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251 Wrappers with and without State Typically every CORBA object is represented by one corresponding object of the implementation language of the server (e.g. a C++ object). In the case of a database wrapper this means that the C++ server contains a copy of the database entry (Figure 4a). If a client invokes for the first time a method on an object reference the corresponding database entry is loaded and the C++ object is instantiated. It stays in memory for subsequent method invocations until the server actively removes it to avoid maintaining a copy of the whole database. This approach can be fast because the CORBA server is a cache for database entries but it makes a system for garbage collection necessary. a) b) Figure 4: CORBA wrappers with a) and without state b) In the second possibility (Figure 4b) the CORBA server is stateless. One language object implements a set of different CORBA objects each representing another database entry. For every request the server extracts the object identifier from the request in order to find out which CORBA object it has to emulate. In this approach no garbage collection is needed but every request requires access to the underlying database. Object-Relational Mapping Relational database management systems (RDBMS) are still the standard today. Several major databases at EBI, such as the EMBL Nucleotide Sequence Database and TREMBL, use the RDBMS ORACLE. Since the relational model is very different from an object-oriented conceptual model, object-relational mapping [15] becomes the central problem for CORBA wrappers. There are several tools, which can help to define object-oriented views on top of a relational database. An example is the object-relational mapping tool “Persistence” [20]. It follows the philosophy of object-oriented databases [1] [4] in the sense that it makes database entries directly available as C++ objects. Persistence is therefore well suited for implementing CORBA servers, which cache database entries in main memory. The main disadvantage of this tool is that the developer has not much freedom to define object schemas. The Persistence object schema is very close to the original table structure of the relational database. Relational views and a certain amount of hand coding are often necessary to create the required mapping. Another type of mapping tool supports the mapping of object-oriented queries to SQL queries. (for example OPM
25 2 [5]). Such a tool is potentially better suited for stateless wrappers and for the implementation of a CORBA Query Service. CORBA Prototypes There are now a large number of CORBA applications and prototypes available at EBI [17]. They include: Database Wrappers EMCORBA A fine-grained CORBA interface to the EMBL Nucleotide Sequence Database by Jeroen Coppieters, Carsten Helgesen, Philip Lijnzaad and Timothy Slidel. The server was implemented in C++ using the RDBMS ORACLE, the object-relational mapping tool Persistence and the ORB ORBIX [18]. RHdb There are two CORBA servers to the Radiation Hybrid Database by Carsten Helgesen, Philip Lijnzaad and Patricia Rodriguez-Tomé. One is implemented in C++ using ORACLE, Persistence and ORBIX. The second server is implemented in Java using JDBC to query the ORACLE database and the ORB VisiBroker for Java [21]. The object-relational mapping was purely hand-coded. Applications AppLab A general-purpose CORBA wrapper for a large class of command-linedriven applications by Martin Senger. It uses the GCG configuration file language. Mapplet A Java Applet, which displays radiation hybrid maps by Kim Jungfer. The CORBA server for this applet uses the flat-file version of RHdb. Hyperbolic Viewer A Java applet for the visualization of tree like data structures by Alan Robinson. In this example a taxonomy tree is displayed. Chris Dodge and Timothy Slidel provided the CORBA taxonomy server using Sun’s ORB NEO and the RDBMS Sybase. Genome Builder A tool for building virtual sequences using the EMCORBA server by Juha Muilu. Protein Sequence Space Viewer Displays families of proteins and their residues in a multidimensional space, projected into 2D and 3D by Chris Dodge. The algorithm is computationally intensive and therefore separated from the Java viewer in a specialized CORBA server.
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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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(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
Page 210 and 211: Introduction 17 BIOKLEISLI:INTEGRAT
Page 212 and 213: 203 In the remainder of this chapte
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Page 216 and 217: BioKleisli in Action: Querying Biom
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Page 220 and 221: References 211 13. NCBI ASN. 1 Spec
Page 222 and 223: Introduction 18 SRS: ANALYZING AND
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Page 226 and 227: 217 Operands also include named set
Page 228 and 229: Figure 1. The SRS Top Page. Figure
Page 230 and 231: Analyzing Data in SRS with Applicat
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Page 234 and 235: Figure 7. A SWISS-PROT entry with i
Page 236 and 237: Figure 8. The results of a query fo
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Page 242 and 243: Introduction 19 BIOLOGY WORKBENCH:
Page 244 and 245: Figure 1. Organization and Flow of
Page 246 and 247: 237 In order to make wrapper develo
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Page 252 and 253: PROFILESCAN Comparison of protein o
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Page 256 and 257: 247 the Object Request Broker (ORB)
Page 258 and 259: 249 Every database entry is represe
Page 262 and 263: Discussion 25 3 CORBA and IDL minim
Page 264 and 265: Introduction 21 BIOWIDGETS:REUSABLE
Page 266 and 267: Visualization Solutions 257 Turnkey
Page 268 and 269: 259 The architecture also exploits
Page 270 and 271: possible because the bioWidget arch
Page 272 and 273: 263 4. Gish, Warren (1994-1997). un
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Page 276 and 277: 267 On the other hand, most users c
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Page 288 and 289: Introduction 23 LABBASE: DATA AND W
Page 290 and 291: 28 1 We will use as a running examp
Page 292 and 293: LabBase Details 283 A LabBase objec
Page 294 and 295: When the procedure completes, LabFl
Page 296 and 297: 287 sequence-assembly Worker in our
Page 298 and 299: 289 The Worker converts the object
Page 300 and 301: 291 Genome Research and are beginni
Page 302 and 303: INDEX
Page 304 and 305: INDEX A of Agricultural Genome Info
Page 306 and 307: of vertebrate genes, 21-33 developm
Page 308 and 309: 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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