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possible because the bioWidget architecture extends the standards put forth in the JavaBeans specification. 26 1 Finally, the third group of users is comprised of Java programmers who wish to use the bioWidget architecture to achieve a goal that cannot be accomplished by connecting existing components. Examples include creating new widgets or reading data in a completely new format into existing widgets. These users are users not so much of the bioWidgets themselves, but of the underlying bioWidget architecture. The bioWidget Consortium The bioWidgets project has adopted a consortium model [18]. But, unlike groups such as the OMG[19] and the ODMG[20], the bioWidgets project is concerned with producing both specifications and implementations. Thus the consortium’s administrative structure must facilitate and coordinate the release and versioning of both design standards and also the programs and applications designed according to those standards. A Central Repository The essential resource provided by (and to) the consortium is a central repository both for finished components and software, and for technical proposals and/or ratified and evolving standards and specifications. The repository is responsible for versioning the bioWidgets, providing components to software developers and users, and tracking subsequent bug reports and requests for additional features. These reports and requests are in turn made available to anyone interested in working on existing widgets, whether or not they happen to be the original authors. By acting as a mediator in this fashion, the repository goes a long way towards guaranteeing the components’ ease-of-use. Quality Control Nat Goodman’s group at the Jackson Laboratory maintains the consortium’s central repository, and has also assumed the responsibility of providing quality control for the entire endeavor. An essential part of any software sharing effort, the quality control process ensures that, prior to submission into the repository, each widget meets certain criteria with respect to documentation, engineering, and reusability. Future Work We will proceed to develop new widgets to support the display of a wider variety of data; future bioWidgets will handle data describing pedigrees, metabolic pathways, 2-D gels, gene expression arrays (SAGE, DNA chips, etc.), and database schemas. The development of particular widgets will soon exploit the upcoming support in web browsers for improvements in the Java language; these improvements address
262 such issues as printing and cutting and pasting. For example, one will be able to select a sequence and paste it directly into a word processor document. Or one will be able to copy sequences from a number of web pages and paste them into a multiple alignment widget. Finally, we hope to use the same facilities we have defined for inter-widget communication to enable communication between the widgets and a variety of remote data sources, in a more dynamic fashion. For example, one might highlight a region of sequence and request a BLAST[20] or motif search. The widget would communicate with a specialized remote server that would perform the requested task and return the results directly to the widget for display and possible subsequent analysis. Using the widgets in this manner overcomes the main limitation of existing web- based systems, namely that, for the most part, they have no memory of what a user has done in the past, beyond what can be encoded in a URL. We also plan to expand the power of the widgets to act as interfaces for composing and answering queries on the data they display, rather than merely acting as browsers. This can be done by integrating the widgets with the kind of flexible query engine provided by systems like BioKleisli (see Chapter ??) and Multi-Database OPM (see Chapter ??). Conclusions The time has come for a component-based revolution in bioinformatics. The software technology, including the World Wide Web, Java and its diverse facilities, and other object-based component architectures such as CORBA will drive the effort. The growing abundance of data in need of analysis, the commonality of visualization needs across genomics applications and laboratory environments and the limits of developer resources will combine to create an intense "market"for GUI components. The bioWidgets Consortium will fill the necessary role of coordinator of widget development efforts and dispenser of widgets, and the bioWidget architecture will provide the technical backbone that ensures reusability and interoperability. References 1. Goodman, N., Rozen, S., and Stein, L. (1995) "The Case for Componentry in Genome Informatics Systems". http://www- genome. wi.mit.edu/informatics/componentry. html 2. Searls, D.B. (1995) "bioTk: Componentry for Genome Informatics Graphical User Interfaces" Gene 163(2):GC1-16. 3. Java TM language. http://www.javasoft.com/
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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
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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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Page 222 and 223: Introduction 18 SRS: ANALYZING AND
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Page 242 and 243: Introduction 19 BIOLOGY WORKBENCH:
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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
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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
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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 282 and 283: 273 database the class Map refers t
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Page 288 and 289: Introduction 23 LABBASE: DATA AND W
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Page 292 and 293: LabBase Details 283 A LabBase objec
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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,
Page 310 and 311: Metabolic Pathway Database, 38 Meta
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