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figure 1, and all flows into the ID database, which is described in a subsequent section. Conversion to ASN.l Transforming data into the same format allows them to be used by software independently of the original source, a major requirement for integration. Within this uniform format the accuracy of the identifiers used to link, or point, to other records determines the accuracy and actual meaning of the links themselves. For literature articles, NCBI has added an integral identifier, the PubMed identifier. For convenience, many articles contain both a MEDLINE unique identifier and a PubMed identifier. Genetic sequence identifiers are more complex and can occur in different forms and places within each data record. Before this integration and use of sequence identifiers can be explained, the way in which NCBI represents the data must be clear. So that the databases and other later applications can use a common format and be insulated from parsing the input data, the diverse streams of data are converted to Abstract Syntax Notation (ASN. 1), an international standard [7]. When using ASN.l, one file describes the format to which other files (messages) of data must correspond. This file can be considered the definition of the message format. The data conforming to the message format can be understood by later application software that is thereby insulated from details of the original input formats. The messages use a “tag-value” format, in that an identifier describes the value that follows; however, analogous to programming language record definitions, the ability to use both recursion and user defined types in the definition of the message format allows for almost infinite complexity in the messages themselves. Since ASN. 1 messages can thus become rather complex and are not intended to be human readable, other report formats, such as GenBank flatfile format, are used to display this data. “asn.all” ASN. 1 message definition The particular format, or message definition, plays a central role. It is what describes the syntax into which all the sequence record information must be parsed. The original definition proposed by Jim Ostell [8] has been used with minor modifications for over five years. It is available as the file, “asn.all” in the NCBI toolkit distribution (ftp:ncbi.nlm.nih.gov; directory toolbox/ncbi_tools) and is discussed in detail in the NCBI Programmers Toolkit documentation [8]. “asn.all” describes the format of both the literature and genetic sequence messages. NCBI also makes use of other ASN. 1 message definitions. So that the data in the ASN.l messages can be used by software, C language structures that map fairly closely to the “asn.all” definitions were designed, as well as software (object loaders) that could read and write these messages from C language structures to files and vice versa. These original structures and object loaders were hand crafted. More recently, the program “asncode” was written by the author and 13
14 made available as part of the NCBI toolkit distribution. “asncode” takes ASN.l message definitions and automatically generates both the C language structure definitions and object loaders. This “asncode”-generated software is used in network communication for the Entrez and Blast systems and the MMDB [9] structure manipulating software and could be easily used by software developers for most ASN.1 applications. Sequence record types In the “asn.all” definition, a “Bioseq” is a biological sequence that can contain information in addition to the sequence. For the purposes of this discussion, a Bioseq can be considered a collection of a “sequence” block, an “id” block, a “history” block, a “descriptor” block, and an “annotation” block. Each Bioseq can have a set of synonymous sequence identifiers that it contains in its “id” block. The semantics of the definition in “asn.all” are that this set of sequence identifiers are names for this Bioseq. Sequence identifiers that occur elsewhere in the record are pointers to Bioseqs that contain those sequence identifiers in their “id” block. “Descriptors” provide information that can apply to the entire sequence, such as citations to the literature and taxonomic classifications for the source of the material that led to the sequence information. Feature “annotation” applies to a particular region of the sequence, such as a coding region. These feature annotations use a sequence identifier to point to the Bioseq containing the sequence being described. Bioseq sets In the “asn.all” definition, a sequence entry can either be a Bioseq, or a more complex set of Bioseqs. One example of a more complex set is the set of protein Bioseqs combined with the nucleic acid that encodes them. The Bioseq can either contain actual sequence, or can incorporate sequence information solely by reference to other Bioseqs that actually contain the sequence. An example of this incorporation by reference is in the set of Bioseqs comprising the exons of a gene. This is the most common type of a “segmented set”. In this case, the set begins with a Bioseq that points to the Bioseqs that contain the exon sequences. These pointers use sequence identifiers to specify the order of the exons by referencing the name (sequence identifier) of the Bioseqs containing the exon sequence. The Bioseqs containing the actual raw sequences for the exons are, in this case, part of the Bioseq set that includes the Bioseq pointing to them. Significantly, this “pointer” Bioseq, which has no sequence of its own and only incorporates sequence by reference, can be processed by the NCBI software system in the same way as any other Bioseq. The entries in the Genomes division of Entrez are another example how pointers incorporate sequence data by reference to other Bioseqs. However, they differ from the sequence entry of the segmented set in that the Bioseqs containing the raw sequence are not in the same entry. This makes it critical for the sequence identifiers to be used accurately and uniquely.
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 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 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
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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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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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