From Functional Genomics to Systems Biology

Total Page:16

File Type:pdf, Size:1020Kb

From Functional Genomics to Systems Biology From Functional Genomics to Systems Biology Julio R. Fernández Masso Centro de Ingeniería Genética y Biotecnología. Ave 31 e/ 158 y 190, PO Box 6 162, Cubanacan, Cuba E-mail: [email protected] After the Transcriptome conferences held in Paris was presented by Mutsumi Kanamori from RIKEN. REPORT (2000), Seattle (2002), and Tokyo (2003), Prof. The comprehensive analysis of this cDNA collection Charles Auffray from the Centre National de la gave a great deal of information on the organization Recherche Scientifique (France), Pro. Zhu Chen, from of mammalian transcriptome. The project found an the Chinese Academy of Science and Chinese National unexpected number of alternative spliced transcripts Human Genome Research Centre (China) and Prof. and a significantly large amount of non coding RNAs Leroy Hood, from the Institute of Systems Biology (ncRNA). The expression analysis of a substantial (USA) launched a new series of conferences specially subset of predicted ncRNAs indicates that these designed to foster the transition of life science research RNAs are regulated in a tissue-specific manner and from Functional Genomics to Systems Biology. The that many of them may be involved in an antisense first of this new series of conferences was held in control mechanism. Their study concludes that Shanghai, China from November 5th to 9th 2005, under ncRNAs are a major component of the transcriptome. the topic of Integrative Systems Biology for Health- The following step they propose is the analysis of Predictive and Preventive Medicine. The conference expression regulatory regions, expression profiles and covers aspects such as Expression Profiling, RNAi, protein-RNA interactions, to unravel the Genome Proteomics, Metabolomics, Systems Biology on Network. Kanamori’s group established a large scale Model Organism, Systems Biology in Medicine, Systems to identify promoter regions and trans- Computation and Modeling and Integrative Annotation criptional starting sites. More than 10 million sites of the Genome. Conferences were presented by the were analyzed to discover new promoters and genes. academic and industrial sectors. From the combination of mapping full length cDNAs and promoter regions they identified low and high Understanding the genome activity chromosomal regions. The epigenetic interface between the genotype and One of the most surprising results of the human the phenotype links the genetic information and gene genome sequence is the scarcity of genes and the expression of an organism. The state of DNA vast tentions of meaningless genomic deserts, with methylation is the most widely studied epigenetic ultraconserved elements. Gustavo Glusman from the mechanism. A number of presentations expressed the Institute for Systems Biology proposes a novel importance of measuring the epigenetic changes as a approach to gene prediction. The new algorithms he measure of disease condition. Prototype techniques developed are based on the detection of genomic for the study of DNA methylation is a rapidly evolving signatures of transcription accumulated over field that should be closely followed since it will mature evolutionary time. He predicted thousands of rapidly in the scale of information and in performance. additional human genes including many that do not The relevance in tracking mutations in the genome code for proteins and genes with long introns and through the use of SNP or comparative genome lacking sequence conservation. He expects these new hybridatization to complement gene expression studies tools to add valuable information when combined in diseases that involve dynamic changes in the genome with algorithms based on gene structure and sequence such as cancer or mental retardation, and to understand similarity as a step forward in achieving the biological processes in stem cell research such as sensitivity and specificity required to fully automate pluripotence or cell differentiation, was pointed out. whole genome annotation. The complexity of the transcriptome Up and down regulated genes: from Full length cDNA sequence has been used to explore expression data to Biological the complexity of the transcriptome. Lukas Wagner meaning from NCBI presented the results obtained from One of the problems in microarray experiments is sequence 13000 human full length cDNA. While the that of developing tools to turn the data into coverage of alternative splicing was not a goal for the mechanistic understanding. The consensus at the project, the large number of EST and fully sequenced meeting was that most of the microarray papers cDNA provides an overview into the challenge of published in foremost journals still present a list of surveying splice variants. Sumio Sugano from the up and down regulated genes, the validation of certain University of Tokio determined the 5´end of genes by Western blot or qPCR, and the formulation 1,145,855 cDNA isolated from 134 full length of new hypotheses on the biological significance of enriched cDNA libraries. They identified a large some of these findings, but lack a significant population of cDNA that code for small proteins, a contribution to new biological understanding. The large number of alternative splices and alternative challenge in microarray studies has moved from the promoters. FANTOM3: The comprehensive mouse generation of high quality data sets to their analysis full length cDNA collection and seque nce database and interpretation. 144 Reportes Reports Xuegong Zhang from Tsinghua University presents genes could be deleterious for cells or could produce a subject-oriented literature mining tool developed in side effect on animals. Jacques Mallet from CNRS his lab for combining the information found in the presented a novel polymerase promoter inducible by literature with microarray gene expression data to doxycycline minimal RNA III that permits the control construct reliable and manageable gene networks with of siRNA levels and RNA interference by induction specific biological processes. The comparison and activation. possible advantages over other available tools was The Taicor method as a tool in the use of moderate discussed. RNAi screens to discover genes that are differentially Several talks refered to the importance of the regulated between cell lines was presented by Anyndya combination of microarray studies with other Dutta from the Univ. of Virginia Health Sciences technologies as CHIP to chip to increase the confidence Center. Using this technology it was possible to of gene expression studies and to allow the definition identify genes that are essential for cell cycle of gene and protein regulatory networks. Technical progression in p53+ cells but dispensable in p53- cells. advances in the field of microarray design for the genome, CHIP to chip and gene expression studies Annotation, data integration, and were presented during the meeting by Affymetrix and mining Agilent Technologies. Sequenom presented the data The process of genome annotation and the integration obtained from several papers using automated mass of different types of information generated by the spectrometry for gene expression and promoter use of high throughput technologies are challenges of methylation studies. today’s biological sciences. Several approaches such as H-invitational and Gene Desk databases and data The Proteome and the Metabolome integration tools such as BioMart were presented at Few presentations at the meeting cover the use of the meeting. Nevertheless the importance of proteomic or metabolomic tools. Nevertheless, it was developing new concepts, theories, computational clear that the understanding of the proteome and its and mathematical tools to explore and identify integration with transcriptome, the phosphorylation relevant interactions and networks was acknowledged profile and a network of protein-protein interactions by the participants. clearly provide a strong insight on mechanisms of biological Systemss. Mark McDonald from Waters Systems Biology described a new label free method for quantitative The concept of Systems Biology was used at the protein profiling that is based on the LC-MS conference at different scales of biological complexity: methodology that makes it possible to determine relative from metabolic pathways to cells and model changes in the amount of peptides in complex digest organisms. Charles Auffrey presented the Systemos- mixtures. The combination of chromatographic cope Consortium and his vision of Systems Biology separation and mass accuracy enables the identification as an integrational and iterative process based on inter- of thousands of ions. The SCAPE method based on the disciplinarity and networking. He proposed a working selective capture of peptides using chromatographic hypothesis that self organization of living systems separation was presented by Gabriel Padrón from CIGB. result from the conjunction of a stable organization This technology simplifies complex mixtures of proteins and chaotic fluctuations. Dr. Auffrey recognized the for their identification in proteome analysis. need to measure small variations of a large number of The expression of open reading frames is of special weak signals with high throughput technologies value to facilitate functional studies at the proteome developed under quality assurance. To achieve the level and to establish the biochemical and biophysical understanding of biological systems and stimulate properties of individual proteins and their molecular applications on preventive and predictive
Recommended publications
  • Chapter 14: Functional Genomics Learning Objectives
    Chapter 14: Functional Genomics Learning objectives Upon reading this chapter, you should be able to: ■ define functional genomics; ■ describe the key features of eight model organisms; ■ explain techniques of forward and reverse genetics; ■ discuss the relation between the central dogma and functional genomics; and ■ describe proteomics-based approaches to functional genomics. Outline : Functional genomics Introduction Relation between genotype and phenotype Eight model organisms E. coli; yeast; Arabidopsis; C. elegans; Drosophila; zebrafish; mouse; human Functional genomics using reverse and forward genetics Reverse genetics: mouse knockouts; yeast; gene trapping; insertional mutatgenesis; gene silencing Forward genetics: chemical mutagenesis Functional genomics and the central dogma Approaches to function; Functional genomics and DNA; …and RNA; …and protein Proteomic approaches to functional genomics CASP; protein-protein interactions; protein networks Perspective Albert Blakeslee (1874–1954) studied the effect of altered chromosome numbers on the phenotype of the jimson-weed Datura stramonium, a flowering plant. Introduction: Functional genomics Functional genomics is the genome-wide study of the function of DNA (including both genes and non-genic regions), as well as RNA and proteins encoded by DNA. The term “functional genomics” may apply to • the genome, transcriptome, or proteome • the use of high-throughput screens • the perturbation of gene function • the complex relationship of genotype and phenotype Functional genomics approaches to high throughput analyses Relationship between genotype and phenotype The genotype of an individual consists of the DNA that comprises the organism. The phenotype is the outward manifestation in terms of properties such as size, shape, movement, and physiology. We can consider the phenotype of a cell (e.g., a precursor cell may develop into a brain cell or liver cell) or the phenotype of an organism (e.g., a person may have a disease phenotype such as sickle‐cell anemia).
    [Show full text]
  • Masterpath: Network Analysis of Functional Genomics Screening Data
    MasterPATH: network analysis of functional genomics screening data Natalia Rubanova, Guillaume Pinna, Jeremie Kropp, Anna Campalans, Juan Pablo Radicella, Anna Polesskaya, Annick Harel-Bellan, Nadya Morozova To cite this version: Natalia Rubanova, Guillaume Pinna, Jeremie Kropp, Anna Campalans, Juan Pablo Radicella, et al.. MasterPATH: network analysis of functional genomics screening data. BMC Genomics, BioMed Central, 2020, 21 (1), pp.632. 10.1186/s12864-020-07047-2. hal-02944249 HAL Id: hal-02944249 https://hal.archives-ouvertes.fr/hal-02944249 Submitted on 26 Nov 2020 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. Rubanova et al. BMC Genomics (2020) 21:632 https://doi.org/10.1186/s12864-020-07047-2 METHODOLOGY ARTICLE Open Access MasterPATH: network analysis of functional genomics screening data Natalia Rubanova1,2,3* , Guillaume Pinna4, Jeremie Kropp1, Anna Campalans5,6,7, Juan Pablo Radicella5,6,7, Anna Polesskaya8, Annick Harel-Bellan1 and Nadya Morozova1,4 Abstract Background: Functional genomics employs several experimental approaches to investigate gene functions. High- throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list).
    [Show full text]
  • 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.
    [Show full text]
  • Human Functional Genomics Project Begins Unraveling Links Between
    Human Functional Genomics Project Begins Unraveling Links Between Genes, Immune Response Jul 28, 2016 | Elizabeth Newbern Premium NEW YORK (GenomeWeb) – Earlier this month, researchers involved in the Human Functional Genomics Project (HFGP) published data on the relationship between genetics and human cytokine response in Nature Medicine. The data was derived from a first cohort, the 200 Functional Genomics project, and the researchers plan to publish more data from this cohort in Nature Medicine in August. In addition, the HFGP just finished collecting patient data from a second cohort made up of 500 healthy individuals from Western Europe and aptly named the 500 Functional Genomics project, late last year. The HFGP researchers also recently finished collecting data from their LifeLines-deep cohort, made up of 1,600 healthy individuals from Western Europe. The researchers are assessing and analyzing the 500 FG and LifeLines-deep data now and hope to publish preliminary results at the end of this year, Mihai Netea, member of the HFGP's scientific advisory board and professor of the Radboud Center for Infectious Diseases (RCI) in the Netherlands, told GenomeWeb. They also finished recruitment and started data collection for their 300 Functional Genomics project, which is a cohort of 300 obese individuals, he added. The human immune system is composed of a complex network of organs, tissues, cells, and molecules and has evolved to both protect the host against infections and to play roles in immune surveillance. However, it has previously been unclear what role genetics plays in influencing cytokine responses in health and disease. The HFGP researchers aim to find out more about the role of genetics in immune response by characterizing and better understanding variation in human response through the use of various omics analyses — genomic, transcriptomic, metabolomic, and microbiomic — and in-depth functional phenotyping.
    [Show full text]
  • High-Throughput Automated Microfluidic Sample Preparation for Accurate Microbial Genomics
    High-throughput automated microfluidic sample preparation for accurate microbial genomics The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Kim, Soohong et al. “High-Throughput Automated Microfluidic Sample Preparation for Accurate Microbial Genomics.” Nature Communications 8 (2017): 13919. © 2017 Macmillan Publishers Limited As Published http://dx.doi.org/10.1038/ncomms13919 Publisher Nature Publishing Group Version Final published version Citable link http://hdl.handle.net/1721.1/108270 Terms of Use Creative Commons Attribution 4.0 International License Detailed Terms http://creativecommons.org/licenses/by/4.0/ ARTICLE Received 14 Oct 2016 | Accepted 11 Nov 2016 | Published 27 Jan 2017 DOI: 10.1038/ncomms13919 OPEN High-throughput automated microfluidic sample preparation for accurate microbial genomics Soohong Kim1,2, Joachim De Jonghe1,3, Anthony B. Kulesa1,2, David Feldman1,4, Tommi Vatanen1,5, Roby P. Bhattacharyya1,6, Brittany Berdy7, James Gomez1, Jill Nolan1, Slava Epstein7 & Paul C. Blainey1,2 Low-cost shotgun DNA sequencing is transforming the microbial sciences. Sequencing instruments are so effective that sample preparation is now the key limiting factor. Here, we introduce a microfluidic sample preparation platform that integrates the key steps in cells to sequence library sample preparation for up to 96 samples and reduces DNA input requirements 100-fold while maintaining or improving data quality. The general-purpose microarchitecture we demonstrate supports workflows with arbitrary numbers of reaction and clean-up or capture steps. By reducing the sample quantity requirements, we enabled low-input (B10,000 cells) whole-genome shotgun (WGS) sequencing of Mycobacterium tuberculosis and soil micro-colonies with superior results.
    [Show full text]
  • IBM Functional Genomics Platform, a Cloud-Based Platform for Studying Microbial Life at Scale
    IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS , VOL. 16, NO. 7, DECEMBER 2019 1 IBM Functional Genomics Platform, A Cloud-Based Platform for Studying Microbial Life at Scale Edward E. Seabolt*, Gowri Nayar*, Harsha Krishnareddy, Akshay Agarwal, Kristen L. Beck, Ignacio Terrizzano, Eser Kandogan, Mark Kunitomi, Mary Roth, Vandana Mukherjee, and James H. Kaufman Abstract—The rapid growth in biological sequence data is revolutionizing our understanding of genotypic diversity and challenging conventional approaches to informatics. With the increasing availability of genomic data, traditional bioinformatic tools require substantial computational time and the creation of ever-larger indices each time a researcher seeks to gain insight from the data. To address these challenges, we pre-computed important relationships between biological entities spanning the Central Dogma of Molecular Biology and captured this information in a relational database. The database can be queried across hundreds of millions of entities and returns results in a fraction of the time required by traditional methods. In this paper, we describe IBM Functional Genomics Platform (formerly known as OMXWare), a comprehensive database relating genotype to phenotype for bacterial life. Continually updated, IBM Functional Genomics Platform today contains data derived from 200,000 curated, self-consistently assembled genomes. The database stores functional data for over 68 million genes, 52 million proteins, and 239 million domains with associated biological activity annotations from Gene Ontology, KEGG, MetaCyc, and Reactome. IBM Functional Genomics Platform maps all of the many-to-many connections between each biological entity including the originating genome, gene, protein, and protein domain. Various microbial studies, from infectious disease to environmental health, can benefit from the rich data and connections.
    [Show full text]
  • After the Draft Sequence, What Next for the Human Genome Mapping Project Resource Centre?
    Comparative and Functional Genomics Comp Funct Genom 2001; 2: 176–179. DOI: 10.1002/cfg.83 Interview: Duncan Campbell After the draft sequence, what next for the Human Genome Mapping Project Resource Centre? Dr R. Duncan Campbell is the Director of the Medical Research Council (MRC) funded UK Human Genome Mapping Project Resource Centre (HGMP-RC), on the Wellcome Trust Genome Campus at Hinxton, near Cambridge, UK. The Centre provides both biological and bioinformatics resources to the UK genomics community, and also has research groups supporting these activities and investigating separate projects. CFG: The HGMP-RC is broadly arranged into three tools for DNA and protein sequence analysis, Divisions: Bioinformatics, Biology Services and which will expand the functions offered by the Research. Let’s tackle these one at a time. The Genetics Computer Group (GCG) package. We are draft sequence release will no doubt have encouraged currently redeveloping our website in order to make many researchers to get on-line and use bio- it bigger, better and more interactive for our users, informatics tools to mine the dataset, can you tell and our hope is that we will have a new website me about the projects that the Bioinformatics team which will go live in the next few months. The site are working on to facilitate such investigations? will include access and ordering for the existing services and information and support for new RDC: With the huge amount of draft sequence now services as they come on-line. available, we have seen increased use of the bioinformatics tools already on offer from the CFG: The Biology Services Division supplies a HGMP-RC, such as NIX and PIX.
    [Show full text]
  • Toward a Protein–Protein Interaction Map of the Budding Yeast: A
    Toward a protein–protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins Takashi Ito*†, Kosuke Tashiro‡, Shigeru Muta‡, Ritsuko Ozawa*§, Tomoko Chiba*§, Mayumi Nishizawa‡, Kiyoshi Yamamoto‡, Satoru Kuhara‡, and Yoshiyuki Sakaki*§ *Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan; ‡Molecular Gene Technics, Graduate School of Genetic Resources Technology, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan; and §RIKEN Genomic Sciences Center, Wako, Saitama 351-0198, Japan Communicated by Satoshi Omura, The Kitasato Institute, Tokyo, Japan, November 22, 1999 (received for review October 15, 1999) Protein–protein interactions play pivotal roles in various aspects of interaction modules contributes not only to the studies of the the structural and functional organization of the cell, and their particular pathway but also to much wider fields of biomedical complete description is indispensable to thorough understanding research. A large set of protein–protein interaction data would of the cell. As an approach toward this goal, here we report a lay a foundation for the search of such modules by both comprehensive system to examine two-hybrid interactions in all of experimental and computational means. the possible combinations between proteins of Saccharomyces Despite the need for comprehensive studies on protein– cerevisiae. We cloned all of the yeast ORFs individually as a protein interactions, no efforts for a genome-wide scale screen- DNA-binding domain fusion (‘‘bait’’) in a MATa strain and as an ing have ever been reported, although pioneering works using activation domain fusion (‘‘prey’’) in a MAT␣ strain, and subse- the yeast two-hybrid system (2) were reported on Drosophila cell quently divided them into pools, each containing 96 clones.
    [Show full text]
  • A Massively Parallel Barcoded Sequencing Pipeline Enables Generation of the First Orfeome and Interactome Map for Rice
    A massively parallel barcoded sequencing pipeline enables generation of the first ORFeome and interactome map for rice Shayne D. Wierbowskia,b,1, Tommy V. Vob,1,2, Pascal Falter-Braunc,d, Timothy O. Jobee, Lars H. Krusef, Xiaomu Weia, Jin Liangb, Michael J. Meyera,b, Nurten Akturkb, Christen A. Rivera-Erickb, Nicolas A. Corderob, Mauricio I. Paramob,g, Elnur E. Shayhidinb, Marta Bertolottib, Nathaniel D. Tippensa,b, Kazi Aktherh, Rita Sharmai, Yuichi Katayosej, Kourosh Salehi-Ashtianik,l,m,n, Tong Haol,m, Pamela C. Ronaldo,p,q, Joseph R. Eckerr,s, Peter A. Schweitzert, Shoshi Kikuchiu, Hiroshi Mizunov, David E. Hilll,m, Marc Vidall,m, Gaurav D. Moghef, Susan R. McCouchh,3, and Haiyuan Yua,b,3 aDepartment of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853; bWeill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853; cInstitute of Network Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Munich, Germany; dMicrobe-Host Interactions, Faculty of Biology, Ludwig-Maximilians-Universität München, 80539 Munich, Germany; eBotanical Institute, Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, 50674 Cologne, Germany; fPlant Biology Section, School of Integrative Plant Sciences, Cornell University, Ithaca, NY 14853; gDepartment of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853; hSection of Plant Breeding and Genetics, School of Integrated Plant Sciences, Cornell University, Ithaca, NY 14853-1901; iSchool
    [Show full text]
  • Arabidopsis Thaliana Functional Genomics Project Annual Report 2008
    The Multinational Coordinated Arabidopsis thaliana Functional Genomics Project Annual Report 2008 Xing Wang Deng [email protected] Chair Joe Kieber [email protected] Co-chair Joanna Friesner [email protected] MASC Coordinator and Executive Secretary Thomas Altmann [email protected] Sacha Baginsky [email protected] Ruth Bastow [email protected] Philip Benfey [email protected] David Bouchez [email protected] Jorge Casal [email protected] Danny Chamovitz [email protected] Bill Crosby [email protected] Joe Ecker [email protected] Klaus Harter [email protected] Marie-Theres Hauser [email protected] Pierre Hilson [email protected] Eva Huala [email protected] Jaakko Kangasjärvi [email protected] Julin Maloof [email protected] Sean May [email protected] Peter McCourt [email protected] Harvey Millar [email protected] Ortrun Mittelsten- Scheid [email protected] Basil Nikolau [email protected] Javier Paz-Ares [email protected] Chris Pires [email protected] Barry Pogson [email protected] Ben Scheres [email protected] Randy Scholl [email protected] Heiko Schoof [email protected] Kazuo Shinozaki [email protected] Klaas van Wijk [email protected] Paola Vittorioso [email protected] Wolfram Weckwerth [email protected] Weicai Yang [email protected] The Multinational Arabidopsis Steering Committee—July 2008 Front Cover Design Philippe Lamesch, Curator at TAIR/Carnegie Institute for Science, and Joanna Friesner, MASC Coordinator at the University of California, Davis, USA Images Map of geographical distribution of ecotypes of Arabidopsis thaliana Updated representation contributed by Philippe Lamesch, adapted from Jonathon Clarke, UK (1993).
    [Show full text]
  • Comparative Genomics for Reliable Protein-Function Prediction from Genomic Data
    340 Update TRENDS in Genetics Vol.20 No.8 August 2004 24 Yu, K. et al. (2003) R-loops at immunoglobulin class switch regions in 31 Michael, N. et al. (2003) The E box motif CAGGTG enhances somatic the chromosomes of stimulated B cells. Nat. Immunol. 4, 442–451 hypermutation without enhancing transcription. Immunity 19, 25 Reaban, M.E. and Griffin, J.A. (1990) Induction of RNA-stabilized 235–242 DNA conformers by transcription of an immunoglobulin switch region. 32 Ito, S. et al. (2004) Activation-induced cytidine deaminase shuttles Nature 348, 342–344 between nucleus and cytoplasm like apolipoprotein B mRNA 26 Daniels, G.A. and Lieber, M.R. (1995) RNA:DNA complex formation editing catalytic polypeptide 1. Proc. Natl. Acad. Sci. U. S. A. 101, upon transcription of immunoglobulin switch regions: implications for 1975–1980 the mechanism and regulation of class switch recombination. Nucleic 33 McBride, K.M. et al. (2004) Somatic hypermutation is limited by Acids Res. 23, 5006–5011 CRM1-dependent nuclear export of activation-induced deaminase. 27 Shinkura, R. et al. (2003) The influence of transcriptional orientation J. Exp. Med. 199, 1235–1244 on endogenous switch region function. Nat. Immunol. 4, 435–441 34 Brar, S. et al. Activation-induced cytidine deaminase (AID) is actively 28 Longacre, A. and Storb, U. (2000) A novel cytidine deaminase affects exported out of the nucleus but retained by the induction of DNA antibody diversity. Cell 102, 541–544 breaks. J. Biol. Chem. (in press) 29 Yoshikawa, K. et al. (2002) AID enzyme-induced hypermutation in an actively transcribed gene in fibroblasts.
    [Show full text]
  • Classical Genetics 3. the Beginnings of Genomic Biol
    Table of Contents: Preface 3.3.2. Eukaryotic chromosome structure Websites of Interest 3.3.3. Heterochromatin & Euchromatin 3.4. DNA Replication Glossary 3.4.1. DNA replication is semiconservative 1. Introduction 3.4.2. DNA polymerases 1.1. What is a Gene? 3.4.3. Initiation of replication 1.2. What is a Genome? 3.4.4. DNA replication is semidiscontinuous 1.3. What is Genomic Biology? 3.4.5. DNA replication in Eukaryotes. 1.3.1. Structural Genomics 3.4.6. Replicating ends of chromosomes 1.3.2. Comparative Genomics 3.5. Transcription 1.3.3. Functional Genomics 3.5.1. Cellular RNAs are transcribed from DNA 1.4. Genomic Databases 3.5.2. RNA polymerases catalyze transcription 3.5.3. Transcription in Prokaryotes 2. The beginnings of Genomic Biology – classical 3.5.4. Transcription in Prokaryotes - Polycistronic mRNAs genetics are produced from operons 2.1. Mendel & Darwin – traits are conditioned by genes 3.5.5. Beyond Operons – Modification of expression in 2.2. Genes are carried on chromosomes Prokaryotes 2.3. The chromosomal theory of inheritance 3.5.6. Transcriptions in Eukaryotes 2.4. Additional Complexity of Mendelian Inheritance 3.5.7. Processing primary transcripts into mature mRNA 2.4.1. Multiple alleles 3.6. Translation 2.4.2. Incomplete dominance and co-dominance 3.6.1. The Nature of Proteins 2.4.3. Sex linked inheritance 2.4.4. Epistasis 3.6.2. The Genetic Code 2.4.5. Epigenetics 3.6.3. tRNA – The decoding molecule 2.5. The Law of Independent Assortment 3.6.4.
    [Show full text]