Databases and Systems

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2. W. J. Wilbur, A retrieval system based on automatic relevance weighting of search teams, Proceedings of the 55th American Society of Information 3. Science, Annual meeting (Ed.) D. Sahr (1992(, Pittsburg, PA. 29,216-220. W. J. Wilbur and Y. Yang, An analysis of statistical term strength and it use in the indexing and retrieval of molecular biology texts, Computers and Biology in Medicine, 26, 209-222. 4. S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, Basic local alignment search tool, Journal of Molecular Biology, 215, 403-410 (1990). 5. J. F. Gibrat, T. Madej, S. H. Bryant, Surprising similarities in structure comparison, Current Opinion in Structural Biology, 6, 377-385 (1996). 6. Improving GenBank’s Taxonomy, NCBI News February, 1994. 7. M. T. Rose, “The Open Book: A Practical Perspective on OSI”, Prentise Hall, Englewood Cliffs, New Jersey, 1990. 8. NCBI Software Toolkit Manual (1993) National Center of Biotechnology Information, Bldg. 38A, NIH, 8600 Rockville Pike, Bethesda MD 20894. 9. T. Madej, J.-F. Gibrat, and S. H. Bryant, Threading a database of protein cores, Proteins, 23,356-369 (1995) 10. M. S. Boguski, T. M. Lowe, and C. M. Tolstoshev, dbEST - database for ‘expressed sequence tags, Nature Genetics 4, 332-333 (1993) 11. D. A. Benson, M. S. Boguski, D. J. Lipman, and J. Ostell, GenBank, Nucleic Acids Research, 25, 1-6 (1997) 12. For information on Sybase products, see their web pages, Http://svbooks.sybase.com/cgi-bidnph-dvnaweb 19
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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 8 and 9: TABLE OF CONTENTS INTRODUCTION ....
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 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
Page 64 and 65: within compound windows uses a conc
Page 66 and 67: Figure 4: The EcoCyc linear map bro
Page 68 and 69: Figure 5: The software architecture
Page 70 and 71: 6. Karp. Frame representation and r
Page 72 and 73: Introduction 5 KEGG: FROM GENES TO
Page 74 and 75: Binary relations Figure 1 : Level o
Page 76 and 77: hierarchical text. The genome map i
Page 78 and 79:
(2) Java applets to search, compare
Page 80 and 81:
can be used for gene function assig
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influenzae lacks the upper portion
Page 84 and 85:
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
Page 102 and 103:
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
Page 164 and 165:
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
Page 174 and 175:
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:
Page 216 and 217:
BioKleisli in Action: Querying Biom
Page 218 and 219:
209 savings in execution time is si
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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
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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,
Page 310 and 311:
Metabolic Pathway Database, 38 Meta
Page 312 and 313:
conversion to ‘gi’ identifiers
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