US20030074363A1 - Method and system for providing a polymorphism database - Google Patents

Abstract

Systems and methods for organizing information relating to study of polymorphisms. A database model is provided which interrelates information about one or more of, e.g, subjects from whom samples are extracted, primers used in extracting the DNA from the subjects, about the samples themselves, about experiments done on samples, about particular oligonucleotide probe arrays used to perform experiments, about analysis procedures performed on the samples, and about analysis results. The model is readily translatable into database languages such as SQL. The database model scales to permit storage of information about large numbers of subjects, samples, experiments, chips, etc.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS
• The present application claims priority from U.S. Prov. App. No. 60/053,842 filed Jul. 25, 1997, entitled COMPREHENSIVE BIO-INFORMATICS DATABASE, from U.S. Prov. App. No. 60/069,198 filed on Dec. 11, 1997, entitled COMPREHENSIVE DATABASE FOR BIOINFORMATICS, and from U.S. Prov. App. No. 60/069,436, entitled GENE EXPRESSION AND EVALUATION SYSTEM, filed on Dec. 11, 1997. The contents of all three provisional applications are herein incorporated by reference. [0001]
• The subject matter of the present application is related to the subject matter of the following three co-assigned applications filed on the same day as the present application. GENE EXPRESSION AND EVALUATION SYSTEM (Attorney Docket No. 018547-035010), METHOD AND APPARATUS FOR PROVIDING A BIOINFORMATICS DATABASE (Attorney Docket No. 018547-033810), METHOD AND SYSTEM FOR PROVIDING A PROBE ARRAY CHIP DESIGN DATABASE (Attorney Docket No. 018547-033830). The contents of these three applications are herein incorporated by reference.[0002]
BACKGROUND OF THE INVENTION
• The present invention relates to the collection and storage of information pertaining to chips for processing biological samples and thereby identifying polymorphisms. [0003]
• The genomes of all organisms undergo spontaneous mutation in the course of their continuing evolution generating variant forms of progenitor sequences (Gusella, [0004] Ann. Rev. Biochem. 55, 831-854 (1986)). The variant form may confer an evolutionary advantage or disadvantage relative to a progenitor form or may be neutral. In some instances, a variant form confers a lethal disadvantage and is not transmitted to subsequent generations of the organism. In other instances, a variant form confers an evolutionary advantage to the species and is eventually incorporated into the DNA of many or most members of the species and effectively becomes the progenitor form. In many instances, both progenitor and variant form(s) survive and co-exist in a species population. The coexistence of multiple forms of a sequence gives rise to polymorphisms.
• Despite the increased amount of nucleotide sequence data being generated in recent years, only a minute proportion of the total repository of polymorphisms in humans and other organisms has so far been identified. The paucity of polymorphisms hitherto identified is due to the large amount of work required for their detection by conventional methods. For example, a conventional approach to identifying polymorphisms might be to sequence the same stretch of oligonucleotides in a population of individuals by dideoxy sequencing. In this type of approach, the amount of work increases in proportion to both the length of sequence and the number of individuals in a population and becomes impractical for large stretches of DNA or large numbers of persons. [0005]
• Devices and computer systems for forming and using arrays of materials on a substrate have been developed. These devices and systems have been used for identifying polymorphisms. For example, PCT application WO92/10588, incorporated herein by reference for all purposes, describes techniques for sequencing or sequence checking nucleic acids and other materials. Arrays for performing these operations may be formed in arrays according to the methods of, for example, the pioneering techniques disclosed in U.S. Pat. No. 5,143,854 and U.S. Pat. No. 5,571,639, both incorporated herein by reference for all purposes. [0006]
• According to one aspect of the techniques described therein, an array of nucleic acid probes is fabricated at known locations on a chip or substrate. A fluorescently labeled nucleic acid is then brought into contact with the chip and a scanner generates an image file indicating the locations where the labeled nucleic acids bound to the chip. Based upon the identities of the probes at these locations, it becomes possible to extract information such as the identity of polymorphic forms in of DNA or RNA. Such systems have been used to form, for example, arrays of DNA that may be used to study and detect mutations relevant to cystic fibrosis, the P53 gene (relevant to certain cancers), HIV, and other genetic characteristics. [0007]
• It would be highly useful to apply such arrays to the study of polymorphisms on a large scale. For example, it would be useful to conduct large scale studies on the correlation between certain polymorphisms and individual characteristics such as susceptibility to diseases and effectiveness of drug treatments. To achieve these benefits, it is contemplated that the operations of chip design, construction, sample preparation, and analysis will occur on a very large scale. The quantity of information related to each of these steps to store and correlate is vast. For large scale polymorphism studies, it will be necessary to store this information in a way to facilitate later advantageous querying and retrieval. What is needed is a system and method suitable for storing and organizing large quantities of information used in conjunction with polymorphism studies. [0008]
SUMMARY OF THE INVENTION
• The present invention provides systems and methods for organizing information relating to study of polymorphisms. A database model is provided which interrelates information about one or more of, e.g, subjects from whom samples are extracted, primers used in extracting the DNA from the subjects, about the samples themselves, about experiments done on samples, about particular oligonucleotide probe arrays used to perform experiments, about analysis procedures performed on the samples, and about analysis results. The model is readily translatable into database languages such as SQL. The database model scales to permit storage of information about large numbers of subjects, samples, experiments, chips, etc. [0009]
• Applications include linkage studies to determine resistance to drugs, susceptibility to diseases, and study of every characteristic of humans and other organisms that is related genetic variability. Another application of a database constructed according to this model is quality control of the various steps of performing a polymorphism study. By preserving information about every step of a polymorphism study, one can assess the reliability of the results or use the preserved information as feedback to improve procedures. [0010]
• A further understanding of the nature and advantages of the inventions herein may be realized by reference to the remaining portions of the specification and the attached drawings.[0011]
BRIEF DESCRIPTION OF THE DRAWINGS
• FIG. 1 illustrates an overall system and process for forming and analyzing arrays of biological materials such as DNA or RNA. [0012]
• FIG. 2A illustrates a computer system suitable for use in conjunction with the overall system of FIG. 1. [0013]
• FIG. 2B illustrates a computer network suitable for use in conjunction with the overall system of FIG. 1. [0014]
• FIG. 3 illustrates a key for interpreting a database model. [0015]
• FIGS. [0016] 4A-4H illustrate a database model for maintaining information for the system and process of FIG. 1 according to one embodiment of the present invention.
DESCRIPTION OF SPECIFIC EMBODIMENTS
• Investigation of Polymorphisms [0017]
• A. Preparation of Samples [0018]
• Polymorphisms are detected in a target nucleic acid from an individual being analyzed. For assay of genomic DNA, virtually any biological sample (other than pure red blood cells) is suitable. For example, convenient tissue samples include whole blood, semen, saliva, tears, urine, fecal material, sweat, buccal, skin and hair. For assay of cDNA or mRNA, the tissue sample must be obtained from an organ in which the target nucleic acid is expressed. For example, if the target nucleic acid is a cytochrome P450, the liver is a suitable source. [0019]
• Many of the methods described below require amplification of DNA from target samples. This can be accomplished by e.g., PCR. See generally [0020] PCR Technology: Principles and Applications for DNA Amplification (ed. H. A. Erlich, Freeman Press, NY, N.Y., 1992); PCR Protocols: A Guide to Methods and Applications (eds. Innis, et al., Academic Press, San Diego, Calif., 1990); Mattila et al., Nucleic Acids Res. 19, 4967 (1991); Eckert et al., PCR Methods and Applications 1, 17 (1991); PCR (eds. McPherson et al., IRL Press, Oxford); and U.S. Pat. No. 4,683,202 (each of which is incorporated by reference for all purposes).
• Other suitable amplification methods include the ligase chain reaction (LCR) (see Wu and Wallace, [0021] Genomics 4, 560 (1989), Landegren et al., Science 241, 1077 (1988), transcription amplification (Kwoh et al., Proc. Natl. Acad. Sci. USA 86, 1173 (1989)), and self-sustained sequence replication (Guatelli et al., Proc. Nat. Acad. Sci. USA, 87, 1874 (1990)) and nucleic acid based sequence amplification (NASBA). The latter two amplification methods involve isothermal reactions based on isothermal transcription, which produce both single stranded RNA (ssRNA) and double stranded DNA (dsDNA) as the amplification products in a ratio of about 30 or 100 to 1, respectively.
• B. Detection of Polymorphisms in Target DNA [0022]
• There are two distinct types of analysis depending whether a polymorphism in question has already been characterized. The first type of analysis is sometimes referred to as de novo characterization. This analysis compares target sequences in different individuals to identify points of variation, i.e., polymorphic sites. By analyzing groups of individuals representing the greatest ethnic diversity among humans and greatest breed and species variety in plants and animals, patterns characteristic of the most common alleles/haplotypes of the locus can be identified, and the frequencies of such populations in the population determined. Additional allelic frequencies can be determined for subpopulations characterized by criteria such as geography, race, or gender. The second type of analysis is determining which form(s) of a characterized polymorphism are present in individuals under test. There are a variety of suitable procedures, which are discussed in turn. [0023]
• 1. Allele-Specific Probes [0024]
• The design and use of allele-specific probes for analyzing polymorphisms is described by e.g., Saiki et al., [0025] Nature 324, 163-166 (1986); Dattagupta, EP 235,726, Saiki, WO 89/11548. Allele-specific probes can be designed that hybridize to a segment of target DNA from one individual but do not hybridize to the corresponding segment from another individual due to the presence of different polymorphic forms in the respective segments from the two individuals. Hybridization conditions should be sufficiently stringent that there is a significant difference in hybridization intensity between alleles, and preferably an essentially binary response, whereby a probe hybridizes to only one of the alleles. Some probes are designed to hybridize to a segment of target DNA such that the polymorphic site aligns with a central position (e.g., in a 15 mer at the 7 position; in a 16 mer, at either the 8 or 9 position) of the probe. This design of probe achieves good discrimination in hybridization between different allelic forms.
• Allele-specific probes are often used in pairs, one member of a pair showing a perfect match to a reference form of a target sequence and the other member showing a perfect match to a variant form. Several pairs of probes can then be immobilized on the same support for simultaneous analysis of multiple polymorphisms within the same target sequence. [0026]
• 2. Tiling Arrays [0027]
• The polymorphisms can also be identified by hybridization to nucleic acid arrays, some example of which are described by WO 95/11995 (incorporated by reference in its entirety for all purposes). WO 95/11995 also describes subarrays that are optimized for detection of a variant forms of a precharacterized polymorphism. Such a subarray contains probes designed to be complementary to a second reference sequence, which is an allelic variant of the first reference sequence. The second group of probes is designed by the same principles as described in the Examples except that the probes exhibit complementarily to the second reference sequence. The inclusion of a second group (or further groups) can be particular useful for analyzing short subsequences of the primary reference sequence in which multiple mutations are expected to occur within a short distance commensurate with the length of the probes (i.e., two or more mutations within 9 to 21 bases). [0028]
• 3. Allele-Specific Primers [0029]
• An allele-specific primer hybridizes to a site on target DNA overlapping a polymorphism and only primes amplification of an allelic form to which the primer exhibits perfect complementarily. See Gibbs, [0030] Nucleic Acid Res. 17, 2427-2448 (1989). This primer is used in conjunction with a second primer which hybridizes at a distal site. Amplification proceeds from the two primers leading to a detectable product signifying the particular allelic form is present. A control is usually performed with a second pair of primers, one of which shows a single base mismatch at the polymorphic site and the other of which exhibits perfect complementarily to a distal site. The single-base mismatch prevents amplification and no detectable product is formed. The method works best when the mismatch is included in the 3′-most position of the oligonucleotide aligned with the polymorphism because this position is most destabilizing to elongation from the primer. See, e.g., WO 93/22456.
• 4. Direct-Sequencing [0031]
• The direct analysis of the sequence of polymorphisms of the present invention can be accomplished using either the dideoxy chain termination method or the Maxam Gilbert method (see Sambrook et al., [0032] Molecular Cloning, A Laboratory Manual (2nd Ed., CSHP, New York 1989); Zyskind et al., Recombinant DNA Laboratory Manual, (Acad. Press, 1988)).
• 5. Denaturing Gradient Gel Electrophoresis [0033]
• Amplification products generated using the polymerase chain reaction can be analyzed by the use of denaturing gradient gel electrophoresis. Different alleles can be identified based on the different sequence-dependent melting properties and electrophoretic migration of DNA in solution. Erlich, ed., [0034] PCR Technology, Principles and Applications for DNA Amplification, (W. H. Freeman and Co, New York, 1992), Chapter 7.
• 6. Single-Strand Conformation Polymorphism Analysis [0035]
• Alleles of target sequences can be differentiated using single-strand conformation polymorphism analysis, which identifies base differences by alteration in electrophoretic migration of single stranded PCR products, as described in Orita et al., [0036] Proc. Nat. Acad. Sci. 86, 2766-2770 (1989). Amplified PCR products can be generated as described above, and heated or otherwise denatured, to form single stranded amplification products. Single-stranded nucleic acids may refold or form secondary structures which are partially dependent on the base sequence. The different electrophoretic mobilities of single-stranded amplification products can be related to base-sequence difference between alleles of target sequences.
• Biological Material Analysis System [0037]
• One embodiment of the present invention operates in the context of a system for analyzing biological or other materials using arrays that themselves include probes that may be made of biological materials such as RNA or DNA. The VLSIPS™ and GeneChip™ technologies provide methods of making and using very large arrays of polymers, such as nucleic acids, on chips. See U.S. Pat. No. 5,143,854 and PCT Patent Publication Nos. WO 90/15070 and 92/10092, each of which is hereby incorporated by reference for all purposes. Nucleic acid probes on the chip are used to detect complementary nucleic acid sequences in a sample nucleic acid of interest (the “target” nucleic acid). [0038]
• FIG. 1 illustrates an [0039] overall system 100 for forming and analyzing arrays of biological materials such as RNA or DNA. A part of system 100 is a polymorphism database 102. Polymorphism database 102 includes information about, e.g., biological sources, preparation of samples, design of arrays, raw data obtained from applying experiments to chips, analysis procedures applied, and analysis results, etc. Polymorphism database 102 facilitates large scale study of polymorphisms.
• A chip design system [0040] 104 is used to design arrays of polymers such as biological polymers such as RNA or DNA. Chip design system 104 may be, for example, an appropriately programmed Sun Workstation or personal computer or workstation, such as an IBM PC equivalent, including appropriate memory and a CPU. Chip design system 104 obtains inputs from a user regarding chip design objectives including polymorphisms of interest, and other inputs regarding the desired features of the array. Optionally, chip design system 104 from external databases such as GenBank. The output of chip design system 104 is a set of chip design computer files in the form of, for example, a switch matrix, as described in PCT application WO 92/10092, and other associated computer files. The chip design computer files form a part of polymorphism database 102. Systems for designing chips for study of polymorphisms are disclosed in U.S. Pat. No. 5,571,639 and in PCT application WO 95/11995, the contents of which are herein incorporated by reference.
• The chip design files are input to a mask design system (not shown) that designs the lithographic masks used in the fabrication of arrays of molecules such as DNA. The mask design system designs the lithographic masks used in the fabrication of probe arrays. The mask design system generates mask design files that are then used by a mask construction system (not shown) to construct masks or other synthesis patterns such as chrome-on-glass masks for use in the fabrication of polymer arrays. [0041]
• The masks are used in a synthesis system (not shown). The synthesis system includes the necessary hardware and software used to fabricate arrays of polymers on a substrate or chip. The synthesis system includes a light source and a chemical flow cell on which the substrate or chip is placed. A mask is placed between the light source and the substrate/chip, and the two are translated relative to each other at appropriate times for deprotection of selected regions of the chip. Selected chemical reagents are directed through the flow cell for coupling to deprotected regions, as well as for washing and other operations. The substrates fabricated by the synthesis system are optionally diced into smaller chips. The output of the synthesis system is a chip ready for application of a target sample. [0042]
• Information about the mask design, mask construction, and probe array synthesis is presented by way of background. A [0043] biological source 112 is, for example, tissue from a plant or animal. Various processing steps are applied to material from biological source 112 by a sample preparation system 114. Operation of sample preparation system 114 in the context of a polymorphism study is discussed below in further detail.
• The prepared samples include nucleic acid sequences such as DNA. When the sample is applied to the chip by a [0044] sample exposure system 116, the nucleic acids may or may not bond to the probes. The nucleic acids can be tagged with fluoroscein labels to determine which probes have bonded to nucleotide sequences from the sample. The prepared samples will be placed in a scanning system 118. Scanning system 118 includes a detection device such as a confocal microscope or CCD (charge-coupled device) that is used to detect the location where labeled receptors have bound to the substrate. The output of scanning system 118 is an image file(s) indicating, in the case of fluorescein labeled receptor, the fluorescence intensity (photon counts or other related measurements, such as voltage) as a function of position on the substrate. These image files may also form a part of polymorphism database 102. Since higher photon counts will be observed where the labeled nucleic acid(s) has bound more strongly to the array of probes, and since the monomer sequence of the probes on the substrate is known as a function of position, it becomes possible to analize the sequence(s) of the nucleic acid(s) that are complementary to the probes.
• The image files and the design of the chips are input to an [0045] analysis system 120 that, e.g., calls bases. Such analysis techniques are described in EPO Pub. No. 0717113A, the contents of which are herein incorporated by reference.
• Chip design system [0046] 104, analysis system 120 and control portions of exposure system 116, sample preparation system 114, and scanning system 118 may be appropriately programmed computers such as a Sun workstation or IBM-compatible PC. An independent computer for each system may perform the computer-implemented functions of these systems or one computer may combine the computerized functions of two or more systems. One or more computers may maintain chip design database 102 independent of the computers operating the systems of FIG. 1 or chip design database 102 may be fully or partially maintained by these computers.
• FIG. 2A depicts a block diagram of a [0047] host computer system 10 suitable for implementing the present invention. Host computer system 210 includes a bus 212 which interconnects major subsystems such as a central processor 214, a system memory 216 (typically RAM), an input/output (I/O) adapter 218, an external device such as a display screen 224 via a display adapter 226, a keyboard 232 and a mouse 234 via an I/O adapter 218, a SCSI host adapter 236, and a floppy disk drive 238 operative to receive a floppy disk 240. SCSI host adapter 236 may act as a storage interface to a fixed disk drive 242 or a CD-ROM player 244 operative to receive a CD-ROM 246. Fixed disk 244 may be a part of host computer system 210 or may be separate and accessed through other interface systems. A network interface 248 may provide a direct connection to a remote server via a telephone link or to the Internet. Network interface 248 may also connect to a local area network (LAN) or other network interconnecting many computer systems. Many other devices or subsystems (not shown) may be connected in a similar manner.
• Also, it is not necessary for all of the devices shown in FIG. 2A to be present to practice the present invention, as discussed below. The devices and subsystems may be interconnected in different ways from that shown in FIG. 2A. The operation of a computer system such as that shown in FIG. 2A is readily known in the art and is not discussed in detail in this application. Code to implement the present invention, may be operably disposed or stored in computer-readable storage media such as [0048] system memory 216, fixed disk 242, CD-ROM 246, or floppy disk 240.
• FIG. 2B depicts a [0049] network 260 interconnecting multiple computer systems 210. Network 260 may be a local area network (LAN), wide area network (WAN), etc. Bioinformatics database 102 and the computer-related operations of the other elements of FIG. 2B may be divided amongst computer systems 210 in any way with network 260 being used to communicate information among the various computers. Portable storage media such as floppy disks may be used to carry information between computers instead of network 260.
• Overall Description of Database [0050]
• [0051] Polymorphism database 102 is preferably a relational database with a complex internal structure. The structure and contents of chip design database 102 will be described with reference to a logical model depicted in FIGS. 4A-4H that describes the contents of tables of the database as well as interrelationships among the tables. A visual depiction of this model will be an Entity Relationship Diagram (ERD) which includes entities, relationships, and attributes. A detailed discussion of ERDs is found in “ERwin version 3.0 Methods Guide” available from Logic Works, Inc. of Princeton, N.J., the contents of which are herein incorporated by reference. Those of skill in the art will appreciate that automated tools such as Developer 2000 available from Oracle will convert the ERD from FIGS. 4A-4H directly into executable code such as SQL code for creating and operating the database.
• FIG. 3 is a key to the ERD that will be used to describe the contents of [0052] chip design database 102. A representative table 302 includes one or more key attributes 304 and one or more non-key attributes 306. Representative table 302 includes one or more records where each record includes fields corresponding to the listed attributes. The contents of the key fields taken together identify an individual record. In the ERD, each table is represented by a rectangle divided by a horizontal line. The fields or attributes above the line are key while the fields or attributes below the line are non-key. An identifying relationship 308 signifies that the key attribute of a parent table 310 is also a key attribute of a child table 312. A non-identifying relationship 314 signifies that the key attribute of a parent table 316 is also a non-key attribute of a child table 318. Where (FK) appears in parenthesis, it indicates that an attribute of one table is a key attribute of another table. Both the depicted non-identifying and identifying relationship are one to one-or-more relationships where one record in the parent table corresponds to one or more records in the child table. An alternative non-identifying relationship 324 is a one to zero-or-more relationship where one record in a parent table 320 corresponds to zero or more records in a child table 322.
• Database Model [0053]
• FIGS. [0054] 4A-4H are entity relationship diagrams (ERDs) showing elements of polymorphism database 102 according to one embodiment of the present invention. Each rectangle in the diagram corresponds to a table in database 102. First, the relationships and general contents of the various tables will be described.
• The interrelationships and general contents of the tables of [0055] database 102 will be described first. Then a chart will be presented listing and describing all of the fields of the various tables.
• FIG. 4A illustrates core elements of [0056] database 102 according to one embodiment of the present invention. A subject table 402 lists organisms from which samples have been extracted for polymorphism analysis or other tissue sources. Samples may also be obtained from tissue collections not associated with any one identified organism. Information stored within subject table 402 includes the name, gender, family, position with family, (e.g., father, mother, etc.), and ethnic group. For human subjects, the name and family will preferably be represented in coded form to assure privacy. Associated with each subject is a species as listed in a species table 404. Also, a relationship may be defined among subjects a subject relationship table 406 which includes records corresponding to related subjects. These relationships may be father-mother, sibling, twins, etc. Subjects may be part of a group that is being studied, e.g., a group with a congenital disease, or a toxic reaction to a particular drug. The groups are listed in a subject group table 408. Participation of subjects in groups is defined by a subject participation table 410 which lists all group memberships.
• Samples and their attributes are listed in a sample table [0057] 412. Each sample has an associated sample type. The sample types are listed in a sample type table 414. Possible sample types include blood, urine, etc. Companies or institutions that provide samples are listed in a sample source table 416.
• [0058] Database 102 provides an item table 418 that includes records for items. There are various types of items that correspond to different stages of the sample preparation process. An “item derivation” transforms an item of one type into an item of another type. The following table lists various item types and item derivation types for a representative embodiment.

  Item Type	Derived from	by Item Derivation Type
  Sample	other samples	pooling
  Sample	other sample	splitting
  Extracted DNA	Sample	DNA Extraction
  Target (Sequences of	Extracted DNA	PCR
  interest amplified)
  Fluorescently Labeled	Target	Labeling
  Target
  Hybridized Chip	Labeled Target	Hybridization (application
  		of target to chip)
  Stained Hybridized Chip	Hybridized Chip	Staining

• [0059]

  Item Type	Derived from	by Item Derivation Type
  Fluorescently Labeled	Target	Labeling
  Target
  Hybridized Chip	Labeled Target	Hybridization (application
  		of target to chip)
  Stained Hybridized Chip	Hybridized Chip	Staining

• Item derivations are listed in an item derivation table [0060] 420. It should be noted that derivations need not produce a change between item types. Each item derivation occurs in accordance with a protocol that characterizes the step or steps in the derivation. Protocols are listed in a protocol table 428. Each item derivation is performed by an employee listed in employee table 432.
• Unused chips are listed in a chip table [0061] 422. Hybridized chips (i.e., chips that have had target applied) are listed in a hybridized chip table 424. A hybridized sample map table 426 lists the relationships between hybridized chips and the samples that have been applied to them.
• Stained hybridized chips are scanned in a process referred to here as a scan experiment. Scan experiments are listed in a scan experiment table [0062] 430. The scan experiment occurs in accordance with a protocol listed in protocol table 428. The scan experiment is performed by an employee listed in employee table 432.
• FIG. 4B depicts further details of the data model for items and item derivations. The various item types are listed in an item type table [0063] 434 and the various item derivation types are listed in an item derivation type table 436. The relationships between successive item types, e.g., sample and target are defined in an item type derivation table 438. An item has associated attributes. For example, for a target, database 102 may store the concentration, volume, location and/or remaining amount. All item attributes are stored in an item attribute table 440. Item attributes may be shared among multiple items. For example, a series of targets may all share a preparation date. An item attribute item map table 442 implements a many-to-many relationship between item attributes and items. The various types of item attributes such as preparer, preparation date, etc. are listed in an item attribute type table 444. Each item type has corresponding attribute types. Some attribute types are, however, shared among various item types. Accordingly, there is a many-to-many relationship among item attribute types and item types that is implemented by an item type map table 446.
• The tables of FIG. 4B represent a powerfully general model of the sample preparation process. Changes in process steps that require changes in the type of information that should be stored may be implemented by changing and adding table contents rather than providing new tables or changing relationships among tables. [0064]
• FIG. 4C depicts a detailed data model for storing information about protocols according to the present invention. Protocols as stored in protocol table [0065] 428 represent information about particular processes that have been performed including item derivations, analyses, and scan experiments. Each protocol has an associated protocol template. Protocol templates identify protocol types. For example, one protocol template may be a PCR template. All protocols associated with the PCR template identify parameters for performing a PCR procedure. Protocol templates are listed in a protocol template table 448. A parameter table 450 lists all the parameters and their values for all the protocols listed in protocol table 428. A parameter template table 452 lists the various parameter types along with default values. An examples of a parameter template would be a PCR reaction temperature. The parameter template would include a default value for this parameter. Parameter table 450 might then list many different PCR reaction temperature values that would be used by many different protocols. If a parameter value has not been modified by the user, it inherits the standard value of the associated parameter template. A parameter template set is a set of parameter templates that are used for a particular purpose, e.g., in association with protocols according to one or more protocol templates. Parameter template sets are listed in a parameter template set table 454. There are different types of parameter template set and these are listed in a parameter template set table 456. A mapping between parameter template sets and protocol templates is defined by a protocol template set map table 458.
• Protocol templates may have associated lengthy verbal information about how to perform protocol steps. A protocol template document table [0066] 460 stores references to documents that include instructions for performing protocols.
• As with the items, the data model for protocols defined by FIG. 4C is highly general and allows significant changes in the way item derivations, analyses, and experiments are performed without changing the underlying data model. [0067]
• Referring again to FIG. 4A, there are tables to record information concerning the use of primers in PCR. A fragment table [0068] 462 lists all the sequence fragments investigated in conjunction with database 102. Associated with each fragment are one or more primer pairs used to amplify the fragment in a PCR process. A primer pair table 464 lists all the primer pairs including information about whether the primer pair actually worked to amplify the fragment. In order to develop the information about the effectiveness of primer pairs, there is a PCR table 466 that lists records identifying the outcome of multiple PCR operations. The individual PCR operations are identified by reference to item derivation table 420.
• A single PCR operation may be used to amplify many different fragments and thus employ many different primer pairs. Of course, a single primer pair may be used in multiple PCR operations. There is therefore a many-to-many relationship between PCR operations and primer pairs that is recorded by a primer pair PCR map table [0069] 468.
• Information about individual primers is stored in a primer table [0070] 470. Also, each primer has an associated protocol in protocol table 428 that characterizes the primer preparation process. Information about primer orders is listed in a primer order table 472. Each primer order is to a vendor and the vendors are listed in a vendor table 474. Each primer order is made by an employee listed in employee table 432. A primer order design map table 476 implements a many-to-many relationship between primer orders and primers.
• The data model described here thus preserves information about primers used in PCR reactions. One can improve results by using primers that have successfully amplified a given fragment in the past. Sometimes particular groups of primer pairs cannot be multiplexed together in the same PCR process. The information preserved here thus permits experimenters to make optimal use of expensive and time consuming PCR procedures. [0071]
• It is also useful to preserve information about the chip production process and the origin of individual chips. A wafer table [0072] 478 lists wafers. When chips are produced, many chips are produced at the same time as part of a single wafer. Chip table 422 stores references to wafer table 478 for each chip and the location of each chip on its wafer at production time. Sometimes there is analytic significance associated with the location of a chip on the wafer. Each wafer is produced as part of a lot and the identify of the lot for each wafer is recorded by wafer table 478 as a reference to a lot table 480 that lists each lot.
• FIG. 4D depicts further details of tables pertaining to chip design that are preferably maintained within [0073] polymorphism database 102 according to one embodiment of the present invention. A tiling design table 482 lists tiling designs. Each tiling design represents the application of a particular tiling format to a sequence to be investigated. Tiling formats indicate probe orientation, probe length, and the position within a probe of a single nucleotide polymorphism being investigated. In a preferred embodiment, there may be very few tiling formats and they are listed in a tiling format table 484.
• A particular tiling design includes many atom designs specifying the design of a single atom. In one embodiment, an atom is a group of typically four probes used to investigate a single base position with each probe hybridizing to a sequence including a different base at that position. Atom designs are listed in an atom design table [0074] 486. Records identifying the designs of individual probes are listed in a probe design table 488. A probe design role table 490 indicates the roles of probes listed in probe design table 488 in the atom designs of atom design table 486. For combinations of probe design and atom design, probe design role table 490 indicates which base the probe hybridizes to at the substitution position and whether the probe represents a match or a mismatch to the wild type.
• A probe data table [0075] 492 gives the hybridization intensity values for particular probes designs as determined in particular scan experiments. Each record of the table also gives the number of pixels used to determine the intensity value and the standard deviation of intensity as measured among the pixels.
• FIGS. [0076] 4E-4G depict aspects of polymorphism database 102 related to analysis procedures and their results according to one embodiment of the present invention. An analysis table 494 lists analyses performed. An analysis generally refers to a non-trivial transformation of data. Records of analysis table 494 include references to protocol table 428 to specify parameters used for each analysis. Analyses may take as their input raw data or the results of previous analyses. An analysis dependency table 496 lists dependencies among analyses where one analysis depends on the data developed by another analysis. An analysis input table 498 lists inputs for analyses listed in analysis table 494.
• On the right side of FIG. 4E are various tables used to support analyses. A chip design sequence map table [0077] 500 correlates particular fragments with chip designs. A sequence position table 502 lists investigated sequence positions indicating their positions on a fragment. Records of sequence position table 502 reference a genomic sequence position table 504 which gives sequence positions in the genome rather than within individual fragments.
• A scan experiment set table [0078] 506 lists sets of scan experiments. This allows for groupings of experiments for individuals or populations to serve as the basis for polymorphism analysis. A scan experiment used table 508 lists records indicating memberships of a scan experiment in a scan experiment set.
• A tiling data table [0079] 510 lists records identifying tiling designs as implemented in particular chips measured by particular scan experiments. An atom data table 512 lists the intensities measured for particular sequence positions as measured in scan experiments identified by the tiling data records. A subject sequence position data table 514 lists combinations of sequence position and scan experiment.
• A series of tables in FIGS. [0080] 4E-4G correspond to different types of analysis that occur during the course of a polymorphism investigation. The types presented here are merely representative. A parallel series of tables provide the analysis results. A polymorphism analysis table 516 lists references to analysis table 494. The results of the performed polymorphism analyses are listed in a polymorphism position result table 518. A record of this table gives a result for a polymorphism analysis for a particular position as determined based on a particular set of scan experiments. In one embodiment the result is whether a particular mutation is certain, likely, possible, or not possible at the position. The result may also be that the reference is wrong.
• A user polymorphism analysis table [0081] 520 lists user interpretations of results as listed in polymorphism position result table 518. The records of user polymorphism analysis table 520 are references to analysis table 494. The user interpretations themselves are stored in a user polymorphism analysis result table 522. Each result is a likelihood of a particular mutation at a position as considered by a user plus an accompanying user comment.
• A P-Hat analysis estimates the relative concentrations of wild type sequence and sequence having a particular mutation as determined in a particular scan experiment. A P-Hat analysis table [0082] 524 lists references to analysis table 494. An atom result table 526 gives estimates of the relative concentration along with upper and lower bounds and a maximum intensity. For heterozygous mutations, the estimates of relative concentration will cluster around 0.5 For homozygous mutations, the estimates should cluster around 1.0.
• Base call analyses are determinations of the base at a particular position for a particular individual that may be based on more than one experiments. A base call analysis table [0083] 528 lists references to analysis table 494. A base call result table 530 lists the called bases for particular combinations of sequence position and subject.
• A P-Hat grouping analysis determines a measure of likelihood that data in a set of scan experiments results from separate genotypes. P-hat grouping analyses are listed in a p-hat grouping analysis table [0084] 532 by reference to analysis table 494. P-hat grouping analysis results are listed in a mutation fraction result table 534. A group separation is given for various combinations of sequence position and scan experiment set.
• A clustering analysis determines an alternative measure of likelihood that data in a set of scan experiments results from separate genotypes. Clustering analyses are listed in a clustering analysis table [0085] 536 by reference to analysis table 494. Clustering analysis results are listed in a clustering result table 538. A clustering factor is given for various combinations of sequence position and scan experiment set.
• FIG. 4F shows tables which support normalization and footprint finding operations that support the analyses referred to in FIG. 4E. Hybridization intensity measurements made in scan experiments should be normalized over a set of scan experiments. The normalization should take into account differences in amplification level produced by different PCR processes. [0086]
• Normalization is done by region of sequence. A normalization region analysis determines the boundaries of a region to be normalized. The determination of boundaries takes into account that different fragments of sequence are amplified by different PCR procedures. A normalization region analysis table [0087] 540 lists normalization region analyses by reference to analysis table 494. A normalization region result table 542 lists the boundaries for each determined normalization region.
• Normalization values for identified normalization regions are themselves determined by normalization analyses. Normalization analyses are listed in a normalization analysis table [0088] 544 by reference to analysis table 494. A normalization result table 546 lists the normalization values for regions.
• A footprint analysis determines regions of sequence for which the hybridization intensity is elevated for the purposes of quality control. Footprint analyses are listed in a footprint analysis table [0089] 548 by reference to analysis table 494. Footprints are identified by sequence starting point and ending point in a particular scan experiment in a footprint table 550.
• FIG. 4G depicts tables pertaining to measurement quality according to one embodiment of the present invention. A tiling data quality analysis determines the quality of results from a scan experiment. These analyses are listed in a tiling data quality analysis table [0090] 552 by reference to analysis table 494. Tiling data quality analysis results are listed in a tiling data quality result table 554. The results include an average hybridization intensity value for perfect match or mismatch probes. A wild type call rate gives the fraction of atom data where the probe corresponding to the reference base has the highest hybridization intensity. A wild type call rate of around 1.0 indicates good quality. Where the call rate is less than 0.75, the scan experiment should be rejected. An accept data field indicates whether the analysis indicates rejection or acceptance.
• Where scan experiment measurements indicate two or more non-wild type bases within a probe length, this indicates a measurement problem for the affected region of sequence. These regions are identified by difficult region analyses listed in a difficult region analysis table [0091] 556 by reference to analysis table 494. A difficult region result table 558 lists the regions identified as being difficult.
• Analysis dependency table [0092] 496 indicates interrelationships among the various analyses of FIGS. 4E-4G. A footprint analysis may depend on a normalization analysis which may in turn depend on a normalization region analysis. A basecall analysis or PHatGrouping analysis may depend on an atom analysis. A polymorphism analysis may depend on any of these analyses and/or a user polymorphism analysis and/or a clustering analysis.
• Another aspect of the investigation of polymorphisms is seeking patent protection for identified polymorphisms. FIG. 4H shows tables of [0093] polymorphism database 102 related to efforts to seek patent protection according to one embodiment of the present invention. A polymorphism patent sequence table 560 lists sequences for which patent protection is sought. A patent application table 562 lists patent applications directed toward the protection of polymorphisms. A polymer patent application sequence map table 564 implements a many-to-many relationship between patent applications and sequences. A prior application table 566 lists relationships between patent applications and prior related patent applications. An attorney table 568 lists attorneys responsible for preparing patent applications listed in patent application table 562. A law firm table 570 lists the law firms to which the attorneys listed in attorney table 568 belong.
• An employee group table [0094] 572 lists groups of inventors for the patent applications listed in table 562. Individual inventors are listed in employee table 432. An employee group map table 574 implements a many-to-many relationship between inventors and groups of inventors.
• The data model of FIG. 4H greatly facilitates the process of securing patent protection for polymorphisms and thereby increases the commercial incentive for investigation of polymorphisms. [0095]
• Database Contents [0096]
• The contents of the tables introduced above will now be presented in greater detail in the following chart. [0097]

  TABLE	FIELD	COMMENT
  tblSubject	SubjectId: INTEGER	Identifies
  		biological source
  		of sample.
  	SpeciesID: INTEGER	Species of
  		subject.
  	Name: VARCHAR2(20)	Name of subject
  		(anonimized for
  		human subjects).
  	Gender: VARCHAR2(10)	Gender of
  		subject.
  	Family: VARCHAR2(20)	Family of subject
  		(anonimized for
  		human subjects).
  	Member: SMALLINT	Position in family
  		(father, mother,
  		etc.).
  	Group: VARCHAR2(20)	Ethnic group.
  	CellLineID: VARCHAR2(20)	Identifier for
  		sample source not
  		associated with
  		particular
  		organism.
  	IsReference: SMALLINT	Whether or not
  		subject is in a
  		group.
  tblSpecies	SpeciesId: INTEGER	Species identifier.
  	Name: VARCHAR2(30)	Name of species.
  SubjectRelationship	Subject1: INTEGER	First subject in
  		relationship
  	Subject2: INTEGER	Second subject in
  		relationship.
  	Position: VARCHAR2(2)	Nature of
  		relationship.
  tblSubjectGroup	GroupId: INTEGER	Identifier of
  		group of subjects
  		(not same as
  		ethnic group).
  	GroupCode: VARCHAR2(20)	Code identifier
  		for group.
  	Comments: LONG VARCHAR	User comments
  		on group.
  	upsize_ts: DATE	Creation date for
  		group.
  tblSubjectParticipation	SubjectId: INTEGER	Reference to
  		subject table.
  	GroupId: INTEGER	Reference to
  		subject group
  		table.
  tblSample	SampleId: INTEGER	Sample identifier.
  	SubjectID: INTEGER	Reference to
  		subject table.
  	SampleSourceId: CHAR(18)	Institutional
  		source of sample.
  	Code: VARCHAR2(20)	Code representing
  		individual subject.
  	Recipient: VARCHAR2(20)	Person accepting
  		sample.
  	Provider: VARCHAR2(20)	Person or
  		institution
  		providing sample.
  	DateReceived: DATE	Date sample
  		received.
  	ProtocolId: INTEGER	Reference to
  		protocol table.
  	SampleTypeId: INTEGER	Reference to
  		sample type table.
  tblSampleType	SampleTypeId: INTEGER	Sample type
  		identifier.
  	Description: VARCHAR2(50)	Description of
  		sample type.
  tblSample Source	SampleSourceId: CHAR(18)	Identifier of
  		institutional
  		sample source.
  	ProviderName: VARCHAR2(20)	Name of
  		individual or
  		institutional
  		sample provider.
  Item	ItemId: INTEGER	Item identifier.
  	ItemTypeId: INTEGER	Item type
  		identifier.
  	ItemName: VARCHAR2(50)	Name of item.
  ItemDerivation	Item1Id: INTEGER	Derivation
  		source.
  	Item2Id: INTEGER	Derivation result.
  	EmployeeId: INTEGER	Employee
  		responsible for
  		derivation.
  	DerivationTypeId: INTEGER	Derivation type
  		identifier.
  	Protocolid: VARCHAR2(18)	Reference to
  		protocol table.
  	Date: DATE	Date of
  		derivation.
  tblChip	ChipId: INTEGER	Rename reference
  		to item table.
  	ChipDesignPlacementId: INTEGER	Placement on
  		wafer.
  	LocationId: INTEGER	Location of chip.
  	WaferId: INTEGER	Wafer the chip
  		was on.
  tblHybedChip	HybedChipId: INTEGER	Rename reference
  		to item table.
  	SubjectID: INTEGER	Reference to
  		subject table.
  	ProtocolId: INTEGER	Reference to
  		protocol table.
  	Repetition: SMALLINT	Refers to number
  		of times chip has
  		been washed and
  		reused.
  tblHybSampleMap	ItemId: INTEGER	Reference to item
  		table.
  Protocol	ProtocolId: INTEGER	Protocol
  		identifier.
  	ProtocolTemplateId: INTEGER	Protocol template
  		identifier.
  	Name: VARCHAR2(100)	Name of protocol.
  tblScanExperiment	ScanExptId: INTEGER	Scan experiment
  		identifier.
  	ItemId: INTEGER	Reference to item
  		table.
  	ScanCode: VARCHAR2(25)	File for scan
  		results.
  	ProtocolId: INTEGERP	Reference to
  		protocol table.
  	ScanRatingId: INTEGER	Assessment of
  		scan quality.
  	ExperimenterId: INTEGER	Experimenter
  		identifier.
  	Date: DATE	Date of
  		experiment.
  	ConversionTool: VARCHAR2(50)	Program used to
  		convert from scan
  		image to
  		intensities.
  	ConversionDate: DATE	Date of
  		conversion.
  	ScanStatus: VARCHAR2(50)	whether or not
  		scan image has
  		been converted to
  		intensities
  	Comments: LONG VARCHAR	Comments.
  Employee	EmployeeId: INTEGER	Employee
  		identifier.
  	EmployeeCode: VARCHAR2(5)	Code for
  		employee
  	FName: VARCHAR2(20)	First name of
  		employee.
  	MName: VARCHAR2(20)	Middle name of
  		employee.
  	LName: VARCHAR2(20)	Last name of
  		employee.
  ItemType	ItemId: INTEGER	Item type
  		identifier.
  	ItemTypeName: VARCHAR2(30)	Name of item
  		type.
  	FormName: VARCHAR2(100)	Reference to user
  		interface form for
  		item type.
  ItemDerivationType	DerivationTypeId: INTEGER	Derivation type
  		identifier.
  	DerivationType: VARCHAR2(50)	Description of
  		derivation type.
  ItemTypeDerivation	NextItemTypeId: INTEGER	Result type of
  		derivation.
  	ItemTypeId: INTEGER	Source type of
  		derivation.
  ItemAttribute	itemAttributeId: INTEGER	Item attribute
  		identifier.
  	ItemAttributeTypeId: INTEGER	Reference to item
  		attribute type
  		table.
  	Attribute: VARCHAR2(50)	Attribute value.
  ItemAttributeItemMap	ItemAttributeId: INGEGER	Reference to item
  		attribute table.
  	ItemId: INTEGER	Reference to item
  		table.
  ItemAttributeType	ItemAttributetypeId: INTEGER	Item attribute
  		identifier.
  	ItemAttributeName: VARCHAR2(30)	Name of item
  		attribute type.
  ItemTypeMap	ItemAttributeTypeId: INTEGER	Reference to item
  		attribute type
  		table.
  	ItemTypeId: INTEGER	Reference to item
  		type table.
  ProtocolTemplate	ProtocolTemplateId: INTEGER	Protocol template
  		identifier.
  	Name: VARCHAR2(100)	Name of protocol
  		template.
  	DateCreated: DATE	Date protocol
  		template created.
  	FormName: VARCHAR2(50)	Name of the
  		electronic form
  		used for protocol
  		template.
  Parameter	ParameterId: INTEGER	Parameter
  		identifier.
  	ParameterTemplateId: INTEGER	Reference to
  		parameter
  		template table.
  	Value: VARCHAR2(20)	Value of
  		parameter.
  	ProtocolID: INTEGER	Reference to
  		protocol table.
  ParameterTemplate	ParameterTemplateId: INTEGER	Parameter
  		template
  		identifier.
  	Name: VARCHAR2(100)	Name of
  		parameter
  		template.
  	ParamTemplateSetId: INTEGER	Reference to
  		parameter
  		template set table.
  	StandardValue: VARCHAR2(100)	Default value for
  		parameter.
  ParamTemplateSet	ParamTemplateSetId: INTEGER	Parameter
  		template set
  		identifier.
  	TypeId: INTEGER	Renamed
  		reference to
  		parameter
  		template set type
  		table.
  	Name: VARCHAR2(20)	Name of
  		parameter
  		template set.
  ParamTemplateSetType	ParamTempSetTypeId: INTEGER	Parameter
  		template set type
  		identifier.
  	Description: VARCHAR2(50)	Description of
  		parameter
  		template set type.
  ParameterTemplateSetMap	ProtocolTemplateId: INTEGER	Reference to
  		protocol template
  		table.
  	ParamTemplateSetId: INTEGER	Reference to
  		parameter
  		template set table.
  ProtocolTemplateDoc	ProtocolDocId: INTEGER	Protocol Template
  		document
  		identifier.
  	ProtocolTemplateId: INTEGER	Reference to
  		protcol template
  		table.
  	Name: VARCHAR2(100)	Name of protocol
  		template.
  	PathAndFileName: VARCHAR2(50)	File name for
  		protocol template
  		document.
  	AuthorName: INTEGER	Author of
  		protocol template
  		document.
  	CreationDate: DATE	Creation date of
  		protocol template
  		document.
  tbFragment	FragmentId: INTEGER	Fragment
  		identifier.
  	ChipSequence: LONG VARCHAR	Sequence of
  		fragment.
  	Code: VARCHAR2(50)	Code representing
  		fragment.
  tblPrimerPair	PrimerPairId: INTEGER	Identifier for
  		primer pair.
  	LeftPrimerId: INTEGER	Left primer
  		identifier.
  	RightPrimerId: INTEGER	Right primer
  		identifier.
  	PCRSize: INTEGER	length of
  		amplified
  		fragment
  	Worked: SMALLINT	Whether or not
  		pair successfully
  		amplified
  		fragment.
  	FragmentId: INTEGER	Reference to
  		fragment table.
  tblPCR	Item1Id: INTEGER	First part of
  		reference to item
  		derivation table.
  	Item2Id: INTEGER	Second part of
  		reference to item
  		derivation table.
  	Reactionworked: SMALLINT	Whether or not
  		PCR reaction
  		worked.
  PrimePairPCRMap	PrimerPairId: INTEGER	Reference to
  		primer pair table.
  	Item1Id: INTEGER	First part of
  		referenced item
  		derivation table.
  	Item2Id: INTEGER	Second part of
  		referenced item
  		derivation table.
  tblPrimer	PrimerId: INTEGER	Primer identifier.
  	ProtocolId: INTEGER	Reference to
  		protocol table.
  	OligoSeq: VARCHAR2(35)	Sequence of
  		primer.
  	Position: INTEGER	Position of primer
  		on fragment.
  	Length: INTEGER	Length of primer.
  	MeltingTemp: INTEGER	Melting
  		temperature of
  		primer.
  	Direction: VARCHAR2(20)	Direction
  		(forward or
  		reverse).
  tblPrimerOrder	OrderId: INTEGER	Order identifier.
  	EmployeeId: INTEGER	Employee who
  		made order.
  	VendorId: INTEGER	Vendor for order.
  	OrderDate: DATE	Date of order.
  	Owner: VARCHAR2(50)	Name of
  		employee making
  		order.
  	Vendor: VARCHAR2(50)	Name of vendor.
  tbl Vendor	VendorId: INTEGER	Vendor identifier.
  	Vendor: VARCHAR2(50)	Name of vendor.
  	PhoneNumber: VARCHAR2(15)	Phone number of
  		vendor.
  	FaxNumber: VARCHAR2(15)	Fax Number of
  		vendor.
  	Address: VARCHAR2(50)	Address of
  		vendor.
  	City: VARCHAR2(50)	City of vendor.
  	State: VARCHAR2(50)	State of vendor.
  	Zip: VARCHAR2(50)	Zip code of
  		vendor.
  tblPrimerOrderDesignMap	PrimerId: INTEGER	Reference to
  		primer table.
  	OrderId: INTEGER	Reference to
  		order table.
  tblWafer	WaferId: INTEGER	Wafer identifier.
  	LotId: INTEGER	Lot to which
  		wafer belongs.
  	Code: VARCHAR2(8)	Code for wafer.
  	SynthesisDate_delete: DATE	Synthesis date for
  		wafer.
  	Released: DATE	Date wafer
  		available.
  	Done: SMALLINT	Whether wafer
  		production is
  		complete.
  	ExpirationDate: DATE	Expiration date of
  		wafer.
  	ExpectedLife: CHAR(18)	Expected useful
  		life of wafer.
  tblLot	LotId: INTEGER	Lot identifier.
  	WaferDesignId: INTEGER	Identifier for
  		wafer design.
  	LotNumber: VARCHAR2(12)	Lot number.
  	WaferPN: VARCHAR2(50)	Part number for
  		wafer.
  tblTiling Design	TilingDesignID: INTEGER	Tiling design
  		identifier.
  	ChipDesignSequenceMapID: NUMBER	Reference to chip
  		design sequence
  		map.
  	TilingFormatID: INTEGER	Reference to
  		tiling format
  		table.
  	UnitNumber: INTEGER	1 for sense, 0 for
  		antisense
  	AtomOffset: INTEGER	# to add to
  		translate atom
  		position in tiling
  		to atom position
  		in chip design
  tblTiling Format	TilingFormatID: INTEGER	Tiling format
  		identifier
  	Orientation: CHAR(18)	Orientation for
  		tiling.
  	ProbeLength: SMALLINT	Length of probes.
  	SubstitutionPosition: SMALLINT	Substitution
  		position for
  		mutation base in
  		probes.
  tblAtomDesign	AtomDesignId: NUMBER	Atom design
  		identifier.
  	TilingDesignID: INTEGER	Reference to
  		tiling design
  		table.
  	Position: INTEGER	Position of atom
  		in sequence.
  tblProbeDesign	ProbeDesignID: NUMBER	Probe design
  		identifier.
  	ChipDesignId: INTEGER	Reference to chip
  		design
  	x: SMALLINT	x position of
  		probe.
  	y: SMALLINT	y position of
  		probe.
  tblProbeDesignRole	ProbeDesignID: NUMBER	Reference to
  		probe design
  		table.
  	AtomDesignID: NUMBER	Reference to atom
  		design table.
  	Substitution: CHAR(18)	Substitution
  		position in probe
  		design.
  	Mismatches: NUMBER	Whether probe is
  		match or
  		mismatch.
  tblProbeData	ProbeDesignID: NUMBER	Reference to
  		probe design
  		table.
  	ScanExptID: INTEGER	Reference to scan
  		experiment table.
  	Intensity: FLOAT	Measured
  		hybridization
  		intensity for
  		probe.
  	NPixels; NUMBER	Number of pixels
  		used for intensity
  		calculation.
  	StDev: NUMBER	Standard
  		deviation for
  		pixels.
  tblAnalysis	AnalysisId: INTEGER	Analysis
  		identifier.
  	Analysis VersionID: INTEGER	Reference to
  		version of
  		analysis.
  	ProtocolID: INTEGER	Reference to
  		protocol table.
  	DatePerformed: DATE	Date analysis
  		performed.
  	NeedsUpdate: NUMBER	Whether analysis
  		is current.
  tblAnalysisDependency	ParentAnalysisId: INTEGER	Analysis
  		providing input.
  	SubAnalysisId: INTEGER	Analysis receiving
  		input.
  	Role: VARCHAR2(20)	Role of data
  		provided by
  		parent analysis.
  TblAnalysisInput	AnalysisinputID: INTEGER	Analysis input
  		identifier.
  	AnalysisId: INTEGER	Analysis receiving
  		input.
  	Inputtype: VARCHAR2(20)	Type of input.
  	ObjectID: INTEGER	Reference to input
  		data.
  tblChipDesignSeguenceMap	ChipDesignSequenceMapID: NUMBER	Chip design
  		sequence map
  		identifier.
  	FragmentID: INTEGER	Reference to
  		fragment table.
  	ChipDesignId: INTEGER	Chip design
  		identifier.
  	AtomOffset: NUMBER	# to add to
  		translate atom
  		position in tiling
  		to atom position
  		in chip design
  tblSequencePosition	SequencePositionID: NUMBER	Sequence position
  		identifier.
  	ChipDesignSequenceMapID: NUMBER	Reference to chip
  		design sequence
  		map table.
  	Position: NUMBER	Position in
  		fragment.
  	GenomicSequencePositionID: INTEGER	Reference to
  		genomic sequence
  		position table.
  	RefBase: INTEGER	Reference base.
  tblGenomicSequencePosition	GenomicSequencePositionID: INTEGER	Genomic
  		sequence position
  		identifier.
  tblScanExperimentSet	ScanExperimentSetID: NUMBER	Scan experiment
  		set identifier.
  tbsScanExperimentUsed	ScanExptID: INTEGER	Reference to scan
  		experiment table.
  	ScanExperimentSetID: NUMBER	Reference to scan
  		experiment set
  		table.
  tblTilingData	TilingDataID: NUMBER	Tiling data
  		identifier.
  	ScanExptID: INTEGER	Reference to scan
  		experiment table.
  	TilingDesignID: INTEGER	Reference to
  		tiling design
  		table.
  tblAtomData	AtomDataID: INTEGER	Atom data
  		identifier.
  	TilingDataID: NUMBER	Reference to
  		tiling data table.
  	SubjectSequencePositionID: INTEGER	Reference to
  		subject sequence
  		position table.
  tblSubjectSequencePosition	SubjectSequencePositionID: INTEGER	Subject sequence
  		position identifier.
  	SubjectID: INTEGER	Reference to
  		subject table.
  	SequencePositionID: NUMBER	Reference to
  		sequence position
  		table.
  tblPolymorphismAnalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tblPolyPositionResult	AnalysisId: INTEGER	Reference to
  		analysis table.
  	PolyPositionID: INTEGER	Polymorphism
  		position identifier.
  	ScanExperimentSetID: NUMBER	Reference to scan
  		experiment set
  		table.
  	PolyPositiontypeID: INTEGER	Refers to
  		possibility of
  		polymorphism at
  		position, e.g.,
  		certain, likely,
  		possible,
  		mismatch
  		(reference is
  		wrong).
  	WTBase: CHAR(18)	Wild type base at
  		position.
  	MuBase: INTEGER	Mutation base at
  		position.
  tblUserPolyanalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tblUserPolyanalysisResult	AnalysisId: INTEGER	Reference to
  		analysis table.
  	SequencePositionID: NUMBER	Reference to
  		sequence position
  		table.
  	ScanExperimentSetID: NUMBER	Reference to scan
  		experiment set
  		table.
  	PolyPositionTypeID: INTEGER	See
  		polymorphism
  		position result
  		table.
  	UserComment: VARCHAR2(256)	User comment
  		done
  		polymorphism
  		analysis.
  tblAtomanalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tblAtomResult	AnalysisId: INTEGER	Reference to
  		analysis table.
  	AtomDataID: INTEGER	Reference to atom
  		data table.
  	PHat: FLOAT	Relative
  		concentration of
  		mutant and wild
  		type.
  	PHatUpperbound: FLOAT	Upperbound for
  		relative
  		concentration.
  	PHatLowerbound: FLOAT	Lowerbound for
  		relative
  		concentration.
  	MaxIntensity: FLOAT	Maximum
  		measured
  		intensity for
  		atom.
  	WTIntensity: FLOAT	Measured wild
  		type intensity.
  	MutIntensity: FLOAT	Measured
  		mutation
  		intensity.
  	LocalWTCallRate: FLOAT	rate at which
  		atoms associated
  		with surrounding
  		sequence call
  		reference base
  	IntensityRatio: FLOAT	Ratio of intensity
  		of wild type probe
  		over intensity of
  		mutation probe.
  tblBaseCallAnalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tblBaseCallResult	AnalysisId: INTEGER	Reference to
  		analysis table.
  	SubjectSequencePositionID: INTEGER	Reference to
  		sequence position
  		table.
  	ScanExperimentSetID: NUMBER	Reference to skin
  		experiments set
  		table.
  	CalledBase: VARCHAR2(1)	Base called for
  		subject based on
  		experiment set.
  	SuggestCheck: NUMBER	Used to indicate
  		whether this
  		sample should be
  		used for
  		resequencing
  tblClusteringAnalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tblClusteringResult	AnalysisId: INTEGER	Reference to
  		analysis table.
  	SequencePositionID: NUMBER	Reference to
  		sequence position
  		table.
  	ScanExperimentSetID: NUMBER	Reference to scan
  		experiment set
  		table.
  	ClusteringFactor: FLOAT	Result of
  		clustering
  		analysis.
  tblNormalizationRegionAnalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tblNormalizationRegion	NormalizationRegionID: INTEGER	Normalization
  		region identifier.
  	AnalysisId: INTEGER	Reference to
  		analysis table.
  	ChipDesignSequenceMapID: NUMBER	Reference to chip
  		design sequence
  		map table.
  	NumberScanExpt.Set	Reference to scan
  		experiment set
  		table.
  	RegionEnd: INTEGER	Indication of end
  		of the
  		normalization
  		region.
  	RegionStart: INTEGER	Indication of
  		beginning of the
  		normalization
  		region.
  tblNormalizationAnalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tblNormalizationResult	NormalizationResultID: INTEGER	Normalization
  		result identifier.
  	AnalysisId: INTEGER	Reference to
  		analysis table.
  	TilingDataID: INTEGER	Reference to
  		tiling data table.
  	NormalizationRegionResultID: INTEGER	Reference to
  		normalization
  		result.
  	NormalizationValue: NUMBER	Value used for
  		normalization.
  	DataOK: NUMBER	Indication
  		whether
  		normalization
  		result is usable.
  tblFootprintAnalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tblFootprint	FootprintID: NUMBER	Footprint
  		identifier.
  	AnalysisId: INTEGER	Analysis
  		identifier.
  	ChipDesignSequenceMapID: NUMBER	Reference to chip
  		design sequence
  		map table.
  	ScanExperimentSetID: NUMBER	Reference to scan
  		experiment set
  		table.
  	FFStart: NUMBER	Start of footprint
  		and sequence.
  	FPEnd: NUMBER	End of footprint
  		and sequence.
  tblTilingDataQualityAnalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tbltilingDataQualityResult	TilingDataID: NUMBER	Reference to
  		tiling data table.
  	AnalysisId: INTEGER	Reference to
  		analysis table.
  	AvgWTIntensity: NUMBER	Average wiId type
  		intensity.
  	WTCallRate: NUMBER	Fraction of atoms
  		where brightest of
  		probes is one with
  		reference space.
  	AcceptData: INTEGER	Whether data is of
  		acceptable
  		quality.
  tblDifficult Regionanalysis	AnalysisId: INTEGER	Reference to
  		analysis table.
  tblDifficultRegionResult	ScanExptId: INTEGER	Reference to scan
  		experiment table.
  	AnalysisId: INTEGER	Reference to
  		analysis table.
  	ChipDesignSequenceMapID: NUMBER	Reference to chip
  		design sequence
  		map table.
  	RgnStart: NUMBER	Beginning of
  		difficult region in
  		sequence.
  	RgnEnd: NUMBER	End of difficult
  		region in
  		sequence.
  	Reason: INTEGER	Code indicating
  		reason for
  		difficult region,
  		e.g., two or more
  		non-wild type
  		bases and less
  		than a probe
  		length.q
  tblPolyPatentSeq	PolyPatentSeqId: NUMBER	Polymorphism
  		sequence
  		identifier.
  	Polyscreen: VARCHAR2(50)	reference to
  		internal grouping
  		of polymorphisms
  	FragmentCode: VARCHAR2(50)	Fragment
  		sequence found in
  	Position: LONG	Position of
  		polymorphism.
  	RefAllel: CHAR(2)	Wild type base at
  		position.
  	FreqP: FLOAT	Frequency of wild
  		type.
  	AltAllele: CHAR(2)	Mutation base at
  		position.
  	FreqQ: FLOAT	Frequency of
  		mutation base.
  	Heterozygocity: FLOAT	Heterozygocity
  		value.
  	SequenceTag: VARCHAR2(50)	Sequence
  		containing
  		polymorphism
  		including
  		ambiguity code at
  		polymorphism
  		position.
  	GeneName: VARCHAR2(50)	Name of gene.
  	ChromosomeNum: VARCHAR2(20)	Chromosome
  		number.
  	ChromosomeLoc: VARCHAR2(20)	Location of gene
  		on chromosome.
  	ForwardPrimer: VARCHAR2(50)	Identifier for
  		forward primer
  		used to implement
  		fragment.
  	ReversePrimer: VARCHAR2(50)	Identifier of
  		primer used to
  		amplify fragment.
  tblPatentApp	PatentAppId: NUMBER	Patent application
  		identifier.
  	GroupId: NUMBER	Reference to
  		employee group
  		table.
  	AttorneyId: NUMBER	Reference to
  		attorney table.
  	DocketNum: VARCHAR2(30)	Docket number
  		for patent
  		application.
  	FilingDate: DATE	Filing date for
  		filing application.
  	Classification: VARCHAR2(30)	Patent office
  		classification for
  		patent application.
  	SerialNumber: VARCHAR2(50)	Serial number
  		assigned by patent
  		office.
  	CountryCode: VARCHAR2(50)	Country in which
  		patent application
  		was filed.
  	InventionTitle: VARCHAR2(100)	Title for patent
  		application
  tblPolyPatentSeqMap	PatentAppId: NUMBER	Reference to
  		patent application
  		table.
  	PolyPatentSeqId: NUMBER	Reference to
  		polymorphism
  		patent sequence
  		table.
  tblPriorApp	PriorAppId: NUMBER	Reference to
  		related prior
  		patent application
  		in patent
  		application table.
  	AppId: NUMBER	Reference to
  		application to
  		which prior
  		application is
  		related.
  tblAttorney	AttorneyId: NUMBER	Attorney
  		identifier.
  	LawFirmId: NUMBER	Law firm where
  		attorney works.
  	FirstName: VARCHAR2(20)	First name of
  		attorney.
  	MiddleName: VARCHAR2(5)	Middle name of
  		attorney.
  	LastName: VARCHAR2(30)	Last name of
  		attorney.
  	RegistrationNum: VARCHAR2(25)	Patent office
  		registration
  		number of
  		attorney.
  tblLawFirm	LawFirmId: NUMBER	Law firm
  		identifier.
  	Company: VARCHAR2(100)	Name of law
  		firm.
  	Address: VARCHAR2(100)	Address of law
  		firm.
  	City: VARCHAR2(30)	City address of
  		law firm.
  	State: VARCHAR2(20)	State address of
  		law firm.
  	ZipCode: VARCHAR2(15)	Zip Code of law
  		firm.
  	Country: VARCHAR2(15)	Country of law
  		firm.
  	Telephone: VARCHAR2(30)	Telephone
  	Fax: VARCHAR2(30)	number of law
  		firm.
  	TELEX: VARCHAR2(20)	Facsimile number
  		of law firm.
  		Telex number of
  		law firm.
  tblEmployeeGroup	GroupId: NUMBER	Identifier for
  		inventor group.
  	GroupName: VARCHAR2(50)	Name of inventor
  		group.
  	Comments: VARCHAR2(50)	Comments.
  	GroupList: VARCHAR2(255)	Written out list of
  		inventor names.
  tblEmployeeGrpMap	EmployeeId: INTEGER	Reference to
  		employee table
  		for
  		inventor/em-
  		ployees.
  	GroupId: NUMBER	Reference to
  		inventor group
  		table.

• It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. For example, tables may be deleted, contents of multiple tables may be consolidated, or contents of one or more tables may be distributed among more tables than described herein to improve query speeds and/or to aid system maintenance. Also, the database architecture and data models described herein are not limited to biological applications but may be used in any application. All publications, patents, and patent applications cited herein are hereby incorporated by reference. [0098]

Claims (23)

What is claimed is:

1. A computer-readable storage medium having stored thereon:

an item table listing a plurality of item records identifying items;

an item attribute table listing a plurality of item attribute records identifying attributes of said items; and

wherein there is a many-to-many relationship between item records and item attribute records.

2. The computer-readable storage medium of claim 1 wherein an item attribute item map table implements said many-to-many relationship between item records and item attribute records, said item attribute item map table listing a plurality of map records identifying both a particular item attribute and a particular item.

3. The computer-readable storage medium of claim 1 having further stored thereon:

an item derivation table listing a plurality of item derivation records identifying transformations between ones of said items used in biological analysis.

4. The computer-readable storage medium of claim 3 having further stored thereon:

a protocol table listing a plurality of protocol records specifying parameters of said transformation.

5. The computer-readable storage medium wherein said items are used in a biological analysis.

6. The computer-readable storage medium of claim 1 wherein said biological analysis comprises a polymorphism analysis.

7. A computer-readable storage medium having stored thereon:

an atom result table listing a plurality of atom result records, specifying relative wild-type and mutant sequence concentrations in targets; and

a subject sequence position table listing a plurality of subject sequence position records, specifying combinations of subjects from whom said targets are derived and sequence positions, each said atom result record being associated with one or more atom result records.

8. The computer-readable storage medium of claim 7 wherein said atom result records further specify upper and lower bounds for said concentrations.

9. The computer-readable storage medium of claim 7 having further stored thereon:

a subject table listing subject records specifying said subjects.

10. A computer-readable storage medium having stored thereon:

a polymorphism table listing polymorphism sequence records specifying sequences known to contain polymorphisms; and

a patent application table listing patent application records specifying one or more polymorphisms specified by said polymorphism sequence records.

11. The computer-readable storage medium of claim 10 wherein said polymorphism sequence records specify for each one of said polymorphisms a polymorphism position, a reference allele, and a base allele.

12. The computer-readable storage medium of claim 11 wherein said polymorphism sequence records further specify for each one of said polymorphisms a measured heterozygocity.

13. A computer-implemented method comprising:

creating n item table listing a plurality of item records identifying items used in biological analysis; and

creating an item attribute table listing a plurality of item attribute records identifying attributes of said items; and

wherein there is a many-to-many relationship between item records and item attribute records.

14. The computer-implemented method of claim 13 further comprising the step of:

creating an item attribute item map table implements said many-to-many relationship between item records and item attribute records, said item attribute item map table listing a plurality of map records identifying both a particular item attribute and a particular item.

15. The computer-implemented method of claim 13 comprising: an item derivation table listing a plurality of item derivation records identifying transformations between ones of said items used in biological analysis.

16. The computer-implemented method of claim 15 further comprising:

creating a protocol table listing a plurality of protocol records specifying parameters of said transformation.

17. The computer-implemented method of claim 13 wherein said biological analysis comprises a polymorphism analysis.

18. A computer-implemented method comprising:

creating an atom result table listing a plurality of atom result records, specifying relative wild-type and mutant sequence concentrations in targets; and creating a subject sequence position table listing a plurality of subject sequence position records, specifying combinations of subjects from whom said targets are derived and sequence positions, each said atom result record being associated with one or more atom result records.

19. The computer-implemented method of claim 18 wherein said atom result records further specify upper and lower bounds for said concentrations.

20. The computer-implemented method of claim 18 further comprising:

creating a subject table listing subject records specifying said subjects.

21. A computer-implemented method comprising:

creating a polymorphism table listing polymorphism sequence records specifying sequences known to contain polymorphisms; and

creating a patent application table listing patent application records specifying one or more polymorphisms specified by said polymorphism sequence records.

22. The computer-implemented method of claim 21 wherein said polymorphism sequence records specify for each one of said polymorphisms a polymorphism position, a reference allele, and a base allele.

23. The computer-implemented method of claim 22 wherein said polymorphism sequence records further specify for at least one of said polymorphisms a measured heterozygocity.

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