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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. -
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. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
The Acute-Phase Protein Orosomucoid Regulates Food Intake and Energy Homeostasis Via Leptin Receptor Signaling Pathway
1630 Diabetes Volume 65, June 2016 Yang Sun,1 Yili Yang,2 Zhen Qin,1 Jinya Cai,3 Xiuming Guo,1 Yun Tang,3 Jingjing Wan,1 Ding-Feng Su,1 and Xia Liu1 The Acute-Phase Protein Orosomucoid Regulates Food Intake and Energy Homeostasis via Leptin Receptor Signaling Pathway Diabetes 2016;65:1630–1641 | DOI: 10.2337/db15-1193 The acute-phase protein orosomucoid (ORM) exhibits a intake and energy expenditure. Energy homeostasis in the variety of activities in vitro and in vivo, notably modulation body is maintained by the integrated actions of multiple of immunity and transportation of drugs. We found in this factors (1,2), including adipose hormones (such as leptin study that mice lacking ORM1 displayed aberrant energy and adiponectin), gastrointestinal hormones (such as in- homeostasis characterized by increased body weight and sulin, ghrelin, and cholecystokinin), and nutrient-related fat mass. Further investigation found that ORM, predom- signals (such as free fatty acids). In addition to acting on fi inantly ORM1, is signi cantly elevated in sera, liver, and peripheral tissues, these actions can also influence central – adipose tissues from the mice with high-fat diet (HFD) circuits in the hypothalamus, brainstem, and limbic system db/db induced obesity and mice that develop obesity to modulate food intake and energy expenditure (1,3). spontaneously due to mutation in the leptin receptor Notably, the adipose tissue–produced leptin is a major (LepR). Intravenous or intraperitoneal administration of regulator of fat, and the level of leptin in circulation is exogenous ORM decreased food intake in C57BL/6, HFD, proportional to body fat (4) and is a reflection of long- and leptin-deficient ob/ob mice, which was absent in db/db OBESITY STUDIES fi term nutrition status as well as acute energy balance. -
High-Throughput Bioinformatics Approaches to Understand Gene Expression Regulation in Head and Neck Tumors
High-throughput bioinformatics approaches to understand gene expression regulation in head and neck tumors by Yanxiao Zhang A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Bioinformatics) in The University of Michigan 2016 Doctoral Committee: Associate Professor Maureen A. Sartor, Chair Professor Thomas E. Carey Assistant Professor Hui Jiang Professor Ronald J. Koenig Associate Professor Laura M. Rozek Professor Kerby A. Shedden c Yanxiao Zhang 2016 All Rights Reserved I dedicate this thesis to my family. For their unfailing love, understanding and support. ii ACKNOWLEDGEMENTS I would like to express my gratitude to Dr. Maureen Sartor for her guidance in my research and career development. She is a great mentor. She patiently taught me when I started new in this field, granted me freedom to explore and helped me out when I got lost. Her dedication to work, enthusiasm in teaching, mentoring and communicating science have inspired me to feel the excite- ment of research beyond novel scientific discoveries. I’m also grateful to have an interdisciplinary committee. Their feedback on my research progress and presentation skills is very valuable. In particular, I would like to thank Dr. Thomas Carey and Dr. Laura Rozek for insightful discussions on the biology of head and neck cancers and human papillomavirus, Dr. Ronald Koenig for expert knowledge on thyroid cancers, Dr. Hui Jiang and Dr. Kerby Shedden for feedback on the statistics part of my thesis. I would like to thank all the past and current members of Sartor lab for making the lab such a lovely place to stay and work in. -
B Inhibition in a Mouse Model of Chronic Colitis1
The Journal of Immunology Differential Expression of Inflammatory and Fibrogenic Genes and Their Regulation by NF-B Inhibition in a Mouse Model of Chronic Colitis1 Feng Wu and Shukti Chakravarti2 Fibrosis is a major complication of chronic inflammation, as seen in Crohn’s disease and ulcerative colitis, two forms of inflam- matory bowel diseases. To elucidate inflammatory signals that regulate fibrosis, we investigated gene expression changes under- lying chronic inflammation and fibrosis in trinitrobenzene sulfonic acid-induced murine colitis. Six weekly 2,4,6-trinitrobenzene sulfonic acid enemas were given to establish colitis and temporal gene expression patterns were obtained at 6-, 8-, 10-, and 12-wk time points. The 6-wk point, TNBS-w6, was the active, chronic inflammatory stage of the model marked by macrophage, neu- trophil, and CD3؉ and CD4؉ T cell infiltrates in the colon, consistent with the idea that this model is T cell immune response driven. Proinflammatory genes Cxcl1, Ccl2, Il1b, Lcn2, Pla2g2a, Saa3, S100a9, Nos2, Reg2, and Reg3g, and profibrogenic extra- cellular matrix genes Col1a1, Col1a2, Col3a1, and Lum (lumican), encoding a collagen-associated proteoglycan, were up-regulated at the active/chronic inflammatory stages. Rectal administration of the NF-B p65 antisense oligonucleotide reduced but did not abrogate inflammation and fibrosis completely. The antisense oligonucleotide treatment reduced total NF-B by 60% and down- regulated most proinflammatory genes. However, Ccl2, a proinflammatory chemokine known to promote fibrosis, was not down- regulated. Among extracellular matrix gene expressions Lum was suppressed while Col1a1 and Col3a1 were not. Thus, effective treatment of fibrosis in inflammatory bowel disease may require early and complete blockade of NF-B with particular attention to specific proinflammatory and profibrogenic genes that remain active at low levels of NF-B. -
Alpha 1 Acid Glycoprotein (ORM1) (NM 000607) Human Untagged Clone Product Data
OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for SC119782 Alpha 1 Acid Glycoprotein (ORM1) (NM_000607) Human Untagged Clone Product data: Product Type: Expression Plasmids Product Name: Alpha 1 Acid Glycoprotein (ORM1) (NM_000607) Human Untagged Clone Tag: Tag Free Symbol: ORM1 Synonyms: AGP-A; AGP1; HEL-S-153w; ORM Vector: pCMV6-XL4 E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: None Fully Sequenced ORF: >OriGene ORF within SC119782 sequence for NM_000607 edited (data generated by NextGen Sequencing) ATGGCGCTGTCCTGGGTTCTTACAGTCCTGAGCCTCCTACCTCTGCTGGAAGCCCAGATC CCATTGTGTGCCAACCTAGTACCGGTGCCCATCACCAACGCCACCCTGGACCAGATCACT GGCAAGTGGTTTTATATCGCATCGGCCTTTCGAAACGAGGAGTACAATAAGTCGGTTCAG GAGATCCAAGCAACCTTCTTTTACTTCACCCCCAACAAGACAGAGGACACGATCTTTCTC AGAGAGTACCAGACCCGACAGGACCAGTGCATCTATAACACCACCTACCTGAATGTCCAG CGGGAAAATGGGACCATCTCCAGATACGTGGGAGGCCAAGAGCATTTCGCTCACTTGCTG ATCCTCAGGGACACCAAGACCTACATGCTTGCTTTTGACGTGAACGATGAGAAGAACTGG GGGCTGTCTGTCTATGCTGACAAGCCAGAGACGACCAAGGAGCAACTGGGAGAGTTCTAC GAAGCTCTCGACTGCTTGCGCATTCCCAAGTCAGATGTCGTGTACACCGATTGGAAAAAG GATAAGTGTGAGCCACTGGAGAAGCAGCACGAGAAGGAGAGGAAACAGGAGGAGGGGGAA TCCTAG Clone variation with respect to NM_000607.2 113 g=>a This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 3 Alpha 1 Acid Glycoprotein -
Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................ -
ORM1 Antibody / Orosomucoid 1 (R32415)
ORM1 Antibody / Orosomucoid 1 (R32415) Catalog No. Formulation Size R32415 0.5mg/ml if reconstituted with 0.2ml sterile DI water 100 ug Bulk quote request Availability 1-3 business days Species Reactivity Human, Mouse, Rat Format Antigen affinity purified Clonality Polyclonal (rabbit origin) Isotype Rabbit IgG Purity Antigen affinity Buffer Lyophilized from 1X PBS with 2.5% BSA and 0.025% sodium azide UniProt P02763 Localization Cytoplasmic Applications Western blot : 0.1-0.5ug/ml IHC (FFPE) : 0.5-1ug/ml ELISA : 0.1-0.5ug/ml (human protein tested); request BSA-free format for coating Limitations This ORM1 antibody is available for research use only. Western blot testing of 1) rat liver, 2) mouse liver and 3) human PANC lysate with ORM1 antibody. Expected molecular weight: 24/41-60 kDa (unmodified/glycosylated). IHC testing of FFPE human intestinal cancer tissue with ORM1 antibody. HIER: Boil the paraffin sections in pH 6, 10mM citrate buffer for 20 minutes and allow to cool prior to testing. Description Alpha-1-acid glycoprotein 1 is a protein that in humans is encoded by the ORM1 gene. The structural gene for orosomucoid (ORM1) is assigned to the end of the long arm of chromosome 9 by demonstration of linkage to ABO and AK1. This gene encodes a key acute phase plasma protein. Because of its increase due to acute inflammation, this protein is classified as an acute-phase reactant. The specific function of this protein has not yet been determined; however, it may be involved in aspects of immunosuppression. Application Notes Optimal dilution of the ORM1 antibody should be determined by the researcher. -
"The Genecards Suite: from Gene Data Mining to Disease Genome Sequence Analyses". In: Current Protocols in Bioinformat
The GeneCards Suite: From Gene Data UNIT 1.30 Mining to Disease Genome Sequence Analyses Gil Stelzer,1,5 Naomi Rosen,1,5 Inbar Plaschkes,1,2 Shahar Zimmerman,1 Michal Twik,1 Simon Fishilevich,1 Tsippi Iny Stein,1 Ron Nudel,1 Iris Lieder,2 Yaron Mazor,2 Sergey Kaplan,2 Dvir Dahary,2,4 David Warshawsky,3 Yaron Guan-Golan,3 Asher Kohn,3 Noa Rappaport,1 Marilyn Safran,1 and Doron Lancet1,6 1Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel 2LifeMap Sciences Ltd., Tel Aviv, Israel 3LifeMap Sciences Inc., Marshfield, Massachusetts 4Toldot Genetics Ltd., Hod Hasharon, Israel 5These authors contributed equally to the paper 6Corresponding author GeneCards, the human gene compendium, enables researchers to effectively navigate and inter-relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways. Our recently launched Version 4 has a revamped infrastructure facilitating faster data updates, better-targeted data queries, and friendlier user experience. It also provides a stronger foundation for the GeneCards suite of companion databases and analysis tools. Improved data unification includes gene-disease links via MalaCards and merged biological pathways via PathCards, as well as drug information and proteome expression. VarElect, another suite member, is a phenotype prioritizer for next-generation sequencing, leveraging the GeneCards and MalaCards knowledgebase. It au- tomatically infers direct and indirect scored associations between hundreds or even thousands of variant-containing genes and disease phenotype terms. Var- Elect’s capabilities, either independently or within TGex, our comprehensive variant analysis pipeline, help prepare for the challenge of clinical projects that involve thousands of exome/genome NGS analyses. -
Rna-Sequencing Applications: Gene Expression Quantification and Methylator Phenotype Identification
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 8-2013 RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION Guoshuai Cai Follow this and additional works at: https://digitalcommons.library.tmc.edu/utgsbs_dissertations Part of the Bioinformatics Commons, Computational Biology Commons, and the Medicine and Health Sciences Commons Recommended Citation Cai, Guoshuai, "RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION" (2013). The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences Dissertations and Theses (Open Access). 386. https://digitalcommons.library.tmc.edu/utgsbs_dissertations/386 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]. RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION -
Cep-2020-00633.Pdf
Clin Exp Pediatr Vol. 64, No. 5, 208–222, 2021 Review article CEP https://doi.org/10.3345/cep.2020.00633 Understanding the genetics of systemic lupus erythematosus using Bayesian statistics and gene network analysis Seoung Wan Nam, MD, PhD1,*, Kwang Seob Lee, MD2,*, Jae Won Yang, MD, PhD3,*, Younhee Ko, PhD4, Michael Eisenhut, MD, FRCP, FRCPCH, DTM&H5, Keum Hwa Lee, MD, MS6,7,8, Jae Il Shin, MD, PhD6,7,8, Andreas Kronbichler, MD, PhD9 1Department of Rheumatology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea; 2Severance Hospital, Yonsei University College of Medicine, Seoul, Korea; 3Department of Nephrology, Yonsei University Wonju College of Medicine, Wonju, Korea; 4Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea; 5Department of Pediatrics, Luton & Dunstable University Hospital NHS Foundation Trust, Luton, UK; 6Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea; 7Division of Pediatric Nephrology, Severance Children’s Hospital, Seoul, Korea; 8Institute of Kidney Disease Research, Yonsei University College of Medicine, Seoul, Korea; 9Department of Internal Medicine IV (Nephrology and Hypertension), Medical University Innsbruck, Innsbruck, Austria 1,3) The publication of genetic epidemiology meta-analyses has analyses have redundant duplicate topics and many errors. increased rapidly, but it has been suggested that many of the Although there has been an impressive increase in meta-analyses statistically significant results are false positive. In addition, from China, particularly those on genetic associa tions, most most such meta-analyses have been redundant, duplicate, and claimed candidate gene associations are likely false-positives, erroneous, leading to research waste. In addition, since most suggesting an urgent global need to incorporate genome-wide claimed candidate gene associations were false-positives, cor- data and state-of-the art statistical inferences to avoid a flood of rectly interpreting the published results is important.