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28 1 We will use as a running example a hypothetical laboratory project whose purpose is to sequence and analyze a large number of cDNA clones drawn from several libraries. A database for such a project would store information about (i) the libraries being sequenced, (ii) the clones picked for sequencing, (iii) sequence-reads performed on those clones, (iv) assemblies of sequence-reads (to coalesce multiple reads from the same clone, and to detect and exploit situations in which duplicate clones are picked), and (v) analyses of assembled sequences. The system would include many components, including (i) software to control robots involved in clonepicking and preparation of sequencing templates, (ii) base calling software, (iii) software to strip vector and for quality screening of raw sequences, e.g., to detect E. coli contamination and check for repetitive elements, (iv) sequence assembly software, (v) sequence analysis software, and (vi) some means for laboratory personnel to review the results. LabBase and LabFlow are research software and are incomplete in many ways. We will endeavor to point out the major holes that we are aware of. The software is freely available and redistributable (see http://goodman.jax.org for details). LabBase Data Management System LabBase provides four main concepts for modeling laboratory (or other) databases: Objects, Materials, Steps, and States. Objects are structural objects, similar to those found in ACEDB [12], OPM [13], lore [14], UnQL [15], and many other systems. Materials are Objects that represent the identifiable things that participate in a laboratory protocol, such as libraries and clones. Steps are Objects reporting the results of a laboratory or analytical procedure, such as sequencing a clone, or running BLAST [16] on a sequence. States are Objects that represent places in a laboratory protocol, e.g., “ready for sequencing” or “ready for BLAST analysis”. We use the term object (lower-case) to refer to any kind of Object including a Material, Step, or State. The most compelling feature of LabBase is that it provides built-in support for two relationships among Materials, Steps, and States that lie at the core of typical laboratory databases. One is a relationship connecting Steps to the Materials upon which they operate. When a Step is stored in the database, LabBase automatically links the Step to its operand Materials in a chronological history and provides a means to access Step-data directly from these Materials; for example, one can retrieve a clone’s sequence or a sequence’s BLAST analysis by querying the respective Materials rather than the Steps. The second built-in relationship connects Materials to States. When a Material is created, LabBase provides a means to place the Material in an initial State; then as Steps operating on the Material are created, the system provides a means to move the Material to the appropriate next State thereby tracking its progress through the protocol. Both of these relationships are many-to-many. We discuss these relationships further when we describe LabBase operations.
282 To create a database for a specific laboratory protocol, the main tasks are to give names to the Materials and Steps of interest, and to describe the data to be reported in each Step. Steps are generally obvious, because they correspond to the actual work being done in the laboratory protocol. The main subtlety is ensuring that Steps correspond to useful points of contact between the laboratory and the computer. In our running example, possible Steps include ones reporting (i) that a library has been constructed, (ii) that a clone has been picked and plated, (iii) that sequence-template has been prepared from a clone, (iv) that sequence-template has been loaded onto a sequencing machine and run, (v) the results of a sequencing-run, e.g., base-calls, quality indicators, and chromatographs, (vi) the results of vector stripping and quality screening of sequencing results, (vii) the results of assembling sequences, and (viii) the results of analyzing sequence-assemblies. Many Materials are equally as obvious, because they correspond to the major reagents employed in the protocol, e.g., libraries and clones, or the major data produced by the protocol, e.g., sequence-reads and assemblies. As with Steps, the main danger is excess: Materials should only be defined for things that are really worth tracking. Limitations in our current software push strongly in the direction of parsimony. The mechanism mentioned above for connecting Step-data to Materials only works for Steps operating directly on a Material; it does not work transitively over related Materials. While it is easy to get the base-calls for a sequence-read, and a list of all sequence-reads for a given clone, and a list of all clones picked from a library, the software offers no special help for getting all base-calls for all sequencereads for a given clone or library. A second limitation is that LabFlow (see later section) only supports workflows in which a single kind of Material marches through a protocol. The effect of these limitations is to encourage database designs in which multiple real-world material are elided into a single database-Material. In our example, it would probably be best to represent libraries as Objects (not Materials), and to merge clones and sequence-reads into one Material; assemblies would probably remain as separate Materials. The end result is a database with just two kinds of Materials: sequence-reads and sequence-assemblies. To recapitulate, the database for our running example would have just two kinds of Materials, sequence-reads and sequence-assemblies, and many kinds of Steps, each operating on one Material. One of the possible Steps listed earlier, namely, the one reporting on library construction, must fall by the wayside, since we have decided to represent libraries as Objects, not Materials; data on library construction would be stored as fields of these library Objects. The most obvious, practical shortcoming of this example database is that without a clone Material, we lose the most natural means of coordinating multiple reads from the same clone. In the database as given, one would probably coordinate multiple reads per clone in the context of sequence-assemblies; this may be workable but is certainly not ideal.
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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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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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Page 242 and 243: Introduction 19 BIOLOGY WORKBENCH:
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Page 256 and 257: 247 the Object Request Broker (ORB)
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Page 262 and 263: Discussion 25 3 CORBA and IDL minim
Page 264 and 265: Introduction 21 BIOWIDGETS:REUSABLE
Page 266 and 267: Visualization Solutions 257 Turnkey
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Page 276 and 277: 267 On the other hand, most users c
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Page 288 and 289: Introduction 23 LABBASE: DATA AND W
Page 292 and 293: LabBase Details 283 A LabBase objec
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Page 298 and 299: 289 The Worker converts the object
Page 300 and 301: 291 Genome Research and are beginni
Page 302 and 303: INDEX
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Page 310 and 311: Metabolic Pathway Database, 38 Meta
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