Genomes Assembly, Gene Finding, Annotation & Gene
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Cytogenomic SNP Microarray - Fetal ARUP Test Code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells
Patient Report |FINAL Client: Example Client ABC123 Patient: Patient, Example 123 Test Drive Salt Lake City, UT 84108 DOB 2/13/1987 UNITED STATES Gender: Female Patient Identifiers: 01234567890ABCD, 012345 Physician: Doctor, Example Visit Number (FIN): 01234567890ABCD Collection Date: 00/00/0000 00:00 Cytogenomic SNP Microarray - Fetal ARUP test code 2002366 Maternal Contamination Study Fetal Spec Fetal Cells Single fetal genotype present; no maternal cells present. Fetal and maternal samples were tested using STR markers to rule out maternal cell contamination. This result has been reviewed and approved by Maternal Specimen Yes Cytogenomic SNP Microarray - Fetal Abnormal * (Ref Interval: Normal) Test Performed: Cytogenomic SNP Microarray- Fetal (ARRAY FE) Specimen Type: Direct (uncultured) villi Indication for Testing: Patient with 46,XX,t(4;13)(p16.3;q12) (Quest: EN935475D) ----------------------------------------------------------------- ----- RESULT SUMMARY Abnormal Microarray Result (Male) Unbalanced Translocation Involving Chromosomes 4 and 13 Classification: Pathogenic 4p Terminal Deletion (Wolf-Hirschhorn syndrome) Copy number change: 4p16.3p16.2 loss Size: 5.1 Mb 13q Proximal Region Deletion Copy number change: 13q11q12.12 loss Size: 6.1 Mb ----------------------------------------------------------------- ----- RESULT DESCRIPTION This analysis showed a terminal deletion (1 copy present) involving chromosome 4 within 4p16.3p16.2 and a proximal interstitial deletion (1 copy present) involving chromosome 13 within 13q11q12.12. This -
Environmental Influences on Endothelial Gene Expression
ENDOTHELIAL CELL GENE EXPRESSION John Matthew Jeff Herbert Supervisors: Prof. Roy Bicknell and Dr. Victoria Heath PhD thesis University of Birmingham August 2012 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. ABSTRACT Tumour angiogenesis is a vital process in the pathology of tumour development and metastasis. Targeting markers of tumour endothelium provide a means of targeted destruction of a tumours oxygen and nutrient supply via destruction of tumour vasculature, which in turn ultimately leads to beneficial consequences to patients. Although current anti -angiogenic and vascular targeting strategies help patients, more potently in combination with chemo therapy, there is still a need for more tumour endothelial marker discoveries as current treatments have cardiovascular and other side effects. For the first time, the analyses of in-vivo biotinylation of an embryonic system is performed to obtain putative vascular targets. Also for the first time, deep sequencing is applied to freshly isolated tumour and normal endothelial cells from lung, colon and bladder tissues for the identification of pan-vascular-targets. Integration of the proteomic, deep sequencing, public cDNA libraries and microarrays, delivers 5,892 putative vascular targets to the science community. -
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, -
Complexin-1 Is a Biomarker of Synucleinopathy
DMM Advance Online Articles. Posted 20 January 2017 as doi: 10.1242/dmm.028035 Access the most recent version at http://dmm.biologists.org/lookup/doi/10.1242/dmm.028035 BLOOD RNA BIOMARKERS IN PRODROMAL PARK4 AND REM SLEEP BEHAVIOR DISORDER SHOW ROLE OF COMPLEXIN-1 LOSS FOR RISK OF PARKINSON’S DISEASE Suna Lahut1,2, Suzana Gispert1, Özgür Ömür1,2, Candan Depboylu3, Kay Seidel4, Jorge Antolio Domínguez-Bautista1, Nadine Brehm1, Hülya Tireli5, Karl Hackmann6, Caroline Pirkevi2, Barbara Leube7, Vincent Ries3, Kerstin Reim8, Nils Brose8, Wilfred F. den Dunnen9, Madrid Johnson10, Zsuzsanna Wolf11, Marc Schindewolf12, Wiebke Schrempf13, Kathrin Reetz14, Peter Young15, David Vadasz3, Achilleas S. Frangakis10, Evelin Schröck6, Helmuth Steinmetz1, Marina Jendrach1, Udo Rüb4, Ayşe Nazlı Başak2¶, Wolfgang Oertel3¶, Georg Auburger1¶ 1Exp. Neurology, Goethe University Medical School, 60590 Frankfurt/Main, Germany; 2NDAL, Boğaziçi University, 34342 Istanbul, Turkey; 3Dept. of Neurology, Philipps University, Baldingerstrasse, 35043 Marburg, Germany; 4Dr. Senckenberg Chronomedical Institute, Goethe University, 60590 Frankfurt/Main, Germany; 5Dept. of Neurology, Haydarpaşa Numune Training and Research Hospital, 34668 Istanbul, Turkey; 6Institute for Clinical Genetics, Faculty of Medicine Carl Gustav Carus, TU Dresden, Fetscherstrasse 74, 01307 Dresden, Germany; 7Institute of Human Genetics, Heinrich Heine University, 40225 Düsseldorf, Germany; 8Dept. of Molecular Neurobiology and Center for the Molecular Physiology of the Brain, Max Planck Institute of Experimental Medicine, 37075 Göttingen, Germany; 9Dept. of Pathology and Medical Biology, Medical Center, University, 9700 RB Groningen, Netherlands; 10Institut für Biophysik and Frankfurt Institute for Molecular Life Sciences, Goethe University, 60438 Frankfurt/Main, Germany; 11Hemophilia Centre, Medical Clinic III, Institute of Transfusion Medicine and Immunhematology Goethe University, 60590 Frankfurt/Main, Germany; 12Dept. -
Swine in Biomedical Research Conference 2005
Swine in Biomedical Research Conference 2005 www.swinegenomics.com Organizing Committee: Lawrence B. Schook, Ph.D. (Chair) Dept. Animal Sciences, Veterinary Pathobiology & Nutritional Sciences, Institute for Genomic Biology, University of Illinois Christopher Counter, Ph.D. Dept. of Oncology and Cancer Biology, Duke University Medical Center Eric Forsberg, Ph.D Vice President of Business Development, Infigen Merete Fredholm, D.V.M., Ph.D., Dr. Vet. Sci., Royal Veterinary and Agricultural Univ.Inst. Animal Science & Animal Health Medicine Thalachallour Mohanakumar, Ph.D. Dept. of Surgery, Pathology and Immunology, Washington University School of Medicine Randall Prather, Ph.D. Department of Reproductive Biotechnology, University of Missouri Steven Niemi, D.V.M. Center for Comparative Medicine, Massachusetts General Hospital Hiroshi Yasue, Ph.D. National Institute of Agrobiological Sciences, Sukuba, Japan University of Illinois Hosting Committee: Jonathan Beever, Animal Sciences Sharon Donovan, Food Science & Human Nutrition H. Rex Gaskins, Animal Sciences Russell Jamison, Materials Science & Engineering Lawrence Schook, Animal Sciences Kelley Tappenden, Food Science & Human Nutrition Michael Tumbleson, Agricultural Engineering (honorary chair) Matthew Wheeler, Animal Sciences Federico Zuckermann, Veterinary Pathobiology Poster Session Moderators: Bioengineering, Russ Jamison, UIUC Immunology & Infectious Diseases, Federico Zuckermann, UIUC Transplantation (allo & xeno), Doug Smith, Baylor & Mark Rutherford, U.Minn. Nutrition (Obesity and Diabetes), Sharon Donovan, UIUC Genomics and Cloning, Jon Beever, UIUC & Max Rothschild, ISU Cardiovascular, Rex Gaskins, UIUC Physiology, Jack Odle, NCSU Cancer, Craig Beattie, UNR Clinical Models, Steve Niemi, Harvard Sponsored by: Institute for Genomic Biology, College of Agricultural, Consumer, and Environmental Services, College of Liberal Arts & Sciences, College of Veterinary Medicine, and Office of the Vice-Chancellor of Research at the University of Illinois at Urbana-Champaign National Institutes for Health (Grant no. -
Single-Cell RNA-Seq of Dopaminergic Neurons Informs Candidate Gene Selection for Sporadic
bioRxiv preprint doi: https://doi.org/10.1101/148049; this version posted August 31, 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-NC-ND 4.0 International license. 1 Single-cell RNA-seq of dopaminergic neurons informs candidate gene selection for sporadic 2 Parkinson's disease 3 4 Paul W. Hook1, Sarah A. McClymont1, Gabrielle H. Cannon1, William D. Law1, A. Jennifer 5 Morton2, Loyal A. Goff1,3*, Andrew S. McCallion1,4,5* 6 7 1McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of 8 Medicine, Baltimore, Maryland, United States of America 9 2Department of Physiology Development and Neuroscience, University of Cambridge, 10 Cambridge, United Kingdom 11 3Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, 12 Maryland, United States of America 13 4Department of Comparative and Molecular Pathobiology, Johns Hopkins University School of 14 Medicine, Baltimore, Maryland, United States of America 15 5Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, 16 United States of America 17 *, To whom correspondence should be addressed: [email protected] and [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/148049; this version posted August 31, 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-NC-ND 4.0 International license. -
Regulates Target Gene Transcription Via Single-Stranded DNA Response Elements
TLS/FUS (translocated in liposarcoma/fused in sarcoma) regulates target gene transcription via single-stranded DNA response elements Adelene Y. Tan1, Todd R. Riley, Tristan Coady, Harmen J. Bussemaker, and James L. Manley2 Department of Biological Sciences, Columbia University, New York, NY 10027 Contributed by James L. Manley, February 29, 2012 (sent for review December 9, 2011) TLS/FUS (TLS) is a multifunctional protein implicated in a wide range TLS has also been linked to splicing. It contains an RNP-type of cellular processes, including transcription and mRNA processing, RNA-binding domain and associates directly with SR protein as well as in both cancer and neurological disease. However, little is splicing factors (11). TET proteins have been detected in spliceo- currently known about TLS target genes and how they are recog- somes (12), and TLS was found associated with RNAP II and nized. Here, we used ChIP and promoter microarrays to identify snRNPs in a transcription and splicing complex in vitro (13). It is genes potentially regulated by TLS. Among these genes, we detected unclear whether and how TLS recruits splicing factors to sites of a number that correlate with previously known functions of TLS, active transcription, but one possibility is through its interaction and confirmed TLS occupancy at several of them by ChIP. We also with TBP and the TFIID complex. detected changes in mRNA levels of these target genes in cells where Here we provide insight into TLS regulation of RNAP II-tran- scribed genes. We used ChIP followed by promoter microarray TLS levels were altered, indicative of both activation and repression. -
Functional Analysis of Structural Variation in the 2D and 3D Human Genome
FUNCTIONAL ANALYSIS OF STRUCTURAL VARIATION IN THE 2D AND 3D HUMAN GENOME by Conor Mitchell Liam Nodzak A dissertation submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Bioinformatics and Computational Biology Charlotte 2019 Approved by: Dr. Xinghua Mindy Shi Dr. Rebekah Rogers Dr. Jun-tao Guo Dr. Adam Reitzel ii c 2019 Conor Mitchell Liam Nodzak ALL RIGHTS RESERVED iii ABSTRACT CONOR MITCHELL LIAM NODZAK. Functional analysis of structural variation in the 2D and 3D human genome. (Under the direction of DR. XINGHUA MINDY SHI) The human genome consists of over 3 billion nucleotides that have an average distance of 3.4 Angstroms between each base, which equates to over two meters of DNA contained within the 125 µm3 volume diploid cell nuclei. The dense compaction of chromatin by the supercoiling of DNA forms distinct architectural modules called topologically associated domains (TADs), which keep protein-coding genes, noncoding RNAs and epigenetic regulatory elements in close nuclear space. It has recently been shown that these conserved chromatin structures may contribute to tissue-specific gene expression through the encapsulation of genes and cis-regulatory elements, and mutations that affect TADs can lead to developmental disorders and some forms of cancer. At the population-level, genomic structural variation contributes more to cumulative genetic difference than any other class of mutation, yet much remains to be studied as to how structural variation affects TADs. Here, we study the func- tional effects of structural variants (SVs) through the analysis of chromatin topology and gene activity for three trio families sampled from genetically diverse popula- tions from the Human Genome Structural Variation Consortium. -
Pathogenetic Subgroups of Multiple Myeloma Patients
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector ARTICLE High-resolution genomic profiles define distinct clinico- pathogenetic subgroups of multiple myeloma patients Daniel R. Carrasco,1,2,8 Giovanni Tonon,1,8 Yongsheng Huang,3,8 Yunyu Zhang,1 Raktim Sinha,1 Bin Feng,1 James P. Stewart,3 Fenghuang Zhan,3 Deepak Khatry,1 Marina Protopopova,5 Alexei Protopopov,5 Kumar Sukhdeo,1 Ichiro Hanamura,3 Owen Stephens,3 Bart Barlogie,3 Kenneth C. Anderson,1,4 Lynda Chin,1,7 John D. Shaughnessy, Jr.,3,9 Cameron Brennan,6,9 and Ronald A. DePinho1,5,9,* 1 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115 2 Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts 02115 3 The Donna and Donald Lambert Laboratory of Myeloma Genetics, Myeloma Institute for Research and Therapy, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205 4 The Jerome Lipper Multiple Myeloma Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115 5 Center for Applied Cancer Science, Belfer Institute for Innovative Cancer Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 6 Neurosurgery Service, Memorial Sloan-Kettering Cancer Center, Department of Neurosurgery, Weill Cornell Medical College, New York, New York 10021 7 Department of Dermatology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, 02115 8 These authors contributed equally to this work. 9 Cocorresponding authors: [email protected] (C.B.); [email protected] (J.D.S.); [email protected] (R.A.D.) *Correspondence: [email protected] Summary To identify genetic events underlying the genesis and progression of multiple myeloma (MM), we conducted a high-resolu- tion analysis of recurrent copy number alterations (CNAs) and expression profiles in a collection of MM cell lines and out- come-annotated clinical specimens. -
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. -
Genome-Wide DNA Methylation Dynamics During Epigenetic
Gómez‑Redondo et al. Clin Epigenet (2021) 13:27 https://doi.org/10.1186/s13148‑021‑01003‑x RESEARCH Open Access Genome‑wide DNA methylation dynamics during epigenetic reprogramming in the porcine germline Isabel Gómez‑Redondo1*† , Benjamín Planells1†, Sebastián Cánovas2,3, Elena Ivanova4, Gavin Kelsey4,5 and Alfonso Gutiérrez‑Adán1 Abstract Background: Prior work in mice has shown that some retrotransposed elements remain substantially methylated during DNA methylation reprogramming of germ cells. In the pig, however, information about this process is scarce. The present study was designed to examine the methylation profles of porcine germ cells during the time course of epigenetic reprogramming. Results: Sows were artifcially inseminated, and their fetuses were collected 28, 32, 36, 39, and 42 days later. At each time point, genital ridges were dissected from the mesonephros and germ cells were isolated through magnetic‑ activated cell sorting using an anti‑SSEA‑1 antibody, and recovered germ cells were subjected to whole‑genome bisulphite sequencing. Methylation levels were quantifed using SeqMonk software by performing an unbiased analysis, and persistently methylated regions (PMRs) in each sex were determined to extract those regions showing 50% or more methylation. Most genomic elements underwent a dramatic loss of methylation from day 28 to day 36, when the lowest levels were shown. By day 42, there was evidence for the initiation of genomic re‑methylation. We identifed a total of 1456 and 1122 PMRs in male and female germ cells, respectively, and large numbers of transpos‑ able elements (SINEs, LINEs, and LTRs) were found to be located within these PMRs. Twenty‑one percent of the introns located in these PMRs were found to be the frst introns of a gene, suggesting their regulatory role in the expression of these genes. -
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Published December 3, 2012 JCB: Article KIF14 negatively regulates Rap1a–Radil signaling during breast cancer progression Syed M. Ahmed,1 Brigitte L. Thériault,4 Maruti Uppalapati,2 Catherine W.N. Chiu,1 Brenda L. Gallie,4 Sachdev S. Sidhu,2 and Stéphane Angers1,3 1Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, 2Terrence Donnelly Center for Cellular and Biomolecular Research, and 3Department of Biochemistry, Faculty of Medicine, University of Toronto, Toronto, Ontario M5S 1A1, Canada 4Campbell Family Cancer Research Institute, Ontario Cancer Institute, University Health Network, Toronto, Ontario M5G 2M9, Canada he small GTPase Rap1 regulates inside-out integrin tethering Radil on microtubules. The depletion of KIF14 activation and thereby influences cell adhesion, mi- led to increased cell spreading, altered focal adhesion dy- T gration, and polarity. Several Rap1 effectors have namics, and inhibition of cell migration and invasion. We been described to mediate the cellular effects of Rap1 in a also show that Radil is important for breast cancer cell context-dependent manner. Radil is emerging as an im- proliferation and for metastasis in mice. Our findings pro- Downloaded from portant Rap effector implicated in cell spreading and vide evidence that the concurrent up-regulation of Rap1 migration, but the molecular mechanisms underlying its activity and increased KIF14 levels in several cancers is functions are unclear. We report here that the kinesin KIF14 needed to reach optimal levels of Rap1–Radil signaling, associates with the PDZ domain of Radil and negatively integrin activation, and cell–matrix adhesiveness required regulates Rap1-mediated inside-out integrin activation by for tumor progression.