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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. -
Investigating the Genetic Basis of Cisplatin-Induced Ototoxicity in Adult South African Patients
--------------------------------------------------------------------------- Investigating the genetic basis of cisplatin-induced ototoxicity in adult South African patients --------------------------------------------------------------------------- by Timothy Francis Spracklen SPRTIM002 SUBMITTED TO THE UNIVERSITY OF CAPE TOWN In fulfilment of the requirements for the degree MSc(Med) Faculty of Health Sciences UNIVERSITY OF CAPE TOWN University18 December of Cape 2015 Town Supervisor: Prof. Rajkumar S Ramesar Co-supervisor: Ms A Alvera Vorster Division of Human Genetics, Department of Pathology, University of Cape Town 1 The copyright of this thesis vests in the author. No quotation from it or information derived from it is to be published without full acknowledgement of the source. The thesis is to be used for private study or non- commercial research purposes only. Published by the University of Cape Town (UCT) in terms of the non-exclusive license granted to UCT by the author. University of Cape Town Declaration I, Timothy Spracklen, hereby declare that the work on which this dissertation/thesis is based is my original work (except where acknowledgements indicate otherwise) and that neither the whole work nor any part of it has been, is being, or is to be submitted for another degree in this or any other university. I empower the university to reproduce for the purpose of research either the whole or any portion of the contents in any manner whatsoever. Signature: Date: 18 December 2015 ' 2 Contents Abbreviations ………………………………………………………………………………….. 1 List of figures …………………………………………………………………………………... 6 List of tables ………………………………………………………………………………….... 7 Abstract ………………………………………………………………………………………… 10 1. Introduction …………………………………………………………………………………. 11 1.1 Cancer …………………………………………………………………………….. 11 1.2 Adverse drug reactions ………………………………………………………….. 12 1.3 Cisplatin …………………………………………………………………………… 12 1.3.1 Cisplatin’s mechanism of action ……………………………………………… 13 1.3.2 Adverse reactions to cisplatin therapy ………………………………………. -
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. -
Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen, -
Browsing Genes and Genomes with Ensembl
The Bioinformatics Roadshow Tórshavn, The Faroe Islands 28-29 November 2012 BROWSING GENES AND GENOMES WITH ENSEMBL EXERCISES AND ANSWERS 1 BROWSER 3 BIOMART 8 VARIATION 13 COMPARATIVE GENOMICS 18 2 Note: These exercises are based on Ensembl version 69 (October 2012). After in future a new version has gone live, version 69 will still be available at http://e69.ensembl.org/. If your answer doesn’t correspond with the given answer, please consult the instructor. ______________________________________________________________ BROWSER ______________________________________________________________ Exercise 1 – Exploring a gene (a) Find the human F9 (coagulation factor IX) gene. On which chromosome and which strand of the genome is this gene located? How many transcripts (splice variants) have been annotated for it? (b) What is the longest transcript? How long is the protein it encodes? Has this transcript been annotated automatically (by Ensembl) or manually (by Havana)? How many exons does it have? Are any of the exons completely or partially untranslated? (c) Have a look at the external references for ENST00000218099. What is the function of F9? (d) Is it possible to monitor expression of ENST00000218099 with the ILLUMINA HumanWG_6_V2 microarray? If so, can it also be used to monitor expression of the other two transcripts? (e) In which part (i.e. the N-terminal or C-terminal half) of the protein encoded by ENST00000218099 does its peptidase activity reside? (f) Have any missense variants been discovered for the protein encoded by ENST00000218099? (g) Is there a mouse orthologue predicted for the human F9 gene? (h) If you have yourself a gene of interest, explore what information Ensembl displays about it! ______________________________________________________________ Answer (a) 8 Go to the Ensembl homepage (http://www.ensembl.org/). -
Nucleolin and Its Role in Ribosomal Biogenesis
NUCLEOLIN: A NUCLEOLAR RNA-BINDING PROTEIN INVOLVED IN RIBOSOME BIOGENESIS Inaugural-Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf vorgelegt von Julia Fremerey aus Hamburg Düsseldorf, April 2016 2 Gedruckt mit der Genehmigung der Mathematisch-Naturwissenschaftlichen Fakultät der Heinrich-Heine-Universität Düsseldorf Referent: Prof. Dr. A. Borkhardt Korreferent: Prof. Dr. H. Schwender Tag der mündlichen Prüfung: 20.07.2016 3 Die vorgelegte Arbeit wurde von Juli 2012 bis März 2016 in der Klinik für Kinder- Onkologie, -Hämatologie und Klinische Immunologie des Universitätsklinikums Düsseldorf unter Anleitung von Prof. Dr. A. Borkhardt und in Kooperation mit dem ‚Laboratory of RNA Molecular Biology‘ an der Rockefeller Universität unter Anleitung von Prof. Dr. T. Tuschl angefertigt. 4 Dedicated to my family TABLE OF CONTENTS 5 TABLE OF CONTENTS TABLE OF CONTENTS ............................................................................................... 5 LIST OF FIGURES ......................................................................................................10 LIST OF TABLES .......................................................................................................12 ABBREVIATION .........................................................................................................13 ABSTRACT ................................................................................................................19 ZUSAMMENFASSUNG -
Large Meta-Analysis of Genome-Wide Association Studies
medRxiv preprint doi: https://doi.org/10.1101/2020.10.01.20200659; this version posted October 4, 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. It is made available under a CC-BY-NC-ND 4.0 International license . Large meta-analysis of genome-wide association studies expands knowledge of the genetic etiology of Alzheimer’s disease and highlights potential translational opportunities Céline Bellenguez1,*,#, Fahri Küçükali2,3,4*, Iris Jansen5,6*, Victor Andrade7,8*, Sonia Morenau- Grau9,10,*, Najaf Amin11,12, Benjamin Grenier-Boley1, Anne Boland13, Luca Kleineidam7,8, Peter Holmans14, Pablo Garcia9,10, Rafael Campos Martin7, Adam Naj15,16, Yang Qiong17, Joshua C. Bis18, Vincent Damotte1, Sven Van der Lee5,6,19, Marcos Costa1, Julien Chapuis1, Vilmentas Giedraitis20, María Jesús Bullido10,21, Adolfo López de Munáin10,22, Jordi Pérez- Tur10,23, Pascual Sánchez-Juan10,24, Raquel Sánchez-Valle25, Victoria Álvarez26, Pau Pastor27, Miguel Medina10,28, Jasper Van Dongen2,3,4, Christine Van Broeckhoven2,3,4, Rik Vandenberghe29,30, Sebastiaan Engelborghs31,32, Gael Nicolas33, Florence Pasquier34, Olivier Hanon35, Carole Dufouil36, Claudine Berr37, Stéphanie Debette36, Jean-François Dartigues36, Gianfranco Spalletta38, Benedetta Nacmias39,40, Vincenzo Solfrezzi41, Barbara Borroni42, Lucio Tremolizzo43, Davide Seripa44, Paolo Caffarra45, Antonio Daniele46,47, Daniela Galimberti48,49, Innocenzo Rainero50, Luisa Benussi51, Alesio Squassina52, Patrizia Mecoci53, Lucilla Parnetti54, Carlo Masullo55, Beatrice Arosio56, John Hardy57, Simon Mead58, Kevin Morgan59, Clive Holmes60, Patrick Kehoe61, Bob Woods62, EADB, Charge, ADGC, Jin Sha15,16, Yi Zhao15,63, Chien-Yueh Lee15,63, Pavel P. -
Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase -
Onderstaande Coverage Is Berekend Over 1000 Exomen, Welke Geprept Zijn Met De Agilent Sureselect XT Exome V6 Kit
Onderstaande coverage is berekend over 1000 exomen, welke geprept zijn met de Agilent SureSelect XT exome v6 kit. Het sequencen is uitgevoerd op een Illumina NextSeq500 met een gemiddelde coverage van 100X , dekking 20x >95% over het gehele exoom. Gemiddelde Gen Coverage 20x A1BG 124 89,46 A1CF 123 98,03 A2ML1 120 98,19 A2M 114 95,38 A3GALT2 123 97,65 A4GALT 222 98,21 A4GNT 153 98,21 AAAS 147 98,21 AACS 139 98,20 AADACL2 118 97,73 AADACL3 139 98,21 AADACL4 156 98,21 AADAC 118 98,05 AADAT 107 89,53 AAED1 67 83,20 AAGAB 106 97,95 AAK1 115 97,79 AAMDC 101 88,39 AAMP 117 98,14 AANAT 124 98,16 AAR2 103 76,39 AARD 82 98,61 AARS2 133 98,12 AARSD1 105 84,15 AARS 123 98,17 AASDHPPT 127 97,08 AASDH 98 97,40 AASS 102 97,35 AATF 139 97,98 AATK 115 96,20 ABAT 111 94,77 ABCA1 124 97,79 ABCA2 152 97,17 ABCA3 129 98,12 ABCA4 126 98,14 ABCA5 59 88,83 ABCA6 88 95,52 ABCA7 163 98,07 ABCA8 102 96,10 ABCA9 109 97,71 ABCA10 74 85,59 ABCA12 116 97,77 ABCA13 129 96,43 ABCB1 114 97,45 ABCB4 96 96,93 ABCB5 105 97,75 ABCB6 140 98,21 ABCB7 126 99,13 ABCB8 140 98,05 ABCB9 139 98,13 ABCB10 85 89,17 ABCB11 118 97,74 ABCC1 123 92,71 ABCC2 127 98,16 ABCC3 147 97,94 ABCC4 112 96,52 ABCC5 121 92,63 ABCC6 115 91,98 ABCC8 129 98,17 ABCC9 108 97,76 ABCC10 144 97,99 ABCC11 126 98,16 ABCC12 134 98,20 ABCD1 113 72,00 ABCD2 96 97,46 ABCD3 90 91,11 ABCD4 118 98,16 ABCE1 63 86,86 ABCF1 116 98,04 Pagina 1 van 295 Onderstaande coverage is berekend over 1000 exomen, welke geprept zijn met de Agilent SureSelect XT exome v6 kit. -
The Tumor Suppressor Notch Inhibits Head and Neck Squamous Cell
The Texas Medical Center Library DigitalCommons@TMC The University of Texas MD Anderson Cancer Center UTHealth Graduate School of The University of Texas MD Anderson Cancer Biomedical Sciences Dissertations and Theses Center UTHealth Graduate School of (Open Access) Biomedical Sciences 12-2015 THE TUMOR SUPPRESSOR NOTCH INHIBITS HEAD AND NECK SQUAMOUS CELL CARCINOMA (HNSCC) TUMOR GROWTH AND PROGRESSION BY MODULATING PROTO-ONCOGENES AXL AND CTNNAL1 (α-CATULIN) Shhyam Moorthy Shhyam Moorthy Follow this and additional works at: https://digitalcommons.library.tmc.edu/utgsbs_dissertations Part of the Biochemistry, Biophysics, and Structural Biology Commons, Cancer Biology Commons, Cell Biology Commons, and the Medicine and Health Sciences Commons Recommended Citation Moorthy, Shhyam and Moorthy, Shhyam, "THE TUMOR SUPPRESSOR NOTCH INHIBITS HEAD AND NECK SQUAMOUS CELL CARCINOMA (HNSCC) TUMOR GROWTH AND PROGRESSION BY MODULATING PROTO-ONCOGENES AXL AND CTNNAL1 (α-CATULIN)" (2015). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 638. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/638 This Dissertation (PhD) is brought to you for free and open access by the The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences at DigitalCommons@TMC. It has been accepted for inclusion in The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access) by an authorized administrator of DigitalCommons@TMC. For more information, please contact [email protected]. THE TUMOR SUPPRESSOR NOTCH INHIBITS HEAD AND NECK SQUAMOUS CELL CARCINOMA (HNSCC) TUMOR GROWTH AND PROGRESSION BY MODULATING PROTO-ONCOGENES AXL AND CTNNAL1 (α-CATULIN) by Shhyam Moorthy, B.S. -
OR2AJ1 (P-13): Sc-104521
SAN TA C RUZ BI OTEC HNOL OG Y, INC . OR2AJ1 (P-13): sc-104521 BACKGROUND APPLICATIONS Olfactory receptors are G protein-coupled receptors that localize to the cilia OR2AJ1 (P-13) is recommended for detection of OR2AJ1 of human origin of olfactory sensory neurons where they display affinity for and bind to a by Western Blotting (starting dilution 1:200, dilution range 1:100-1:1000), variety of odor molecules. The genes encoding olfactory receptors comprise immunofluorescence (starting dilution 1:50, dilution range 1:50-1:500) and the largest family in the human genome. The binding of olfactory receptor solid phase ELISA (starting dilution 1:30, dilution range 1:30-1:3000); may proteins to odor molecules triggers a signal transduction that propagates cross-react with OR2T27. nerve impulses throughout the body, ultimately leading to transmission of the OR2AJ1 (P-13) is also recommended for detection of OR2AJ1 in additional signal to the brain and the subsequent perception of smell. OR2AJ1 (olfac - species, including equine, canine, bovine and porcine. tory receptor 2AJ1) is a 328 amino acid protein. The gene encoding OR2AJ1 maps to human chromosome 1. RECOMMENDED SECONDARY REAGENTS REFERENCES To ensure optimal results, the following support (secondary) reagents are recommended: 1) Western Blotting: use donkey anti-goat IgG-HRP: sc-2020 1. Malnic, B., Hirono, J., Sato, T. and Buck, L.B. 1999. Combinatorial receptor (dilution range: 1:2000-1:100,000) or Cruz Marker™ compatible donkey codes for odors. Cell 96: 713-723. anti- goat IgG-HRP: sc-2033 (dilution range: 1:2000-1:5000), Cruz Marker™ 2. -
An Evolutionary Based Strategy for Predicting Rational Mutations in G Protein-Coupled Receptors
Ecology and Evolutionary Biology 2021; 6(3): 53-77 http://www.sciencepublishinggroup.com/j/eeb doi: 10.11648/j.eeb.20210603.11 ISSN: 2575-3789 (Print); ISSN: 2575-3762 (Online) An Evolutionary Based Strategy for Predicting Rational Mutations in G Protein-Coupled Receptors Miguel Angel Fuertes*, Carlos Alonso Department of Microbiology, Centre for Molecular Biology “Severo Ochoa”, Spanish National Research Council and Autonomous University, Madrid, Spain Email address: *Corresponding author To cite this article: Miguel Angel Fuertes, Carlos Alonso. An Evolutionary Based Strategy for Predicting Rational Mutations in G Protein-Coupled Receptors. Ecology and Evolutionary Biology. Vol. 6, No. 3, 2021, pp. 53-77. doi: 10.11648/j.eeb.20210603.11 Received: April 24, 2021; Accepted: May 11, 2021; Published: July 13, 2021 Abstract: Capturing conserved patterns in genes and proteins is important for inferring phenotype prediction and evolutionary analysis. The study is focused on the conserved patterns of the G protein-coupled receptors, an important superfamily of receptors. Olfactory receptors represent more than 2% of our genome and constitute the largest family of G protein-coupled receptors, a key class of drug targets. As no crystallographic structures are available, mechanistic studies rely on the use of molecular dynamic modelling combined with site-directed mutagenesis data. In this paper, we hypothesized that human-mouse orthologs coding for G protein-coupled receptors maintain, at speciation events, shared compositional structures independent, to some extent, of their percent identity as reveals a method based in the categorization of nucleotide triplets by their gross composition. The data support the consistency of the hypothesis, showing in ortholog G protein-coupled receptors the presence of emergent shared compositional structures preserved at speciation events.