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Future Directions Figure 4. KEGG as a deductive database for path computation. Computing paths of reasoning We have mentioned two specific examples of path computation problems: computing multiple links in DBGET/LinkDB and computing chemical reaction pathways in KEGG. This type of deduction from binary relations can further be extended to include different types of relations that are summarized in Table 5. Table 5. Different types of binary relations While factual, similarity, and genomic relations are well computerized, the majority of functional relations, such as pairs of interacting molecules and upstream- downstream relationships in biochemical pathways, exist only in printed materials or experts’ brains. In BRITE we have collected a small set of molecular interaction data from primary literature, which is extremely labor intensive and requires expert knowledge. We are also working on to develop computational technologies to automatically extract pairs of interacting molecules from the written text. However, an ultimate solution for fully computerizing interaction data would be direct submission from the authors. 75
76 Designing new systematic experiments The current knowledge of metabolic pathways, especially on the intermediary metabolism, is already well represented in KEGG. The next question is how to organize divergent sets of regulatory pathways. We are collecting pathway data mostly from review articles on various aspects of cellular functions, but the existing literature is the result of the traditional reductionistic approach in molecular biology, which probably represents only a fragmentary portion of actual regulatory pathways in the cell. It is therefore necessary to design new systematic experiments, for example, for protein-protein interactions by yeast two-hybrid systems and for genegene interactions by observing gene expression profiles on microarrays. KEGG will provide reference data sets and computational technologies to uncover underlying gene regulatory networks in such experimental data. Acknowledgments The KEGG project has been supported by the Human Genome Program of the Ministry of Education, Science, Sports and Culture in Japan. References 1. Kanehisa, M. A database for post-genome analysis. Trends Genet. 13, 375-376 (1997). 2. Kanehisa, M. Linking databases and organisms: GenomeNet resources in Japan. Trends Biochem Sci. 22,442-444 (1997). 3. Goto, S., Bono, H., Ogata, H., Fujibuchi, W., Nishioka, T., Sato, K., and Kanehisa, M. Organizing and computing metabolic pathway data in terms of binary relations. Pacific Symp. Biocomputing 2, 175-186 (1996). 4. Fujibuchi, W., Goto, S., Migimatsu, H., Uchiyama, I., Ogiwara, A., Akiyama, Y., and Kanehisa, M. DBGET/LinkDB: an Integrated Database Retrieval System. Pacific Symp. Biocomputing 3,683-694 (1997). 5. Goto, S., Nishioka, T., and Kanehisa, M. LIGAND: Chemical database for enzyme reactions. Bioinformatics 14, in press (1998). 6. Riley, M. Functions of the gene products of Escherichia coli. Microbiol. Rev. 57, 862-952 (1993). 1. Bono, H., Ogata, H., Goto, S., and Kanehisa, M. Reconstruction of amino acid biosynthesis pathways from the complete genome sequence. Genome Research 8, 203- 210 (1998)
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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
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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Page 86 and 87: Introduction 6 OMIM: ONLINE MENDELI
Page 88 and 89: The Entry Each OMIM entry is assign
Page 90 and 91: Searching OMIM is as simple as typi
Page 92 and 93: How OMIM is Curated OMIM is maintai
Page 94 and 95: 7 GDB: INTEGRATING GENOMIC MAPS Int
Page 96 and 97: in one of two ways: by being wholly
Page 98 and 99: Figure 3: Universal Coordinate Disp
Page 100 and 101: Figure 5: Dispersion of linear (lef
Page 102 and 103: which minimizes dispersion. The opt
Page 104 and 105: Figure 10: A piece of an "comprehen
Page 106 and 107: alignments are better for the speci
Page 108 and 109: Summary 8 HGMD: THE HUMAN GENE MUTA
Page 110 and 111: Data coverage and structure 101 By
Page 112 and 113: Conclusions and Outlook 103 Being b
Page 114 and 115: Introduction 9 SENSELAB: MODELING H
Page 116 and 117: 107 For the purposes of the rest of
Page 118 and 119: 109 users might advocate creating a
Page 120 and 121: 111 The Associations table describe
Page 122 and 123: Managing the Object Class Hierarchy
Page 124 and 125: 115 In fig. 2, the Objects and Clas
Page 126 and 127: 117 7. Mirsky JS, Nadkarni PM, Hine
Page 128 and 129: 10 THE MOUSE GENOME DATABASE AND TH
Page 130 and 131: genetics community fostered the ear
Page 132 and 133: 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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References 211 13. NCBI ASN. 1 Spec
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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
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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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