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Figure 5: Dispersion of linear (left) and longest chain (right) universal coordinates for chromosome 1 Measuring Dispersion Dispersion can be measured in a number of ways, of which the most straightforward is perhaps the standard deviation of the distance of all the points from the y=x line; this number can be interpreted as a distance error in universal coordinates. A somewhat better measurement would compare each universal coordinate for a locus with the standard-error-weighted average of the universal coordinates for that locus; this would have the desirable property that a locus with N coordinates would contribute only N values to the overall dispersion; the pairwise comparisons shown here generate N*(N-1)/2 values, which means that common loci are overrepresented in the results. Dispersion measures can be computed for individual maps, by comparing the universal coordinates of loci on that map with those from all the other maps; the resulting map dispersion number can be used to rank maps by their overall concordance with other maps, and can also be interpreted as a standard error for the universal coordinates of that map. Similarly, dispersion can be computed on a per-locus basis to identify highly discrepant loci. The scripts which compute universal coordinates for GDB generate HTML reports showing map-dispersion (figure 6) and the most discrepant loci (figure 7) 91
92 Figure 6: Maps ordered by increasing dispersion measure Figure 7: Loci with large dispersion The use of dispersion as a metric to evaluate the quality of alignments suggests the possibility of using dispersion minimization to drive the alignments. A simple way to do this would be to choose a fixed set of control points for each nonstandard map (the xi ’s in figure 8) and choose yt 's that determine a piecewise-linear transformation
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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
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
Page 82 and 83: influenzae lacks the upper portion
Page 84 and 85: Future Directions Figure 4. KEGG as
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 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
Page 134 and 135: 125 In February 1998, a ‘cDNA and
Page 136 and 137: Acknowledgments 127 MGD is funded b
Page 138 and 139: 11 THE EDINBURGH MOUSE ATLAS: BASIC
Page 140 and 141: expression database under developme
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
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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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