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FIGURE 2. A Query for a lacZ-(amber ) lacI-- strain, with no amber suppressor, and an isogenic lacZ+ strain STRAIN Select! Sub! Clear! Go! Clone! Report Pedig! Isos! [?] ID#: CGSC# Designation Choice Source Sex: Episome: Via Use Mutations --------------- -------------------- Isogenic w.r.t. mut: Comment: References: Author Year Journal Vol. Page Priority STRAIN Select! Sub! Clear! Go! Clone! Report Pedig! Isos! [!] ID#: 7668 CGSC# 4977 Designation Choice Source 3.300 U281 1 Paris Sex: Hfr CA86 2 S.Brenner PO: valS < attP4 Episome: Mutations Via Use Mutation lacZ281(Am) lac122 Type Comment LAM- Inherit lac122 was formerly called lac, 3 by Jacob; it originated relA1 in strain 3.300 Hfr. spoT1 Inherit lacZ281 was formerly called lacZu281. thi-1 References: Inherit Am = amber mutation Author Year Journal Vol. Page Priority Pardee et al. 1959 J.Mol.Biol. 1 165 1 Isos! and Report! CGSC Strain # 4977 Strain designation: 3.300 U178 Other Designation: CA86 S.Brenner Sex: Hfr Point of Origin 1 of Hfr 3000 valS--
182 sources --- sequences (GenBank), bibliography abstracts (Medline), and more detailed enzyme information (Enzyme Commission database and SwissProt). This choice reflects the fact that the stock center has always operated with a small staff, usually director, two laboratory research assistants, some part-time help with data entry and editing, and with the advent of the database, a part-time systems administrator, and the database is only an ancillary part of the stock center's functions. The body of data, however, expands and needs modification at a rate that exceeds our modest plan. We need to adjust our curation model to accommodate this. Lessons learned and improvements obvious in hindsight One of the positive lessons was that one biologist beginning at ground level in database experience and one computer scientist with interest and knowledge in biology can work together to accomplish a lot of database functionality in a short time. On the negative side, I would, in hindsight, pay more attention to external use of the database from the outset. The initial development definitely focussed on in-house use because that was the most critical need and because external use was not favored by the then-director of the stock center. Subsequently, we have found external access by users to be extremely helpful and efficient both for users and for stock center staff. The ability to provide a Web interface to the database (10), thanks to the Genera software (8), has done a lot to increase satisfaction of outside users. Yet there are many useful features that we use daily with the in-house aptforms that are not yet available to web users. There are ways that we can accommodate some of these features, even within html limitations, but they have not all been attended to. Among the things I would do differently would be to ensure that external users had access to these more powerful querying capabilities from the outset (although certain parts of the database, such as request-forms and strain-maintenance records, would still be excluded from the public part of the database, since they seem irrelevant to external users' information needs). Also, of course, we would develop more extensive user documentation. External use is also related to curation capabilities. Unexpectedly, being on the web has given us a volunteer corps of proofreaders. Many users are experts in areas of biochemistry or genetics and send us corrections on very specific aspects of the data. It has been suggested by some outside users that there be public access to: (a) a write-in adjunct to the database to allow researchers to add expert knowledge, and corrections, that will become appended to the record and available to anyone examining the record. (b) allowing some SQL-query capability (non-forms).
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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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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
Page 178 and 179: Figure 4 : Available Classes and Su
Page 180 and 181: Figure 6 : Query by Example and Que
Page 182 and 183: Figure 8 : Hypertext ACEDB Object a
Page 184 and 185: 15 CGSC: THE E.COLI GENETIC STOCK C
Page 186 and 187: 177 described once for that gene an
Page 188 and 189: FIGURE 1. A Query for an hsdR- recA
Page 192 and 193: 183 These are suggestions that dese
Page 194 and 195: SYSTEMS
Page 196 and 197: Introduction 16 OPM: OBJECT-PROTOCO
Page 198 and 199: 189 OPM is a data model whose objec
Page 200 and 201: The OPM Database Development Tools
Page 202 and 203: ased on this query tree. Further qu
Page 204 and 205: 195 Interface, except that one can
Page 206 and 207: 197 Our strategy of providing suppo
Page 208 and 209: 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
Page 218 and 219: 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
Page 224 and 225: 215 In SRS, databank structures are
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
Page 232 and 233: 223 other databanks, and define spe
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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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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