Noelia Díaz Blanco
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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 ...................................................................................................................................................................................................... -
Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Multiple Activities of Arl1 Gtpase in the Trans-Golgi Network Chia-Jung Yu1,2 and Fang-Jen S
© 2017. Published by The Company of Biologists Ltd | Journal of Cell Science (2017) 130, 1691-1699 doi:10.1242/jcs.201319 COMMENTARY Multiple activities of Arl1 GTPase in the trans-Golgi network Chia-Jung Yu1,2 and Fang-Jen S. Lee3,4,* ABSTRACT typical features of an Arf-family GTPase, including an amphipathic ADP-ribosylation factors (Arfs) and ADP-ribosylation factor-like N-terminal helix and a consensus site for N-myristoylation (Lu et al., proteins (Arls) are highly conserved small GTPases that function 2001; Price et al., 2005). In yeast, recruitment of Arl1 to the Golgi as main regulators of vesicular trafficking and cytoskeletal complex requires a second Arf-like GTPase, Arl3 (Behnia et al., reorganization. Arl1, the first identified member of the large Arl family, 2004; Setty et al., 2003). Yeast Arl3 lacks a myristoylation site and is an important regulator of Golgi complex structure and function in is, instead, N-terminally acetylated; this modification is required for organisms ranging from yeast to mammals. Together with its effectors, its recruitment to the Golgi complex by Sys1. In mammalian cells, Arl1 has been shown to be involved in several cellular processes, ADP-ribosylation-factor-related protein 1 (Arfrp1), a mammalian including endosomal trans-Golgi network and secretory trafficking, lipid ortholog of yeast Arl3, plays a pivotal role in the recruitment of Arl1 droplet and salivary granule formation, innate immunity and neuronal to the trans-Golgi network (TGN) (Behnia et al., 2004; Panic et al., development, stress tolerance, as well as the response of the unfolded 2003b; Setty et al., 2003; Zahn et al., 2006). -
Manual Annotation and Analysis of the Defensin Gene Cluster in the C57BL
BMC Genomics BioMed Central Research article Open Access Manual annotation and analysis of the defensin gene cluster in the C57BL/6J mouse reference genome Clara Amid*†1, Linda M Rehaume*†2, Kelly L Brown2,3, James GR Gilbert1, Gordon Dougan1, Robert EW Hancock2 and Jennifer L Harrow1 Address: 1Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK, 2University of British Columbia, Centre for Microbial Disease & Immunity Research, 2259 Lower Mall, Vancouver, BC, V6T 1Z4, Canada and 3Department of Rheumatology and Inflammation Research, Göteborg University, Guldhedsgatan 10, S-413 46 Göteborg, Sweden Email: Clara Amid* - [email protected]; Linda M Rehaume* - [email protected]; Kelly L Brown - [email protected]; James GR Gilbert - [email protected]; Gordon Dougan - [email protected]; Robert EW Hancock - [email protected]; Jennifer L Harrow - [email protected] * Corresponding authors †Equal contributors Published: 15 December 2009 Received: 15 May 2009 Accepted: 15 December 2009 BMC Genomics 2009, 10:606 doi:10.1186/1471-2164-10-606 This article is available from: http://www.biomedcentral.com/1471-2164/10/606 © 2009 Amid et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Host defense peptides are a critical component of the innate immune system. Human alpha- and beta-defensin genes are subject to copy number variation (CNV) and historically the organization of mouse alpha-defensin genes has been poorly defined. -
Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. -
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. -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Bioinformatics Analyses of Genomic Imprinting
Bioinformatics Analyses of Genomic Imprinting Dissertation zur Erlangung des Grades des Doktors der Naturwissenschaften der Naturwissenschaftlich-Technischen Fakultät III Chemie, Pharmazie, Bio- und Werkstoffwissenschaften der Universität des Saarlandes von Barbara Hutter Saarbrücken 2009 Tag des Kolloquiums: 08.12.2009 Dekan: Prof. Dr.-Ing. Stefan Diebels Berichterstatter: Prof. Dr. Volkhard Helms Priv.-Doz. Dr. Martina Paulsen Vorsitz: Prof. Dr. Jörn Walter Akad. Mitarbeiter: Dr. Tihamér Geyer Table of contents Summary________________________________________________________________ I Zusammenfassung ________________________________________________________ I Acknowledgements _______________________________________________________II Abbreviations ___________________________________________________________ III Chapter 1 – Introduction __________________________________________________ 1 1.1 Important terms and concepts related to genomic imprinting __________________________ 2 1.2 CpG islands as regulatory elements ______________________________________________ 3 1.3 Differentially methylated regions and imprinting clusters_____________________________ 6 1.4 Reading the imprint __________________________________________________________ 8 1.5 Chromatin marks at imprinted regions___________________________________________ 10 1.6 Roles of repetitive elements ___________________________________________________ 12 1.7 Functional implications of imprinted genes _______________________________________ 14 1.8 Evolution and parental conflict ________________________________________________ -
Protein Interaction Between RNF181 and the Platelet Integrin, Αiibβ3 Seamus Allen Royal College of Surgeons in Ireland, [email protected]
Royal College of Surgeons in Ireland e-publications@RCSI PhD theses Theses and Dissertations 11-1-2015 Characterization of a protein: protein interaction between RNF181 and the platelet integrin, αIIbβ3 Seamus Allen Royal College of Surgeons in Ireland, [email protected] Citation Allen S. Characterization of a protein: protein interaction between RNF181 and the platelet integrin, αIIbβ3.[PhD Thesis]. Dublin: Royal College of Surgeons in Ireland; 2015. This Thesis is brought to you for free and open access by the Theses and Dissertations at e-publications@RCSI. It has been accepted for inclusion in PhD theses by an authorized administrator of e-publications@RCSI. For more information, please contact [email protected]. — Use Licence — Creative Commons Licence: This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License. This thesis is available at e-publications@RCSI: http://epubs.rcsi.ie/phdtheses/179 Characterization of a protein: protein interaction between RNF181 and the platelet integrin, αIIbβ3 Seamus T Allen BSc Platelet Biology Group, Department of Molecular and Cellular Therapeutics RCSI A Thesis submitted to the School of Postgraduate Studies, Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, in fulfillment of the degree of Doctor of Philosophy Supervisor: Professor Niamh Moran July 2015 Thesis Declaration I declare that this thesis, which I submit to RCSI for examination in consideration of the award of a higher degree of Doctor of Philosophy, is my own personal effort. Where any of the content presented is the result of input or data from a related collaborative research programme, this is duly acknowledged in the text such that it is possible to ascertain how much of the work is my own. -
Toxicogenomics Applications of New Functional Genomics Technologies in Toxicology
\-\w j Toxicogenomics Applications of new functional genomics technologies in toxicology Wilbert H.M. Heijne Proefschrift ter verkrijging vand egraa dva n doctor opgeza gva nd e rector magnificus vanWageninge n Universiteit, Prof.dr.ir. L. Speelman, in netopenbaa r te verdedigen op maandag6 decembe r200 4 des namiddagst e half twee ind eAul a - Table of contents Abstract Chapter I. page 1 General introduction [1] Chapter II page 21 Toxicogenomics of bromobenzene hepatotoxicity: a combined transcriptomics and proteomics approach[2] Chapter III page 48 Bromobenzene-induced hepatotoxicity atth etranscriptom e level PI Chapter IV page 67 Profiles of metabolites and gene expression in rats with chemically induced hepatic necrosis[4] Chapter V page 88 Liver gene expression profiles in relation to subacute toxicity in rats exposed to benzene[5] Chapter VI page 115 Toxicogenomics analysis of liver gene expression in relation to subacute toxicity in rats exposed totrichloroethylen e [6] Chapter VII page 135 Toxicogenomics analysis ofjoin t effects of benzene and trichloroethylene mixtures in rats m Chapter VII page 159 Discussion and conclusions References page 171 Appendices page 187 Samenvatting page 199 Dankwoord About the author Glossary Abbreviations List of genes Chapter I General introduction Parts of this introduction were publishedin : Molecular Biology in Medicinal Chemistry, Heijne etal., 2003 m NATO Advanced Research Workshop proceedings, Heijne eral., 2003 81 Chapter I 1. General introduction 1.1 Background /.1.1 Toxicologicalrisk -
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) -
Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected]
University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School July 2017 Role of Amylase in Ovarian Cancer Mai Mohamed University of South Florida, [email protected] Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the Pathology Commons Scholar Commons Citation Mohamed, Mai, "Role of Amylase in Ovarian Cancer" (2017). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/6907 This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Role of Amylase in Ovarian Cancer by Mai Mohamed A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Pathology and Cell Biology Morsani College of Medicine University of South Florida Major Professor: Patricia Kruk, Ph.D. Paula C. Bickford, Ph.D. Meera Nanjundan, Ph.D. Marzenna Wiranowska, Ph.D. Lauri Wright, Ph.D. Date of Approval: June 29, 2017 Keywords: ovarian cancer, amylase, computational analyses, glycocalyx, cellular invasion Copyright © 2017, Mai Mohamed Dedication This dissertation is dedicated to my parents, Ahmed and Fatma, who have always stressed the importance of education, and, throughout my education, have been my strongest source of encouragement and support. They always believed in me and I am eternally grateful to them. I would also like to thank my brothers, Mohamed and Hussien, and my sister, Mariam. I would also like to thank my husband, Ahmed.