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Selecting orthologous genes with HOVERGEN As explained in the introduction, the search for orthologous genes is a complex task that requires a careful analyses of all available data. With HOVERGEN, this search can be done in two steps. The first step consists in using the QUERY_WIN sequence retrieval software to search for homologous genes known in different species, according to the criteria defined by the user. Then, the user has to examine with the graphical interface all the gene families to which these genes belong to distinguish orthologous from paralogous genes. 31
32 Examples of application HOVERGEN has been used in various studies based on statistical analysis of orthologous genes, to study the evolutionary rate of genes [3,25,26], the structure of vertebrate genomes [27-29] or the statistics of genes size [30,31]. In molecular phylogeny, it is important to work on large samples of genes, and it is absolutely necessary to exclude paralogues from the analyses to be able to resolve phylogenetic trees of species. Thanks to the amount of data available in HOVERGEN it has been possible to address several important questions on vertebrates phylogeny, and some unexpected results regarding mammals or birds phylogeny have come out [32-34]. HOVERGEN is also useful as a tool to display a global view of all data available for a given gene family. Thanks to the graphical interface, the user can quickly access to the annotations of the various redundant sequences of a gene, or of the homologues of that gene in the same genome or in other species. Thus, HOVERGEN allows one to enrich the knowledge on a given gene by the information available for its various homologues. Finally, HOVERGEN is also a powerful tool for correcting annotation errors. Newly sequenced genes are now often annotated simply by similarity with an already known sequence. Unfortunately, many errors occur because paralogous genes are not distinguished from orthologous genes. Such errors can easily be checked by looking at the phylogenetic trees provided by HOVERGEN. Comparison with other databases Several other databases include information on homology relationships between sequences. For example, ENTREZ [35] shows all significant similarities detected between protein or DNA sequences. However, ENTREZ does not provide multiple alignments nor phylogenetic trees. The PIR database now provides an entire classification of all published protein sequences, along with multiple alignments [36]. However, contrarily to HOVERGEN, PIR is limited to proteins, and does not give access to the gene sequences. Currently, HOVERGEN is the only database that provides an entire classification of vertebrate genes, with both multiple alignment and phylogenetic trees. Its main originality comes from its query retrieval system that allows one to quickly retrieve sets of homologous genes from different species and its graphical interface that allows one to analyze their evolutionary relationships. Limitations Several limitations of our database have to be mentioned. First of all, the graphical display of phylogenetic trees is limited to families having less than 150 sequences. Currently, only 10 families contain more than 150 sequences (Table 1), but this number should rapidly increase. Due to this problem of displaying large trees, we sometimes split paralogous genes from a same families into different subfamilies.
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 28 and 29: 2. W. J. Wilbur, A retrieval system
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 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
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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
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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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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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