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TABLE OF CONTENTS INTRODUCTION ..........................................................................................................1 Stanley Letovsky DATABASES 1 NCBI: INTEGRATED DATA FOR MOLECULAR BIOLOGY RESEARCH .................11 Karl Sirotkin 2 HOVERGEN: COMPARATIVE ANALYSIS OF HOMOLOGOUS VERTEBRATE GENES .............................................................................................................. 21 Laurent Duret, Guy Perrière and Manolo Gouy 3 WIT/WIT2: METABOLIC RECONSTRUCTION SYSTEMS................................. 37 Ross Overbeek, Niels Larsen, Natalia Maltsev, Gordon D. Pusch, and Evgeni Selkov 4 ECOCYC: THERESOURCE AND THE LESSONS LEARNED.............................. 47 Peter D. Karp and Monica Riley 5 KEGG: FROM GENES TO BIOCHEMICAL PATHWAYS ........................................... 63 Minoru Kanehisa 6 OMIM: ONLINE MENDELIAN INHERITANCE IN MAN...................................77 Alan F. Scott, Joanna Amberger, Brandon Brylawski and Victor A. McKusick 7 GDB: INTEGRATING GENOMIC MAPS ............................................................. 85 Stanley Letovsky 8 HGMD: THE HUMAN GENE MUTATION DATABASE ....................................... 99 Michael Krawczak, Edward V. Ball, Peter Stenson and David N. Cooper 9 SENSELAB: MODELING HETEROGENOUS DATA ON THE NERVOUS SYSTEM ............................................................................................................................. 105 Prakash Nadkarni, Jason Mirsky, Emmanouil Skoufos, Matthew Healy, Michael Hines, Perry Miller and Gordon Shepherd 10 THE MOUSE GENOME DATABASE AND THE GENE EXPRESSION DATABASE: GENOTYPE TO PHENOTYPE ...............................................................................119 Janan T. Eppig, Joel E. Richardson, Judith A. Blake, Muriel T. Davisson, James A. Kadin, and Martin Ringwald THE EDINBURGH MOUSE ATLAS: BASIC STRUCTURE AND INFORMATICS ....129 11 12 Richard A Baldock, Christophe Dubreuil, Bill Hill and Duncan Davidson FLYBASE: GENOMIC AND POST-GENOMIC VIEWPOINTS ............................... 141 The FlyBase Consortium
viii 13 MAIZEDB: THE MAIZE GENOME DATABASE..............................................151 Mary Polacco and Ed Coe 14 AGIS: USING THE AGRICULTURAL GENOME INFORMATION SYSTEM ..... 163 Stephen M. Beckstrom-Sternberg and D. Curtis Jamison 15 CGSC: THE E.COLI GENETIC STOCK CENTER DATABASE .........................175 Mary K.B. Berlyn SYSTEMS 16 OPM: OBJECT-PROTOCOL MODEL DATA MANAGEMENT TOOLS ‘97 ......... 187 Victor M. Markowitz, I-Min A. Chen, Anthony S. Kosky, and Ernest Szeto 17 BIOKLEISLI: INTEGRATING BIOMEDICAL DATA AND ANALYSIS PACKAGES 201 Susan B. Davidson, O. Peter Buneman, Jonathan Crabtree, Val Tannen, G. Christian Overton and Limsoon Wong 18 SRS: ANALYZING AND USING DATA FROM HETEROGENOUS TEXTUAL DATABANKS...................................................................................... 213 Phil Carter, Thierry Coupaye, David P. Kreil, and Thure Etzold 19 BIOLOGY WORKBENCH: ACOMPUTING AND ANALYSIS ENVIRONMENT FOR THE BIOLOGICAL SCIENCES ................................................................... 233 Roger Unwin, James Fenton. Mark Whitsitt, Curt Jamison, Mark Stupar, Eric Jakobsson and Shankar Subramaniam 20 EBI: CORBA AND THE EBI DATABASES........................................... 245 Kim Jungfer, Graham Cameron and Tomas Flores 21 BIOWIDGETS: REUSABLE VISUALIZATION COMPONENTS FOR BIOINFORMATICS................................................................................... 255 Jonathan Crabtree, Steve Fischer, Mark Gibson and G. Christian Overton 22 ACEDB:THE ACE DATABASE MANAGER ............................................. 265 Jean Thierry-Mieg, Danielle Thierry-Mieg and Lincoln Stein 23 LABBASE: DATA AND WORKFLOW MANAGEMENT FOR LARGE SCALE BIOLOGICAL RESEARCH............................................................. 279 Nathan Goodman, Steve Rozen, and Lincoln Stein INDEX ............................................................................................................... 293
Page 2 and 3: BIOINFORMATICS: Databases and Syste
Page 4 and 5: BIOINFORMATICS: Databases and Syste
Page 6 and 7: To Tish, Emily, Miles and Josephine
Page 10 and 11: INTRODUCTION Stanley Letovsky Bioin
Page 12 and 13: for wisdom. Where algorithms tend t
Page 14 and 15: gain performance by distributing qu
Page 16 and 17: systems described (OPM, BioKleisli,
Page 18 and 19: DATABASES
Page 20 and 21: 1 NCBI: INTEGRATED DATA FOR MOLECUL
Page 22 and 23: figure 1, and all flows into the ID
Page 24 and 25: There are other sets of data that c
Page 26 and 27: 2. Explicit declaration by the hist
Page 28 and 29: 2. W. J. Wilbur, A retrieval system
Page 30 and 31: 2 HOVERGEN: COMPARATIVE ANALYSIS OF
Page 32 and 33: Figure 1: Phylogenetic tree of the
Page 34 and 35: fragment. In some cases, a same gen
Page 36 and 37: Figure 2: Distribution of families
Page 38 and 39: the user can visualize all the anno
Page 40 and 41: Selecting orthologous genes with HO
Page 42 and 43: Thus, HOVERGEN allows one to do exh
Page 44 and 45: 21. Saitou, N. Nei, M. The neighbor
Page 46 and 47: 3 WIT/WIT2: METABOLIC RECONSTRUCTIO
Page 48 and 49: protein sequence are used (e.g., OR
Page 50 and 51: After we have accumulated an initia
Page 52 and 53: Coordinating the Development of Met
Page 54 and 55: Availability of the Pathways, Softw
Page 56 and 57: 4 ECOCYC: THE RESOURCE AND THE LESS
Page 58 and 59:
epresented as an instance of the cl
Page 60 and 61:
The Gene--Reaction Schematic The ma
Page 62 and 63:
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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(2) Java applets to search, compare
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can be used for gene function assig
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influenzae lacks the upper portion
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Future Directions Figure 4. KEGG as
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Introduction 6 OMIM: ONLINE MENDELI
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The Entry Each OMIM entry is assign
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Searching OMIM is as simple as typi
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How OMIM is Curated OMIM is maintai
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7 GDB: INTEGRATING GENOMIC MAPS Int
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in one of two ways: by being wholly
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Figure 3: Universal Coordinate Disp
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Figure 5: Dispersion of linear (lef
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which minimizes dispersion. The opt
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Figure 10: A piece of an "comprehen
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alignments are better for the speci
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Summary 8 HGMD: THE HUMAN GENE MUTA
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Data coverage and structure 101 By
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Conclusions and Outlook 103 Being b
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Introduction 9 SENSELAB: MODELING H
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107 For the purposes of the rest of
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109 users might advocate creating a
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111 The Associations table describe
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Managing the Object Class Hierarchy
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115 In fig. 2, the Objects and Clas
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117 7. Mirsky JS, Nadkarni PM, Hine
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10 THE MOUSE GENOME DATABASE AND TH
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genetics community fostered the ear
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123 Data acquisition for MGD includ
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125 In February 1998, a ‘cDNA and
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Acknowledgments 127 MGD is funded b
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11 THE EDINBURGH MOUSE ATLAS: BASIC
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expression database under developme
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133 For blastocysts the shape of th
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135 which is a cut at an arbitrary
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137 This defines a viewing directio
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139 plate spline [19] or polynomial
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12 FLYBASE: GENOMIC AND POST- GENOM
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143 Genes, including links via publ
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Public Access to FlyBase Data The c
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147 There are two classes of annota
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149 screens to identify direct or i
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13 MAIZEDB: THE MAIZE GENOME DATABA
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The bins map strategy has been empl
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Clicking on one of the orthologous
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* hyper-text linked to MaizeDB enti
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DISSEMINATION TO EXTERNAL DATABASES
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Appendix 2: part of the Query Form
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Introduction 14 AGIS: USING THE AGR
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Horn Fly--Haematobia Screwworm--Coc
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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
Page 220 and 221:
References 211 13. NCBI ASN. 1 Spec
Page 222 and 223:
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
Page 278 and 279:
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
Page 288 and 289:
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
Page 308 and 309:
GenBank database query software of,
Page 310 and 311:
Metabolic Pathway Database, 38 Meta
Page 312 and 313:
conversion to ‘gi’ identifiers
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