Supplemental Data Inter-Individual Variability in Gene Expression
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Browsing Genes and Genomes with Ensembl
The Bioinformatics Roadshow Copenhagen, Denmark 16 & 17 June 2011 BROWSING GENES AND GENOMES WITH ENSEMBL EXERCISES AND ANSWERS Note: These exercises are based on Ensembl version 62 (13 April 2011). After in future a new version has gone live, version 62 will still be available at http://e62.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? 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 CodeLink 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, responsible for the cleavage of factor X to yield its active form factor Xa, reside? (f) How many non-synonymous variants have been discovered for the protein encoded by ENST00000218099? (g) Is there a mouse ortholog predicted for the human F9 gene? (h) On which cytogenetic band and on which contig is the F9 gene located? Is there a BAC clone that contains the complete F9 gene? (i) If you have yourself a gene of interest, explore what information Ensembl displays about it! ______________________________________________________________ Answer (a) Go to the Ensembl homepage (http://www.ensembl.org). -
Analysis of Trans Esnps Infers Regulatory Network Architecture
Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Anat Kreimer All rights reserved ABSTRACT Analysis of trans eSNPs infers regulatory network architecture Anat Kreimer eSNPs are genetic variants associated with transcript expression levels. The characteristics of such variants highlight their importance and present a unique opportunity for studying gene regulation. eSNPs affect most genes and their cell type specificity can shed light on different processes that are activated in each cell. They can identify functional variants by connecting SNPs that are implicated in disease to a molecular mechanism. Examining eSNPs that are associated with distal genes can provide insights regarding the inference of regulatory networks but also presents challenges due to the high statistical burden of multiple testing. Such association studies allow: simultaneous investigation of many gene expression phenotypes without assuming any prior knowledge and identification of unknown regulators of gene expression while uncovering directionality. This thesis will focus on such distal eSNPs to map regulatory interactions between different loci and expose the architecture of the regulatory network defined by such interactions. We develop novel computational approaches and apply them to genetics-genomics data in human. We go beyond pairwise interactions to define network motifs, including regulatory modules and bi-fan structures, showing them to be prevalent in real data and exposing distinct attributes of such arrangements. We project eSNP associations onto a protein-protein interaction network to expose topological properties of eSNPs and their targets and highlight different modes of distal regulation. -
Dissecting the Genetic Relationship Between Cardiovascular Risk Factors and Alzheimer's Disease
UC San Diego UC San Diego Previously Published Works Title Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer's disease. Permalink https://escholarship.org/uc/item/7137q6g1 Journal Acta neuropathologica, 137(2) ISSN 0001-6322 Authors Broce, Iris J Tan, Chin Hong Fan, Chun Chieh et al. Publication Date 2019-02-01 DOI 10.1007/s00401-018-1928-6 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Acta Neuropathologica https://doi.org/10.1007/s00401-018-1928-6 ORIGINAL PAPER Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease Iris J. Broce1 · Chin Hong Tan1,2 · Chun Chieh Fan3 · Iris Jansen4 · Jeanne E. Savage4 · Aree Witoelar5 · Natalie Wen6 · Christopher P. Hess1 · William P. Dillon1 · Christine M. Glastonbury1 · Maria Glymour7 · Jennifer S. Yokoyama8 · Fanny M. Elahi8 · Gil D. Rabinovici8 · Bruce L. Miller8 · Elizabeth C. Mormino9 · Reisa A. Sperling10,11 · David A. Bennett12 · Linda K. McEvoy13 · James B. Brewer13,14,15 · Howard H. Feldman14 · Bradley T. Hyman10 · Margaret Pericak‑Vance16 · Jonathan L. Haines17,18 · Lindsay A. Farrer19,20,21,22,23 · Richard Mayeux24,25,26 · Gerard D. Schellenberg27 · Kristine Yafe7,8,28 · Leo P. Sugrue1 · Anders M. Dale3,13,14 · Danielle Posthuma4 · Ole A. Andreassen5 · Celeste M. Karch6 · Rahul S. Desikan1 Received: 22 September 2018 / Revised: 28 October 2018 / Accepted: 28 October 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Cardiovascular (CV)- and lifestyle-associated risk factors (RFs) are increasingly recognized as important for Alzheimer’s disease (AD) pathogenesis. Beyond the ε4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV-associated genes also increase risk for AD. -
Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model
Downloaded from http://www.jimmunol.org/ by guest on September 25, 2021 T + is online at: average * The Journal of Immunology , 34 of which you can access for free at: 2016; 197:1477-1488; Prepublished online 1 July from submission to initial decision 4 weeks from acceptance to publication 2016; doi: 10.4049/jimmunol.1600589 http://www.jimmunol.org/content/197/4/1477 Molecular Profile of Tumor-Specific CD8 Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. Waugh, Sonia M. Leach, Brandon L. Moore, Tullia C. Bruno, Jonathan D. Buhrman and Jill E. Slansky J Immunol cites 95 articles Submit online. Every submission reviewed by practicing scientists ? is published twice each month by Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts http://jimmunol.org/subscription Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html http://www.jimmunol.org/content/suppl/2016/07/01/jimmunol.160058 9.DCSupplemental This article http://www.jimmunol.org/content/197/4/1477.full#ref-list-1 Information about subscribing to The JI No Triage! Fast Publication! Rapid Reviews! 30 days* Why • • • Material References Permissions Email Alerts Subscription Supplementary The Journal of Immunology The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2016 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. This information is current as of September 25, 2021. The Journal of Immunology Molecular Profile of Tumor-Specific CD8+ T Cell Hypofunction in a Transplantable Murine Cancer Model Katherine A. -
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. -
Identification of Key Candidate Genes and Molecular Pathways in White Fat
Pan et al. Human Genomics (2019) 13:55 https://doi.org/10.1186/s40246-019-0239-x PRIMARY RESEARCH Open Access Identification of key candidate genes and molecular pathways in white fat browning: an anti-obesity drug discovery based on computational biology Yuyan Pan, Jiaqi Liu* and Fazhi Qi* Abstract Background: Obesity—with its increased risk of obesity-associated metabolic diseases—has become one of the greatest public health epidemics of the twenty-first century in affluent countries. To date, there are no ideal drugs for treating obesity. Studies have shown that activation of brown adipose tissue (BAT) can promote energy consumption and inhibit obesity, which makes browning of white adipose tissue (WAT) a potential therapeutic target for obesity. Our objective was to identify genes and molecular pathways associated with WAT and the activation of BAT to WAT browning, by using publicly available data and computational tools; this knowledge might help in targeting relevant signaling pathways for treating obesity and other related metabolic diseases. Results: In this study, we used text mining to find out genes related to brown fat and white fat browning. Combined with biological process and pathway analysis in GeneCodis and protein-protein interaction analysis by using STRING and Cytoscape, a list of high priority target genes was developed. The Human Protein Atlas was used to analyze protein expression. Candidate drugs were derived on the basis of the drug-gene interaction analysis of the final genes. Our study identified 18 genes representing 6 different pathways, targetable by a total of 33 drugs as possible drug treatments. The final list included 18 peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists, 4 beta 3 adrenoceptor (β3-AR) agonists, 1 insulin sensitizer, 3 insulins, 6 lipase clearing factor stimulants and other drugs. -
The Roles of Histone Deacetylase 5 and the Histone Methyltransferase Adaptor WDR5 in Myc Oncogenesis
The Roles of Histone Deacetylase 5 and the Histone Methyltransferase Adaptor WDR5 in Myc oncogenesis By Yuting Sun This thesis is submitted in fulfilment of the requirements for the degree of Doctor of Philosophy at the University of New South Wales Children’s Cancer Institute Australia for Medical Research School of Women’s and Children’s Health, Faculty of Medicine University of New South Wales Australia August 2014 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name: Sun First name: Yuting Other name/s: Abbreviation for degree as given in the University calendar: PhD School : School of·Women's and Children's Health Faculty: Faculty of Medicine Title: The Roles of Histone Deacetylase 5 and the Histone Methyltransferase Adaptor WDR5 in Myc oncogenesis. Abstract 350 words maximum: (PLEASE TYPE) N-Myc Induces neuroblastoma by regulating the expression of target genes and proteins, and N-Myc protein is degraded by Fbxw7 and NEDD4 and stabilized by Aurora A. The class lla histone deacetylase HDAC5 suppresses gene transcription, and blocks myoblast and leukaemia cell differentiation. While histone H3 lysine 4 (H3K4) trimethylation at target gene promoters is a pre-requisite for Myc· induced transcriptional activation, WDRS, as a histone H3K4 methyltransferase presenter, is required for H3K4 methylation and transcriptional activation mediated by a histone H3K4 methyltransferase complex. Here, I investigated the roles of HDAC5 and WDR5 in N-Myc overexpressing neuroblastoma. I have found that N-Myc upregulates HDAC5 protein expression, and that HDAC5 represses NEDD4 gene expression, increases Aurora A gene expression and consequently upregulates N-Myc protein expression in neuroblastoma cells. -
A Clinicopathological and Molecular Genetic Analysis of Low-Grade Glioma in Adults
A CLINICOPATHOLOGICAL AND MOLECULAR GENETIC ANALYSIS OF LOW-GRADE GLIOMA IN ADULTS Presented by ANUSHREE SINGH MSc A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy Brain Tumour Research Centre Research Institute in Healthcare Sciences Faculty of Science and Engineering University of Wolverhampton November 2014 i DECLARATION This work or any part thereof has not previously been presented in any form to the University or to any other body whether for the purposes of assessment, publication or for any other purpose (unless otherwise indicated). Save for any express acknowledgments, references and/or bibliographies cited in the work, I confirm that the intellectual content of the work is the result of my own efforts and of no other person. The right of Anushree Singh to be identified as author of this work is asserted in accordance with ss.77 and 78 of the Copyright, Designs and Patents Act 1988. At this date copyright is owned by the author. Signature: Anushree Date: 30th November 2014 ii ABSTRACT The aim of the study was to identify molecular markers that can determine progression of low grade glioma. This was done using various approaches such as IDH1 and IDH2 mutation analysis, MGMT methylation analysis, copy number analysis using array comparative genomic hybridisation and identification of differentially expressed miRNAs using miRNA microarray analysis. IDH1 mutation was present at a frequency of 71% in low grade glioma and was identified as an independent marker for improved OS in a multivariate analysis, which confirms the previous findings in low grade glioma studies. -
Supplementary Materials
1 Supplementary Materials: Supplemental Figure 1. Gene expression profiles of kidneys in the Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice. (A) A heat map of microarray data show the genes that significantly changed up to 2 fold compared between Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice (N=4 mice per group; p<0.05). Data show in log2 (sample/wild-type). 2 Supplemental Figure 2. Sting signaling is essential for immuno-phenotypes of the Fcgr2b-/-lupus mice. (A-C) Flow cytometry analysis of splenocytes isolated from wild-type, Fcgr2b-/- and Fcgr2b-/-. Stinggt/gt mice at the age of 6-7 months (N= 13-14 per group). Data shown in the percentage of (A) CD4+ ICOS+ cells, (B) B220+ I-Ab+ cells and (C) CD138+ cells. Data show as mean ± SEM (*p < 0.05, **p<0.01 and ***p<0.001). 3 Supplemental Figure 3. Phenotypes of Sting activated dendritic cells. (A) Representative of western blot analysis from immunoprecipitation with Sting of Fcgr2b-/- mice (N= 4). The band was shown in STING protein of activated BMDC with DMXAA at 0, 3 and 6 hr. and phosphorylation of STING at Ser357. (B) Mass spectra of phosphorylation of STING at Ser357 of activated BMDC from Fcgr2b-/- mice after stimulated with DMXAA for 3 hour and followed by immunoprecipitation with STING. (C) Sting-activated BMDC were co-cultured with LYN inhibitor PP2 and analyzed by flow cytometry, which showed the mean fluorescence intensity (MFI) of IAb expressing DC (N = 3 mice per group). 4 Supplemental Table 1. Lists of up and down of regulated proteins Accession No. -
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, -
Anillin Is a Prognostic Factor and Is Correlated with Genovariation in Pancreatic Cancer Based on Databases Analysis
ONCOLOGY LETTERS 21: 107, 2021 Anillin is a prognostic factor and is correlated with genovariation in pancreatic cancer based on databases analysis YUANHUA NIE, ZHIQIANG ZHAO, MINXUE CHEN, FULIN MA, YONG FAN, YINGXIN KANG, BOXIONG KANG and CHEN WANG Department of General Surgery, Lanzhou University Second Hospital, Lanzhou, Gansu 730030, P.R. China Received March 12, 2020; Accepted October 8, 2020 DOI: 10.3892/ol.2020.12368 Abstract. Pancreatic cancer has a low survival rate globally. PC pathways were associated with low expression of ANLN. Anillin (ANLN) is involved in the pathogenesis of pancreatic Overall, ANLN is more highly expressed in PC compared cancer (PC). The present study used databases and reverse with in normal tissue, and is associated with poor differen‑ transcription‑quantitative PCR to investigate the association tiation. The expression of ANLN may be a novel prognostic between ANLN expression, clinical variables and the survival marker of poor survival. Finally, ANLN exert its functions in rate of patients with pancreatic cancer. Gene expression PC through the p53, cell cycle, DNA replication, mismatch of ANLN in normal and cancer tissues was analyzed using repair and nucleotide excision repair and pathways. data from The Cancer Genome Atlas, Oncomine and Gene Expression database of Normal and Tumor tissues 2 and Introduction ANOVA, and the association between ANLN mRNA expres‑ sion and ANLN genovariation was analyzed using cBioPortal. Pancreatic cancer (PC) is a major public health problem The association between ANLN expression and the survival, and is the eleventh most common cancer in the world, with clinical, pathological and prognostic characteristics of PC 458,918 new cases and 432,242 deaths in 2018 (1). -
Myopia in African Americans Is Significantly Linked to Chromosome 7P15.2-14.2
Genetics Myopia in African Americans Is Significantly Linked to Chromosome 7p15.2-14.2 Claire L. Simpson,1,2,* Anthony M. Musolf,2,* Roberto Y. Cordero,1 Jennifer B. Cordero,1 Laura Portas,2 Federico Murgia,2 Deyana D. Lewis,2 Candace D. Middlebrooks,2 Elise B. Ciner,3 Joan E. Bailey-Wilson,1,† and Dwight Stambolian4,† 1Department of Genetics, Genomics and Informatics and Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States 2Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, United States 3The Pennsylvania College of Optometry at Salus University, Elkins Park, Pennsylvania, United States 4Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, United States Correspondence: Joan E. PURPOSE. The purpose of this study was to perform genetic linkage analysis and associ- Bailey-Wilson, NIH/NHGRI, 333 ation analysis on exome genotyping from highly aggregated African American families Cassell Drive, Suite 1200, Baltimore, with nonpathogenic myopia. African Americans are a particularly understudied popula- MD 21131, USA; tion with respect to myopia. [email protected]. METHODS. One hundred six African American families from the Philadelphia area with a CLS and AMM contributed equally to family history of myopia were genotyped using an Illumina ExomePlus array and merged this work and should be considered co-first authors. with previous microsatellite data. Myopia was initially measured in mean spherical equiv- JEB-W and DS contributed equally alent (MSE) and converted to a binary phenotype where individuals were identified as to this work and should be affected, unaffected, or unknown.