Database formats

The diversity of suppliers and the difficulty of maintaining up to date lists made the maintenance of print directories difficult and expensive. Most are now either out of print or have migrated to database format. Nevertheless a review of the older directories is interesting, and they should be retained as they provide valuable background reading. 

There was a time long ago, in the days of SAS 6 and earlier versions, when you needed to rely on software external to SAS to convert some data into SAS data sets so SAS could use it. Conceptual Software produced just such a tool with its DBMS/Copy package, which could easily convert many proprietary software database formats into SAS data sets. In 2002, SAS purchased the DBMS/Copy line of software tools and now distributes that software under its Dataflux subsidiary. So DBMS/Copy is still available for use, but the good news is that it is now largely unnecessary if you have SAS/ACCESS and SAS 9. 

DBMS/Copy exists as a stand-alone program and also as a product called dfpower DBMS/Engines that allows conversion of these files from within SAS itself. If you have an ongoing need to pull a proprietary database format into SAS that SAS 9 with SAS/ACCESS alone cannot read, dfpower DBMS/Engines may be a solution. 

An expert system does much more than extract information from a database, format it, and offer it up to the user it analyzes and processes the information to make deductions and generate recommendations. Because an ES may be required to present alternative strategies and give an estimate of the potential value of different courses of action, it must contain a reasoning capacity, which relies on some sort of general problem-solving method. 

Using the UMIST Astrochemistry Database format for rate constants, calculate the rate constants at 20 and 100 K for the following reactions, giving the units for the rate constants in each case ... 

To improve this unsatisfying situation, many bioinformatics sites construct nonredundant databases from a number of component databases, or they use external nonredundant databases, e.g., OWL (Bleasby et al., 1994). Both strategies considerably improve the situation for the end user, but they require the time- and resource-consuming maintenance of multiple databases or the acceptance of a certain time lag between creation of an entry and its appearance in the nonredundant database. Furthermore, both strategies lead to a loss of information in the individual entry owing to the diversity of database formats. Whereas OWL preserves most information of an entry and some of its structure, the NRDB program requires a conversion of the component databases to FASTA format, which contains only one description line per entry. 

Given query gene expression data, discretize gene expression values using the same procedure as that employed in the above database formatting process. Use the same degree and thresholds to discretize query data as that used in the database discretization. 

To export data files into a database program, a database file format called. DB2 was developed in an early PC database, dBase II. Databases are made up of files, which could be compared to a Rolodex file box full of cards, all containing the same type of information. The Rolodex card would be equivalent to a database record. Each record has on it a series of entries, fields, in the same place on each card. To import data into a database record, all the entries in the report must be matched up with existing fields in the database s format. Most software that uses database formats has export/import subprograms that allow you to align fields between the two formats and allow you to select various ways of determining coding for end-of-file and end-of-record terminators. 

Data acquisition is presented in the upper left comer of Figure 1. The information is read from multiple heterogeneous sources and transformed in our standard format. The acquisition mechanism understands the IDMEF format, our private database format, and several dedicated log sources such as firewall logs (Cisco, Netscreen, Checkpoint, IPtables), access control mechanisms (TCP-wrappers, login), VPN concentrators, IDS sensors and routers. 

It should also be noted that database creation, modification, and use are greatly benefited by the standard methods approach described previously. This approach produces reliable data that lend themselves to a highly consistent database format throughout a project lifetime. 

Most existing (electronic) databases use typical database formats that present all data pertaining to one compound entry (and there may be more than one entry per individual compound) in a data form. While this is a convenient way to see all the information of one particular entry, it prevents getting an overview of all entries on that compound and how the values from other entries compare with the particular one shown. Some databases allow table-type views, which can be useful to gain this overview. Alternatively, all entries may be exported and printed for a more comprehensive view with the use of another software or on paper. 

In order to store chemical information, there is a need to store chemical structures in some type of database format. For most practical purposes chemical structures are stored in 2D formats as described below. 

Fig. 2.1 Initial display for creating a database format waiting to enter the record structure (top). After entering the sequence with requests for a chosen format the monitor will show the lower display. On tne top of both screens a short description (help) of the commands for editing the formats is given.

Fig. 2.2 Display for entering the first record of the newly established database format. In the boxes on the top of the screen is a short description of how the data can be entered ana modified is given.

To facilitate the validation process on the basis of the above approach, the MS electronic data is contributed to the OPCW Laboratory in any of the following electronic formats JCAMP, NIST ASCII, AMDIS, and NIST MS Database. Contributing laboratories provide mostly the NIST MS Database format, with structures. The OPCW Code and the Schedule number are placed in the synonym field of the database. The file is submitted to the OPCW Laboratory either as the NIST MS User Database or the corresponding set of text files representing the MSP (Spectral) and MOL (Structure) information. The NIST MS Search/Analysis programs are used for the management of the MS electronic data and also GC(RI) once merged with the MS data. 

Table 21 shows a variety of thermodynamic data collections and the elements considered. The thermodynamic data are usually not available in a current database format (exception CHEMVAL 6 as dBASE file) but in a form which is needed for the specific program. To use thermodynamic data in PHREEQC which are applicable e g. for EQ 3/6 or PHREEQC, they have to be converted into the respective format (e g. PHREEQC) using a transfer program. 

This relational chemical database format is extended in ISIS to include 3D models, generic structures, and most recently, reactions. In these cases, additional "trees" in the database hierarchy connect 2D structures with 3D models, connect root structures with corresponding Rgroup members, or connect molecules with reactions. 

S.N. Kabekkodu, J. Faber, and T. Fawcett, New Powder Diffraction File (PDF-4) in relational database format advantages and data-mining capabilities, Acta Cryst. B58, 333 (2002). 

Throughout the long history of the technique, its emphasis underwent several evolutionary and revolutionary transformations. Remarkably, none of the new developments have taken away nor diminished the value of earlier applications of the powder diffraction method on the contrary, they enhanced and made them more precise and dependable. A noteworthy example is phase identification from powder diffraction data, which dates back to the late 1930 s (Hanawalt, Rinn, and Frevel). Over the years, this application evolved into the Powder Diffraction File containing reliable patterns of some 300,000 crystalline materials in a readily searchable database format (Powder Diffraction File is maintained and distributed by the International Centre for Diffraction Data, http //www.icdd.com). 

TABLE 21.1 Example Database Format for Disease Progression Model... 

The following is a list of resources from which to obtain quantitative sales, market, and health-care data. This type of information is regularly utilized by market research groups for tracking and forecasting purposes. Most of these sources are available online in database format or as electronic publications via subscription, while a few providers offer customized research and products for internal use. 

For efficiency purposes, we need to put our FASTA-formatted sequences into another format. The author has developed a file format, the Sequence Database format (SDB), that allows for fast random access to multiple sequences stored in a single file. See Note 2b for descriptions of the command-line utilities available (as part of the Mercator distribution) for creating and accessing SDB files. We will use the fa2sdb utility to put our softmasked genomes into SDB format. 

IGRExtract takes as input (1) a file containing the genomic sequence (in FASTA format), (2) an ORF database(s) (NCBI and/or TIGR), and (3) a tRNA/rRNA database formatted as described above. When prompted, enter the name of these files (see Note 5). 

BLAST 2.0 supports databases in XDF (extended Database Format). Thus, before conducting a BLAST comparison, drag and drop the xdformat program icon (located in the BLAST 2.0 directory) into the Terminal window, type -n, then drag and drop the IGR database file. 

A Biopipe protocol represents a series of analyses. Each unit of analysis consists of specifications for input, analysis, and output. The input layer consists of a number of adaptors for various common database formats or for remote fetching from Web sources like GenBank. The role of the input layer is to retrieve data into a common format for a subsequent analysis. The complementary output layer contains adaptors to push the analysis result out to the desired database or format. The analysis layer functions through the action of wrapper Biopipe Perl modules that make standard Bioperl runnable binaries accessible to the Biopipe system. An explicit design goal of Biopipe is to reuse the encapsulations of binary tools, importers, and exporters that Bioperl already includes, with thin wrappers that specify the inputs that the input layer must provide in a workflow context. 

A much more ambitious database that builds on the IUBMB classification is BRENDA, maintained by the Institute of Biochemistry at the University of Cologne. In addition to the data provided by the ENZYME database, the BRENDA curators have extracted a large body of information from the enzyme literature and incorporated it into the database. The database format strives to be readable by both humans and machines. The categories of data stored in BRENDA comprise the EC-number, systematic and recommended names, synonyms, CAS-registry numbers, the reaction catalyzed, a list of known substrates and products, the natural substrates, specific activities, KM values, pH and temperature optima, cofactor and ion requirements, inhibitors, sources, localization, purification schemes, molecular weight, subunit structure, posttranslational modifications, enzyme stability, database links, and last but not least an extensive bibliography. Currently, BRENDA holds entries for approximately 3500 different enzymes. 

Database records are used to hold raw sequence data as well as an array of ancillary annotations. In a smvey of the various database formats, we can observe that, although different sets of rules are applied, it is still possible in many cases to inter-... 

Most chemical research groups using MACCS created one or more databases of proprietary chemical structures and associated property data. They also utilised the FCD database that was available from MDL in MACCS database format. That database was derived from the Fine Chemicals Directory published by Fraser Williams (Scientific Systems) Limited. 

For real retrospective data analyses, such as stability tests, it is often necessary to compare data from different measurement points with each other. For this purpose, the use of electronically stored data is advantageous, because it is very easy to superimpose and compare chromatograms and search within the databases. However, electronic filing systems, for example, file or database formats, provide some challenges regarding the recoverability of the data during the regulatory retention period (see Section 8.2.4). 

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