Gong1#, Amit Pande1, Ulrike Lisewski2, Torsten Röpke2, Bettina Purfürst1, Lei liang3, Shiqi Jia4, Sebastian Frühler1, Anca Margineanu1, Chun Zeng5,6, Han Zhu5,6, Peter Kühnen9, Semik Khodaverdi7, Winfried Krill7, Wei Chen8, Maike Sander5,6,*, Klemens Raile9,*, Zsuzsanna Izsvak1,* 1Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany. 2Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, 13125 Berlin, Germany. 3Department of Pediatrics, Anhui Provincial Children’s Hospital, 23000 Hefei, China 4The First Affiliated Hospital of Jinan University, 510000 Guangzhou, China. 5Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA. 6Department of Pediatrics, University of California San Diego, La Jolla, CA 92037, USA. 7Department of Pediatrics, Klinikum Hanau, 63450 Hanau, Germany. 8Department of Biology, Southern University of Science and Technology, 518000 Shenzhen, China. 9Charité, Universitätsmedizin Berlin, Virchow-Klinikum, 13125 Berlin, Germany #These authors contributed equally to this work. * Correspondence e-mail: [email protected] (Zsuzsanna Izsvak); [email protected] (Klemens Raile); [email protected] (Maike Sander). bioRxiv preprint doi: https://doi.org/10.1101/2021.08.24.457485; this version posted August 26, 2021. -
A Framework to Identify Contributing Genes in Patients with Phelan-Mcdermid Syndrome
www.nature.com/npjgenmed Corrected: Author Correction ARTICLE OPEN A framework to identify contributing genes in patients with Phelan-McDermid syndrome Anne-Claude Tabet 1,2,3,4, Thomas Rolland2,3,4, Marie Ducloy2,3,4, Jonathan Lévy1, Julien Buratti2,3,4, Alexandre Mathieu2,3,4, Damien Haye1, Laurence Perrin1, Céline Dupont 1, Sandrine Passemard1, Yline Capri1, Alain Verloes1, Séverine Drunat1, Boris Keren5, Cyril Mignot6, Isabelle Marey7, Aurélia Jacquette7, Sandra Whalen7, Eva Pipiras8, Brigitte Benzacken8, Sandra Chantot-Bastaraud9, Alexandra Afenjar10, Delphine Héron10, Cédric Le Caignec11, Claire Beneteau11, Olivier Pichon11, Bertrand Isidor11, Albert David11, Laila El Khattabi12, Stephan Kemeny13, Laetitia Gouas13, Philippe Vago13, Anne-Laure Mosca-Boidron14, Laurence Faivre15, Chantal Missirian16, Nicole Philip16, Damien Sanlaville17, Patrick Edery18, Véronique Satre19, Charles Coutton19, Françoise Devillard19, Klaus Dieterich20, Marie-Laure Vuillaume21, Caroline Rooryck21, Didier Lacombe21, Lucile Pinson22, Vincent Gatinois22, Jacques Puechberty22, Jean Chiesa23, James Lespinasse24, Christèle Dubourg25, Chloé Quelin25, Mélanie Fradin25, Hubert Journel26, Annick Toutain27, Dominique Martin28, Abdelamdjid Benmansour1, Claire S. Leblond2,3,4, Roberto Toro2,3,4, Frédérique Amsellem29, Richard Delorme2,3,4,29 and Thomas Bourgeron2,3,4 Phelan-McDermid syndrome (PMS) is characterized by a variety of clinical symptoms with heterogeneous degrees of severity, including intellectual disability (ID), absent or delayed speech, and autism spectrum disorders (ASD). It results from a deletion of the distal part of chromosome 22q13 that in most cases includes the SHANK3 gene. SHANK3 is considered a major gene for PMS, but the factors that modulate the severity of the syndrome remain largely unknown. In this study, we investigated 85 patients with different 22q13 rearrangements (78 deletions and 7 duplications). -
Product Size GOT1 P00504 F CAAGCTGT
Table S1. List of primer sequences for RT-qPCR. Gene Product Uniprot ID F/R Sequence(5’-3’) name size GOT1 P00504 F CAAGCTGTCAAGCTGCTGTC 71 R CGTGGAGGAAAGCTAGCAAC OGDHL E1BTL0 F CCCTTCTCACTTGGAAGCAG 81 R CCTGCAGTATCCCCTCGATA UGT2A1 F1NMB3 F GGAGCAAAGCACTTGAGACC 93 R GGCTGCACAGATGAACAAGA GART P21872 F GGAGATGGCTCGGACATTTA 90 R TTCTGCACATCCTTGAGCAC GSTT1L E1BUB6 F GTGCTACCGAGGAGCTGAAC 105 R CTACGAGGTCTGCCAAGGAG IARS Q5ZKA2 F GACAGGTTTCCTGGCATTGT 148 R GGGCTTGATGAACAACACCT RARS Q5ZM11 F TCATTGCTCACCTGCAAGAC 146 R CAGCACCACACATTGGTAGG GSS F1NLE4 F ACTGGATGTGGGTGAAGAGG 89 R CTCCTTCTCGCTGTGGTTTC CYP2D6 F1NJG4 F AGGAGAAAGGAGGCAGAAGC 113 R TGTTGCTCCAAGATGACAGC GAPDH P00356 F GACGTGCAGCAGGAACACTA 112 R CTTGGACTTTGCCAGAGAGG Table S2. List of differentially expressed proteins during chronic heat stress. score name Description MW PI CC CH Down regulated by chronic heat stress A2M Uncharacterized protein 158 1 0.35 6.62 A2ML4 Uncharacterized protein 163 1 0.09 6.37 ABCA8 Uncharacterized protein 185 1 0.43 7.08 ABCB1 Uncharacterized protein 152 1 0.47 8.43 ACOX2 Cluster of Acyl-coenzyme A oxidase 75 1 0.21 8 ACTN1 Alpha-actinin-1 102 1 0.37 5.55 ALDOC Cluster of Fructose-bisphosphate aldolase 39 1 0.5 6.64 AMDHD1 Cluster of Uncharacterized protein 37 1 0.04 6.76 AMT Aminomethyltransferase, mitochondrial 42 1 0.29 9.14 AP1B1 AP complex subunit beta 103 1 0.15 5.16 APOA1BP NAD(P)H-hydrate epimerase 32 1 0.4 8.62 ARPC1A Actin-related protein 2/3 complex subunit 42 1 0.34 8.31 ASS1 Argininosuccinate synthase 47 1 0.04 6.67 ATP2A2 Cluster of Calcium-transporting -
Genome-Wide Meta-Analysis Identifies New Susceptibility Loci for Migraine
View metadata, citation and similar papers at core.ac.uk brought to you by CORE Europe PMC Funders Group provided by St George's Online Research Archive Author Manuscript Nat Genet. Author manuscript; available in PMC 2014 June 02. Published in final edited form as: Nat Genet. 2013 August ; 45(8): 912–917. doi:10.1038/ng.2676. Europe PMC Funders Author Manuscripts Genome-wide meta-analysis identifies new susceptibility loci for migraine A full list of authors and affiliations appears at the end of the article. # These authors contributed equally to this work. Migraine is the most common brain disorder, affecting approximately 14% of the adult population, but its molecular mechanisms are poorly understood. We report the results of a meta-analysis across 29 genome-wide association studies, including a total of 23 285 migraine cases and 95 425 population-matched controls. We identified 12 loci associated with migraine susceptibility (P < 5 × 10−8). Five loci are new (near AJAP1 on 1p36, near TSPAN2 on 1p13, within FHL5 on 6q16, within c7orf10 on 7p14, and near MMP16 on 8q21). Three of these loci were identified in disease subgroup analyses. Brain tissue eQTL analysis suggests potential functional candidate genes at four loci: APOA1BP, TBC1D7, FUT9, STAT6, and ATP5B. Recently, significant progress has been made in the identification of common genetic variants associated with migraine susceptibility through genome-wide association (GWA) studies of clinic-based migraine with aura (MA) patients1, migraineurs from the general population2,3, and clinic-based migraine without aura (MO) patients4. To further elucidate Europe PMC Funders Author Manuscripts the genetic susceptibility of migraine, we performed a meta-analysis of 23 285 individuals with migraine from 29 clinic- and population-based studies (Fig.