An Ancient Yeast for Young Geneticists: a Primer on the Schizosaccharomyces Pombe Model System
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Creating the Gene Ontology Resource: Design and Implementation
Resource Creating the Gene Ontology Resource: Design and Implementation The Gene Ontology Consortium2 The exponential growth in the volume of accessible biological information has generated a confusion of voices surrounding the annotation of molecular information about genes and their products. The Gene Ontology (GO) project seeks to provide a set of structured vocabularies for specific biological domains that can be used to describe gene products in any organism. This work includes building three extensive ontologies to describe molecular function, biological process, and cellular component, and providing a community database resource that supports the use of these ontologies. The GO Consortium was initiated by scientists associated with three model organism databases: SGD, the Saccharomyces Genome database; FlyBase, the Drosophila genome database; and MGD/GXD, the Mouse Genome Informatics databases. Additional model organism database groups are joining the project. Each of these model organism information systems is annotating genes and gene products using GO vocabulary terms and incorporating these annotations into their respective model organism databases. Each database contributes its annotation files to a shared GO data resource accessible to the public at http://www.geneontology.org/. The GO site can be used by the community both to recover the GO vocabularies and to access the annotated gene product data sets from the model organism databases. The GO Consortium supports the development of the GO database resource and provides tools enabling curators and researchers to query and manipulate the vocabularies. We believe that the shared development of this molecular annotation resource will contribute to the unification of biological information. As the amount of biological information has grown, it has examining microarray expression data, sequencing genotypes become increasingly important to describe and classify bio- from a population, or identifying all glycolytic enzymes is logical objects in meaningful ways. -
To Find Information About Arabidopsis Genes Leonore Reiser1, Shabari
UNIT 1.11 Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes Leonore Reiser1, Shabari Subramaniam1, Donghui Li1, and Eva Huala1 1Phoenix Bioinformatics, Redwood City, CA USA ABSTRACT The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome, Additionally we describe how members of the community can share data using TAIR’s Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. Keywords: Arabidopsis ● databases ● bioinformatics ● data mining ● genomics INTRODUCTION The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource for the biology of Arabidopsis thaliana (Huala et al., 2001; Garcia-Hernandez et al., 2002; Rhee et al., 2003; Weems et al., 2004; Swarbreck et al., 2008, Lamesch, et al., 2010, Berardini et al., 2016). The TAIR database contains information about genes, proteins, gene expression, mutant phenotypes, germplasms, clones, genetic markers, genetic and physical maps, genome organization, publications, and the research community. In addition, seed and DNA stocks from the Arabidopsis Biological Resource Center (ABRC; Scholl et al., 2003) are integrated with genomic data, and can be ordered through TAIR. -
Former Fellows Biographical Index Part
Former Fellows of The Royal Society of Edinburgh 1783 – 2002 Biographical Index Part Two ISBN 0 902198 84 X Published July 2006 © The Royal Society of Edinburgh 22-26 George Street, Edinburgh, EH2 2PQ BIOGRAPHICAL INDEX OF FORMER FELLOWS OF THE ROYAL SOCIETY OF EDINBURGH 1783 – 2002 PART II K-Z C D Waterston and A Macmillan Shearer This is a print-out of the biographical index of over 4000 former Fellows of the Royal Society of Edinburgh as held on the Society’s computer system in October 2005. It lists former Fellows from the foundation of the Society in 1783 to October 2002. Most are deceased Fellows up to and including the list given in the RSE Directory 2003 (Session 2002-3) but some former Fellows who left the Society by resignation or were removed from the roll are still living. HISTORY OF THE PROJECT Information on the Fellowship has been kept by the Society in many ways – unpublished sources include Council and Committee Minutes, Card Indices, and correspondence; published sources such as Transactions, Proceedings, Year Books, Billets, Candidates Lists, etc. All have been examined by the compilers, who have found the Minutes, particularly Committee Minutes, to be of variable quality, and it is to be regretted that the Society’s holdings of published billets and candidates lists are incomplete. The late Professor Neil Campbell prepared from these sources a loose-leaf list of some 1500 Ordinary Fellows elected during the Society’s first hundred years. He listed name and forenames, title where applicable and national honours, profession or discipline, position held, some information on membership of the other societies, dates of birth, election to the Society and death or resignation from the Society and reference to a printed biography. -
Use and Misuse of the Gene Ontology Annotations
Nature Reviews Genetics | AOP, published online 13 May 2008; doi:10.1038/nrg2363 REVIEWS Use and misuse of the gene ontology annotations Seung Yon Rhee*, Valerie Wood‡, Kara Dolinski§ and Sorin Draghici|| Abstract | The Gene Ontology (GO) project is a collaboration among model organism databases to describe gene products from all organisms using a consistent and computable language. GO produces sets of explicitly defined, structured vocabularies that describe biological processes, molecular functions and cellular components of gene products in both a computer- and human-readable manner. Here we describe key aspects of GO, which, when overlooked, can cause erroneous results, and address how these pitfalls can be avoided. The accumulation of data produced by genome-scale FIG. 1b). These characteristics of the GO structure enable research requires explicitly defined vocabularies to powerful grouping, searching and analysis of genes. describe the biological attributes of genes in order to allow integration, retrieval and computation of the Fundamental aspects of GO annotations data1. Arguably, the most successful example of system- A GO annotation associates a gene with terms in the atic description of biology is the Gene Ontology (GO) ontologies and is generated either by a curator or project2. GO is widely used in biological databases, automatically through predictive methods. Genes are annotation projects and computational analyses (there associated with as many terms as appropriate as well as are 2,960 citations for GO in version 3.0 of the ISI Web of with the most specific terms available to reflect what is Knowledge) for annotating newly sequenced genomes3, currently known about a gene. -
The Impact of Databases on Model Organism Biology Sabina Leonelli
Re-Thinking Organisms: The Impact of Databases on Model Organism Biology Sabina Leonelli (corresponding author) ESRC Centre for Genomics in Society, University of Exeter Byrne House, St Germans Road, EX4 4PJ Exeter, UK. Tel: 0044 1392 269137 Fax: 0044 1392 269135 Email: [email protected] Rachel A. Ankeny School of History and Politics, University of Adelaide 423 Napier, Adelaide 5005 SA, AUSTRALIA. Tel: 0061 8 8303 5570 Fax: 0061 8 8303 3443 Email: [email protected] ‘Databases for model organisms promote data integration through the development and implementation of nomenclature standards, controlled vocabularies and ontologies, that allow data from different organisms to be compared and contrasted’ (Carole Bult 2002, 163) Abstract Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the curators of these databases for what counts as reliable evidence, acceptable terminology, appropriate experimental set-ups and adequate materials (e.g., specimens). On the one hand, these choices are informed by the collaborative research ethos characterising most model organism communities. On the other hand, the deployment of these standards in databases reinforces this ethos and gives it concrete and precise instantiations by shaping the skills, practices, values and background knowledge required of the database users. -
Sheila Watson Fonds Finding Guide
SHEILA WATSON FONDS FINDING GUIDE SPECIAL COLLECTIONS JOHN M. KELLY LIBRARY | UNIVERSITY OF ST. MICHAEL’S COLLEGE 113 ST. JOSEPH STREET TORONTO, ONTARIO, CANADA M5S 1J4 ARRANGED AND DESCRIBED BY ANNA ST.ONGE CONTRACT ARCHIVIST JUNE 2007 (LAST UPDATED SEPTEMBER 2012) TABLE OF CONTENTS TAB Part I : Fonds – level description…………………………………………………………A Biographical Sketch HiStory of the Sheila WatSon fondS Extent of fondS DeScription of PaperS AcceSS, copyright and publiShing reStrictionS Note on Arrangement of materialS Related materialS from other fondS and Special collectionS Part II : Series – level descriptions………………………………………………………..B SerieS 1.0. DiarieS, reading journalS and day plannerS………………………………………...1 FileS 2006 01 01 – 2006 01 29 SerieS 2.0 ManuScriptS and draftS……………………………………………………………2 Sub-SerieS 2.1. NovelS Sub-SerieS 2.2. Short StorieS Sub-SerieS 2.3. Poetry Sub-SerieS 2.4. Non-fiction SerieS 3.0 General correSpondence…………………………………………………………..3 Sub-SerieS 3.1. Outgoing correSpondence Sub-SerieS 3.2. Incoming correSpondence SerieS 4.0 PubliShing records and buSineSS correSpondence………………………………….4 SerieS 5.0 ProfeSSional activitieS materialS……………………………………………………5 Sub-SerieS 5.1. Editorial, collaborative and contributive materialS Sub-SerieS 5.2. Canada Council paperS Sub-SerieS 5.3. Public readingS, interviewS and conference material SerieS 6.0 Student material…………………………………………………………………...6 SerieS 7.0 Teaching material………………………………………………………………….7 Sub-SerieS 7.1. Elementary and secondary school teaching material Sub-SerieS 7.2. UniverSity of BritiSh Columbia teaching material Sub-SerieS 7.3. UniverSity of Toronto teaching material Sub-SerieS 7.4. UniverSity of Alberta teaching material Sub-SerieS 7.5. PoSt-retirement teaching material SerieS 8.0 Research and reference materialS…………………………………………………..8 Sub-serieS 8.1. -
Electrophysiology Read-Out Tools for Brain-On-Chip Biotechnology
micromachines Review Electrophysiology Read-Out Tools for Brain-on-Chip Biotechnology Csaba Forro 1,2,†, Davide Caron 3,† , Gian Nicola Angotzi 4,†, Vincenzo Gallo 3, Luca Berdondini 4 , Francesca Santoro 1 , Gemma Palazzolo 3,* and Gabriella Panuccio 3,* 1 Tissue Electronics, Fondazione Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci, 53-80125 Naples, Italy; [email protected] (C.F.); [email protected] (F.S.) 2 Department of Chemistry, Stanford University, Stanford, CA 94305, USA 3 Enhanced Regenerative Medicine, Fondazione Istituto Italiano di Tecnologia, Via Morego, 30-16163 Genova, Italy; [email protected] (D.C.); [email protected] (V.G.) 4 Microtechnology for Neuroelectronics, Fondazione Istituto Italiano di Tecnologia, Via Morego, 30-16163 Genova, Italy; [email protected] (G.N.A.); [email protected] (L.B.) * Correspondence: [email protected] (G.P.); [email protected] (G.P.); Tel.: +39-010-2896-884 (G.P.); +39-010-2896-493 (G.P.) † These authors contributed equally to this paper. Abstract: Brain-on-Chip (BoC) biotechnology is emerging as a promising tool for biomedical and pharmaceutical research applied to the neurosciences. At the convergence between lab-on-chip and cell biology, BoC couples in vitro three-dimensional brain-like systems to an engineered microfluidics platform designed to provide an in vivo-like extrinsic microenvironment with the aim of replicating tissue- or organ-level physiological functions. BoC therefore offers the advantage of an in vitro repro- duction of brain structures that is more faithful to the native correlate than what is obtained with conventional cell culture techniques. -
The Mouse Genome Database: Integration of and Access to Knowledge About the Laboratory Mouse Judith A
D810–D817 Nucleic Acids Research, 2014, Vol. 42, Database issue Published online 26 November 2013 doi:10.1093/nar/gkt1225 The Mouse Genome Database: integration of and access to knowledge about the laboratory mouse Judith A. Blake*, Carol J. Bult, Janan T. Eppig, James A. Kadin and Joel E. Richardson The Mouse Genome Database Groupy Bioinformatics and Computational Biology, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA Received October 1, 2013; Revised November 4, 2013; Accepted November 5, 2013 ABSTRACT genes and as a comprehensive data integration site and repository for mouse genetic, genomic and phenotypic The Mouse Genome Database (MGD) (http://www. data derived from primary literature as well as from informatics.jax.org) is the community model major data providers (1,2). organism database resource for the laboratory The central mission of the MGD is to support the trans- mouse, a premier animal model for the study of lation of information from experimental mouse models to genetic and genomic systems relevant to human uncover the genetic basis of human diseases. As a highly biology and disease. MGD maintains a comprehen- curated and comprehensive model organism database, sive catalog of genes, functional RNAs and other MGD provides web and programmatic access to a genome features as well as heritable phenotypes complete catalog of mouse genes and genome features and quantitative trait loci. The genome feature including genomic sequence and variant information. catalog is generated by the integration of computa- MGD curates and maintains the comprehensive listing of functional annotations for mouse genes using Gene tional and manual genome annotations generated Ontology (GO) terms and contributes to the development by NCBI, Ensembl and Vega/HAVANA. -
Functionalization of Brain Region-Specific Spheroids With
www.nature.com/scientificreports OPEN Functionalization of Brain Region- specifc Spheroids with Isogenic Microglia-like Cells Received: 5 February 2019 Liqing Song1,6, Xuegang Yuan1, Zachary Jones2, Cynthia Vied 3, Yu Miao1, Mark Marzano1, Accepted: 15 July 2019 Thien Hua4, Qing-Xiang Amy Sang4,5, Jingjiao Guan1, Teng Ma1, Yi Zhou2 & Yan Li1,5 Published: xx xx xxxx Current brain spheroids or organoids derived from human induced pluripotent stem cells (hiPSCs) still lack a microglia component, the resident immune cells in the brain. The objective of this study is to engineer brain region-specifc organoids from hiPSCs incorporated with isogenic microglia- like cells in order to enhance immune function. In this study, microglia-like cells were derived from hiPSCs using a simplifed protocol with stage-wise growth factor induction, which expressed several phenotypic markers, including CD11b, IBA-1, CX3CR1, and P2RY12, and phagocytosed micron-size super-paramagnetic iron oxides. The derived cells were able to upregulate pro-infammatory gene (TNF-α) and secrete anti-infammatory cytokines (i.e., VEGF, TGF-β1, and PGE2) when stimulated with amyloid β42 oligomers, lipopolysaccharides, or dexamethasone. The derived isogenic dorsal cortical (higher expression of TBR1 and PAX6) and ventral (higher expression of NKX2.1 and PROX1) spheroids/ organoids displayed action potentials and synaptic activities. Co-culturing the microglia-like cells (MG) with the dorsal (D) or ventral (V) organoids showed diferential migration ability, intracellular Ca2+ signaling, and the response to pro-infammatory stimuli (V-MG group had higher TNF-α and TREM2 expression). Transcriptome analysis exhibited 37 microglia-related genes that were diferentially expressed in MG and D-MG groups. -
Biomolecule and Bioentity Interaction Databases in Systems Biology: a Comprehensive Review
biomolecules Review Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review Fotis A. Baltoumas 1,* , Sofia Zafeiropoulou 1, Evangelos Karatzas 1 , Mikaela Koutrouli 1,2, Foteini Thanati 1, Kleanthi Voutsadaki 1 , Maria Gkonta 1, Joana Hotova 1, Ioannis Kasionis 1, Pantelis Hatzis 1,3 and Georgios A. Pavlopoulos 1,3,* 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; zafeiropoulou@fleming.gr (S.Z.); karatzas@fleming.gr (E.K.); [email protected] (M.K.); [email protected] (F.T.); voutsadaki@fleming.gr (K.V.); [email protected] (M.G.); hotova@fleming.gr (J.H.); [email protected] (I.K.); hatzis@fleming.gr (P.H.) 2 Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece * Correspondence: baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.); Tel.: +30-210-965-6310 (G.A.P.) Abstract: Technological advances in high-throughput techniques have resulted in tremendous growth Citation: Baltoumas, F.A.; of complex biological datasets providing evidence regarding various biomolecular interactions. Zafeiropoulou, S.; Karatzas, E.; To cope with this data flood, computational approaches, web services, and databases have been Koutrouli, M.; Thanati, F.; Voutsadaki, implemented to deal with issues such as data integration, visualization, exploration, organization, K.; Gkonta, M.; Hotova, J.; Kasionis, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more I.; Hatzis, P.; et al. Biomolecule and and more difficult for an end user to know what the scope and focus of each repository is and how Bioentity Interaction Databases in redundant the information between them is. -
Structure and Function of Cells
Biophysics: An Introduction CHAPTER 1 STRUCTURE AND FUNCTION OF CELLS The cell (from Latin cella, meaning "small room") is the basic structural, functional and biological unit of all known living organisms. Cells are the smallest unit of life that can replicate independently, and are often called the "building blocks of life" (http://en.wikipedia.org/wiki/Cell_biology). The cell is the structural integrity, functional. and hereditary smallest of living creatures in the form of a small space bounded by membranes and contains a concentrated liquid. In Becker. et al (2000:2) mentioned that the cell is the basic unit of biology. Cells consist of a protoplasm enclosed within a membrane, which contains many biomolecules such as proteins and nucleic acids. Organisms can be classified as unicellular (consisting of a single cell; including most bacteria) or multicellular (including plants and animals). While the number of cells in plants and animals varies from species to species, humans contain about 100 trillion (1014) cells. The cells come from preexisting cells and have a life of their own in addition to their joint role in the multicellular organism. Most living things are composed of single cells. or so-called unicellular organisms. such as bacteria and amoeba. Other living things. including plants. animals. and humans, are multicellular organisms composed of many specialized cell types with their respective functions Most plant and animal cells are visible only under the microscope, with dimensions between 1 and 100 micrometres. The human body is composed of more than 1013 cells. Nevertheless. the whole body of all organisms derived from a single cell division results. -
Biologie Moléculaire De LA CELLULE Biologie Moléculaire De Sixième Édition
Sixième édition BRUCE ALEXANDER JULIAN DAVID MARTIN KEITH PETER ALBERTS JOHNSON LEWIS MORGAN RAFF ROBERTS WALTER Biologie moléculaire de LA CELLULE Biologie moléculaire de Sixième édition LA CELLULESixième édition Biologie moléculaire de moléculaire Biologie LA CELLULE LA BRUCE ALBERTS BRUCE ALBERTS ALEXANDER JOHNSON ALEXANDER JOHNSON JULIAN LEWIS JULIAN LEWIS DAVID MORGAN DAVID MORGAN MARTIN RAFF MARTIN RAFF KEITH ROBERTS KEITH ROBERTS PETER WALTER PETER WALTER -:HSMCPH=WU[\]\: editions.lavoisier.fr 978-2-257-20678-7 20678-Albers2017.indd 1-3 08/09/2017 11:09 Chez le même éditeur Culture de cellules animales, 3e édition, par G. Barlovatz-Meimon et X. Ronot Biochimie, 7e édition, par J. M. Berg, J. L. Tymoczko, L. Stryer L’essentiel de la biologie cellulaire, 3e édition, par B. Alberts, D. Bray, K. Hopkin, A. Johnson, A. J. Lewis, M. Ra", K. Roberts et P. Walter Immunologie, par L. Chatenoud et J.-F. Bach Génétique moléculaire humaine, 4e édition, par T. Strachan et A. Read Manuel de poche de biologie cellulaire, par H. Plattner et J. Hentschel Manuel de poche de microbiologie médicale, par F. H. Kayser, E. C. Böttger, P. Deplazes, O. Haller, A. Roers Atlas de poche de génétique, par E. Passarge Atlas de poche de biotechnologie et de génie génétique, par R.D. Schmid Les biosimilaires, par J.-L. Prugnaud et J.-H. Trouvin Bio-informatique moléculaire : une approche algorithmique (Coll. IRIS), par P. A. Pevzner et N. Puech Cycle cellulaire et cytométrie en "ux, par D. Grunwald, J.-F. Mayol et X. Ronot La cytométrie en "ux, par X. Ronot, D.