Structure and Function of the Human Genome
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Illegitimate DNA Integration in Mammalian Cells
Gene Therapy (2003) 10, 1791–1799 & 2003 Nature Publishing Group All rights reserved 0969-7128/03 $25.00 www.nature.com/gt REVIEW Illegitimate DNA integration in mammalian cells HWu¨ rtele1, KCE Little2 and P Chartrand2,3 1Programme de Biologie Mole´culaire, Universite´ de Montre´al, Montre´al, Canada; 2Department of Medicine, Division of Experimental Medicine, McGill University, Montre´al, Que´bec, Canada; and 3Centre Hospitalier de l’Universite´ de Montre´al and De´partement de Pathologie et de Biologie Cellulaire, Universite´ de Montre´al, Montre´al, Que´bec, Canada Foreign DNA integration is one of the most widely exploited therapy procedures can result in illegitimate integration of cellular processes in molecular biology. Its technical use introduced sequences and thus pose a risk of unforeseeable permits us to alter a cellular genome by incorporating a genomic alterations. The choice of insertion site, the degree fragment of foreign DNA into the chromosomal DNA. This to which the foreign DNA and endogenous locus are modified process employs the cell’s own endogenous DNA modifica- before or during integration, and the resulting impact on tion and repair machinery. Two main classes of integration structure, expression, and stability of the genome are all mechanisms exist: those that draw on sequence similarity factors of illegitimate DNA integration that must be con- between the foreign and genomic sequences to carry out sidered, in particular when designing genetic therapies. homology-directed modifications, and the nonhomologous or Gene Therapy (2003) 10, 1791–1799. doi:10.1038/ ‘illegitimate’ insertion of foreign DNA into the genome. Gene sj.gt.3302074 Keywords: illegitimate DNA integration; DNA repair; transgenesis; recombination; mutagenesis Introduction timate integration. -
Genomics and Its Impact on Science and Society: the Human Genome Project and Beyond
DOE/SC-0083 Genomics and Its Impact on Science and Society The Human Genome Project and Beyond U.S. Department of Energy Genome Research Programs: genomics.energy.gov A Primer ells are the fundamental working units of every living system. All the instructions Cneeded to direct their activities are contained within the chemical DNA (deoxyribonucleic acid). DNA from all organisms is made up of the same chemical and physical components. The DNA sequence is the particular side-by-side arrangement of bases along the DNA strand (e.g., ATTCCGGA). This order spells out the exact instruc- tions required to create a particular organism with protein complex its own unique traits. The genome is an organism’s complete set of DNA. Genomes vary widely in size: The smallest known genome for a free-living organism (a bac- terium) contains about 600,000 DNA base pairs, while human and mouse genomes have some From Genes to Proteins 3 billion (see p. 3). Except for mature red blood cells, all human cells contain a complete genome. Although genes get a lot of attention, the proteins DNA in each human cell is packaged into 46 chro- perform most life functions and even comprise the mosomes arranged into 23 pairs. Each chromosome is majority of cellular structures. Proteins are large, complex a physically separate molecule of DNA that ranges in molecules made up of chains of small chemical com- length from about 50 million to 250 million base pairs. pounds called amino acids. Chemical properties that A few types of major chromosomal abnormalities, distinguish the 20 different amino acids cause the including missing or extra copies or gross breaks and protein chains to fold up into specific three-dimensional rejoinings (translocations), can be detected by micro- structures that define their particular functions in the cell. -
From the Human Genome Project to Genomic Medicine a Journey to Advance Human Health
From the Human Genome Project to Genomic Medicine A Journey to Advance Human Health Eric Green, M.D., Ph.D. Director, NHGRI The Origin of “Genomics”: 1987 Genomics (1987) “For the newly developing discipline of [genome] mapping/sequencing (including the analysis of the information), we have adopted the term GENOMICS… ‘The Genome Institute’ Office for Human Genome Research 1988-1989 National Center for Human Genome Research 1989-1997 National Human Genome Research Institute 1997-present NHGRI: Circa 1990-2003 Human Genome Project NHGRI Today: Characteristic Features . Relatively young (~28 years) . Relatively small (~1.7% of NIH) . Unusual historical origins (think ‘Human Genome Project’) . Emphasis on ‘Team Science’ (think managed ‘consortia’) . Rapidly disseminating footprint (think ‘genomics’) . Novel societal/bioethics research component (think ‘ELSI’) . Over-achievers for trans-NIH initiatives (think ‘Common Fund’) . Vibrant (and large) Intramural Research Program A Quarter Century of Genomics Human Genome Sequenced for First Time by the Human Genome Project Genomic Medicine An emerging medical discipline that involves using genomic information about an individual as part of their clinical care (e.g., for diagnostic or therapeutic decision- making) and the other implications of that clinical use The Path to Genomic Medicine ? Human Realization of Genome Genomic Project Medicine Nature Nature Base Pairs to Bedside 2003 Heli201x to 1Health A Quarter Century of Genomics Human Genome Sequenced for First Time by the Human Genome Project -
Visual Exploration Across Biomedical Databases
1 Visual Exploration Across Biomedical Databases Michael D. Lieberman∗†‡ Sima Taheri∗ Huimin Guo∗ Fatemeh Mir-Rashed∗ [email protected] [email protected] [email protected] [email protected] Inbal Yahav§ Aleks Aris∗ Ben Shneiderman∗† [email protected] [email protected] [email protected] Abstract—Though biomedical research often draws on knowl- mentioned in d. d may also have associations with other edge from a wide variety of fields, few visualization methods PubMed documents that cite d as a reference, as well as for biomedical data incorporate meaningful cross-database ex- associations to the PubMed documents that d itself cites. ploration. A new approach is offered for visualizing and explor- ∈ ing a query-based subset of multiple heterogeneous biomedical Furthermore, each gene g G could have associations with databases. Databases are modeled as an entity-relation graph the proteins for which g codes, or the DNA sequences in containing nodes (database records) and links (relationships which g’s code appears. Usually, the various types of records between records). Users specify a keyword search string to in these databases also have many attributes associated with retrieve an initial set of nodes, and then explore intra- and inter- them. For example, PubMed documents might be annotated database links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually with the date of publication, authors, and general topics, while present in biomedical data. Comments from domain experts gene records could be annotated with the relevant species, indicate that this visualization method is potentially advantageous location on chromosome, or function. -
The Economic Impact and Functional Applications of Human Genetics and Genomics
The Economic Impact and Functional Applications of Human Genetics and Genomics Commissioned by the American Society of Human Genetics Produced by TEConomy Partners, LLC. Report Authors: Simon Tripp and Martin Grueber May 2021 TEConomy Partners, LLC (TEConomy) endeavors at all times to produce work of the highest quality, consistent with our contract commitments. However, because of the research and/or experimental nature of this work, the client undertakes the sole responsibility for the consequence of any use or misuse of, or inability to use, any information or result obtained from TEConomy, and TEConomy, its partners, or employees have no legal liability for the accuracy, adequacy, or efficacy thereof. Acknowledgements ASHG and the project authors wish to thank the following organizations for their generous support of this study. Invitae Corporation, San Francisco, CA Regeneron Pharmaceuticals, Inc., Tarrytown, NY The project authors express their sincere appreciation to the following indi- viduals who provided their advice and input to this project. ASHG Government and Public Advocacy Committee Lynn B. Jorde, PhD ASHG Government and Public Advocacy Committee (GPAC) Chair, President (2011) Professor and Chair of Human Genetics George and Dolores Eccles Institute of Human Genetics University of Utah School of Medicine Katrina Goddard, PhD ASHG GPAC Incoming Chair, Board of Directors (2018-2020) Distinguished Investigator, Associate Director, Science Programs Kaiser Permanente Northwest Melinda Aldrich, PhD, MPH Associate Professor, Department of Medicine, Division of Genetic Medicine Vanderbilt University Medical Center Wendy Chung, MD, PhD Professor of Pediatrics in Medicine and Director, Clinical Cancer Genetics Columbia University Mira Irons, MD Chief Health and Science Officer American Medical Association Peng Jin, PhD Professor and Chair, Department of Human Genetics Emory University Allison McCague, PhD Science Policy Analyst, Policy and Program Analysis Branch National Human Genome Research Institute Rebecca Meyer-Schuman, MS Human Genetics Ph.D. -
Contributions to Biostatistics: Categorical Data Analysis, Data Modeling and Statistical Inference Mathieu Emily
Contributions to biostatistics: categorical data analysis, data modeling and statistical inference Mathieu Emily To cite this version: Mathieu Emily. Contributions to biostatistics: categorical data analysis, data modeling and statistical inference. Mathematics [math]. Université de Rennes 1, 2016. tel-01439264 HAL Id: tel-01439264 https://hal.archives-ouvertes.fr/tel-01439264 Submitted on 18 Jan 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. HABILITATION À DIRIGER DES RECHERCHES Université de Rennes 1 Contributions to biostatistics: categorical data analysis, data modeling and statistical inference Mathieu Emily November 15th, 2016 Jury: Christophe Ambroise Professeur, Université d’Evry Val d’Essonne, France Rapporteur Gérard Biau Professeur, Université Pierre et Marie Curie, France Président David Causeur Professeur, Agrocampus Ouest, France Examinateur Heather Cordell Professor, University of Newcastle upon Tyne, United Kingdom Rapporteur Jean-Michel Marin Professeur, Université de Montpellier, France Rapporteur Valérie Monbet Professeur, Université de Rennes 1, France Examinateur Korbinian Strimmer Professor, Imperial College London, United Kingdom Examinateur Jean-Philippe Vert Directeur de recherche, Mines ParisTech, France Examinateur Pour M.H.A.M. Remerciements En tout premier lieu je tiens à adresser mes remerciements à l’ensemble des membres du jury qui m’ont fait l’honneur d’évaluer mes travaux. -
Microbes and Metagenomics in Human Health an Overview of Recent Publications Featuring Illumina® Technology TABLE of CONTENTS
Microbes and Metagenomics in Human Health An overview of recent publications featuring Illumina® technology TABLE OF CONTENTS 4 Introduction 5 Human Microbiome Gut Microbiome Gut Microbiome and Disease Inflammatory Bowel Disease (IBD) Metabolic Diseases: Diabetes and Obesity Obesity Oral Microbiome Other Human Biomes 25 Viromes and Human Health Viral Populations Viral Zoonotic Reservoirs DNA Viruses RNA Viruses Human Viral Pathogens Phages Virus Vaccine Development 44 Microbial Pathogenesis Important Microorganisms in Human Health Antimicrobial Resistance Bacterial Vaccines 54 Microbial Populations Amplicon Sequencing 16S: Ribosomal RNA Metagenome Sequencing: Whole-Genome Shotgun Metagenomics Eukaryotes Single-Cell Sequencing (SCS) Plasmidome Transcriptome Sequencing 63 Glossary of Terms 64 Bibliography This document highlights recent publications that demonstrate the use of Illumina technologies in immunology research. To learn more about the platforms and assays cited, visit www.illumina.com. An overview of recent publications featuring Illumina technology 3 INTRODUCTION The study of microbes in human health traditionally focused on identifying and 1. Roca I., Akova M., Baquero F., Carlet J., treating pathogens in patients, usually with antibiotics. The rise of antibiotic Cavaleri M., et al. (2015) The global threat of resistance and an increasingly dense—and mobile—global population is forcing a antimicrobial resistance: science for interven- tion. New Microbes New Infect 6: 22-29 1, 2, 3 change in that paradigm. Improvements in high-throughput sequencing, also 2. Shallcross L. J., Howard S. J., Fowler T. and called next-generation sequencing (NGS), allow a holistic approach to managing Davies S. C. (2015) Tackling the threat of anti- microbial resistance: from policy to sustainable microbes in human health. -
Bioinformatics Tools for RNA-Seq Gene and Isoform Quantification
on: Sequ ati en er c n in e g G & t x A Journal of e p Zhang, et al., Next Generat Sequenc & Applic p N l f i c o 2016, 3:3 a l t a i o n r ISSN: 2469-9853n u s DOI: 10.4172/2469-9853.1000140 o Next Generation Sequencing & Applications J Review Article Open Access Bioinformatics Tools for RNA-seq Gene and Isoform Quantification Chi Zhang1, Baohong Zhang1, Michael S Vincent2 and Shanrong Zhao1* 1Early Clinical Development, Pfizer Worldwide R&D, Cambridge, MA, USA 2Inflammation and Immunology RU, Pfizer Worldwide R&D, Cambridge, MA, USA *Corresponding author: Shanrong Zhao, Early Clinical Development, Pfizer Worldwide R&D, Cambridge, MA, 02139, USA, Tel: + 1-212-733-2323; E-mail: [email protected] Rec date: Oct 27, 2016; Acc date: Dec 15, 2016; Pub date: Dec 17, 2016 Copyright: © 2016 Zhang C, et al. This is an open-access article distributed under the terms of the creative commons attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract In recent years, RNA-seq has emerged as a powerful technology in estimation of gene or transcript expression. ‘Union-exon’ and transcript based approaches are widely used in gene quantification. The ‘Union-exon’ based approach is simple, but it does not distinguish between isoforms when multiple alternatively spliced transcripts are expressed from the same gene. Because a gene is expressed in one or more transcript isoforms, the transcript based approach is more biologically meaningful than the ‘union exon’-based approach. -
Sequence Analysis Instructions
Sequence Analysis Instructions In order to predict your drug metabolizing phenotype from your CYP2D6 gene sequence, you must determine: 1) The assembled sequence from your two opposing sequencing reactions 2) If your PCR product even represents the human CYP2D6 gene, 3) The location of your sequence within the CYP2D6 gene, 4) Whether differences between your alleles and CYP2D6*1 sequence represents sequencing errors or polymorphisms in your sequence, and 5) The effect(s) of any polymorphisms on CYP2D6 protein sequence. As you analyze your gene sequence, copy and paste your analyses and results into a text file for your final lab report. If some factor, like the quality of your sequence, prevents you from carrying out the complete analysis, your grade will not be penalized—just complete as many of the steps below as possible, and include an explanation of why you could not complete the analysis in your final report. Font appearing in bold green italics describes questions to answer and items to include in your final report. Part 1: Assembling your CYP2D6 sequence from both directions The sequencing reaction only produces 700-800 bases of good sequence. To get most of the sequence of the 1.2 Kbp PCR product, sequencing reactions were performed from both directions on the PCR product. These need to be assembled into one sequence using the overlap of the two sequences. In order to assure that it is going in the forward direction, it is best to derive the reverse complement sequence from the reverse sequencing file. Take the text file and paste it into a program like http://www.bioinformatics.org/sms/rev_comp.html. -
Agenda: Gene Prediction by Cross-Species Sequence Comparison Leila Taher1, Oliver Rinner2,3, Saurabh Garg1, Alexander Sczyrba4 and Burkhard Morgenstern5,*
Nucleic Acids Research, 2004, Vol. 32, Web Server issue W305–W308 DOI: 10.1093/nar/gkh386 AGenDA: gene prediction by cross-species sequence comparison Leila Taher1, Oliver Rinner2,3, Saurabh Garg1, Alexander Sczyrba4 and Burkhard Morgenstern5,* 1International Graduate School for Bioinformatics and Genome Research, University of Bielefeld, Postfach 10 01 31, 33501 Bielefeld, Germany, 2GSF Research Center, MIPS/Institute of Bioinformatics, Ingolsta¨dter Landstrasse 1, 85764 Neuherberg, Germany, 3Brain Research Institute, ETH Zuurich,€ Winterthurerstrasse 190, CH-8057 Zuurich,€ Switzerland, 4Faculty of Technology, Research Group in Practical Computer Science, University of Bielefeld, Postfach 10 01 31, 33501 Bielefeld, Germany and 5Institute of Microbiology and Genetics, University of Go¨ttingen, Goldschmidtstrasse 1, 37077 Go¨ttingen, Germany Received March 5, 2004; Revised and Accepted March 24, 2004 ABSTRACT Thus, with the huge amount of data produced by sequencing projects, the development of high-quality gene-prediction Automatic gene prediction is one of the major tools has become a priority in bioinformatics. For eukaryotic challenges in computational sequence analysis. organisms, gene prediction is particularly challenging, since Traditional approaches to gene finding rely on statis- protein-coding exons are usually separated by introns of vari- tical models derived from previously known genes. By able length. Most traditional methods for gene prediction are contrast, a new class of comparative methods relies intrinsic methods; they use hidden Markov models (HMMs) or on comparing genomic sequences from evolutionary other stochastic approaches describing the statistical composi- related organisms to each other. These methods are tion of introns, exons, etc. Such models are trained with already based on the concept of phylogenetic footprinting: known genes from the same or a closely related species; the they exploit the fact that functionally important most popular of these tools is GenScan (1). -
The Chromosome-Centric Human Proteome Project for Cataloging Proteins Encoded in the Genome
CORRESPONDENCE The Chromosome-Centric Human Proteome Project for cataloging proteins encoded in the genome To the Editor: utility for biological and disease studies. Table 1 Features of salient genes on The Chromosome-Centric Human With development of new tools for in- chromosomes 13 and 17 Proteome Project (C-HPP) aims to define depth characterization of the transcriptome Genea AST nsSNPs the full set of proteins encoded in each and proteome, the HPP is well positioned Chromosome 13 chromosome through development of a to have a strategic role in addressing the BRCA2 3 54 standardized approach for analyzing the complexity of human phenotypes. With this RB1 2 3 massive proteomic data sets currently being in mind, the HUPO has organized national IRS2 1 3 generated from dedicated efforts of national chromosome teams that will collaborate and international teams. The initial goal with well-established laboratories building Chromosome 17 of the C-HPP is to identify at least one complementary proteotypic peptides, BRCA1 24 24 representative protein encoded by each of antibodies and informatics resources. ERBB2 6 13 the approximately 20,300 human genes1,2. An important C-HPP goal is to encourage TP53 14 5 aEnsembl protein and AST information can be found at The proteins will be characterized for tissue capture and open sharing of proteomic http://www.ensembl.org/Homo_sapiens/. localization and major isoforms, including data sets from diverse samples to enhance AST, alternative splicing transcript; nsSNP, nonsyno- mous single-nucleotide polyphorphism assembled from post-translational modifications (PTMs), a gene- and chromosome-centric display data from the 1000 Genomes Projects. -
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cells Article Transcriptome and Methylome Analysis Reveal Complex Cross-Talks between Thyroid Hormone and Glucocorticoid Signaling at Xenopus Metamorphosis Nicolas Buisine 1,† , Alexis Grimaldi 1,†, Vincent Jonchere 1,† , Muriel Rigolet 1, Corinne Blugeon 2 , Juliette Hamroune 2 and Laurent Marc Sachs 1,* 1 UMR7221 Molecular Physiology and Adaption, CNRS, Museum National d’Histoire Naturelle, 57 Rue Cuvier, CEDEX 05, 75231 Paris, France; [email protected] (N.B.); [email protected] (A.G.); [email protected] (V.J.); [email protected] (M.R.) 2 Genomics Core Facility, Département de Biologie, Institut de Biologie de l’ENS (IBENS), École Normale Supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France; [email protected] (C.B.); [email protected] (J.H.) * Correspondence: [email protected] † Co-first authors, alphabetic order. Abstract: Background: Most work in endocrinology focus on the action of a single hormone, and very little on the cross-talks between two hormones. Here we characterize the nature of interactions between thyroid hormone and glucocorticoid signaling during Xenopus tropicalis metamorphosis. Methods: We used functional genomics to derive genome wide profiles of methylated DNA and measured changes of gene expression after hormonal treatments of a highly responsive tissue, tailfin. Clustering classified the data into four types of biological responses, and biological networks were Citation: Buisine, N.; Grimaldi, A.; modeled by system biology. Results: We found that gene expression is mostly regulated by either Jonchere, V.; Rigolet, M.; Blugeon, C.; T or CORT, or their additive effect when they both regulate the same genes. A small but non- Hamroune, J.; Sachs, L.M.