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10 THE MOUSE GENOME DATABASE AND THE GENE EXPRESSION DATABASE:GENOTYPETO PHENOTYPE Introduction Janan T. Eppig, Joel E. Richardson, Judith A. Blake, Muriel T. Davisson, James A. Kadin, and Martin Ringwald The Jackson Laboratory, Bar Harbor, ME 04609 Two important resources for mouse biological and genomic data and analysis exist, and continue to expand at The Jackson Laboratory (U.S.A.). These resources, the Mouse Genome Database (MGD) and the Gene Expression Database (GXD), have as their long term goal the facilitation of research through access to integrated genomic structural data, gene expression information, and phenotypic descriptions. The World Wide Web (WWW) provides a method for easily navigating these integrated data resources to address complex questions of biological importance (Figure 1). The goals for the MGD and GXD resources are 1) to provide high quality, elemental genomic data suitable for analysis and integration with other biological data, 2) to develop software for data importation, analysis and display, and 3) to develop easy-to-use flexible interfaces for access by the scientific community. Emphasis is on acquisition of primary data, since these are most suitable for analysis and combination with new data.
120 Figure 1. Mouse Genome Informatics Home Page. The Mouse Genome Informatics World Wide Web site (http://www.informatics.jax.org) serves as an integrating point of access for the Mouse Genome Database (MGD) and the Gene Expression Database (GXD). This unifying WWW site provides users with seamless easy access to information on the genetics and biology of the laboratory mouse. Mouse Genome Database (MGD) The Beginnings The Mouse Genome Database developed as an outgrowth and extension of various information resources, both paper and electronic, on the laboratory mouse. The tradition of pre-publication and anecdotal information exchange in the mouse
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BIOINFORMATICS: Databases and Syste
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BIOINFORMATICS: Databases and Syste
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To Tish, Emily, Miles and Josephine
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TABLE OF CONTENTS INTRODUCTION ....
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INTRODUCTION Stanley Letovsky Bioin
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for wisdom. Where algorithms tend t
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gain performance by distributing qu
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systems described (OPM, BioKleisli,
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DATABASES
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1 NCBI: INTEGRATED DATA FOR MOLECUL
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figure 1, and all flows into the ID
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There are other sets of data that c
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2. Explicit declaration by the hist
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2. W. J. Wilbur, A retrieval system
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2 HOVERGEN: COMPARATIVE ANALYSIS OF
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Figure 1: Phylogenetic tree of the
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fragment. In some cases, a same gen
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Figure 2: Distribution of families
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the user can visualize all the anno
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Selecting orthologous genes with HO
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Thus, HOVERGEN allows one to do exh
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21. Saitou, N. Nei, M. The neighbor
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3 WIT/WIT2: METABOLIC RECONSTRUCTIO
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protein sequence are used (e.g., OR
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After we have accumulated an initia
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Coordinating the Development of Met
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Availability of the Pathways, Softw
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4 ECOCYC: THE RESOURCE AND THE LESS
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epresented as an instance of the cl
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The Gene--Reaction Schematic The ma
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EcoCyc contains extensive informati
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within compound windows uses a conc
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Figure 4: The EcoCyc linear map bro
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Figure 5: The software architecture
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6. Karp. Frame representation and r
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Introduction 5 KEGG: FROM GENES TO
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Binary relations Figure 1 : Level o
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hierarchical text. The genome map i
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Page 80 and 81: can be used for gene function assig
Page 82 and 83: influenzae lacks the upper portion
Page 84 and 85: Future Directions Figure 4. KEGG as
Page 86 and 87: Introduction 6 OMIM: ONLINE MENDELI
Page 88 and 89: The Entry Each OMIM entry is assign
Page 90 and 91: Searching OMIM is as simple as typi
Page 92 and 93: How OMIM is Curated OMIM is maintai
Page 94 and 95: 7 GDB: INTEGRATING GENOMIC MAPS Int
Page 96 and 97: in one of two ways: by being wholly
Page 98 and 99: Figure 3: Universal Coordinate Disp
Page 100 and 101: Figure 5: Dispersion of linear (lef
Page 102 and 103: which minimizes dispersion. The opt
Page 104 and 105: Figure 10: A piece of an "comprehen
Page 106 and 107: alignments are better for the speci
Page 108 and 109: Summary 8 HGMD: THE HUMAN GENE MUTA
Page 110 and 111: Data coverage and structure 101 By
Page 112 and 113: Conclusions and Outlook 103 Being b
Page 114 and 115: Introduction 9 SENSELAB: MODELING H
Page 116 and 117: 107 For the purposes of the rest of
Page 118 and 119: 109 users might advocate creating a
Page 120 and 121: 111 The Associations table describe
Page 122 and 123: Managing the Object Class Hierarchy
Page 124 and 125: 115 In fig. 2, the Objects and Clas
Page 126 and 127: 117 7. Mirsky JS, Nadkarni PM, Hine
Page 130 and 131: genetics community fostered the ear
Page 132 and 133: 123 Data acquisition for MGD includ
Page 134 and 135: 125 In February 1998, a ‘cDNA and
Page 136 and 137: Acknowledgments 127 MGD is funded b
Page 138 and 139: 11 THE EDINBURGH MOUSE ATLAS: BASIC
Page 140 and 141: expression database under developme
Page 142 and 143: 133 For blastocysts the shape of th
Page 144 and 145: 135 which is a cut at an arbitrary
Page 146 and 147: 137 This defines a viewing directio
Page 148 and 149: 139 plate spline [19] or polynomial
Page 150 and 151: 12 FLYBASE: GENOMIC AND POST- GENOM
Page 152 and 153: 143 Genes, including links via publ
Page 154 and 155: Public Access to FlyBase Data The c
Page 156 and 157: 147 There are two classes of annota
Page 158 and 159: 149 screens to identify direct or i
Page 160 and 161: 13 MAIZEDB: THE MAIZE GENOME DATABA
Page 162 and 163: The bins map strategy has been empl
Page 164 and 165: Clicking on one of the orthologous
Page 166 and 167: * hyper-text linked to MaizeDB enti
Page 168 and 169: DISSEMINATION TO EXTERNAL DATABASES
Page 170 and 171: Appendix 2: part of the Query Form
Page 172 and 173: Introduction 14 AGIS: USING THE AGR
Page 174 and 175: Horn Fly--Haematobia Screwworm--Coc
Page 176 and 177: Figure 2 : Main AGIS Menu 167
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Figure 4 : Available Classes and Su
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Figure 6 : Query by Example and Que
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Figure 8 : Hypertext ACEDB Object a
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15 CGSC: THE E.COLI GENETIC STOCK C
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177 described once for that gene an
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FIGURE 1. A Query for an hsdR- recA
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FIGURE 2. A Query for a lacZ-(amber
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183 These are suggestions that dese
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SYSTEMS
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Introduction 16 OPM: OBJECT-PROTOCO
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189 OPM is a data model whose objec
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The OPM Database Development Tools
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ased on this query tree. Further qu
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195 Interface, except that one can
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197 Our strategy of providing suppo
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Acknowledgments 199 Between 1992 an
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Introduction 17 BIOKLEISLI:INTEGRAT
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203 In the remainder of this chapte
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pages : “4267-4274", abstract:
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BioKleisli in Action: Querying Biom
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209 savings in execution time is si
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References 211 13. NCBI ASN. 1 Spec
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Introduction 18 SRS: ANALYZING AND
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215 In SRS, databank structures are
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217 Operands also include named set
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Figure 1. The SRS Top Page. Figure
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Analyzing Data in SRS with Applicat
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223 other databanks, and define spe
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Figure 7. A SWISS-PROT entry with i
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Figure 8. The results of a query fo
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parser and documentation files, and
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23 1 12. Altschul, S.F., Gish, W.,
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Introduction 19 BIOLOGY WORKBENCH:
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Figure 1. Organization and Flow of
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237 In order to make wrapper develo
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the browser window containing a men
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either during the current Workbench
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PROFILESCAN Comparison of protein o
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20 EBI: CORBA AND THE EBI DATABASES
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247 the Object Request Broker (ORB)
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249 Every database entry is represe
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251 Wrappers with and without State
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Discussion 25 3 CORBA and IDL minim
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Introduction 21 BIOWIDGETS:REUSABLE
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Visualization Solutions 257 Turnkey
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259 The architecture also exploits
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possible because the bioWidget arch
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263 4. Gish, Warren (1994-1997). un
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22 ACEDB: THE ACE DATABASE MANAGER
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267 On the other hand, most users c
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269 Street, City and State into "ma
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27 1 to a running database after ed
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273 database the class Map refers t
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275 were waiting for the Biowidget
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277 table of 13 columns and 1 milli
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Introduction 23 LABBASE: DATA AND W
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28 1 We will use as a running examp
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LabBase Details 283 A LabBase objec
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When the procedure completes, LabFl
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287 sequence-assembly Worker in our
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289 The Worker converts the object
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291 Genome Research and are beginni
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INDEX
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INDEX A of Agricultural Genome Info
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of vertebrate genes, 21-33 developm
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GenBank database query software of,
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Metabolic Pathway Database, 38 Meta
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conversion to ‘gi’ identifiers
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