HUT75550A - Comparative gene transcript analysis - Google Patents

Abstract

The present invention relates to methods for the diagnosis of gene transcription as well as gene expression of cells and tissues. The present invention relates to the fields of application of both molecular biology and computer science. More specifically, the present invention provides methods for testing samples containing gene transcripts, determining the relative frequency of mRNAs in biological samples, and generating gene transcript images. Our number: 84215-2444C / PÁ

Description

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DISCLOSURE

EXAMPLE Representative: DANUBIA SZABADALMI és VÉDJEGY IRODA Kft.

FIELD OF THE INVENTION The present invention relates to methods for the gene expression of gene transcription as well as gene expression of cells and tissues. The invention relates to the fields of application of molecular biology and computer science. More particularly, the present invention relates to methods for testing samples containing gene transcripts, determining the relative frequency of mRNAs in biological samples, and generating gene transcript images. The present invention is advantageously applicable in the fields of diagnostics, toxicology and medicine; it can be used, inter alia, to distinguish between normal and patient cellular abnormalities, for example, mutant and wild-type cells, to predetermine the toxicity and efficacy of drugs, and other similar biological samples. to solve tasks based on the comparison of

Until recently, molecular biology activities were based on the principle of "one gene at a time". Researchers have observed the physical changes in the cell, mixtures of cells or their environment have been isolated, proteins have been purified, and their sequences have been determined and tested.

We have made a number of 84215-2444C / PAs to search for the right gene. In recent years, different countries have set up large-scale projects to determine the billions of billions of bases constituting the human genome. The work to accomplish this usually starts by dividing the genomic mass into chromosome pieces, and then determining the sequence of these fragments, which are later identified as "motive" identities with known protein or parts thereof, known as "motif". The majority of genomic DNAs do not encode protein, so they can be expected to exert an effect on the protein-producing ability of the cell, the medical significance of which is currently unknown.

According to a third method, only transcripts encoding "cellular machinery" that is actively involved in the preparation of proteins, i.e., mRNAs, are sequenced. The advantage of this is that at this stage, the cellar omitted all the non-coding DNAs, and it is relatively simple to identify the protein coding regions of the RNAs. The advantage of the approach to genomics for genome researchers is not clear. In fact, when the possibility of cDNA sequencing was first raised, its genomic sequencing commitment was openly rejected. For example, the United States Human Genome Project Manager ignored cDNA sequencing as a worthless method for the project and did not approve the financing of the plans. Described herein are methods for testing DNA (including cDNA libraries). According to our studies and researches, each gene product is considered to be an information element ("pixel"), which refers exclusively to the expression of a particular gene. We describe procedures by which each gene expression information image is a single gene transcription image ("image"), in which the individual genes can be simultaneously displayed, so that the relationship between the gene image elements can be made easily visible or not. understandable.

We also disclose a new method called "electronic subtraction". Azelectronic subtraction allows the genetic engineer to transform a picture into a moving image that, at the level of a cell or a complete tissue, describes the temporal and dynamics of gene expression. The above-mentioned "cellular or organ-level" movement of the cellular machine forms the basis of the invention. This approach reveals the physiological processes of the living cell and provides a promising opportunity to develop new medical and diagnostic methods.

We also discover a new method called "electronic northern analysis". With this approach, a gene can be traced through many cell and tissue types. Nucleic acids (DNA and RNA), in their sequences, inherit the hereditary information, and consequently form life-oriented molecules. Nucleic acids are found in all living organisms such as bacteria, fungi, viruses, plants and animals. It is important to determine the relative distribution of different nucleic acids over time (with different conditions, treatments and lifestyles) in different cells, tissues and organisms.

All human cells in the human body contain the same set of 23 pairs of chromosomes. It is estimated that these physical and sex chromosomes contain about 100,000 genes. Differences between different cell types probably reflect different expression of about 100,000 genes. The basic questions of biology can be answered in the knowledge of which genes are transcribed, and the relative distribution of transcripts in different cells. Earlier biotechnological methods have allowed only a few genes to be tested at one time using conventional molecular biological techniques (eg, polymerase chain reaction, northern blot analysis, or other types of DNA probe analysis, such as in situ hybridization). Each of these procedures is characterized by allowing only known genes / transcription of only a few genes simultaneously [Nucl. Acids Slit. 19, 7097 (1991);

Nucl. Acids Slit. 18: 4833 (1990); Nucl. Acids Slit. 18, 2789 (1989); European J. Neuroscience 2, 1063 (1990);

Analytical Biochem. 187, 364 (1990); Gene Annals Techn. Appl. 2, 64 (1990); GATA 8 (4), 129 (1991); Proc. Natl. Acad. Sci. USA 85, 1696 (1988); : Nucl. Acids Slit. 19, 1954 (1991); Proc. Natl. Acad Sci. USA 88: 1943; Nucl. i

Acids Slit. 1: 6123 (1991); Proc. Natl. Acad. Sci. USA 85,5738 (1988); Nucl. Acids Slit. 16, 10937 (1988)].

For many years, research has been carried out - using different techniques - to investigate the number and type of genes for transcription, such as transcription of genes, such as genes. activation, differentiation, aging, viral transformation, morphogenesis mitosis - induce or regulate. One of the first methods was to isolate and test the level of proteins present in the cell, tissue, organ system, and even whole organisms before and after a particular process. One method of many protein assays in a sample is two-dimensional gel electrophoresis, as a result of which proteins can generally be identified as discrete bands and quantified, and eventually reduced to a discrete signal. Presence of two-dimensional analysis separates approximately 15% of proteins. For the purpose of testing the separated proteins, each band should be cleaved from the membrane, using protein sequence analysis using "Edman degradation". Unfortunately, most bands are too small to get a reliable sequence and many of the bands contain more than one protein. Another difficulty is that many N-terminals of proteins are blocked, which complicates sequencing. The study of differentiation at the level of gene transcription eliminates the aforementioned difficulties and disadvantages because the performance of the recombinant DNA technique allows signal amplification with very small amounts of material. The most common method (called "hybridisation subtraction") consists in isolating mRNA from a biological sample prior to and after the developmental process in question (A and B), converting one mRNA set to cDNA. , sample B is extracted from the contents of sample A (mRNA from acDNA) by content hybridization, and a cDNA library is prepared from non-hybridized mRNAs. A number of research teams have successfully used this method, and different methods have been described or developed by applying their basic scheme [Nucl. Acids Slit. 19, 7097 (1991); Nucl. Acids Rev. 18, 4833 (1990); Nucl. Acids Slit. 18, 2789 (1989); EuropeanJ. Neuroscience. 2, 1063 (1990); Analytical Biochem. 187,364 (1990); Genet. Annals Techn. Appl. 7, 64 (1990); GATA8 (4) 129 (1991); Proc. Natl. Acad. Sci. USA 85: 1696 (1988); Nucl. Acids Slit. 19, 1954 (1991); Proc. Natl. Acad.

Sci. USA 88: 1943; Nucl. Acids Slit. 19, 6123 (1991);

Proc. Natl. Acad. Sci. USA 85: 5738; Nucl. Acids Slit. 16, 10937 (1988)]. Although each of these techniques has weaknesses in advantage, there are some adverse and undesirable factors in these processes. First of all, we need to mention the very high time and effort required to build such libraries. Generally, for a skilled molecular biologist (depending on his ability, experience and luck), it takes about 3 to 6 months to create and characterize such a library. Secondly,

Usually, subtracting libraries are less valuable than conventional libraries. An average traditional library contains at least 106 clones, and the average size of inserts is 1-3 kilobases. In this way, subtracting libraries contain a total of 10 or 10 clones and an average insert size of 0.2 kb. Accordingly, such libraries contain substantially less cloning sequence information than conventional cDNA libraries. Thirdly, this method allows only the capture of induced genes in sample A (for sample B), but not vice versa, and comparisons with a third (C) pattern also make it difficult. a very large amount of "driver" mRNA (sample B) (hundreds of micrograms) is required, which significantly limits the number and type of possible extractions, as large quantities of large tissue and cell are very difficult to extract. Fifth, the resolution of the subtraction depends on the physical properties of DNA hybridization to DNA or RNA. The ability of a given sequence to find a hybridization pair depends on its unique CoT value. The CoT value is the product of the number of copies (concentration) of the given sequence and the duration of the hybridization (T). Hence, in the case of frequent sequences, hybridization events occur very rapidly (low CoT value), while rare sequences form duplexes with very high CoT values. In a suitable time frame, it is difficult to form CoT-9 values that allow such rare sequences to form duplexes or to make their selection effective. Thus, the hybridization subtraction is not suitable for studying the relative level of rare mRNA species. Sixth, this problem is further complicated by the fact that duplex formation also depends on the base composition of a given sequence. High-guanine and cytosine-rich sequences produce stronger duplexes than sequences rich in adenine and thymine, so that the hybridization subtraction can be used to remove the former sequences. Seventh, hybridization may also occur between pairs that do not match exactly, in which case the expression of the homologous gene may "mask" the expression of the gene under examination, which distorts the results for the particular gene.

The use of Matsubara and Okubo partial cDNA sequences has been proposed for generating expression profiles of genes that can be used for functional analysis of the human genome. Matsubara and Okubo protested against the use of random chaining because of the formation of multiple DNA fragments from each mRNA, which could distort the examination of the number of specific mRNAs per library. Randomly selected members of a 3'-directed cDNA library were sequenced and found to have different EST-frequency. A comparison of the list of ESTs from different cell types was recommended to classify genes. The genes expressed in many cell types were called "housekeeper" and those expressing specific cells in some cells were cell-specific, even if the entire sequence of the gene or the biological activity of the gene product was not known. The present invention eliminates the disadvantages of prior art processes. By the method according to the invention, the relative frequency of many gene transcripts in a given biology sample can be determined by high throughput sequence-specific analysis of each RNA and / or their corresponding cDNAs. The present invention provides several advantages over the present method of isolating the individual proteins based on their biological effects by means of protein exploration processes. By practicing the method according to the invention, it is possible to make a detailed diagnostic comparison of the cell expression profiles, which shows a number of changes in the expression of single transcripts. Advantages of the present invention against current extraction processes include more complex 3 6 7 library analysis (10 to 10 clones instead of 10 'clones), which allow for low incidence rates and increasing or decreasing frequency. These large libraries - compared to previous libraries - can be very easy to produce. The process of the invention also makes it easy to distinguish homologs. The method according to the invention is very advantageous since it converts a large amount of data into an understandable and processable format. The most important differences are highlighted by the electronic feature. Detailed analyzes are thus more convenient. The present invention provides several advantages over prior electronic methods of analyzing cDNAs. Our procedure is particularly effective when more than 100, preferably more than 1000, gene transcripts are tested. In such cases, new, low frequency transcripts can be detected and tissue typed. High resolution analysis of gene expression can be used to identify disease-specific genes directly as a diagnostic profile or to develop more classical diagnostic methods.

This method is referred to as gene transcript frequency analysis and the quantitative quantitative analysis of gene transcripts as a result of comparative gene transcription analysis. The present invention relates to a method for testing a sample of gene transcripts, wherein (i) a library comprising biological sequences is constructed; (ii) generating a set of transcript sequences in which each transcript sequence refers to another set of biological sequences in the library; processing a sequence value identified for each transcript sequence that identifies sequence sequences for sequence annotation, and alignment between one of the biological sequences in the library and at least one of the reference sequences; and (iv) processing all identified sequence values to obtain final data values that indicate that each identified sequence value is present in the library a few times. The invention further relates to a method for comparing a sample containing two-gene transcripts. In the process, the first sample is operated as described above, while the second sample is used to produce a sequence of biological sequences, from which a second set of transcript sequences is generated, each set of transcripts being in the second directory. refers to one of its biological sequences. The second set of transcript sequences are then processed with a programmed computer to generate a second sequence of identified sequence values, namely, newly identified sequence values, each of which is a sequence annotation, and a biological sequence of the second library and a reference sequence. refers to the degree of fit between at least one of the sequences. By processing the newly identified sequence values, new final data values are generated that indicate that each of the newly identified sequence values is present in the second directory. Final data values from the first sample and further sequence values from the second sample are processed to determine the ratios of transcript sequences that indicate differences in the number of gene transcripts in the two samples. The invention further relates to a method for determining the mRNA relational frequency in a biological sample in which (i) a population of mRNA transcripts is isolated from a biological sample; (ii) identifying, by a sequence-specific method, genes that have been transcribed by mRNA transcription; (iii) determining the number of mRNA transcripts corresponding to each gene; and (iv) the number of mRNA transcripts used to determine the relative frequency of mRNA transcripts within the mRNA transcript population.

The invention also relates to a method for producing a gene transcript image. In the process, a first mRNA mixture is prepared from which cDNA copies are prepared. The cDNA is then inserted into a suitable vector used for transfection of cells of a suitable host strain. Cells are smeared and cells are grown into clones, each clone representing each mRNA. A reprotective population of clones transfected with cDNA is isolated, in which population each clone is identified by a sequence-specific method that identifies the gene from which the individual mRNA was transcribed. In order to evaluate the frequency of the gene transcript, it is determined how many times the gene has been identified with a clone. Genes - together with their frequency of occurrence - create a sequential order of transcriptional gene transcription according to frequency. «« 13

According to another preferred embodiment of the previous method, the relative frequency of gene transcripts in a cell type or tissue is compared to the relative frequency of gene transcripts in another cell type or tissue to identify differences and similarities. In another preferred embodiment of the invention, the method comprises a library system of biological sequences which comprises a method for detecting a set of transcript sequences (wherein each transcript sequence refers to one of the biological sequence sequences in the library) and an atrancript. sequences consists of a method of processing a computer system (in which a database of reference transcript sequences referring to reference sequences is stored). The computer is used to identify the sequence sequences for the single-transcript sequences (each of which is a sequence annotation of the identified sequence values and the match between the biological sequence of one of the library sequences and one of the reference sequences), and the presence of each identified sequence value in the library in the library. To program the final data values referring to the frequency of the data, it is programmed with the software for processing said sequence sequence. The present invention therefore relates essentially to a method and one system (for determining the frequency of relative occurrence of 14 gene transcripts in a biological sample). The invention further relates to a method of comparing the transcript of a derivative of two or more biological samples by which the two samples can be distinguished from each other and one or more genes, which are expressed differently in the two samples, can be identified. Accordingly, the gene transcript and the comparison can be used for di-agnostic purposes. In a preferred embodiment of the method, many RNAs or their corresponding cDNAs are subjected to high throughput, sequence-specific analysis (gene transcription). In another preferred embodiment of the method, agen transcript mapping analysis is performed by high throughput cDNA sequence analysis. In addition, a comparison of two or more gene transcript images may be used to detect or diagnose a particular biological condition or disease (which is associated with the recurrent frequency of gene transcripts in a given cell or cell group). The following is information about the tables and figures.

In Table 1, see Figures 2-5. tables are used. Table 2 lists the 100 most common gene transcripts. This list contains some of the isolates obtained from the sequenced HUVEC cDNA library as described below. The numbers in the balol column indicate the order of frequency of the 15 sequences listed in the table. In the next column, "number", the reference reference clone number of the first HUVEC sequence corresponding to the sequence in the "entry" column is given. Non-sequenced isolates a2. not shown in table. In the next column, "N", the total number of cDNAs with the same alignment as the reference transcript sequence in the "entry" column is shown.

In the column "entry", enter the "NIH GENBANK" locus, which corresponds to the directory sequence numbers. The letter "s" in some places indicates the species from which the reference sequence is derived (the codes for column "s" are given in Table 1). In the column labeled "descriptor", a clear (decoded) name of the sequence corresponding to the "NIHGENBANK" locus in the "entry" column is given. Table 3 shows a comparison of the 15 most common gene transcripts occurring in normal monocytes and activated macrophage cells. Table 4 provides an overview of a library extraction analysis comparing THP-1 and human macrophage cDNA sequences. In this table we use the same codes as in Table 2. A new column number is called "bgfreq" [frequency number in the subtractant library], "rfend" (frequency number in the steel directory) and "ratio" (target directory frequency divided by the frequency of the extraction library). how often the frequency number "0" in the extract directory is divided by the frequency number observed in the target directory. Since we cannot divide by 0, this is one way to get results and to distinguish the result the proportions that are obtained for the frequency number 1 of the extraction library.

Table 5 shows a computer program written in source code for generating gene transcription protocols. Table 6 shows a partial listing of database entries used in the electronic "northern blot" analysis of the invention.

Figure 1 is a flowchart summarizing the stored data collected for the sequence generation and testing portion of library creation. THE 2 . FIG. 1A illustrates a flowchart of operations with "frequency organizer" software in certain embodiments of the present invention. Figure 3 is a block diagram of a preferred embodiment of the system according to the invention. Figure 4 shows a more detailed block diagram of the bioinformatics method, from the new (already sequenced but not identified) sequence to the transcript mapping analysis result list. In the development of the present invention, it is an object of the present invention to provide a method for relatively occurring the gene transcripts of different biological samples, each RNA or the corresponding cDNAs (or, alternatively, other biological sequences). representative data) using high throughput analysis - comparable. This procedure is called gene transcription. Quantitative analysis of the relative prevalence of the gene transcript set is referred to as "gene transcription mapping" or "gene transcript frequency analysis". By practicing the present invention, the gene transcription profile of any cell group or tissue from any organism can be determined. According to the invention, the solution is suitable for determining a profile of a sample of a single cell (or clones of a single cell), a plurality of cells, or a tissue comprising multiple cell types. The present invention provides significant advantages in terms of diagnostics, toxicology, and pharmacology. We can perform a sophisticated diagnostic test for a patient (not diagnosed). A biological sample consisting of body fluids or tissues of the patient is prepared, the gene transcripts in the sample are isolated and amplified to the extent necessary for their identification. below. The hatch transcript sequence frequencies are compared to the sequence frequencies in the reference data base (including normal data from patient or healthy patients) 18

I put together. The patient suffers from the disease (s) with which the patient's series of data shows the closest similarity. Gene transcript frequency analysis, for example, can be used to distinguish normal cells or tissues from patient cells or tissues, as well as the differences between normal monocytes and activated macrophages (see Table 3). The key issue in toxicology is which test is most effective in predicting or demonstrating a toxic effect. Gene transcription imaging provides very detailed information about the cell and tissue environment, some of which would not be available using conventional, less detailed screening procedures. Agenic transcription imaging is a more effective way of predicting the toxicity and efficacy of drug drugs, as is the case with previous methods. Gene transcription can be used to selectively select protein categories that are supposed to affect, for example, enzymes that degrade toxins. Another embodiment of the invention may be used to distinguish between serotonin comparative gene transcript frequency anti-cancer agents and non-responsive cancer cells. Examples of anticancer agents include tamoxifen, vincristine, vinblastine, podophyllotoxins, etoposide, tenisposide, cisplatin, biological modifiers, e.g. interferon, IL-2, GM-CSF, enzymes, hormones and the like. This provides the preferred method for classifying the gene according to functional categories of gene transcripts. In the case of cancer cells, transcription factors or other essential regulatory molecules form very important categories for the analysis of different libraries. Another embodiment of the invention is to compare serine synthase transcript frequency control with liver cell and to distinguish liver cells isolated from patients treated with experimental drugs (e.g., FIAU) to distinguish between pathology caused by underlying disease and drug-induced pathology. In a further embodiment of the invention, comparative gene transcript frequency analysis is used to differentiate brain tissue from lithium-treated or lithium-treated patients. Another embodiment of the invention is used to distinguish serine synthesized gene transcript frequency analysis cyclosporin and FK506-treated cells or normal cells. In a further embodiment of the invention, the comparative gene transcript frequency analysis is used to differentiate human cells infected with virus (including HIV virus) and non-infected human cells. Frequency analysis of gene transcription in HIV \ t

It is also used for rapid screening of gene transcripts of resistant, HIV-infected and HIV-sensitive cells. A comparison of the frequency of the 20 gene transcripts indicates the success of the treatment and / or the new test options.

In another embodiment, comparative gene transcript frequency analysis is used to differentiate between rumen flushing fluids from individuals suffering from various diseases.

In another embodiment, comparative gene transcript frequency analysis is used to differentiate between mutant and wild-type plant, microbial and animal cells. In addition, the transcript frequency program has been adapted to evaluate transcription of a gene in many different tissues. Such comparisons can identify deletion mutants that do not produce the gene product, or point mutants that produce less frequent or different messages. Such mutations may affect basic biochemical and pharmacological processes, such as the use of the enzyme. mineral nutrition and metabolism, and such mutations can be isolated by those skilled in the art. Thus, crops with higher yields, resistance to insect pests and other advantages can be developed. Another embodiment of the invention is to use serine comparative gene transcript frequency analysis as a tree comparative analysis that allows the selection of better pharmaceutical animal models. In this embodiment, humans and other animals (e.g., mice) or their cultured cells are treated with a specific 21 testing agent and the relative sequence frequency of each DNA population is determined. If the animal test system proves to be good, the homologous genes of the animal cDNA population change expression similarly to genes in human cells. As far as drug-related side effects are concerned, detailed transcript frequency analysis is performed to screen for changes in brain transcript. We evaluate the model thesaurus by comparing basic physiological changes. Another embodiment of the invention is to use a highly detailed gene transcript profile of a patient's cell or tissue (e.g., blood sample) in a serine synthesis gene transcript frequency analysis assay in a clinical setting. Specifically, agen transcript frequency analysis is used to generate a high-resolution gene expression profile specific to a disease-related condition. In a preferred embodiment of the invention, high throughput cDNA sequencing is used to identify the specific transcripts in question. The resulting cDNA and derived amino acid sequences are then compared to sequences of "GENBANK" or other sequence databases as described below. The method according to the invention provides several advantages over protein detection methods 22 based on presently known two-dimensional gel chromatographic methods (to identify certain proteins in a particular biological activity). In the practice of the inventive method, comparing the profiles of activated and inactive cells shows several changes in the expression of single transcripts. Once we have determined that the given sequence is accurate, similar or non-matching, we add the sequence to the database. The number of cDNA copies corresponding to each gene is then plotted.

Since it is time-consuming and time-consuming to perform this manually, it is a good and quick way to put this information into a table by organizing with a computer program. The number of cDNA copies (optionally divided by the total number of sequences in the data series) depicts the relative frequency of transcripts of each gene. The list of represented genes can then be grouped according to the frequency observed in the cDNA population. There are many other types of comparison, examples of which are described below.

In another method of constructing a gene transcript image, a sample of test mRNA is prepared, and - a representative sequence of unique probes complementary to at least some of the test mRNAs is generated, and the test mRNA is fixed for the queued probes. We give it its \ t The test mRNA was incubated with probes for a sufficient time to hybridize, and mRNA probe hybrids were detected and quantified. Hybrids are identified by their location in a small series. The sum of the individual hybrids is summed up by the population. Each hybrid quantity is divided by the population number to give a relative frequency series of gene transcription mapping analysis. The following examples are provided to illustrate the invention and not to limit the scope of the present invention. Example 1

Tissue Sources and Cell Lines A biological sequence derived from virtually any source can be used for computer-aided analysis according to the invention. The most widespread are tissues from the human body. Tissue can be obtained from any organ of any age, any donor with abnormalities or any immortalized cell line. In some cases, they may be advantageous because of their purity as regards immortalized cell lines; other tissue samples always contain mixed cell types. There is a specialized technique for extracting a single cell (e.g., a cerebral cell) and using an acellular machine to produce sufficient cDNA for sequencing by techniques and analysis according to the invention (cf. U.S. Patent Nos. 5,021,335 and 5,168,038, which are incorporated herein by reference). should be considered as part of the teaching in its entirety). In the examples described below, the following immortalized cell lines were used: monocyte-like U-937 cells; activated macrophage-like THP-1 cells; induced 24 vascular endothelial cells (HUVEC cells); and mast cell-like HMC-1 cells.

The U-937 cell line is a human histitocytic lymphoma cell line with monocyte properties developed by pleural effusion of a patient suffering from diffuse histocytic lymphoma [Sundstrom, C. and Nilsson, K.:

Int. J. Cancer 17: 565 (1976)]. The U-937 cell line is one of a few human cell lines with morphology, cytochemistry, surface receptors and monocyte-like properties of hystocytes. These cells can be induced towards final monocytic differentiation and activated with supernatant of a human lymphocyte culture to express new cell surface molecules. As a result of this type of invitro activation, the cells undergo morphological and functional changes, including an increase in antibody-dependent cellular cytotoxicity (ADCC) (a primary function of macrophages) against erythroid and tumor target cells. In vitro activation of U-937 cells by forbol-12-myristate-13-acetate (PMA) stimulates the production of several compounds, including prostaglandins, leukotrienes, and platelet activating factor (PAF), which are potent inflammatory mediators. Thus, the U-937 cell line is well suited for the identification and isolation of gene transcripts associated with normal monocytes. The HUVEC cell line is an early passage, normal, homogeneous, well characterized endothelial cell culture from human umbilical vein (Cell Systems Corp., 12815 NE 124thStreet, Kirkland, WA 98034). Only the gene transcripts from the induced or treated 25 HUVEC cells were sequenced. A 1 x 108 cell dose was treated with 1E / ml rIL-1b and 100 ng / ml E.coli-lipopolysaccharide (LPS) endotoxin for 5 hours prior to harvest. . Another 2 x 108 cell-containing dose was treated in confluent state prior to harvest with 4 U / ml TNF and 2 U / ml gamma-IFN (gamma-IFN). The THP-1 cell line is a leukemic cell line that exhibits distinct monocytic properties. This cell line is derived from the blood of a one-year-old boy with acute monocytic leukemia (Tsuchiya, S. et al., Int. J. Cancer 171 (1980)). The following cytological and cytochemical criteria were used to determine the monocytic nature of the cell line: (1) the presence of sodium fluoride-inhibited α-naphthyl butyrate esterase activity; (2) lysozyme production; (3) phagocytosis of latex particles and sensitized sheep red blood cells (SRBC); and (4) the potency of activating the T lymphocytes of mitomycin-C treated THP-1 cells (concanavalin A treated). The cytoplasm of THP-1 cells contained small azurophilic particles, the nucleus was toothed and was irregularly shaped due to deep collections. This cell line contains Fc and C3b receptors, which are probably involved in afagocytosis. Proliferation of THP-1 cells treated with 12-o-tetradecanoyl phorbol-13-acetate (TPA) stops and differentiates into macrophage-like cells that mimic the natural monocyte-derived macrophages in several respects. When the cells are deformed, the seed core becomes even more irregular, and phagocytosis vacuoles appear in the cytoplasm with new ones. Differentiated THP-1 cells adhere to the tissue culture tray at an increased level. HMC-1 cells (human mast cell line) originate from peripheral blood of a Mayo Clinic mast cell leukemia patient [Leukemia Slot. 12, 345 (1988)]. Cells in culture have similar appearance to immature, cloned mouse mast cells, contain histamine, and exhibit positive staining for chloroacetic acid esterase, amino-caproic acid esterase, eosinophil major base protein (MBP) and tryptase. HMC-1 cells have lost their ability to synthesize normal IgE receptors. HMC-1 cells collected originally from the patient by leukophoresis show 10, 16 translations that do not occur during culture. Thus, HMC-1 cells form a good mast cell model. Example 2: Production of cDNA libraries In order to compare libraries, the library should be produced in a similar manner. Checking some parameters is particularly important. One such parameter is the process of isolating mRNA. It is important to use the same conditions for removing DNA and heterogeneous nuclear RNA from the reference libraries. CDNA size fractionation fractionation should be done with caution. To construct libraries to be compared, it is preferable to use the same vector clock, and finally to use the same type of vector (e.g., one-way vector) for effective comparison. The use of a one-way vector may be advantageous in terms of simpler testing of output.

In order to obtain only one clone per mRNA transcript when the cDNAs are recovered, one-way oligo-dT primers are preferably used for chaining. It is known that the use of a mixture of oligo-dT and random primers may also be advantageous when targeting genes, since such a mixture results in greater sequence diversity. Similar effects are available with DR2 (Clontech) and HXLOX (U.S. Biochemical), and Invitrogen and Novagen vectors. These vectors must meet two conditions: they must contain a primer for the standard primers (eg, T3 or M13 reverse primers); and include 10 kb inserts.

It is also important that the clone is sampled at random and that large amounts of clone are used. We also generated data for 5,000 clones, however, we want to extract rare genes and / or determine their relative frequency, and it may be necessary to sample 100,000 clones from a library. The size fractionation of the cDNA should be done with caution. Alternatively, plaques may be selected instead of clones.

At present, we believe that the Uni-

In addition to TM ZAP vector system (Stratagene), other similarly directional vectors may be used. Such vectors are believed to include (among others) DR2 (Clontech) and HXLOX (U.S. Biochemical). 28 Details of library creation (see Figure 1) are preferably stored in a database for subsequent retrieval of the sequences to be compared and stored as such. Figure 1 shows essential information for DNA constructors, cell or cDNA carriers, precursor, biological source, culture, mRNA production, and cDNA formation. In the detailed analysis of sequences and libraries, similarly detailed information available from other steps is preferred. Cells should be recovered from RNA and tissue samples and cDNA libraries should be prepared. The cDNA libraries can be prepared by known techniques [see, e.g. for example, Maniatis, T. et al., "Molecular Cloning", Cold Spring Harbor Laboratory, New York (1982)]. You can also buy cDNA libraries. The U-937 cDNA library (catalog number 937207) was obtained from Stratagene, Inc. (11099 M. Torrey Pines Rd., La Jolla, CA 92037). The THP-1 cDNA library was prepared from Stratagene for our order for 48 hours at 100 nM TPA and 4 hours at 1 μρ / πιΐ LPS. The HMC-1 cDNA library from cultured HMC-1 cells was also ordered by Stratagene. The HUVEC cDNA library was induced from two separate processed portions of HUVEC cells, ordered by Stratagene. Essentially, the same method was used to produce all libraries. First, poly (A +) RNA (mRNA) 29 was purified. In the case of U-937- and HMC-1-RNA, cDNA synthesis was initiated only with oligo-dT, while in THP-1 and HUVEC-RNA, oligo-dT and random hexamers were initiated and two cDNA libraries were isolated. treated separately. In order to facilitate their insertion into the cDNA terminals, the Uni-Zap vector system (Stratagene), they ligated synthetic adapter oligonucleotides, which enabled the creation of a high-efficiency, one-way (sense orientation) lambda library, and containing cDNA insertions. It provided the convenience of a plasma system that enabled the detection of clones with blue-white color selection. The two libraries were merged into a single library by blending the same number of bacteriophages. The libraries can be screened by DNA probes or antibody probes, and the pBluescript® phagemid (Stratagene) inverter can be quickly excised. The phagemid allows the use of a plasmid system suitable for light insertion characterization, sequencing, site-specific mutagenesis, unidirectional deletions, and expression of fusion processes. Custom-made library phage particles were infected with E. coli-XL1-Blue® (Stratagene), which, with its high transformation efficiency, increased the likelihood of obtaining rare "underrepresented" clones in the cDNA library. Example 3 Isolation of cDNA Clones

The phagemid forms of each cDNA clone were obtained by the in-carrier cleavage method, whereby the host bacterial strain was infected with the lambda-library and phage helper phage. Proteins from the phage containing the library and the helper phage cleaved ("nick") lambda DNA, and lambda-target DNA - defined sequences - started new DNA synthesis, and less A single-stranded phagemid DNA molecule of the size of pBluescript plasmid and cDNA insert was generated. The phagemid DNA was selected from the cell and purified and used to re-infect fresh host cells in which double-stranded phagemid DNA was prepared. Since the phagemid carries a beta-lactamase gene, the newly transformed bacteria were selected on medium containing ampicillin.

TM The phagemid DNA is Magic Mimpreps DNA Purification System (Promega Catalog No. A7100; Promega Corp., 2800)

Woods Hollow Rd., Madison, WI 53711). This small-scale process provides a simple and reliable way to isolate bacterial cells and to isolate phagemid DNA, which is purified using a patented DNA binding resin. DNA prepared from the cleaning resin for DNA sequencing and other analytical manipulations was eluted. Phagemid DNA was also purified using the "QIAwell-8 Plasmid Purification System" from QIAGEN® DNA Purification System (QIAGEN Inc., 9259 Eton Ave, Chattsworth, CA 91311). This product range provides a convenient, fast, reliable, high throughput process

TM Lyser of bacterial cells - with EMPORE membrane technique of 3M company, in multicellular arrangement using "QIAGEN" anion exchange beads - to isolate highly purified phagemid DNA. DNA from the purification resin and DNA prepared for other analytical manipulations were eluted. Another method of phagemid purification became available in the latter time. For this purpose, the "Miniprep Kit" kit (catalog number 77468; Advanced GeneticTechnologies Corp., 19212 Orbit Drive, Gaithersburg, Maryland) is used. This kit is 96-well and contains 960 reagents for purification. All reagent kits are provided with instructions for use, the procedure of which was followed by the following changes. First, only 1 ml (25 mg / 1carbenicillin and 0.4% glycerol) of sterile "terrific" medium is added to each of the 96 wells. After inoculation in the wells, the bacteria were cultured for 24 hours and lysed with 60 μl lysing buffer. The centrifugation (at 2900 rpm for 5 minutes) was performed before pouring the contents of the block onto the first filter plate. The optional step in which the isopropanol was added to the Tris buffer was not normally used. samples were placed in a 96-well Beckman unit for storage. 32 ΤΜ

Another new DNA purification system is the WIZARD product family, available from Promega (catalog number A7071), and can be adapted to a 96-well layout. Example 4: Sequencing of cDNA clones

The cDNA inserts from the random isolates of the U-937 and THP-1 libraries were partially sequenced. DNA sequencing techniques are well known. Conventional enzymatic procedures from the hybridizing primer to the DNA template in question for the extension of DNA chains to the DNA chains using the DNA polymerase Klenow fragment, "Sequenase" or Taq polymerase. Procedures have been developed for both single-stranded and double-stranded churches. The chain termination reaction products are usually electrophoresed on urea acrylamide gels and detected (with radionuclide labeled precursors) by autoradiography or (fluorescent precursors) fluorescence. Mechanized reaction preparation, sequencing, and the latest developments in fluorescence detection enabled the increase in the number of sequences that can be determined daily (e.g., DNA sequencer "Applied Biosystems373" and "377", "Catalyst 800"). In general, the read lengths available with the described systems are 250-400 bases, and this length is clone dependent. The reading length also depends on the gel run time. Generally, a longer run time will shorten the sequence. To identify the sequence and determine the degree of homology. «- 33 - requires a minimum of 25-50 bases. Gene transcription imaging can be used by any sequence-specific method, including (inter alia) hybridization, mass spectroscopy, capillary electrophoresis, and "505" gel electrophoresis. Example 5

Searching for Homology in the cDNA Clone and Derived Protein (and Further Steps) Databases containing previously identified sequences using nucleotide sequences (sequences of sequences) derived from the cDNA clones as the sequences in question are examined for the presence of homologous (similar) domains. Examples of such databases include "Genbank" and "EMBL". In the following, an example of a homology search algorithm to be used in the practice of the present invention will be described, followed by computer steps for use in the embodiments of the invention. In the description of the computer steps of the present invention, the term "library" means a sequence (or population) of nucleic acid sequences in a biological sample. A library may be comprised of biological sample characteristic cDNA sequences, RNA sequences, or the like. The biological sample may contain cells of one type of human cell, or may be any biological sample of the above type. It is believed that sequences in a library accurately represent or characterize a biological sample (for example, a library of representative cDNAs derived from clones of a single human cell RNA). sequences). In the description of the computer steps of the present invention, the term "database" means a set of stored data representing a sequence of sequences, which in turn represents a collection of biological reference materials. For example, a database may represent many stored cDNA sequences that are characteristic of human cells infected with different viruses, cells of different ages, cells derived from different mammary glands, and so on. In preferred embodiments of the present invention, a computer programmed to implement the following steps (described below) is used: (i) processing data referring to a library of cDNA sequences produced by high throughput cDNA sequencing or by other means , in order to determine whether certain sequences in the library match the DNA sequences of a reference DNA sequence database (and, if so, the reference database - sequence matching), and the reference to indicate the extent of cleavage between the sequence and the sequence of the library), and to assign an identification sequence value based on the degree of sequence alignment and the degree of alignment in each sequence in the library; (Ii) grouping the number of matching identification sequence values in the library into some or all of the entries in the database (although this can be done manually based on a single entry list, preferably by applying the software described below); and (iii) dividing the number of sequences in the individual frequency numbers library (if the size of the libraries is different) so as to obtain relative frequency numbers (i.e. the relative frequency of single gene transcripts) for each of the identified sequence values.

The list of identified sequence values (or their corresponding genes) can then be sorted by frequency in the cDNA population. Many other comparisons are possible. For example, steps (i) and (ii) can also be repeated using two different bookshops (sometimes referred to as "target libraries" or "extraction libraries") (this procedure will be described in more detail below). or a gene ratio) is obtained by dividing the frequency number obtained in the target directory (for the identifier in question in question) by the frequency number obtained in the extraction library (for the identified sequence value in question). Extraction can be performed on multiple libraries. You can combine derivative transcripts from several (eg three) libraries, and then divide them into another set of 36 transcripts from multiple libraries (e.g., three). This operation has the following notation: (A + B + C) / (D + E + F) where each letter represents a complete library. In some cases, the frequency numbers of transcripts (in the aggregated book repositories) can be divided by the total sample size before subtraction. Unlike the conventional hybridization technique, which allows for the single extraction of two libraries according to the present invention, once a set or library of atrancript sequences has been processed and fed into the computer, we can perform any extraction of the library. For example, with this method, the relative frequency values obtained in the first directory can be divided by the values of the second library (or vice versa) by the ratio values.

In the variant of step (i), the library consists of nucleotide sequences derived from cDNA clones. Examples of databases that can be tested for homologous (similar) domains in step (i) include conventional databases, e.g. "Genbank" (NIH), "EMBL" (European Molecular BiologyLabs, Germany) and "GENESEQ" (Intelligenetics, Mountain View, California).

One of the homology search algorithms that can be used to implement step (i) is D.J. Lipman and W.R. The algorithm described by Pearson [Science 227, 1435 (1985)]. In this algorithm, the search for homologous regions occurs in two steps. In the first step, the regions with the highest homology are determined using the homology number table by calculating the fitting number. In this step, the "Ktup parameter" is used to create a minimum window size that can be shifted to compare two sequences. The AKtup parameter also sets the number of bases that need to be matched to remove the regions with the highest homology from the sequences. In this step, no insertion or deletion is used, and we display the homology as an initial value ("INIT"). In the second step, the homologous regions are subordinated to each other ("alignment") in order to obtain the largest number of matches with a "gap" insertion for possible deletion region insertion. The match number obtained in the first step is recalculated using the homology number table and insertion number table, resulting in an optimized value ("OPT"). DNA sequences between two sequences can be graphically analyzed using the Harr method (dot matrix homology curves) [Needleman, S.B. and Wunsch, C.O., J.Mom. Biol. 4, 8, 443 (1970). This method provides a two-dimensional curve that can be advantageously used to determine homologous regions as a function of repeating regions.

In one preferred embodiment, step (i) is accomplished by providing the directory data known as "INHERIT 670 Seguence Analysis System", commercially available from Applied Biosystems Inc. (Foster)

City, California) is processed by a computer program (including "Factura" software [Applied Biosystems Inc.]). The "Factura" program processes every single sequence of the library beforehand, thereby cutting out 38 parts that are unlikely to be significant (such as the vector used to create the library). Further sequences that can be cut or disguised (not taken into account by the search engines) include, but are not limited to, polyA tail, as well as repetitive GAG and CCC sequences. To identify sequences containing such little information, "low-end" search engines can be written, or otherwise, such sequences, e.g. You can ignore the "BLAST" program.

In the program algorithm of the "INHERIT 670 Sequence Analysis System", the "Pattern Specification Language" developed by TRW Inc. is used to determine the homologous regions. "Three parameters determine how the INHERIT analysis will run the sequence comparisons: window size, window offset, and fault tolerance. The window window determines the length of segments that the sequence in question will disintegrate. Determine that - starting from the beginning of the previous segment - the next segment (to be compared) starts to run. The tolerance for insertion deletions and / or substitutions is tolerated beyond the specified word length. The default settings are as follows: window size = 20; window offset = 10; hibernation = 3. " ["INHERIT Analysis Users Manual" 2-15. No. 1.0, Applied Biosystems, Inc. (Oct. 1991)]. Using the combination of the above three parameters, we can examine the presence of sequences containing homologous regions (eg DNA database) 39 and assign starting values to appropriate sequences. The homologous regions are then analyzed using point matrix homology curves to homologous regions. depending on the repeat regions. Smith-Waterman layouts can be used to display the results of the homology search. The INHERIT software can be run using a "Sun" computer system programmed with a UNIX operator system.

Instead of the INHERIT program, the BLAST program (GCG, Genetics Computer Group, WI) and the "Dasher" program (Temple Smith, Boston University, Boston, MA) can be used. To search for anucleotide sequences, "Genbank", EMBL, or individual databases such as databases containing "GENESEQ" (Intelligenetics, Mountain View, CA) or other genes may be used. In addition, we used our own database to search for certain sequences. In preferred embodiments of the invention, the atrancript sequences, for the best match with a reference gene transcript, are analyzed by the "INHERIT" software to identify a sequence identifier and to determine the extent of the homologue, and these together with the identification. For the purpose of the present invention, a "Macintosh" personal computer (Apple) programmed with a frequency management and subtraction analysis (described below) is used for further processing purposes. Prior to running the frequency-ordering and subtraction analysis program (also referred to as the frequency-organizing program), matched values are identified for the identified sequences from the cDNA clones (according to the above parameters) based on the degree of fitting. Its categories are: "accurate" fit (regions with a high degree of identity); homologous human matching (high similarity but not "exact" matches); homologous, non-human fit (regions with high similarity in sequences other than humans); lack of fit (there are no significant homologous regions with previously identified nucleotide sequences stored in the database). Alternatively, the degree of fit can be expressed in numerical value (see later). A link between the reference sequences and the database entries can be identified by reference to the ability of the nucleic acid sequences to derive protein and peptide sequences. By using the derived polypeptide sequence, the identity recognition can be performed in a similar manner to the cDNA sequence alignment. In this case, a protein sequence is compared to the previously identified sequences of databases (e.g., "Swiss / Prot", "PYR" and "NBRF" protein databases) as the sequence to be examined to obtain homologous proteins. These proteins are first scored for homology using the homology number table (Orcutt, BC and Dayoff, MO: "Scoring Matrices", PIR Report MÁT - 0285 (Feb. 1985)), resulting in 41 INIT numbers obtained . The homologous regions are subordinated to each other ("alignment") in order to obtain the largest fit number by inserting a "gap" into a possible deletion region. The fit number is recalculated using the homology number table and insertion number table. Without knowing the exact reading frame of the isolated sequence, the protein homology search is accomplished by examining all three reading sequences, peptide and protein sequence homologies for aDNA sequence homologies. Similarly, we can determine using the "INHERIT 670Seguence Analysis System." The "Pattern Spéciiication Language" and parameter windows are used to examine the protein databases for the presence of sequences with dotted homologous regions. homology curve The additional search tools that can be applied to pattern search databases include homologous regions.

are "Plsearch Blocks" (Henikoff & Henikoff, University of Washington, Seattle) "Dasher" and "GCG". Pattern search databases include (among others) "Protein Blocks" (Henikoff & Henikoff, University of Wa-shington, Seattle), "Brookhaven Protein" (BrookhavenNational Laboratory, Brookhaven, MA), "PROSITE" (AmosBairoch, University of Geneva, Switzerland), "PorDom" (Temple 42

Smith, Boston University) and "PROTEIN MOTIF FINGERPRINT" (University of Leeds, UK). The "ABI Assembler" user software (Applied Biosystems, Inc., Foster City, CA) forming part of the "INHERIT" DNA analyzing system may be used to design and control sequence sequencing projects that generate data from selected sequence fragments into larger sequences. "Assembler" software combines two advanced computer techniques that maximize ascending DNA fragments - assembled sequences ("Assemblages", a special grouping of data, where inter-sequence links are graphical overlaps, "alignment", and statistical projections). The process is based on the "Meyers-Kececioglu" type fragment aggregation program ("INHERIT Assembler User's Manual", AppliedBiosystems, Inc., Foster City, CA), and a highly accurate sequence organizer suitable for fusing DNA fragments is based on graph theory. Other applicable matching programs include "MEGALIGN" (DNASTAR Inc., Madison, WI), "Dasher", and "STATE" (Roger Staden, Cambridge, England). Referring now to Figure 2, the "frequency organizer" program will be described in more detail, with the above-mentioned step (ii) being grouped (the number of library sequences that match each database entry; 43 in other words, grouping the frequency of each database entry). FIG. 2 is a flowchart of a preferred embodiment of a frequency management program. The source code list of this implementation of the grooming program is shown in Table 5. The frequency scheduler program is in "FoxBASE" (Microsoft

Corporation). Although we selected the "FoxBASE" program for the first iteration of this technique, this is not considered to be exclusive, as, as will be apparent to those skilled in the art, many other programming languages may be used, including "Sybase" as a particularly advantageous option. THE 2. The subroutine names shown in FIG. 1B correspond to the subroutines listed in Table 5.

Again, for SEQ ID NO: 2, "identified sequences" refers to transcript sequences that represent each sequence of the library and an appropriate identification of the database entry (havan) by which it fits. In other words, "identified sequences" are transcript sequences that represent the output of step (i) described above. Fig. 3 is a block diagram of a system suitable for carrying out the present invention. This system includes a library generating unit (2) which generates a directory and sets the output stream of transcript sequences referring to the biological sequences containing the library. The programmed processor 44 (4), according to step (i) described above, receives the output stream from unit 2 and generates the identified sequences. Processor 4 can be programmed with commercially available "INHERIT 670 SequenceAnalysis System" and commercially available "Factura" software (both available from AppliedBiosystems Inc.) and the "UNIX" operator system. Still related to Figure 3, the identified sequences are fed to processor 6, which is programmed by the frequency scheduling program, which generates the final transcript sequences indicated in Figures 2 and 3. A4. Figure 5 shows a more detailed block diagram of a planned computer system containing various search techniques and a database set. With respect to FIG. 2, the frequency scheduler program performs an action on the identified sequences first as a "Tempnum", which discards the identified sequences that do not match the selected type of data base entries. For example, the "Tempnum" operation can identify identified sequences that represent database entries with the following types of matches (definitions see above): "exact" matches; human "homologous" matches; "other species" matches (genes in other species - non-human); lack of matching (there are no significant homologous regions with previously identified nucleotide sequences stored in the database); Inclusions ('Incyte' for previously unknown DNA sequences); 'X' matches (matched with ESTs in the reference database) This action eliminates theUU, S, Μ, V, Sequences A, R, and D (see Table 1). Identified sequence values remaining after selection by the "Tempnum" operation are then subjected to a further selection (herbicide) operation known as "Tempred." For example, the identified sequence values selected in the "Tempred" operation are then sorted by the library by the "Tempdesig" operation. or represent sequences derived from two or more libraries.

First, the case is described when the identified sequence values represent origin sequences from a single library. In this case, all identified sequence values determined during the "Tempred" operation are sorted by "Templib" operation followed by "Libsort" operation and finally "Temptarsort" operation. This three-order operation arranges the identified sequences, for example, by a decreasing frequency number, whereby a list of decreasing frequency numbers is obtained, in which each single frequency number corresponds to one identified sequence record 46; or a list of decreasing frequency numbers, where the regularity numbers in each list correspond to a selected type of database entry (with no redundancy in each sorted list). In this case, the operation "Cruncher" can be omitted, so the "final data" values are the sequenced transcript sequences obtained during the "Temport" operation. The following describes the case where the transcript sequences generated in the "Tempred" operation represent sequences from two libraries ("target" libraries and "extract" libraries). For example, the target library may comprise cDNA sequences derived from cDNA sequences from a patient's cell clones, and cDNA sequences from clones of a patient's post-drug treatment cell. As another example, the target library may consist of cDNA sequences derived from clones of a particular cell type from a young person, and the extract library may consist of cDNA sequences derived from clones of the same cell type of another human being.

In this case, the "Tempdesig" operation processes all transcript sequences representing the target directory to the "Templib" (and then "Libsort" and "Temptarsort") operations, and all transcript sequences representing the extract directory are "Tempsub" (majda "Subsort" and "Tempsubsort"). For example, sequential "Templib", "Libsort", and "Temptarsort" sorting operations are identified sequentially from the target directory by a decreasing frequency number, resulting in a list of decreasing frequency numbers in which each frequency number is a single identified sequence corresponds to an entry; or multiple lists of reduced-frequency numbers, where the frequency numbers in the individual lists correspond to database entries of a selected type (with no redundancy in each organized list). The sequential sequencing operations "Tempsub", "Subsort" and "Tempsubsort" sort the identified sequences from the extract directory by decreasing frequency number, resulting in a list of decreasing frequency numbers in which each frequency number is a single identified sequence corresponds to an entry; or multiple lists of descending frequency numbers, where the list numbers in each list correspond to a selected type of database entry (with no redundancy in each sorted list). The transcript sequences obtained at the end of the "Temptarsort" operation usually represent ordered lists from which a histogram can be made, in which the frequency of (target library sequences) on one (e.g., horizontal) axis and the other (e.g., vertical) axis is the number of frequencies. identified sequence value (e.g., human or non-human gene type). Similarly, transcript sequences obtained at the end of the "Tempsubsort" operation usually represent ordered lists from which a histogram can be made, in which one (e.g., the horizontal) axis has a frequency of 48 (extraction library sequences), the other (e.g. vertical) the identified sequence value (e.g., human or non-human gene type). The transcript sequences (ordered listings) at the end of the "Tempsubsort" and "Temptarsort" organizer operations are combined in the "Cruncher" operation. The "Cruncher" operation identifies the pairs of frequencies of the corresponding sequences from the target directory and the extract directory, and divides the two members of the pairs, determining ratios for the respective frequency pairs, and arranges the ratios by decreasing ratio. The output stream from the "Cruncher" operation (Figure 2, "Final Transcript Sequence") is usually a set list from which a histogram can be created, the frequency of which on a single axis (the corresponding identified sequence values from the target and extract directory) and the identified sequence value (eg gene type) on the other axis.

Preferably, before determining the ratio of the frequency values of the two book repositories, the "Cruncher" operation divides each ratio by the total number of sequences in the target directory or extract directory (or both). The list of relational values generated by the "Cruncher" operation is advantageous for many medical, scientific and industrial uses. In another preferred embodiment, the output of the "Cruncher" operation is a single list sequence in which each list represents a series of descending ratio values for another selected subset of database entries (e.g., pro 49 milk family) a.

In one example, the frequency organizer program of the invention arranges a number of mRNA transcripts corresponding to each single gene identified in a database for a directory. These numbers are divided by the total number of clones. The result of the division reflects the relative frequency of the mRNA transcripts in the cell type or tissue from which the sequences are derived. The final data set is referred to as "gene transcription mapping". The resulting, extracted data points show which proteins and genes are overregulated or under-regulated. Example 6 HUVEC cDNA library A 2. Table 3 shows the order of frequency of different gene transcripts in an induced HUVEC library (decreasing frequency). This computer layout simplifies tissue testing and accelerates the identification of essential new proteins specific for this cell type. The cardiac and vascular endothelium is this endothelial cell type, and the more we know about its composition (especially its changes due to activation), the more chances we have of treating the diseases of this tissue (such as atherosclerosis). destinations become available. 50 Example 7

Monocyte and mast cell cDNA libraries In Tables 3 and 4, a shortened comparison of two libraries is shown. In these tables, "normal monocytes" are HMC-1 cells, and "activated macrophages" are PMA pretreated and LPS-activated THP-1 cells. Table 3 lists the most frequent gene transcripts of both cell types in descending order of frequency. This table, as it shows only 15 gene transcripts from both cells, allows a quick qualitative comparison of the most common transcripts. This provides a direct-to-search search toolbox with a parallel display of frequency. In this example, this search tool provides information on: (1) only one of the 15 most common transcripts of activated macrophages (the polyA binding protein) is found among the 15 most common gene transcripts of normal monocytes; and (2) a new gene transcript, not previously included in other data bases, is present in relatively large amounts in activated macrophages, but less common in normal macrophages. Such a search toolkit provides an abridged procedure for finding new proteins (e.g., receptors, cell surface and intracellular signaling molecules) that can serve as drug targets for conventional drug screening programs. Such a toolbox provides significant time savings compared to the need for "hit / miss" search engines for identifying important proteins in the vicinity of the cell, as proteins that carry out the normal cellular functions and which are in standby state mRNA, can be extracted quickly from further characterization.

This illustrates how the altered cellular function changes the gene transcription profiles. It is well known to those skilled in the art that the biochemical composition of cells is altered by other functional changes (e.g., cancer, including various stages of cancer, and poisoning). As a first screening tool for such gene expression and protein assays, a gene transcriptional extraction profile such as that shown in Table 3 can be used well. Example 8.

Elimination analysis of a normal monocyte and activated monocyte cDNA library

After the cDNA data were fed into a computer, the computer program shown in Table 5 was used to calculate the ratios of all transcripts in the two libraries discussed in the previous example, and the gene transcripts were arranged according to their decreasing ratios. If a gene transcript is not represented in one of the libraries, its frequency is unknown, but less than obvious. As an approximation - and to obtain a ratio that would not be possible if the frequency of the unrepresented gene0 is assigned - only one of two genes represented in each library is assigned a frequency of 1/2. If 1/2 is assigned to unrepresented clones, 52 increase the relative importance of the "on" and "off" genes, whose products may be possible drug targets. The resulting result list is called a subtraction table and represents a very valuable screening method as shown by the following data. Table 4 is a subtraction table in which the library of abnormal monocytes was "extracted" electronically from the library of activated macrophages. This table most efficiently highlights the frequency changes of gene transcripts that have occurred as a result of macrophage activation. Even the first 20 listed gene transcripts are also unknown. Thus, electronic subtraction is a preferred means by which the basic biochemical changes between the two cell types can be identified more quickly. Such a tool saves valuable time for universities and pharmaceutical companies that spend on research radar billions in the early stages of research, and can accelerate the drug development cycle, allowing for much earlier launching of drug screening programs. In this way, this search tool provides a way for new drugs to reach the general public faster and more economically.

A withdrawal table can also be prepared for diagnosing patients. A sample from a particular patient (e.g., monocytes obtained from a biopsy or blood sample) can be compared to the data described herein to diagnose conditions associated with amacrophage activation. 53 Table 4 reveals many new gene transcripts (labeled "Incyte" connector). It should be noted that in the activated macrophage, many genes are "on" (i.e., the number of amonocyte frequencies in the "bgfreq" column 0). This asynchronous method is much more advantageous than other screening techniques (e.g., "western blot" analysis), which does not detect such new, independent gene transcripts. The extraction-screening technique in the activated macrophage also revealed a number of cancer gene transcripts (rho, ETS2, rab-2 ras, YPT1-related oncogenes, and acmyeloid leukemia mRNA). These transcripts are known for the use of immoral cell lines and are therefore of interest. This screening technique provides a detailed picture of overregulated transcripts (including on-genes), which helps explain why cancer drugs affect the patient's activated macrophage-mediated immunity. Those skilled in the art, having knowledge of this screening process, can develop even more targeted, even more effective drug screening programs to identify drugs that are differentially effective (1) against a condition characterized by the active cancer macrophages, (2) solely against cancer; and (3) solely against the condition characterized by activated macrophage.

In the activated macrophages, the smooth muscle aging protein (22 kD) was over-regulated, indicating that it is possible in the 54 anti-inflammatory (target target to be blocked).

Extraction analysis of cDNA libraries of normal liver cells and hepatitis virus-infected liver cells

In this example, the rats are infected with the hepatitis virus and kept in the colony until the definite symptoms of hepatitis are shown. According to the diagnosis, half of the liver inflammatory rats are treated with a new anti-inflammatory disease (AHA). Before the infection with the hepatitis virus or after treatment with AHA (or in the untreated group after infection), all rats are sampled. In addition, liver samples can be taken directly before treatment with AHA.

The liver tissue was recovered as described in Examples 2 and 3 for the recovery of mRNA and sequencing of cDNA. The cDNAs from each sample were processed with the computer program described in Table 5 and examined for frequency. The cDNA provides a gene transcript mapping detail of the basal state (control) and infected and / or treated status of each animal. The cDNA data obtained for a group of samples can be pooled into all control samples, a pool of gene transcript profiles obtained from samples from infected rats and from samples from AHA-treated rats. Extracts are performed between the appropriate libraries and grouped libraries. In each animal, the control and post-test samples may be drawn. If there is a sample before and after AHA treatment, data from each animal and treatment group is extracted. Additionally, data for all control samples can be combined and averaged. The control mean can be subtracted from post-test cDNA samples from AHA-treated rats and from post-test cDNA samples from non-AHA-induced rats. If pre- and post-treatment samples are available, they can be compared individually (or averaged electronically) and extracted.

These extraction tables are applied in two general ways. According to the first, the differences are investigated for those brain transcripts that are dependent on hepatic progression or healing. Extraction tables provide a means of distinguishing the effects of drug treatment and the underlying pathology of hepatitis. Since liver inflammation affects many parameters, it has been complicated to detect further liver toxicity in the past, with blood tests for conventional enzymes. The gene transcript profile and excretion provide a much more complex biochemical image that researchers need to analyze such complex problems. According to the second embodiment, the extraction tables provide a means for identifying clinical markers, individual proteins, or other biochemical determinants which are 56 for the predetermination of a clinical endpoint (e.g., disease), drug-induced improvement, or drug-induced illnesses and / or They can be used to evaluate. The extraction tables specifically raise genes for in and out of genes, thus, these tables make the first screening of a set of gene transcripts potentially applicable as clinical markers. Subsequently, the electronic subtraction of further cell and tissue libraries shows which of the potential markers are actually located in different cell or tissue libraries. Additional library-accessible gene transcripts are removed from the pool of potential clinical markers. Blood samples or other appropriate samples (known to fulfill the condition) are then compared to confirm the appropriateness of selecting a clinical marker. In this method, the physiological function of the protein transcript is not determined to qualify as an agonist transcript as a clinical marker. Example 10.

Electronic "Northern blot" analysis

One of the drawbacks of electronic subtraction is that, by applying, more than one pair of images is simultaneously compared. Once it has been determined that the particular gene products are suitable for further testing (e. G., Electronic extraction or other methods), it is preferred that the expression of each gene is studied in several different tissues. 57

In the laboratory, "northern blot" hybridization is used for this purpose. In this technique, a cDNA is labeled as a suitable probe and then blotted hybridized with RNA samples from different tissue and cell types. The expression pattern of a given gene (at one time) can be quantitatively determined by autoradiography in all samples. In a further embodiment of the invention, a computer version of the above method is described, which is referred to as "electronic Northern blot" analysis. According to a variant, the expression of a single gene is examined against the library libraries - prepared and sequenced. By this method, the pattern of any of the selected gene expression can be tested quickly and easily. It is possible to scan multiple selected genes, which allows more frequent and useful discoveries. AZ5. The computer program shown in Table 1A includes a program for executing the operation described in this example. Table 6 shows a partial list of database entries for electronic "northern blot". Example 11 Phase I clinical trials

After verifying the safety and efficacy of the method of the invention in the above animal assays, we begin with Phase I clinical trials. Healthy dogs were subjected to conventional pre-clinical laboratory tests and samples were taken which were subjected to gene transcript analysis. During the test, new samples are taken from the patients at specified intervals. Samples were subjected to a gene transcript analysis as described above. Gene transcript changes recorded in the rat toxicity study are carefully evaluated as clinical markers for patients. Changes in gene transcript analysis are evaluated as clinical indicators and other laboratory results as toxicity indicators. Extraction is also performed on individual patient samples and on average patient samples. The extraction analysis in the treated patient highlights any toxicological change that is a very sophisticated determinant of toxicity. The extraction procedure also includes clinical markers. Further subgroups can be examined by subtraction analysis; these include, for example, (1) segregation according to the occurrence and type of seizure effect, and (2) segregation according to dosage. EXAMPLE 12 Gene Transcript Mapping Analysis in Clinical Trials Gene transcription imaging (or complex gene transcription imaging) may be used advantageously in other clinical trials. Gene transcript analysis can be used to compare cells of the same type from different species, for example, to detect 59 specific differences in the enzyme system responsible for amelioration. Such testing assists in the selection and validation of an animal model for screening or toxicological testing of drugs for use in human or veterinary medicine. When comparisons between animals of different species are presented in columns for each species, the comparison is called inter-species comparison or "zoo-blot". In embodiments of the invention, for example, databases written using the "FoxBASE" program available from Microsoft Corporation may be used. In other embodiments of the invention, other databases are used, e.g. U.S. Patent No. 5,270,170 to Cull et al., December 14, 1993, PCT International Publication No. WO 93/22684, November 11, 1993, PCT International Publication No. WO 93/06121. (April 1, 1993) and WO 91/19818, PCT International Publication (December 26, 1991), a database of a peptide, a polymer database, a synthetic oligomer database, and an oligonucleotide database. Four references (which are to be considered in their entirety as part of the teaching) contain information that can be used to practice other embodiments of the invention. All references mentioned herein are considered to be part of the disclosure in its entirety. It will be appreciated by those skilled in the art that various methods and modifications of the method and system of the invention are possible without departing from the scope of the invention. Although the present invention has been described in the specific preferred embodiments, it is to be understood that the subject matter of the invention and its claims are not limited to these embodiments disclosed. • · - 61 - Table 1 Meaning of letter codes

Definitions (D) Library (L) Distribution (D) E = exact U = U937 C = non-specific H = homologous M = HMC P = cell / tissue spec. 0 = other species T = THP-1 U = unknown N = no match H = HUVEC D = non-coding gene S = spleen Status (I) U = unreadable L = lung 0 = currently R = repetitive RNA Y = T- and B cells are not interesting; A = only poly-A A = adenoid 1 = primary analysis V = vector only Species (S); M = mitochond. DNA H = human 2 = primary analysis S = skip A = monkey completed; I = fitting P = pig 3 = full length- "Incyte" clone D = canine sequence; X = EST fit V = cattle 4 = secondary an B = rabbit 1 z z s; Location (L) R = rat 5 = tissue N = nuclear M = "northern" C = cytoplasmic S = hamster 6 = total long- K = cytoskeleton C = chicken E = cell surface F = amphibian Z = intracell. memb. I = invertebrate M = mitochondrial Z = unicellular S = secreted G = fungus U = unknown X = other • · - 62 - Table 1 Reporting of letter codes (continued)

Function (R) T = translation L = "protein-processing" R = ribosomal protein 0 = oncogenic G = GTP-binding protein V = virus element Y = kinase / phosphatase A = tumor-antigen-linked I = binding proteins D = NA-binding / transcription B = Surface molecule / receptorC = Ca ++ - binding protein S = ligands / effectors H = "stress response" protein E = enzyme F = ferroprotinP = protease / inhibitor Z = oxidative phosphorylation Q = sugar metabolism M = amino acid metabolism N = nucleic acid metabolite. W = lipid metabolism K = structural X = other U = unknown 63 Table 2

Number of clones: 15000 - 20000Libraries: HUVEC

Sort by: incidence Total clones tested: 5000 pcs. 319 gene for a total of 1713 clones number N c entry s descriptor 1 15365 67 HSRPL41 Riboptn L41 2 15004 65 NCY015004 INCYTE 015004 3 15638 63 NCY015638 INCYTE 015633 4 15390 50 NCY015390 INCYTE 015390 5 15193 47 HSFIB1 Fibronectin 6 15220 47 RRRPL9 R Riboptn L9 7 15280 47 NCY01S280 INCYTE 015280 8 15533 33 M62O6 EST HHCHO9 (IGR) 9 15662 31 HSACTCGR Actin, gamma 10 15026 29 NCYO15O26 INCYTE 015026 11 15279 24 HSEF1AR Elf 1-alpha 12 15027 23 NCYO15O27 INCYTE 015027 13 15033 20 NCY015033 INCYTE 015033 NCY015198 INCYTE 015198 15 15309 20 HSCOLL1 Collagenase 16 15221 19 NCYO15221 INCYTE 015221 17 15263 19 NCYO15263 INCYTE 015263 18 15290 19 NCYO1529O INCYTE 015290 19 15350 18 NCY015350 INCYTE 015030 21 15034 17 NCYO15234 INCYTE 015234 NCYO15234 INCYTE 015234 22 15459 23 15353 15 NCY015353 INCYTE 015353 24 15373 15 S76965 Ptn kinase inhibition 25 15255 14 HUMTHYB4 Thymosin beta-4 26 15401 14 HSLIPCR Lipocortin I 27 15425 14 HSPOLYAB Poly-A b p 23 18212 14 HUMTHYMA Thymosin, alpha 29 18216 14 HSMRP1 Motilitv relat ptn; MRP-1 30 15189 13 HS18D Interferon Induc ptn 1-8D 31 15031 12 HUMFKBP FK506 bp 32 15306 12 HSH2AZ Histone H2A 33 15621 12 HUMLEC Lectin, B-galbp, 14kDa 34 15789 11 NCY015789 INCYTE 015789 35 16578 11 HSRPS11 Riboptn LINE 36 16632 11 M61984 EST HHCA13 (IGR) 37 18314 11 NCY018314 INCYTE 018314 38 15367 10 NCY015367 INCYTE 015367 39 15415 10 HSIFNIN1 interferon induc mRNA 40 15633 10 HSLDHAR Lactate dehydrogenase 41 15813 10 CHKNMHCB C Myos heavy chain B 42 18210 10 NCY018210 INCYTE 018210 43 NCY018210 INCYTE 018210 43 HSRPII140 RNA Polyraase II 44 13996 10 NCY018996 INCYTE 018996 45 15088 9 HUMFERL Ferritin Light Chain 46 15714 9 NCY015714 INCYTE 015714 47 15720 9 NCY015720 INCYTE 015720 43 15863 9 NCY015863 INCYTE 015863 49 16121 9 HSY Endothelin 50 18252 9 NCY018252 INCYTE O182S2 HUMALBP Lipid bp, adipocyte 52 15370 8 NCYO1537O INCYTE 015370 64 Table 2 (continued; number N c entry 3 descriptor 53 15670 8 BTCIASHI V NADH-ubiq oxidoreductase 54 15795 8 NCY015795 INCYTE 015795 55 16 245 8 NCY016245 INCYTE 016245 56 18262 8 NCY018262 INCYTE 018262 57 18321 8 HSRPL17 Riboptn L17 58 15126 7 XLRPL1BRF Riboptn L1 59 15133 7 HSAC07 Actin; beta 60 15245 7 NCY01S245 INCYTE 015245 61 15288 7 NCY015288 INCYTE 015288 62 15294 7 HSGAPDR G-3-PD 63 15442 7 HUMLAMB Laminin Receptor 54k0a 64 154SS 7 HSNGMRNA Uracil DNA Glycosylase 65 16646 7 NCY016646 INCYTE 016646 66 18003 7 HELLEN Plsmnogen activ gene 67 15032 6 HUMUB Ubiquitin 68 15267 6 HSRPS8 Riboptn S8 69 15295 6 NCYO15295 INCYTE 015295 70 15458 6 RNRPS10R R Riboptn S10 71 15832 6 RSGALEM R UDP-galactose epimerase 72 15928 6 HUMT3BM40 Tubulin, beta 74 18218 6 NCY018218 INCYTE 018218 75 18499 6 HSP27 Hydrophobic ptn p27 76 18963 6 NCY018963 INCYTE 018963 77 18997 6 NCY018997 INCYTE 018997 78 15432 5 HSAGALAR Galactosidase A, alpha 79 15475 5 NCY015475 INCYTE 015475 80 15721 5 NCY015721 INCYTE 015721 81 15865 5 NCY015865 INCYTE 015721 81 15865 5 NCY015865 INCYTE INCYTE 016270 83 16886 5 NCY016886 INCYTE 016886 84 18500 5 NCY018500 INCYTE 018500 85 18503 5 NCY018503 INCYTE 018503 86 19672 5 RRRPL34 R Riboptn L34 87 15086 4 XLRPL1AR F Riboptn Lla 88 15113 4 HUMIFNWRS tRNA synthetase, trp 89 15242 4 NCY015242 INCYTE 015242 90 15249 4 NCY015249 INCYTE 015249 91 15377 4 NCY015377 INCYTE 015407 93 15473 4 NCY015473 INCYTE 015473 94 15588 4 HSRPS12 Riboptn S12 95 15684 4 HSEF1G Elf gamma 96 15782 4 NCY015782 INCYTE 015782 97 15916 4 HSRPS18 Riboptn S18 98 15930 4 NCY015930 INCYTE 015930 99 16108 4 NCY016108 INCYTE 016108 100 16133 4 NCY016133 INCYTE 016133 65 3. Table 15 The most common 15 genes in normal monocytes and activated macrophages

Normal Monocytes Activated Macrophages 1 Elongation Factor-1-α Interleukin-1-β 2 Ribosomal Phosphoprotein Macrophage Protein-I 3 Ribosomal S8 Homologue Protein Interleukin-8 4 β-globin Lymphocyte Activating Gene 5 Ferritin-H Chain Elongation Factor -1-α 6 ribosomal L7 protein β-actin 7 Nucleoplasmin "Rantes" T-cell specific protein 8 Ribosomal S20 homologous protein Poli-A binding protein 9 Transferrin receptor Osteopontin; nephropontin 10 Poli-A binding protein cc-tumor necrosis is-factor 11 Tumor-regulated tumor protein 011050 INCYTE clone 12 Ribosomal S25 protein Cu / Zn zuperoxide dismutase 13 SRP9 signal recognition Adenylate cyclase (yeast protein homolog) 14 H2A.Z-histone NGF-related B-cell activating molecule 15 Ribosomal Ke-3 protein Glia-protease nexin-66 Table 4

Libraries: THP-I

Extracting Library: HMC

Sort by: Frequency of occurrence Total clones tested: 7375. 1057 genes for a total of 2151 clones number entry s descriptor bgfreq rf end ratio 10022 HUMIL1 IL 1-beta 0 131 262.00 10036 HSMONCF IL-8 0 119 238.00 10089 HSLAG1CDN Activated gene 0 71 142.00 10060 HUMMIP1A MIP- 1 3 121 40.333 10689 HSOP Osteopont 0 20 40.000 11050 NCY011050 INCYTE 011050 0 17 34.000 10937 HSTNFR TNF-alpha 0 17 34.000 10176 HSSOD Superoxide dismutase 0 14 28.000 10886 HSCDW40 B-cell activation, NGF relat 0 10 20.000 10186 HUMAPR Early resp PMA -induc 0 9 18.000 10967 HUMGDN PN-1, glial-derivative 0 9 18.000 11353 NCY011353 INCYTE 011353 0 8 16.000 10298 NCY010298 INCYTE 010298 0 7 14.000 10215 HUM4COLA Collagenase, type XV 0 6 12000 10276 NCY010276 INCYTE 010276 0 6 12.000 10488 NCY010488 INCYTE 010488 0 6 12.000 11138 NCY011138 INCYTE 011138 0 6 12.000 10037 HUMCAPPRO Adenylate cyclase 1 10 10.000 10840 HUMADCY Adenylate cyclase 0 5 10.000 10672 HSCD44E Cell adhesion glptn 0 5 10.000 12837 HUMCYCLOX Cyclooxygenase-2 0 5 10.000 10001 NCY010001 INCYTE 010001 0 5 10.000 10005 NCY010005 INCYTE 010005 0 5 10.000 10294 NCY010294 INCYTE 010294 0 5 10.000 10297 NCY010297 INCYTE 010297 0 5 10.000 10403 NCY010403 INCYTE 010403 0 5 10.000 10699 NCY010699 INCYTE 010699 0 5 10.000 10966 NCY010966 INCYTE 010966 0 5 INCYTE 012092 0 5 10.000 12549 HSRHOB Oncogene rho 0 5 10.000 10691 HUMARF1BA ADP-ribosylation fctr 0 4 8.000 12106 HSADSS Adenylosuccinate synthetase 0 4 8.000 10194 HSCATHL Cathepsin L 0 4 8000 10479 CLMCYCA I Cyclin A 0 4 8.000 10031 NCY010031 INCYTE 010031 0 4 8000 10203 NCY010203 INCYTE 010203 0 4 8.000 10288 NCY010288 INCYTE 010288 0 4 8.000 10372 NCY010372 INCYTE 010372 0 4 8.000 10471 NCY010471 INCYTE 010471 0 4 8.000 10484 NCY010484 INCYTE 010484 0 4 8.000 10859 NCY010859 INCYTE 010859 0 4 8.000 10890 NCY010890 INCYTE 010890 0 4 8000 11511 INCYTE 010890 0 4 8.000 11511 NCY011511 INCYTE 011511 0 4 8.000 11868 NCY011868 INCYTE 011868 0 4 8.000 12820 NCY012820 INCYTE 012820 0 4 8000 10133 HSX1RAP IL-1 antagonist 0 4 8000 10516 HUMP2A Phosphatase, reg. 2A 0 4 8000 11063 HUMB94 TNF-induc response 0 4 8000 11140 HSHB15RNA HB15 gene; new lg 0 3 6.000 10788 NCY001713 INCYTE 001713 0 3 6.000 10033 NCY010033 INCYTE 010033 0 3 6.000 10035 NCY010035 INCYTE 010035 0 3 6.000 10084 NCY010084 INCYTE 010084 0 3 6.000 10236 NCY010236 INCYTE 010236 0 '3 6.000 10383 NCY010383 INCYTE 010383 0 3 entry s descriptor bgfreq rf end 10450 NCY010450 INCYTE 010450 0 3 10470 NCY010470 INCYTE 010470 0 3 10504 NCY010504 INCYTE 010504 0 3 10507 NCY010507 INCYTE 010598 0 3 10779 NCY010779 INCYTE 010779 0 3 10909 NCY010909 INCYTE 010909 NCY010909 INCYTE INCYTE 010976 0 3 10985 NCY010985 INCYTE 010985 0 3 11052 NCY011052 INCYTE 011052 0 3 11068 NCY011134 INCYTE 010134 0 3 11136 NCY011136 INCYTE 011136 0 3 11191 NCY011191 INCYTE 011191 0 3 11219 NCY011219 INCYTE 011219 0 3 11386 NCY011386 INCYTE 011386 NCY011386 INCYTE 0 3 11403 NCY011403 INCYTE 011403 0 3 11460 NCY011460 INCYTE 011460 0 3 11618 NCY011618 INCYTE 011618 0 3 11686 NCY011686 INCYTE 011686 0 3 12021 NCY012021 I NCYTE 012021 0 3 12025 NCY012025 INCYTE 012025 0 3 12320 NCY012320 INCYTE 012320 0 3 12330 NCY012330 INCYTE 012330 0 3 12853 NCY0128S3 INCYTE 012853 0 3 14386 NCY014386 INCYTE 014386 0 3 14391 NCY014391 INCYTE 014391 0 3 ratio 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6,000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 6.000 68 Table 5 • Mister menu tor S03TRACT2CN outputSET TALK 0 ?? S3I SAFST? OFFS2T E3CACT CNSST TTPSAH2AD TO 0CLZAR ”

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BEXF ff xsiafceb -7.7 'WCnS' BEX7.ff ANAUl 7 'Sorted by MONDAY' · BEI, ff ANAL-2 7 'Arranvad by 7CNCTOCN' BEI? 71? 'Total clonea repreaenctdd: 1 77 STRITES.S.O) 7 1 Total · clonea an &amp; late: 1 ?? STR (STARTOT, 3.0)? 'Tot4l.conputattion.tine: 7? 3TR {CCM5MOÍ, S, 2) "77 1 unique"? · 7'd> deactivation £ - diatriciation a = location. Ra functtion aa ap «ci ia s ia s SCRESN 1 TYFS 0 SSADZN5 'Scree 1' AT 40.2 '3122 283,453 PZXSLS FONT * Geaeva', 9 COLOK 0 0 0DO CAS3 · CAS3 ANALAL7? STR (ADNIQUE, 4.0) '7? 1 genes. Forum of the total of' STR (ANALTOT, .4, O) 7? 1 clonea '? ·. SCS2SN 1 ΤΪΡ2 0 HEADUG' Sequence 1 'AT 40,2 SX22 283,492 PZXSLS FONT' Geie / a ', 7 COLOK 0,0,0, OFF OFF Fialda nunher, D, F, Z , RjBTOKY, S, BESCRX? T0R, BCFRSQ, RF2ND, RMTO, ISS! HUNT OFF 'CL0S3, DATABA3E3, FoxSASS + ZMac: £ αχ filea; clonea. Dbf * CÁSS.ARAL «2 * argan / fiactica

SS? PRINTCRSS? EEACSC CN SCREZN 1 WPS. 0 HBAimG 'Series 1' AT 40,2 SZZS 285,492 PIX2L3 PHOTO 'Helvettica ·, 253' COLOK 07 '? 'ΒΕίΠΣΜΟ PRCfESZNS' 1 SCR2EÍ1 ΤΏΊ 0 HBADIN3 'Sort by l'A? 40.2 SZZS 283,492 72X2L3 PHOTO 'Helvefcica', 265 COLOS 0? 'Suxfaca siolaculaa and its receptors' SCH3EN 1 ΤΎ72 0 KSADDG' Sort by 1 * AT 40,2 SZS 286,492 PZX2LS PHOTO 'Cenevt', 7 COLOK 0,0,0, GF? fielda numb «r, D, F, Z, R, ETORYY, S, CESCRZ? T0R, 3GFS2Q-R» 7ND, RAn: 0, I TOR Ra'2 · SCR22H 1 ΤΥΡΕ 0 HEADD53 'Sortén 1''λΤ 40, 2 SZZS 286,4.93 P2ÉLS .FOTO, 'Helvattica', 265 COLOK 0> 'Calciua-binding protein t' SCR2SN 1 TYP2 0 E2ADING 'Sort 1' AT 40,2'SIZE 286,492 PZXSLS FONT 'Geieva', 7 COLCR 0, 0.0, cauldron OFF field n «nber, D, F, Z, R, ETOK2, S, EHSCRI? TOR, aGFM5Q, <r £ SD, RATIO.I TOR Rs'C 'SCKZE24 * 1 WPS 0 ΚΞΛΟΙΝδ' Sort 1 * AT 40.2 SZS 283.492 PZ3EL3 PHOTO 'Kelvwttica ·, 265 COLOK 07' Liganda'and i 'SCS2SN 1 TYP2 0'SACDO' Scraea 1 'AT 40,2 SZZZ 286,492 PDCSLS FONT' Geneva ', 7 COLCR 0,0 , 0, due to CFF field nuaber, D, F, Z, R, ETO «', S, £ ESCRZPTOR, aCFMQ, XyBC, KATZO, I TOR R-'S' SCKZZN 1 TYPT 0 HEADZU3 'Scrtei 1 * AT 40, 2 SZ2S 236,493 PZ2SLS TORT 'Relvettica', 255 COLOK 07 'Stack of Protein Protein: 1 SCKSEN 1 ΤΥΡΕ-0 SILVER' Sertel 1 * AT'40,2 SZZS 236,492 P23ELS PHOTO 'Geneva', 7 COLCR 0,0,0, list OFF fl «lda'suEbett, 0, F, Z, R, QTOXT, S, CSSCRZPT0R, B0FRSQ, R7BO, RAT: 0.Z TOR Rs'Z '' 7. · · SCR2SN 1 ΤΪ72 0 HEABZNG Sortes 1 * AT 40.2 SZSS 286,492 PZXSLS TONT 'Helvetica', 268 COLOK 07 * ORCOCHOS '7. '· SCKSZM 1 ΤΥΡΕ 0 KSADZNG' Sort 1 * AT 40,2 SZ22 286,492 PZX3LS PHOTO 'Helvetica ·, 265 COLOR 0? 1General onogeneue * ,, 9CR3EM 1 TY72 0 KSAQQG''Sora 1 'AT-40.2 SZZZ 286,492 PZXSLS FOOTO "Geieva", 7 COLCR 0,0,0, Due to OFF field aattber, Dt'F, Z, R, EOTRY, S, DESCRZPTOR, BOFRSQ, WEND, RATZO, TOR R-'O 'SCRZC4 1 Year 0 KSADZNS' Sort 1 * AT 40.2 SZS 286,492 PZXSLS PHOTO 'Helvettica', 265 COLOR 07 'GTP Biading Proteins1 SCS22H 1 TYP2 0 HEAntNG 'SerMc 1' AT 40.2 SZ2Z 286.492 PZXSLS FQtTO 'Geneva', 7 COLCR 0.0.0, dirt OFF field with scratch, D, F, ZR, ETORY, S, CBSCR1PTOH, aFFZZ, RATXO.Z TOR R -'O '72 SCRE2T 1?' ViralSCREEN 1h Carbon black TYPS 0 H2ABIW3 "Screen 1 'AT 40.2 SIZS 235,493 PIXELS FONTELENEN' TYP2 0 HEADDJC" Scrsen 1 'AT. 40.2 SIZE 386,493 PIXELS POINT £ iod ?, Z, R, ENIRY, S, OESCRIPTOR, SGFREQ, RFSJD, HATXO, • Hellweed ", 3S5 COLOR 0 • c £ á & £", 7 cS £ rs, o, I TOR R »'V' w SCREEN 1 TYFE 0 HEADDJC "Screen 1 · AT 40.2 SIZE 230.432 PIXELS TONT" Kelvetica "," 255 COLOR 07 'Xinaaea and Phoephatae i *' SCREEN 1 TYPS 0 HEADDJC "1x AT 40.2 SIZE 335,493 PIXELS FONT "G 7 "COLOK 0,0,0, CERT STORY field nunber, E, F, Z, R, SJTHY, S, OESCR2? TOR, BCFRaQ, RFElD, RATIQ.I FORUM Rs'Y 'SCREEN 1-TYPB 0 HEADDJC "Screen 1 * AT 40,3 S223 285,492 PIXELS MAIN" Hélvetica ", 2S5 COLCR 07" Tumor-relacad antigen »'SC3ZZX 1 TYPS 0 HEADING" Ser ser. 1 * AT 40.2 SIZS 235,492 PIXELS FONT "Geneva", 7 COLOR 0.0,0, li's: OFF '£ ialis nunber, D, F, Z, R, SNTRY, 3, UNCCRIPTOR, SCFHSQ.RFaJD, RATXO, I TOR R «'A' 7. SCRSSJ 1 TYPS 0 HEADD3G" Screen 1 · AT 40.2 SIZE 235,492 PIXELS FCOT "Kelvetica." , 258 COLOR 0? · 'PROTEIN SYNTHETIC MACHDJERY PROTEINS' 1. SCREEN 1 TYPS 0 HEADDC "Screen 1 * AT 40.2 SIZ3 285,492 PIXELS FONT" Kelvetica ".255 COLOR 0? 'Tranacriptic and Nucleic Acid-binding Preteine' SCREEN 1 TYPS 0 HEADDJG" Screen 1 * AT 40.2 SIZS 285.492 PIXELS FONT "Geneva", 7 COLOR 0.0.0, dirt OFF field nunber, O, F, Z, R, sNTRY, S, DESCRI? TOR, BGFRS3, RFEND, RATIO.I TOR Ra'D 'SCREEN 1 ΤΥΡΕ 0 HEADDJG "Serien 1 * AT 40.2 SIZS 236,492 PIXELS FONT" Kelvetica "265 COLOR 07" Translation: '<' SCRS2Í 1 1 0 H2ADDJG 'Screen 1 * AT 40.2 SIZE 286,492 PIXELS FONT Geneva, 7 COLOS 0.0.0, dirt OFF 'fialda nunber, D, F, Z, R, 2OTRY, S, 0ESCRI? TOR, SGFRSQ, RFSND, RAno, I TOR R »' T * - SCREEN 1 ΤΥΡΕ 0 HEADDJG" Screen 1 · AT'40.2 SIZS 285.492 PIXZL3 FOJT "Helvetica", 265 COLOR 07 'Rowan protease:' SCREEN 1 TYPS 0 HEADDJG 'Screen 1' AT 40,2 SIZS 285,493 PIXELS FCNT '&amp; η <νη · 7 COLCR 0.0.0, CFF FieldS nttrfcer, D, F, Z, R, 2NTRY, S, CESCRI? TOR.S3FR3Q, KFS®, RkTIO.I FOUND »R 'SCREEN 1 TYPS 0 HEADDJG." Screen 1 "AT 40.2 SIZS'286,492 PDSLS TO NT "Kelvetica" / 265 COLOR 07 'Protein Processing: · SCREEN 1 TYPS 0 HEADDJG "Screen 1" AT 40.2 SIZS 235,492 PIXELS FONT "Geneva", 7.COLCR 0,0,0, Dirt OFF fielda nunber, D, F, Z, R.EJTRY, S, OgSCa? TOR.BSF? SQ, RF2ND, RATIO, I TOR Rm'L '-i' SCREEN 1 TYPS 0 HEADDJG "Screen 1" AT 40, .2 SIZS 286,433 PIXELS. FONT "Helvetica", 268 COLOR 0 "SCREEN 1 ΤΥΡΕ 0 HEADINC" Screen 1 'AT 40,2 3132 286,492 PIXELS FONT "Helvetica", 265 COLOR 0? ·' Ferrcprotein: 'SCREEN 1 ΤΥΡΕ Q HEADDJC "Screen 1' AT 40.2 SIZE 286,492 PIXELS FONT "Geneva", 7 COLOR 0.0.0, Dirt OFF field number, D, F, 3, R, 3ISIS, S, DESCR2PTCa, 9GFRS3, RFEND.RATXO, I TOR R »'F "SCREEN 1 TYPS 0 HEADDJG" Screen-1 "AT 40.2 SIZS 286,492 PIXELS TONT" Helvetica ", 25COLOR 07 'Proteasas and Inhibitors:' SCBEEt 1 ΤΥΡΕ Q HEADDJG 'Screen 1 * AT 40.2 SIZS 285,493 5IXSXS FCOT Geneva ", 7 CCLOR 0.0.0, dirt OF7 field nu», O, P, Z, R, DITHY, S, OSSCaiFIOR.BGFRZQ, RFEND, RAT3O.I TOR R-'P 'SCREEN 1 TYPS 0 HEADDJC "Screen 1 'AT 40.2 SIZS 295,492 PIXELS TONT" Helvetica ", 255 COLOR 0? 'Oxldative phosphosylation: SCREEN 1 TYPS 0 HEADDJG' Screen 1 * AT 40.2 SIZS 285.492 PXXZLS FONT "Geneva", 7 COLOR 0.0.0, dirt OFF fielda nuróer.OPZRXNIRY.S, 08SCRlPTOR.aGFREQ, RFEND, RATIO , I POR R »'Z' SCREEN 1 ΤΥΡΕ 0 HEADDJG '' Screen 1 * AT 40,2 SIZS 235,492 PIXELS FONT" Kelvetica ", 255 COLCR 07 'Radius 4 - 1 ·' 'SCREEN 1 TYPS 0 HEADDJG" Screen 1 * AT 40.2 SIZS 235,492 PIXELS FONT "Genome" .7 OOLOR 0,0,0, dirt CFF field number, D, F, Z, R, a «HY.S.DESCRIFTOR, BGFREQ.RFEND, RA3TO, I TOR R-'Q 'SCREEN 1 ΤΥΡΕ 0 HEADDJG "Screen 1" AT 40.2 3IZ2 286,492 PIXELS FONT "Helvetica", 255 COLCR 07' Acid Acid Tracking: '· SCREEN 1 TYPS 0 HEADDJG "Screen 1" AT 40.2 SIZS 236,492 PIXELS FONT "Geneva", 7 COLOR 0.0,0 / 73 Sets OFP fields ntstber, 0, F, Z, R, 2HTRY, S.0E3CHI? TR, BCFRSQ, RFSNO, RATZOI POR R = 'M' SOSai 1 «SS 0. HSADIM! "Screen 1 · AT 40.3 SXZ3 285,492 PXXSLS POINT &amp;?" &Amp; 5: "2 &amp; O'S 0? 'Nueleic Acid Methachate:' · SCRSStl.« P2 O'HEAYJJ "Screen · 1 "AT 40.2 3ΙΖΞ 285.492 PXXSLS FCJTT" Geneva "(7 COLCR 0.0.0, 'Off, OFF' fields nunber, D, F, Ζ,, ΕΝΤΗΤ, S, OESCRIFTOR, 8GFREQ, RFEND, RATIO, T POR R-'N '' SOSai'1 «PS 0 HSADINS" Serein 1 · AT 40.2 SIZS 295,492 PTXSXS 'FCOT Helvetica, 255 COLCR 0?' Lips Meeaboys: 'sassá 1 «? S 0 HSADffiO" Bronze 1 'AT 40.2 SXZS 285.492 FXX2LS Gen "Geneva", 7 COLOR 0.0.0, Off OFF fields mmfaer, D, F, Z, R.5X «TF.F (S, DESCRX? RCR, 3C7F3Q, RFZNC, HATX0, r POR R-'W 'SCRSat 1 TSS 0 KEADXSG "Serien 1" AT 40.2 SXZS 285,492 PXXSLS FONT "Helvetica", 255 COLOR 07' Cther enzymes. * 'SOSE! X «ΡΕ 0 HSADIKG 3creen 1 "AT 40.2 SIZS 286.492 PXXSL3 FONT" Geneva ", 7 COLOR 0.0.0, Uie OFF fields nunber, n ', P, Z, R, 2S7TSi, S, DESCRIPTOR, BGPR2Q, RFEND, RATIO, I R = 'S' 7 · · · · · · · · '3CR2SN 1 «Ρ2 0 HSADXNG" Screen 1 "AT 40,2 SXZS 286,492 PX XEL3 POINT "Helvetica", 263 COLOR 07 '' ·? 'MXSCSLtANECCS CATSGORXSfi1? SCJSai 1 «SS 0 HZAOIKG" Serien X "AT 40.2 SIZS 286,492 PIXSLS POINT" Helvetica ", 255 CCLOR 0? 'stress respoaset' sasai 1 «ΡΕ 0 HSAXXO" Screen 1 "AT 40,2 3222 285,492 PIXEL3 POINT" GENE ", 7 COLOR 0,0,0, Hit OFF fields nuaberiD, F ', Z, .R, a7TRf, S , DE3CRXPTOR, SGFRSQ, Rо®, SATIO, X R »· RS · SCRSai 1« 7Ε 0 HZADINO "Screen X 'AT 40.2 SIZS 286,492 PXXSLS POINT" Helvetica ", 255 CCLOR'07' Stniczú: 'sasai X «72 0 H2ADXNS" Serien X "AT 40.2 SXZS 286.492 PCCBLS FONT" Genova ", 7 COLOS 0.0.0, Cauldron OFF fields nu» l3er, D, F, Z, R, 3NI5S, S, DSSCR2PTOR, BGFRSa, RParo, RATIO, 2; FOR R = 'K' s X Τ5Τ2 O HSADXSB 'Serien X' AT 40 402 SIZS 286,492 PIX2L3 FONT "Helvetica", 255 CCLOR 07 · 'other clonei:' sasai X «72 0 HSSflaa Screen X "AT 40.2 SXZS 285.492 PXXSLS · FCfiT" Geneva ", 7th COLCR 0.0.0k OFF fields nuraber.O,?, Z, R, aWTCi, 3, ISSCRI? TOR, aCFRSQ, RFEiC, RATIO, I FOUND R '' X 'sasai X «73 O ΕΞΑΟΙΝ3" Serien X * AT 40,2 SXZS 286,492 PIXSLS FONT "HeXvetiea *, 255 CCLOR 07' Claceaof unteiown funetian: 'sas X' ΡΕ 0 KSADXNG" Serein X "AT 40 , 2 SIZS 286,492 PL.OLS FONT "Genev a, 7 COLOR 0.0.0, cauldron OFF fiald! nunber, O,?, Z, R.aiTRY, S, DESCRIPT0R, BGFREQ, RraiD, RAT20, I FOR R «'U' MCASc OO" Tea print.prg "SET FR2NT OFP. ssr osvxcz το sasai

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LOOP a®oo 74 • judged (single), version 11-25-34eless datafcaseeS2T rauc CT? ssr print ο ?? ' SST ZSCACT HEATCL2AR - STOR2 · TO Xcbjeet STORS '' TO Dobject STORS 0 TO NUnS. STORS OTO ZOffSTORS 1 TO Ballco wkxls, T. »Program. i Northern {aiagle). fiat • Daca .... i 8 / 8/94 • Version.! .PoxSASS + Zííac, 'revisicn 1.10 • Noees. : 'Drilling in Northern (aiagle) • · · · SCR2SN 1 TY? 2 0 HEADQJG' Screer. 1 'AT 40,2 SXZS 235,432 PXX2L3 PCNT' Genava · 12 COLOK '0,0,09 PZXSLS 15,81 TO 46,397 SITIÉ 28447 COLOR 0,0, -1, -25600, -1, -19 PESLS 39,79 TO 132,422 STYL3 28447 COLOR 0,0,0, -25600, -1, -13 POC25S 115,98 SAY 'Sitry t,' STYLS. 55536 POINT 'Oeva', 12 COLOS 0,0,0, -1, -1, -18 PDSL3 115,173 G3T Bobject STYLS 0 TORT · ΰβι <ν »·, 12 SI23 15,142 COIL 0,0,0, -1, - 1, -1 8 PIX2LS 145,39 SAY 'Deacription' STYL2 65536 TCOT 'CanavaMl COLOR 0,0,0, -1, -1, -1 · 9 PXJffitS 145,173 C3T Dobject STYLS 0 FCNT' Geaeva ', 12 3122 15,241 COLOK 0.0.0, -1, -1, -1 9 PI5ELS 35.39 SAY 'Siagla Jlortbera aeareh aereea * STYL3 65536 PCNT' Genarva '', 274 COLOK 0.0, -9 PIXSLS 220,162 GBT Ball STYLZ 65536 FONT ' Chicago *, 12 PICTOR2 * 8 * R Contiaia; Bail out * SIZ29 PIXSLS 175,98 SAY 'Clone': · STYLS 55536 POINT 'G «n« vn'., 12 COLOK 0,0,0, -1, -1, -1 8 'PIXSLS 175,173 GET Nbmb STYLS 0 MAJOR 'Genava'.U S2ZZ 15,70 COLOK 0,0,0.-l-1', - l8 PIXZLS 80,152 SAY * & xcer usy CNS o £ tba follow * STYLS 65536 POINT * Geneva ', 12 CCLCR - 1 ', ».

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COFY TO 'Hits.dbf * FOr Entry »AE READY * lSEX SAPETY CN 76 * MASTER ANALYSIS 3, VERSION 12-3-94 * Master menu forum analysis output

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SST DEVTCE TO SCREEN SST DEFAULT TO “SmartGuy: FoxSASE + / Mac: fox files lOutput program * ÜSS * SmartGuy: Fox3ASS-i- / Mac: fox files: Clonas.dbf"

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STOP 0 ΤΟ ENTIRE

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STORS 0 TO PTF CO WHILE .T. * Program: Mastar analysis.fimt * Date ....: 12 / 9/94 * Version .: FoxBASE * / Mac, Revision 1.10 * Nctas ....: Form Fiber Master Analysis SCREEN 1 ΤΥΡΕ 0 HEADING 'Screen 1 'AT 40,2 SIZE 236,492 PIXELS FONT * Genenza *, 9 COLOR 0,0,0,9 PIXELS 39,255 TO 277,430 STYLS 23447 COLOR 0,0, -1, -25600, -1.-13 PIXELS 75,120 TO 173,241 STYLS 3871 COLOR 0.0, -1, -25600, -1, -1

9 PIXELS 27.93 SAY Customized Output Menu * STYLS 65536 FCS7T * Genevn *, 274 COLOR 0.0, -1, -1, -19 PIXELS 45.54 GET conden STYLE 65536 FONT 'Chicago ·, 12 PICTORE · β * Ο Condensed · SIZE9 PIXELS 54,261 GET anal STYLS 65536 FONT 'Chicago *, 12 PICTORE * 9 * RV Sort / number; Sort / eatxyi3 PIXELS 117,126 GET EMATOH STYLE 65536 FONT' Chicago ·, 12 PICTOPE * 3 * C Exact · SIZE 15,52 CO 9 PIXELS 135,126 GET HMATOH STYLS 65536 FONT 'Chicago ·. 12 PICTORS · β · Ο HOROLOGOLOGY · SIZE 15,1 a'PXXELS 153,125 GET OMATOH STYLS 55536 FONT 'Chicago * .12 PICTORS * 9 »C Ottoer Spc * SI23 15,84 for PIXELS 90,152 SAY' Matehes: · STYLE 65536 FONT ' Geneva ', 268 COLOR 0.0, -1, -1, -1.-13 PIXELS 63.54 GET PRINTON STYLE 65536 FONT' Chicago *, 12 PICTORS * 9 * C List of Inolude clona * β PIXELS 171,126 GET Imacch STYLS 65535 FONT 'Chicago *, 12 PICTORE * »* C Incyte * SIZE 15,65 COP PIXELS 252,146 GET iaitiafee STYLE 0 FONT' Geneva ', 12 SIZE'15,70 COLOR 0,0, -1, -1, -1, - 19 PIXELS 270,145 GET terminate STYLE 0 FONT * Geneva *, 12 SIZE 15,70 COLOR 0,0, -1, -1.-1, -1a PIXELS 234,134 SAY * Inelude clones · STYLS 65536 FONT * Geneva *, 12 COLOR 0 , 0, -1, -1, -1, -1a PIXELS 270,125 · SAY * -> * STYLS 65536 FOrT -Ger.eya ', 14 COLOR 0.0, -1, -1, -1, -1 3 PIXELS 193,126 GET PTF STYLE 65535 FONT 'Chicago *, 12 PICTORS * 9 * C Priat 60 Fiber * SIZE 15,9a PIXELS 189,0 TO 2S7.120 STYLE 3371' COLOR 0,0, -1,6600, -1, - 1 3 PIXELS 209.3 SAY 'Library Preference * STYLE 65536 FONT * Geneva *, 2S6 COLOR 0.0, γΐ, -1, -1, -1a PIXELS 227.18 GET ENTIRE STYLE 65536 'FONT • Chicago *, 12 PICTORE * e * RV All; 3elected * SIZE 16 »* EOF: Master analysis.fmt

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09E TEMPDESIQ

IF Ena: ch-0 .AND .. Hmateh = 0 .AND. Qsatck »O .AND. IMATCH «O

APPEND FROM TEMPLIB ENDI? IF Etatch »APPEND FRCM TEMPLIB FOOT 0 = · 2 'E © X? IF Hnatch-1 APPEND FROM 7EMPLI3 FORUM D * 'H' ENDI? IF Cteatchsl APPEND FROM TEMPLIB FOUND D = 'O' ENDI? IF Isnaceh »l APPEND FROM TEMPLIB FOUND» 'I' .OR.D · 'X' .OR.D-'N '

ENDIF IF Xsratchnl APPEND FROM TEMPLX3 FORUM D-'X '

ENDIF

COÜNT TO ANALTOT set talk off

CO CASE 78

CASE PT? = OSET D5VIC3 TO PR INTSET PRDIT PTEJECT CASE PTFsl SET ALTERNATE TO 'Total Functional eort.txf

• SET ALTERNATE TO • SET ALTERNATE TO • SET ALTERNATE TO • SET ALTERNATE TO • SET ALTERNATE TO SETTING ALTERNATE TO SET ALTERNATE ONENDCAS3> »··> <·· • H and 0 list item.txt * • Shear Stress HUVEC 2: Aburdar.ee sort.txt "" Shear Stress HUVEC 2: Abur.cance con.txf • Shear Stress HUVEC 2: Functicn sort.txt * • Shear Stress HUVEC 2: DY3 attribute sort.txt "• Shear stress HUVEC l ; C2on · lisc.txfShear Stress HUVEC 2: Locatícn sort.txt "I? PRINTON = 1 81.30 SAY * Databas ± Subsst Analysis * STYLS 6Ξ335 FCNT * Ganeva, 274 COLOR 0.0.0,

ENDIF 7 7 s

7 dateO ?? 1 1 7? ΊΊΜΒΟ 7 'Clona-r.umbers 1 7? STR (INITIATE, 5.0) 7? · Through 1 7? STR (TERMINAT3, 5,0) 7 'Libraries: * IP ENTIRE = 1 7' All Libraries

ENDIF IP ENTIRE = 2MARK-1DO WH-LE, T.

I? MARK> stopień

SXIT

ENDIF USE SELECTEDGO MARX7 1 1 7? TRIM (lirname)

STOR3 MARX + 1 TO MARKLCOP

endo

EOIF? 'Desionaeions:' IP Bnateh = 0 .AND. Hmatch = 0 .AND. Ctoatch = 0 .AND. IXATCH-0 77 'All'

ENDIF IP Smatchsl 77 'Exaot,'

ENDIF IF Hmatch-1 77 'Human, · D3DIF' IF Qmatchal 77 'Other sp.'

ENDIF IF Imatchwl 7? 'Incyte'

SNDIF IF Xrratch = 77 'EST' 1, -1 · · · · - 79 -

ENDIF I? CONDEN-1? 'Condensed forma analysie'

ENDIF IF ANALS? 'Sorted by NUMBER'

ENDIF IF ANAL »2? 'Sorted by ENTRY'

ENDIF X? ANALs3? 'Arranged by ASONDANCE'

ENDIF I? ANAL.4? 'Sort by INTEREST'

ENDIF I? ANAL = 5? 'Arranged by LOCATION' ENDIF 'IF ANAL-5? 'Arrangad by DISTRI3OTI0N'

ENDIF IF ANAL-7? 'Arranged by' JNCTICN '

ENDIF? 'Total clone9 representeds' ?? STR (STARTOT, 6.0)? 'Total clonss analyzeds' ?? STR (ANALTOT, 6, O?? 1 = library d = designaticn f = distributicn z = location r c)?

»It ♦ ♦» *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** H H H H H H H H

USE TEMPQE3IG SCREEN 1 ΤΥΡΕ 0 KEADING 'Screen 1' AT 40,2 SIZE 236,492 PIXELS FONT 'Genarva' ',? COLOS 0,0,0, DO CASECAS3 ANAL = 1 * variety / number

SET HEADING ON IF CONDEN-1

SORT ΤΟ TENP1 CN ENTRY, NUH3ER DO 'CCMPRESSION number.PRG ·

EX .SE

SORT EX TEXPl CN NUMBER USE ΓΕ4Ρ1

Cold off fields number, L, D, F, Z, R, C, ENTRY, S, D3SCRIPT0R

• list off fieláa number, L, D, F, Z, R, C, ENTRY, S, DSSCRIPTOR, LENCTH, RFEND, INIT, I

CLEAR DATA3ASES

ERASS TENP1.D3F

ENDIF CASE ANAL »2

* aort / DESCRIPTOR

SET HEADING ON • SORT ΤΟ TEMP1 CN DS3CRIPT0R, ENTRY, NUM3ER / S forum D »'S' .OR.D-'H '.OR.O'O', OR.D« 'X' .OR.D »' I '• SORT ΤΟ ΤΞΜΡ1 CN ENTRY, DESCRI? TOR, NÜMSER / S Forum Da Έ'.CR.D-Ή' '. OR.D-'0'.OR.D »' X'.OR.D« ' ORT'SORT ΤΟ ΤΞΜΡ1 ON ENTRY, START / S Forum D = Έ'.OR, D = 'K1.03.0 »' 0'.CR.D = 'X'.OR.D = 11' IF CCNDSN on EO ' COMPRESSION entry.PSG '

ELSE

USE TH4PX

list off fiald »nuiaber, L, D,?, Z, R, C, ENTRY, S.DESCRI? TOS, S« 3ra, RJEND, INXT, ICLOSS DATABASESERASE TE4P1.D8FENDIF 80 CASS ANAL-3 * line by abundance

SET HEADINO CN SORT 4 TE4P1 ON ENTRY, NUM3ER forum D- * E '.OR.D =' H '.OR.D-'O' .OR.D-'X '.OR.D-'I1 00' CCmfression abundance.?rg· CASS ANAL-4 * sort / ir.terest

SET HEADOR3 ON IF CONDEN-1 SOFT ΤΟ TEMP1 ON EOTRY.NUMBER FORM I> 0 DO 'CQMPR2SSICN interest. PRO *

ELSE SORT ON I / D.EOTRY ΤΟ TEMF1 FORM I> 1 USB MILK1

list c: f fields numher, L, D, F, Z.R, C, EOTRY, S, DESCRIPTOR, LENGTE, RFEND, INIT, I

CLOSS DATA3ASSS

E? ASS TE-ÍF1.DSF

ENDIF CAS2 ANAL-5 * arrange / lccacicn

SET HSADING CN

ST0R3 4 το AMPLIFIER? 'Nuclear:' SORT ON ENIRY, NDJ2ER FIELDS RFEND, NUH3SR, L, D, F, Z, R, C, ENTRY, S, DESCRIPIOR, LENOTH, NIGHT, I, COMMENIF CCNDEN DO 'Ccrpression location.prg'

ELSE DO • Norma 1 candy 1 *

ENDIF? 'Cytcplasmic: 1 SORT CN EOTRY, NÜM3ER FIELDS RFEND, NUHBER, L, D, F, Z, R, C, 2NTRY, S, DESCRIPTOR, LENOTH, INIT, I, C0MMQ1IF CCNDEN DO' Ccrcpreaaion location.prg ·

ELSE DO 'Normal sourdough 1 * ENDIF _? 'Cytbakeleton:' SORT CN ENTRY, NUNBER FIELDS RFEND, NUH3ER. L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, LENGTE, ΙΝΙΤ, Ι, COMMENIF CCWEN-1 DO 'Ccnpression location.prg *

ELSE DO 'Normal Sourdough 1'

ENDIF? 'Cell suríace:' SORT CN ENTRY.NUKSSR FIELDS SFEND, NUMSER, L, D, F, Z, R, C, SMIRY, S, DESCRIPTOR.LENSTK, INlT.I.COttOIF CCNDEN DO 'Conpression location.prg ·

ELSE DO 'Normal Relais 1'

ENDIF? 'Intracellular membrane: 1 SORT CN EOTRY, NÍMMERER FIELDS RFEND, NUM3ER, L, D, F, Z, R, C, E2ZrRY, S, DESCRIPTOR, LENOTH. ΣΝΙΤ, Σ, COMXENIF CCNDEN DO 'Compression location.prg ·

ELSE DO 'Normal Sourdough 1'

ENDIF? 'Mitochondrial:' SORT CN ENFRY, NUKBER FIELDS RFSND.NUMBER, L, D, F, 2, R, C.EOTRY, S, DESCRIPTOR, LENOTH, ΣΝΙΤ, I. CCMMENIF CCNDENal DO · 0αηρτε8β1οη location.prg *

ELSE DO 'Normal Sourdough 1'

ENDIF 81 7 'Secrets:' SORT CN ENTRY, NUKBER FIELDS RFSND, NGM3ER, L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, FLIGHT, INIT, I, CCMMENI? CONDEN = 1 DO "Compresaion location.prg"

SLSE DO "Normal subroutine 1 'SNDIP7 1Otheri' SORT ON ENTRY, NUMBER FIELDS RFEND, NUMBER, L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, LENGTH, 2OT, I, C» CCNDEN = 1 DO "Compresaion location.prg ·

SLSE DO "Normal subroutina 1 ·

ENDIF? 'UnJciown: 1 SORT CN ENTRY, NUMBER FIELDS RFEND, NUMBER, L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, LENGTE, INIT, I.CCMOII? CCJNDENsl DO “Ccmprassion location.prg”

ELSE DO "Normal aubroutine 1 *

ENDIF IF CONE5N = i

SET DEVICE. TO PRINTER

SET PRINTER ON

EJ3CT DO "Output heading.prg * USS" Ana-lysis location.dbf DO "Create bargraph.prg '

SET -HEADING OFF? 'FUNCTIONAL CLOSE TOTAL UNIQUE NEW% TOTAL1?

LIST OFF FIELDS Ζ, CLONES, GQJES, NEW, PERCENT, GRAPH

CLOSE DATA2ASES

ERASS TEMP2.DBF

SET HEADINQ ON • USE SmartGuy: FoxSASS * / Mac: fox files: TEMPMASTER.dbf

ENDIF

CASE ANAL «S * TAMAGA / DISTRIBUTION

SET HEADING ON

STORS 3 TO AMPLIFIER? 'Cell / tiaue epecific diatribution:' SORT CM ENTRY, NUMBER FIELDS RFEND, NUMBER, L, D, F, Z, R, C, ENTRY, 3, DESCRIPTOR, LEYGTH, INIT.I.CCMMENIF CCNDEN · 1 DO 'Ccmpression dianrib .prg "

ELSE DO "Normal · ubroutija 1"

ENDIF 7 'Non-specific distribucíons' SCRT CN ENTRY, NUMBER FIELDS RFEND, NUMBER, L, D, F, Z, R, C, RNTRY, S, DESCRIPTOR, FLIGHT, INIT, LCOMMBK1F CCNDEM.l DO "Compresaion distrib.prg "

ELSE DO "Normal subroutina 1 *

ENDIF 7 'Unkncwn Diatribution: 1 SORT CN ENTRY, NUMBER FIELDS RFEND, NUMBER, L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, LENGTH, INIT, I.MEMMIP CCNDEN DO "Catpresaion distrib.prg"

ELSE DO "Normal subroucine 1"

ENDIF IF CCNDEN ·!

SET DEVICE TO PRINTER

SET PRINTER CN 82

SJECT DO 'Output neading.prg · US3 "Analysis distribution.dbí' DO 'Create bargraph.prg'

HEAD HEADING OFF? 1 FUNCTIONAL CLOSE TOTAL UNIQUS% TOTAL '7

LIST OFF FIELDS P, NAME, CLQNES, GENES, FERCENT, GRA? H

CLOSE DATASASES

IRR TEMF2.DBF

SYT HEADING QN 'USE' SmartGuy: FoxSASE + / Mac: fox files: Ta <PMÁSTER.dbf "

ENDIF CASS ANAL = 7 * arrange / function

SÍT SEADING ON

STORE 10 TO AMPLIFIER? 'BUJDOSS PRDTEINS' 7? 'Surface no.ecules and recipe:' SORT ON Et.TRY, NUM3ER FIELDS RFSND.NUMBZR, L, D, F, Z, R.C, ENTRY, S, DESCRIPTOR. LENGTE, INIT, I, COMMENIF CONDENL DO 'Compression function.prg ·

ELSE DO 'No subroutine 1'

ENDIF? 'Calcium-binding proteins: 1 SORT QN SNTRY, NUMBSR FIELDS RFND, NMM3ER, L, D,?, Z, R, C, ENTRY, S, DESCRIPTOR, LE'? IH, INIT, I, CWSGNIF CONDEN DO ' Ccrrprassion function.prg ·

ELSE DO 'Mór eubroutine 1 *

ENDIF? 'Ligands and effector' SORT IS EOTRY, NUM3ER FIELDS RySMD, NÜMBER, L, D,?, Z, R, C, ENTRY, S, DESCRIFTCR, LYGTH, INIT, I, CC «MENIF CCWEN-1 DO 'Compression function. · prg

ELSE DO 'Normal subroutine 1 *

ENDIF 7 'Othar binding processes ·' SORT CN ENTRY.NUMSER FIELDS RF2ND, NUMBER, L, D, P, Z, R, C, ENTRY, S, DESCRIPTOR, ΙΒϋΙΗ, INIT, I.COHKSNIF CONDEN.l DO 'Compression function.prg "

ELSE DO 'Normal subroutine 1 ·

ENDIF

* EJECT? 'ONCCGSNES' 9 7 'General cncogenesi' SORT ON ENTRY.NLMSER FIELDS RFEND, NUMBER, L, D, F, Z, R, C, ENTRY, S, DESCRIFT0R, LEM3TH, INIT, ΙIF CONDEN.l DO "Compression fun.ction.prg "

ELSE DO 'Normal subroutine 1 *

ENDIF 7 'GTP binding proteins 1 SORT ON ENTRY.NMBER FIELDS HFEND, NUM3ER, L, D, F, Z, R, C, ENTRY.S, DESCRIPTOR, LENGTE, INIT, I. CCMHENIF CONDEN.l DO "Compresion function .prg "

ELSE DO 'Normal subroutine 1 *

ENDIF 7 'Viral Elements' 83 SORT ENTRY.NUNSER FIELDS RFEND, NUR3ER, L, D, F, Z, R, C, ENTRY. 3. DESCRIPTOR, LENGTE, IMIT.Ι, ΟΟΜΜΕΝIF CCNDEN = 1 CO 'CaKgresaion ftncsion.prg "

ELSE co "Normal subroutine 1"

ENDIF? 'Kinases and Phosphatases:' SORT ON ENTRY, NUM3ER FIELDS RFEND, NUMSSR, L, D, F, Z, R, C, ENTRY, 3, DESCRIPTOR, LQCTH, INIT, I, CCMO? IP CONDSNal CO 'Compression function.prg "

ELSE CO "Normal aubroutile 1"

ENDIF? 1Tumor-related antigen 'SORT ON ENTRY, NÜM3ER FIELDS RFEND.NQGSR, L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, LENGTH, DTCT, I, CCfcMENIF CONDEN = 1 DO "Compression function.prg"

ELSE CO "Normal subroutine 1"

ENDIF

"EJECT?" PROTEIN SWTHETIC HACHIN2RY PROTEIN? "Transcription and Nucleic Acid-Birding Protein3: 'SORT ON ENTRY, NUMBER FIELDS RFSND, NUHSER, L, D, F, Z, R, C, 3TTRY, S, DESCRIPTOR, LENGTH, ΙΝΙΤ, Ι, ΟΟΗΈΝXF CCNDEN = 1 DO "Con ^ reasion function.prg '

ELSE DO "Normal subroutine 1"

ENDIF? 'Transition:' SORT ON ENTRY, NUMBER FIELDS RFEND, NCKBER, L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, LENC-TK, INIT, I, CCMMZNIF CONDEN-1 DO "Compreaaion function.prg "

ELSE DO "Normal subroutine 1"

ENDIF? 'Riboscmal proteins:' SORT ON ENTRY, NUMBER FIELDS RFEND, NUMSER, L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, L2NGTH, D.1T, I, CCMKENIF CONDEN-1 DO "Compression function. PRG *

ELSE DO "Normal subroutine 1 *

ENDIF? 'Protein processing:' SORT CN ENTRY, NUMBER FIELDS RFEND, NUMBER, L, D, F, 2, R, C, ENTRY, S, DESCRIPTOR, LENGTH, IMIT, I.COMMEUIF CONDEN-1 DO "Compression function.prg" .

ELSE CO "Normal subroutine 1 *

ENDIF

'EJECT? 'ENZYMES'? 'Ferroprotein' SORT IS ENTRY, NUMBER FIELDS RFEND, NUMBER, L, D, F, Z. R, C, ENTRY, S, DESCRIPTOR, LENGTH, HOT, I, COfrfrG5lIF CONDEN-1 DO "Compression function.prg"

El .SS DO 'Normal subroutine 1'

ENDIF? 'Proteases and Inhibitors:' SORT ON ENTRY, NUMBER FIELDS RFEND, NLMEER, L. D, F. Z, R, C, ENTRY, S. DESCRIPTOR, LENGIK, DOT, I, CCMKENI? CONDSNsl DO "Compression function.prg"

ELSE 84 DO 'NojkwX subroutine 1'

S®IF? 'Oxidativa pho3prorylatic: 1 SORT ON ENTRY.NDMBER FIELDS RFEND, NIMMER (L, DFZ-RC, ENTRY, S, DESCRIPTOR, LENGTH, INIT.I.CCWMENIF CCNDEN = 1 CO "Conpre33ion function.prg" ELS3 CO "Normal subroutine 1 * EDDI? 'Radius metabolites' SORT ON ZNTRY, NUM3ER FI2LDS RFEO, NÜM3ER, I, D,?, Z, R, C, ENTRY, 3, DSSCRIPTOR, LDK3TH, INTJ7I, COHMENIF CQNDEN-1 DO "Compression function.prg "ELS2 DO" Normal subroutine 1 "Edo? Amino Acid Metabolic: 'SORT ON ENTRY, NUS-BSR FIELDS RFEND.NTOQHR, L, D, F, Z, R, C, ENTRY, S, DESCRIFTOR, LENGTH, DIT, I.COMINE CCNEEN-1 DO "Compression function.prg"

EDSE DO "Normal subroutine 1" 3 © t? ? 'Nucleic acid metabolism:' SORT ON 2N7RY, NÜMB2R FI2LDS RFSND, NUWSSR, L.0, F, Z, R, C, 2NTRY, S, DSSCRIPT0R <IEYGTH <INIT, I, CCWMEN17 CONDEN = 1 DO "Ccmpresaion fur.ccion .prg "

ELSE DO "Normal subroutine 1"

ENDIF? 'Lipid Metabolite:' SORT ON ENTRY.NLHEER FIELDS RF3iD, NBHSER, L, D, F, Z, RrC, ENTRY, S, DESCRIPTOR, LENGTK, INIT, I, CCMMENIF CONDEN-1 DO "Ccnpression function.prg"

ELSE DO 'Normal subroutine 1 "

ENDIF? "Other enzymes" SORT ON ENTRY.NMMBER FIELDS RFaO, M »SER, L.D, F, Z, R, C, SNraY, S, DBSCSIPTOR, lSN5ra, IMIT, I, CC» jENIF CONCEN »! DO "Conpreasion function.prg"

SLSE DO "Normal subroutine 1"

ENDIF

* EJECT? 'MISCELLANECUS CATEGORIES'? ? 'Stress'responae:' SORT CN EOTRY.NUMSER FIELDS FFND, NCM3ER, L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, LENGTH, INIT, I, CCWMENI? CONDSNal DO "Compression funcCion.prg"

ELSE DO "Normal subroutine 1"

ENDIF? 'STRUCTURAL;' SORT QN 2NTRY, NUMB £ R FIELDS RFEND, NCMB £ R, L, 0, F, Z, R, C, ENTRY, S, DESCRIPTOR, LE> CTH, ΙΝΓΓ, I, COMMENIF CCNDENal DO "Compression function.prg" ' sLse DO "Normal subroutine 1"

2NDIF? 'Other clonea.' 'SORT ON INTRY.NUMBER FIELDS RFEND, NUMBER, L, D,?, Z, R, C, ENTRY, S, DESCRIPTOR, LENGTH, ΙΝΓΓ, I, COMMENIF CONDENxl DO "Compression function.prg"

ELSE 85 DO · Νοπγλ1 subroutine 1 *

ENDIF? 'Clor.e9 of unknown fuaceians' SORT ON ENTRY, NUM3ER FIELDS FFEND, NUK3ER, L, D,?, Z, R, C. SNTRY, S, DESCRIPTOR.LSNQra, SUT. I, COMMSIF CQNDEN = 1 DO 'Ccmpreeaion fur.cticn.prg *

ELSB DO 'Normal subroutijia 1'

ENDIF IF CCNDEN = * 1

SJECT

• SST DSVICS TO PRINTER

• SET PRINT CN DO 'Output heaöir.g.prg' »** ETSS 'Ar.aly8ia fuRCtion.dbf' DO 'Create bargraph.prg'

SST K2ADDJ3 OFF SCRESN 1 TYPS 0 HEADING 'Screen 1' AT 40,2 SIZZ 236,492 PIXSLS FONT 'Ganeva', 12 COLOR 0,0,0

? · DOTOT TOTAL NSW DIST? 1 FUNCTICNAL CLASS CLONES GENES GENSS FUNDTICNAL CIASS

• LIST OFF FIELDS Ρ, CLONES, GSNES, NEW, PERCENT, ORA? H, CCH? ANYLIST OFF FIELDS Ρ, CLONES, G-NES, NEW, PERCENT, GRAPKCLOSE DATASASES

ERASE TEMP2.D3F

SST HEADING ON 'USE' SiraxtGüy: FQxSASE + / Macifox files: TEKPMASTER. <Jbí *

ENDIF CASE ANAL »8 DO“ Subgrcup suntnary 3.prg '

ENDCASS DO "Test print.prg ·

SET PRINT OFF

SET DZVICE TO SCREEN

CLOSE DATASASES

• ERASS TEMRLXB.DBF

• ERASE TEMPNÜM.DBF

♦ ERASE TEMPDESIG.DBF

* ERASE SELECTED. DfiP

CLEAR

LCCP

ENEDO 86

* COMPRESSION SU3R0OTINE FÓR ANALYTICAL FRCGRAMS USE ΤΞΚΡ1

COUNT TO LOT RSPLACE ALL RFEND WITH 1 MARXI = 1 SW2-0

CO WHILS SW2 »0 ROLLIF MARXI» »TOOTACX

COÜNT TO UNIQUZ COUOT TO NSWGENES FOUND »Ή'.OR.D · '0' SW2 = 1 LOO?

ENDIF

GO MARXI DUP s 1

STORS EOTRY TO TESTA SW - 0

00 WHILS SW = O TEST

SOT

STORS ENTRY ΤΟ ΤΞ3Τ3IT TESTA a TSSTBDSLETEDU? »DÜP-rlLOOP SOI? GO MARXI.

RSPLACE RFEND WITH OOP

MARXI «MARXI-r DUP SWal

LOOP

ENDDO TEST

LOOP

SNDDO ROLL • GO TO? STORS Z TO LOC 'HSS' Analytical Locat'ion.dbf '

LOCATE FORM Z-LOC

RSPLACE CLONES WITH TOT

RSPLACE GENES WITH UNIQUE

RSPLACE NEW WITH NEWGSNSS SEX TEHP1 SORT ON RFEND '/ D ΤΟ TEMP2 USE ΤΠ4Ρ2 ?? STR (UNIQUE, 5.0) 11 1 genea, forum for the total of 1 ?? STR (TOT, 5.0) ?? '.clonea * 1 1 V Coír.cider.ce' liat off flelda ηυπύοβΓ, ΒΡΏΟΛ, Ρ, Ζ, Η, ς, δ, ΟΕδΟΚΙΡΤΟΧ, ΐΛίΧΠΚ, ΙίΠΤ, Ι

* 3ST PRINT OFF

CLOSE DATA3ASES

ERASS TSMPl.DBF

ERASS T54P2.DBF

USE TEMPDESIG 87 * COMPRSSSION SUBROUTINS FORM ANALYSIS PRCGRAM5 ÜSS TEMP1

COUOT TO LAKE REPLACE ALL RFEND WITH 1 MARXI »1 SW2-0

DO WHILE SW2 = 0 ROLLI? MARXI> = TOOTACK

COUOT TO UNIQUE SW2-1

LOOP

ENDIF

00 MARXI DUP = 1

STORE EOTRY TO TESTA SW - 0

DO WHILE SW = O TEST SKI?

STORE EOTRY TO TESTBIF TESTA = TESTBDELETEDUP = · DU7 + 1LOOP

• ENDIF

GO MARXI

REPLACE RFEND WTTH DUP MARKI = MARXl + Dü? SW = 1

LOOP

ENDDO TEST

LOOP

ENDDO ROLL

'BRCWSE

* SET PRINTER ON SORT ON DATE ΤΟ TEMP2 USE TEMP2 ?? STRCUNIQUE, 4.0)? 7 1 genes, forum for the tocal of '7? STR (TCT, 4.0) 7? * clones? ? 'V Coíncidence' COUOT ΤΟ P4 FOUND 1-4 IF P4> 0 7 STR (P4,3,0) ?? 'genee with priority - 4 (Secondary analysis)' cf o ff field nuwber, RF2 ^, L, D, F, 2, R, C, a7IHY, S, DESCRIPTOR, LENGTH, INIT forum 1-47 themselves? COUOT ΤΟ? 3 FOUND 1-3 I? P3> 0? STR (P3,3,0) ?? 1 genes with priority = 3 (Full Ínsert sequence :) 'list off field number, RFEND, L, D, F, Z, R, C, EOTRY, S, DESCRIPTOR, L3ÍCTH, IMIT forum I »3 □

ENDIF COUOT ΤΟ? 2 FORM 1 = 2. IF P2> 0? STR (? 2,3,0) 77 'genes with priority = 2 (Priwary analysis eonplete)' s list of field nurober, FFEND, L, D, F, Z, R, C, EOTRY, S, DESCRIPTOR, LENGTH, INIT Forum 1-2?

ENDIF COUOT TO PL FOUND 1-1 IF Pl> 0 88? STR (P1,3, O) ?? 'genea wíth priorityíty = 1 (Primary analysis naededi)'

^ EW.L / D.PzZ ^ .C.SNTRY.S.DEaCRI-'TOR.LSNG'rH.IhlT Forum I «1ENDIF • SST PRIOT 0F7

CLOSS DATABASES

EHASS TEMP1.DBF

EHASS TSMP2.DSF USE 'SnartGuy: Fox3AS2f / Mac: fox files idonss.dbf · 89 »COMPEESSICN SU3R0UTINE FORUM ANALYSIS PROGRAM3

USE TEMPI

COUNT TO LAKE REPLACS ALL RFEND WITH 1 MARKI »1 SW2-0

DO WEILE SW2 = 0 ROLLXF MARKI> = TOO

COUNT TO UNIQUE SW2 = 1

LOOP

ENDIF

GO MARKI DUP = 1

STOP ENTRY TO TESTA SW = 0

DO WHILE SW-0 TEST sxip

STORZ ENTRY TO TESTEIF TEST »FOR TESTS TE DUP» DUP + 1

LCOP

ENDIF

GO MARKI REPLACS RFEND WITH DU?

MARKI = MARXl-DUP SW = 1

LOOP

ENDDO TEST

LOOP

ENDDO ROLL

• BROWSE

• SZT PRINTER IS SORT ON NUMBER ΤΟ ΤΞΜΡ2 USE ΤΠ4Ρ2 ?? STR (UNXQU3,4,0) ?? 'genes, the forum of the total of ?? STR (TOT, 5.0) ?? 1 clonea '? 'V Coincidence'

dirt off field niasber, RFIND, L, D, F, Z, R, C, ZNIRY, S, DESCRI? TOR, LENGTH, INIT, I

♦ SET PRUUST OFF

CLOSE DATABASE

ERASE TEMP1.DBF

ERASE TEMP2.DBF USE 'S [nartGuy: FoxBASET / Mac: grass files: slones.dbf' 90

* CCMPRES3XON SU3ROCTIN3 FORWARD AHALYSIS FROGRAMS USS TEMP1

COCNT TO TOWN RSPLACE ALL, RFEXTD WITH 1 MARKI - 1 SW2 = 0

EO WHILE SW2 = 0 ROLLIF MARKI> - TOWER

CCUNT TO UNIQUS COUNX TO NtcGSOIL FORM Ds'H · .OR.Efe'O'sw2 «:

LCOP

ENDIF

GO MARKI DUP · 1 STORE ENTRY ΤΟ ΤΕΞΤΑ sw> ö

EO WHILE SW = 0 TEST skip

STCRE ENTRY TO TSST3IF TSSTA = TSST3DELETED (JP = DUP + 1LOOP

ENDIF

GO MARKI

REPLACE FFEND WITH DOT MARKI - KARK1 + CU? SW »1

LCOP

ENDDO TEST

LCOP

ENDDO ROLL

GO TOP

STORE R TO FUNC USS 'Analysis xuoction.dbf'

LOCATE FORM P ^ FUNC

RSPLACE CLONES WITH TOUR

REFLACE GENSS WITH UNIQÜS PSPLACE NEW WITH ΝΞΜ3ΕΝΕ3 SS ÜSS TEUPL SORT CN RFSND / D ΤΟ ΤΞΜΡ2 CSS TEMP2

SST HEADIN3 CN ?? STR (ULNQCS, 5.0) ?? 'G «n« s, far the total of 1 ?? STRCTOT, 5.0) 17 'clcnee' »♦ *? 1 V Coineiáence '

li «t off field cuaber, RFSND, L, D,?, Z, R, C, ENTRY, S, DESCRX? TOR, LSNGTH, IMIT, I Ftt

'SCRESN 1 ΤΥΡΕ 0 ΗΞΑΠΙΝ0' Screen 1 'AT 40.2 SI2E 286.492 PIXELS FONT' Geneva'.iS COLOR 0.0, st cauliflower field RFEND, S, DESCRI? TOR

* SET PRUsT OFF

CL0S2 DATABASES

ERASB TEKP1.EEF

EAAS2 TSM72.D3F

ÜSS TSMPOESXG 91

* CCMFHESSICN SU3RCOTINE FOS ANALYSIS FOR PROFESSIONAL USB T24P1

COUNT TO LAKE REPLACE ALL RFEND WITH 1 KASKI = 1

DO WHILE SW2 = 0 ROLLIF MARXI> »TOTPACX

COUNT TO UNIQUE SW2 = 1

LCOP

ENDIF

GO MARKI DUS - 1

STCRE ENTRY TO TESTA SW - 0

DO WHILE SW = 0 TEST

SKIP

STCRS ENTRY TO TESTBIF TEST TEST TEST DOT = DOT + 1

LOOP

ENDIF

C-0 MARKI

REPLACE SFEND WITH DOT

KARKI = MARK1 + DUP EW = 1

LOOP

ENDDO TEST

LOOP

ENDDO ROLL GO TO?

STCRE F TO DIST USE "Analysis Distraction.dbf ·

LCCATE FORUM »» DIST

REPLACE CLCNES WITH TOUR

REPLACE GENES WITH UNIQUE USE TEMP1 «art is. rfend / d to TENP2 USE TENP2 ?? STRtUNIQUE, 5.0) 7? 'genex, forum for total 1 77 STR (T0T, 5,0) 77' clccee '7' V Cóincidens: · '

liat of fielda numbex, RFEND, L, D, F, E, R, C, ENTRY, S.DESCRIFTOR.LENOTH, IMIT, I

* SET PRIOT OFF

CLOSE DATA3ASES

ERASE TEMP1.DBF

ERASS TEMP2.DBF

USB TENPEESIG 92 'CCMPRESSION SUBROUT ^ AUS ANALYSIS FROGRAM3 USB TSMP1

COUNT TO LAKE RSPLACS ALL RFEND WITH 1 MARKI »1 SW2-0

DO WHILE SW2 = 0 ROLLIF MARKI> - TOWER

COUNT TO UNIQUE SW2 = 1

LOOP ENDI?

GO MARKI DUP - 1

STORE ENTRY TO TESTA SW - 0

DO WHILE SWAO TEST SKI?

STOP EOTRY TO TEST3IF TESTA = TEST3LELETE DUP »DUP + 1

LOOP

ENDIF

GO MARKI

REPLACE -RFEND WITH DUP

MARKI - MARK1 + EUP SW = 1

LOOP

ENDCY TEST

LOOP ENDDO ROLL 'GO TO? USB ΤΈΜΡ1 7? STR (UNICUS, 5.0) 77 1 genes. forum for the total of '?? STR (TOT, 5.0) ?? 'clonea'? 'V Coincidenc ·'

cauldron Off fields nurrber <RFEND (L, D <F, Z.R.C.Ei7r3Y, StDESCKIPTOR, LEN3TH, INIT (I

* SET PRINT OFF

CLCSE DATA3ASES ERASE TEMPl.DB?

USB TEMPTESIG 93

'COMPRESSICN SU3R0UTINE POR ANALYSIS PROGRAMS, STNARÍCUY: Fox5ASS * ZMac:: ox filet: Clonas.dfc £' copy το tempi forum 'H'.CR.Da'N' .OR.D = 'R' .OR.De'A 'DELETS FÓR Ds'N1 .OR.Ds'O · .OR.Ds'A' .O? .. D = 'U' .CR.D = 'S' .OR.Ds'M '.OR.Da'R' .OR.D = 'V'

PacXon

COUOT TO TÓS

REPLACS ALL RFEND WITH I MARXI »1 SH2 = 0 DO WHILE SW2sQ ROLL ·

X? MARXI> = TOOTACK

THOUGHT TO YOURSELF SW2 »1

LOOP

SNDIF

GO MARXI DUP 1

STORE Q3TRY TO TESTA SW - 0

DO WHILE SW «C TEST

SKXP STORE ENTRY TO TESTBXF TEST TESTBDELETS '

DUP «DUP + 1LOOP salts?

GO MARXI

REPLACS RFEND WXTH DUP

MARXI s MARXI-DUP SWsl

LOOP

ENDDO TEST

LOOP

ENDDO ROLL

'BROWSE

♦ SET PRINTER QN SORT ON RFENDZD, NUM3ER ΤΟ TEXP2 USE TEMP2

REPLACS ALL START WXTH RFENDZXDCÍ ^ 'ICOOO ?? STRCUNIQUE, 5.0) ?? 'he is the total o £' 77 STR (TOT, S, 0) 77 'resident'? 'Coíacidence V V Clor.ea / 10000' set heading o ££

SCRSEN 1 ΤΥΡΕ 0 HSADING 'Sirene 1' AT 40,2 SXZE 286,452 PIXELS FONT 'Geneva', 7 COLOR 0,0,0, Cauliflower, RFEXD, START, L, D, F, Z, R, C, ENTRY, S, DESCRIPTOR, INIT, I'SET PRXNT OFF

CLOSE DATABASES

ERASE TEMP1.DSF

ERASE TEMP2.DBF USE 'SnartGuy: FoxBASEt / Mac: fox ilileazclonea.dbf "94

* CCMPRESSIONS SUBR0UTIN3 FORM ANALYSIS FOR USE TE4P1 COUOT TO IDGENE FOUND D = '3' .OR.D = 'O' .ΟΕ.Ο = · Η '.OR.C =' N '.OR.D =' R ·. OR.D = 'A * DELETE FORM D =' N '.OR.O =' D '.OR.D =' A '.OR.D =' U'.OR.D-'S '.OR-D -'M * .OR.Cte'R '.OR.D =' V '

CCUNT TO LAKE REPLACE ALL RFEND WITH 1 MARKI »1 SW2 = 0

DO WHILE SW2 = 0 ROLLIF MARXI> ”TOO

COUNT TO UNIQUE SW2 = 1

LOOP

EMDIF GO MARKIOU? - 1 STORE EILTRY TO TESTASW - 0 00 WHILE SW = O TESTSKX? STORE ENTRY TO TESTBIF TESTA = TESTBDELSTEDUP - DUP + 1LOOP ·

ENDIF

GO MARXI REPLACE RF2ND WITH DUPMARKI - MARXIrDUPSW «1

LOOP

ENDDO TESTLOOP

ENDOO ROLL ♦ BROWSE

♦ SET PRINTER ON SORT CN RFEND / D, NUMB £ R ΤΟ2USE TSMP2 REPLACE ALL START WITH RFEND / IDGENS * 10000 ?? STR (UNIQUS, 5.0) 7? 1 g «n ·», the tocal of '77 STR (TOT, S, 0), 77 * cyanéi? 'Coincidenee V V Clone9 / 10000' aet heading

SCRESN 1 ΤΥΡΕ 0 HEADING 'Screea 1' AT 40.2 SI2S 286.492 PIXEL5 FONT 'Geneva', 7 COLOR 0.0.0, Field field number, RFEND, START, L, D, F, Ζ ^ Η, Ο, ENTRY , S, DE3CRIPT0R; INTT, I * SET PRIKT OFF CL0S3 0ATA3ASESERASE TEMPLE.DBFERASE ΤΞ4Ρ2.0ΒΡ ÜSS · 33ΆΓς <ϊυγ: Γοχ2Α5Ε · ι · / Μ4θíox · · sclcnes.dbf '95 USE ΤΞΜΡΙ

COCNT T LAKE ?? · Total of * ?? STR (TOT, 4.0) ?? 'Clonea'

It Ilit Off fields from DDer, L, D, F, 2, R, C, EWrRy, EESCRIPTOR, LENGTH, R? ZND, niXT, Xlist o ££ field number.L.D.F.Z.S.C.ENTOY.EESCRIPTORCLO3E DATABASES

ERASS-TSM? 1.DBF

USE TEMFDESIC 96 • Lifescan Menu; Version 8-7-94

CELL TALK OFF eet device to screen

CLEAR HSS · επ »ΓΕΰυγ: ΡοχΕΑ5Ε + / ΜΛσ: £ οχ files: clones.dbf '3TOFE LUFCATEO TO Cpdate

GO BGTTCM STORE RECNOO TO cloneno STORE 6 TO Chooser DO WHIL3 .T. * Program: Lifeseq menu.fmt * Date .... i 1/11/95 * Version .: Fcx £ ASE + / Mac, revision 1.10 * Notes ....: Form Fiii Lifeseq Menu «SCR2EN 1 ΤΥΡΕ 0 KEADIN3 - Screea 1 "AT 40.2 SIZE 286,492 PIXELS FONT" Geneva ", 268 COLOS 0,0,8 PIXEXS 18,126 TO 77,365 STYLE 28479 COLOR 32767, -25600, -1, -16223, -16721, -157238 PIXEXS 110,29 TO 188,217 STYLE 3371 COLOR 0.0, -1, -25600, -1, -1 8 PIXELS 45,161 SAY "LIFESE3 'STYLE 53335 FONT' Geneva ·, 536 COLOK 0.0, -1, -1,7135,58843 PIXELS 36,269 SAY ' IM "STYLE 65536 FONT · δβηβνβ ·, 12 COLOR 0.0, -1, -1,7135,58848 PIXELS 53,143 SAY 'Molecular Biology Desktop * SITTJE 65536 FONT' Helvetica ', 18 COLOR 0.0,0,8 PIXELS 90,252 TO 251,467 STYLE 23447 COLOR 0,0, -1, -25600, -1, -1 8 PIXEXS 117,270 GET Chooser STYLE 65535 FONT 'Chicago · 12 PICTCRS' 3 * RV Transcript profiles3 PIXEXS 135,128 SAY üpdate STYLE 0 FCWT 'Geneva' , 12 SI22 15,79 COLOR 0,0,0, -25600, -1, -1 '0 PIXELS 171,128 SAY cloneno STYLE 0 FCOT' Geneva ', 12 SIZ2 15,79 COLOR 0,0,0, -25600, - í, -18 PIXELS 13 5.44 SAY "Last lipdace: * STYLE 65536 FOOT 'Geneva', 12 CCLOR 0.0, -1, -1, -1, -18 PIXELS 171.44 SAY 'Total Clones: · STYLE 65536 FONT ', 12 CCLOR 0.0, -1, -1, -1, -13 PIXELS 45,296 SAY' vl.30 'STYLE 55536 FONT' Geneva *, 782 COLOR 0.0, -1, -1, -1, - 1 * * EOF: Lifeseq menu. fix

REXD

DO CASE CASE Chocserel DO 'SmaxtCuy: FcxSASE + Mac: £ ox filea: Output program »: Master analysis 3, prg · CASE Chceser» Fox files-.Output program »: Subtracticn 2.prg "CASE Chcser» 3 DO, SmartGuy: FoxSASE + / Mac: fox filas: Output programi: Northern (aingle) .prg * CASE Chocser »4 USS 'Libraries.dbf'

3RCWSE CASE Chcosex »5 DO 'SmartGuy: FoxEASE + / Mac: fox filletsOutput programsiSee individual clone.prg'

CASE CHOCCOS DO · StuartGuy: FcxBASE + / Mac: £ ex filesiLibraries Output programs: Menu.pxg · CASE Chocser = 7

CLEAR

SCREEN 1 OFF

two hours of

ENDCASE

LOOP

ENDDO 97 ¢ 1.30 ΞΑΥ "Database Subset Analyzer * STYLE 5553« FONT "Geneva", 274 COLOR 0.0,0,777 t 7 dateC) ?? '1

77 TIM2O 7 'Clone r.umbers' ?? STR (3NIT1ATE, 6, O) ?? 'through' ?? STR (TERMINATE, 6.0) 7 'Libraries:' IF ENTIRE '! 7 'All libraries'

ENDIF

IF ENTIRE = 2MASXslDO WKILE .1.if mark> sto? ItEXITENDIF US2 SELECTEDGO MARK7 '' 7 7 TRXMdibnetc ·)

STORS HAMUI TO MARKLOOP

ENDDO

ENDIF 7 'Designation ‘IF JbiatchsO .AND. Kmeteh = 0 .AND. Qnatch = 0 7? 'All'

ENDIF i? Enacch «l 7? 'Exact.'

ENDIF IF E.T.atch-1 7? 'Huioan,'

ENDIF I? Ómateh »1 7? 'Other «p.'

ENDIF IF CONDEN-7 'Condeased fomat analysis'

ENDIF IF ANAL? · 'Sorted by NUM3ER'

ENDIF IF ANAL-2 7 'Sorted by ENTRY'

ENDIF IF ANAL-3? 'Arranged by A3VNDANCS'

ENDIF I? ANAL «4? 'Sorted by INTEREST'

ENDIF IF ANAL-5 7 'Arranged by LOCATICN'

ENDIF IF ANAL-5 7 'Arranged by DISTRIBUTICN'

ENDIF IF ANAL-7 7 'Arrangad by FUNCTICN' 1, -1, -1 98

ENDTF? 'Total clones repreaented: 17? STR (STARTOT, 6.0)? 'Total clonea analyzedi' 7? STR (ANALYTIC, 6.0) 7 7 99 USE TEMP1

COÜNT T LAKE ?? 'Total of ?? STR (TCT, 4.0) ?? 'clone3 ·?

'lsd off fields tunÜ3er.L, D, F, Z, R, C, Ek7raY, DESCRI?' ICR, La'C7H, RrZ} O, INIT, Il, ofi fialds number, L, D,?, Z, R, C, EOTRY, DESCRIFTORCLOSE EATABA3ES ERASE TEMP1.D3?

USE TEMRDESIG - 100 - ÜSS TSHP1

COUNT TO HOME ?? 'Total of · ?? 9TR (TOT, 4.0) ?? 'Addresses'. 7

Off list off fielQ3 W-UTÓee, L, D, P, 2, R, CrE ^ JTRY.DSSCRIPTORdaCTH, HFETTO, INIT, Ilit off fields n-.atoer ^ toD.FjZ.RiC.aJTRy.DSSCRXPTORCLOSE DA.TA2ASSS S3ASE TDí ? l.DB?

USE TEMPOESIG - 101 - * Northern (single), version 11-25-94 close databases SET TALX OF? SET PRIOT OF? SET EXACT 0 ??

CLEAR STORE '' TO EOBJECT STORE '' TO Dobject STORE 0 TO Nvurib STORE 0 TO Zog STORE 1 TO Bail DO WHILE .T. * Program: Northern (sir.gle). tin * Date ....: 8 / 3/94 * Version:: Fox3ASS + / Mac, revision 1.10 * Notes ....: Form fiié Northern (single, »SCREEN 1 ΤΥΡΕ 0 HSADING 'Screen 1 * AT 40, 2 SIZE 286,492 PIXELS FONT 'Geneva', 12 COLOR 0,09 PIXELS 15,31 TO 45,397 STIL 28447 COLOR 0,0, -1, -25600, -1, -19 PIXELS 89,79 TO 192,422 STYLS 28447 COLOR 0 , 0.0, -25600, -1, -13 PIXELS 115.98 3AY "Entry #: 'STYLS 65536 FONT' Geneva ', 12 COLOR 0.0,0, -1, -1, -13 PIXELS 115,173 GET Eobject STYLS 0 FONT 'Ger.eva', 12 SIZ3 15,142 COLOR 0,0,0, -1, -1, -13 PIXELS 145,89 5AY "Description" STYLS 65536 FONT "Geneva", 12 COLOR 0,0,0, -1, -1, -13 PIXELS 145,173 GET Dobject STYLE 0 FONT 'Geneva', 12 SIZ3 15,241 COLOR 0,0,0, -1, -1, -13 PIXELS 35,89 SAY 'Single Northern search screen' STYLS 65536 FONT 'G «neva», 274 COLOR 0.0, -3 PIXELS 220,162 GET Bail STYLE 65536 FONT' Chicago *, 12 PICTOKS '3 * R Cont; Ball out * SIZS3 PIXELS- 175,99 SAY' Clone STYLS 65536 FONT 'Geneva'; 12 COLOR 0.0,0, -1, -1, -19 PIXELS 175,173 GST NUJtb STYL S 0 FONT 'Genev &amp;', 12 SIZE 15,70 COLOR 0,0,0, -1, -1, -19 PIXELS 80,152 SAY 'Enter ar.y ONE of the following:' STYLE 65536 FONT 'Geneya', 12 COLOR -1

V «EOF: Northern (single, fint

READ I? Baila »2

CLEAR screen 1 off

R3TORN

ENDIF USE 'StrartGuy: FoxSASS + / Mac: Fox files: Lookup.dbf'

SET TALK'CN IF Eobjecto '' STORE UPPER (Eobject, to Eobject

SET SAFETY OFF SORT CN Entry TO ”Lookup entry.dbf ·

SET SAFETY ON USE 'Lookup entry.dbf' LCCATE FORWARD Look-Eobject

IF .NOT.FCUNDO

CLEAR

LOOP

ENDIF

ERCWSE STORE Entry TO Searehyal

CLCSS DATASASES ERAS2 “Lookup-entry.dbf”

ENDIF IF Drop ecto '1

SET EXACT OFF

SET SAFETY CFF SORT ON descriptor TO 'Lookup descriptor. dbf 'SET SAFETY ON USE' Lookup descriptor .dbf · LOCATE FORUM UPFER (TR1M (descriptor)) = Ü ?? SR (TRIM (Dobject))

IF .NOT.FCUNDO

CLEAR - 102 -

LCQS

ENDIF

BROWSB STORE Er.try TO Searchval

CLOSE DATABASES ERASE "Lookcp descriptor.dbf ·

SET EXACT CN

ENDIF

I? itafeoO USE · Επ «.ΐΧ0υγ:? οχΒΑΕΞ + / Μ3θ:? οχ phils: cloneg.dbf GO Ncmb

BROWSB STORE Entry TO Searchval

ENDIF

CLEAR? 'Northern aralyaia fcr egyry' ?? Searchval o? 'Enter Y co proceed' watt το o: <

CLEAR IP UPP2R (0K) o'Y · acreen 1 off

RETURN ENDIF * * CCMPRESSION SUBRYPER LIBRARY Library, dbf? 'Ccuspreasing the Librariea fiié now, ..'

USE ‘SrartGuy:? Cx3ASE + / Maci? Cx files: librarias.dbfSET SAFETY OFF SORT ON library TO‘ Corpreaaed libraries.dbf · * FORUM entered> 0

SET SAFSTY ON USE "Compresaed libraries .dbf · D3LETE POR er.tered-0

PACX COUNT TO KNOW · MARXI «1 SW2» 0

DO WHILE SW2sO ROLLIP MARKI> «TÓTPACX SW2» 1

LOOP

ENDIF

GO MARKI

STORE library TO TESTA

SKtP

STORE Library TO TESTB

I? TEST TESTB

DELETE

ENDIF MARXI »MARX1 + 1

LOOP

ENDDO ROLL * Northern analyaie

CLEAR? 'Doing the northem now ...'

SET TALK CN

SSS "SsiartGuy: Fax3ASE * / Mac: Fox styles: clor.ea .dbf * SET SAFETY OFF COPY TO 'Hits.dbf FOOT entry» search

SET SAFETY ON - 103

CLOSE DATABASES SELECT 1 USB 'Ccnpressed libraries.dbf * STCRE KSCCOuoto TO Eatriea SELECT 2 USB * Hit «.dbf»

Brand EO WHILE .T. SELECT 1 IF MarJofiatries

EXIT

ENDLF

C-O MARK STORS library TO Jigger SELECT 2 COUNT TO TOOLS library® Jigger SELECT 1 R5PLACS slow with Zog

MarkaMark + 1

LOOP

ENDDO SELECT 1 3RCWSE FIELDS LIBRARY, LISNAME, ENTERED, HITS AT 0.0

CL2AR? 'Filter Y to priat:'

WATT TO PRINSET IF U? 7SR «'7'

SET PRINT CN

CLEAR EJECT · SCREEN 1 ΤΥΡΕ 0 QUALITY 'Sered 1' AT 40,2 SIZE 285,492 PIXELS FONT 'Geceva', 14 COLOR 0,0,0? 'DATABASE ENTRI2S MATCHING ENTRY' ?? Search

? DATEO 7 SCREEN 1 ΤΥΡΕ 0 KEADING 'Sered 1' AT 40,2 SIZE 236,492 PIXELS FONT 'Geneva', 7 COLOR 0,0,0, LIST OFF FIELDS library, libnarce, entered, hit SELECT 2 LIST OFF FIELDS NUMBER, LIBRARY, D, S, F, Z, R, FI, TRY, DESCRIPTOR, R? START, START.RFENDSET TALX OFFSET FRINT OFFQCI7

CLOSE DATABASES

SET TALX OFF

CLEAR DO 'Test priat, prg *

RETORN - 104 - Table 6 library ΑΟΞΝΙΝ0Ο1 STEERING01 ADR = NOT01 AML3NOT01 6MARNOT01 SWARNOTC8 CARONOT01 CHAONOT01 CORNNOTD1 FIERAQT01 FI0RAGTC2 F1BRANT01 FI3SNGT01

FtSnNQTO

FISRNOTOI F.aRNcrraa HWC1NOT01 ENVIRONMENTAL HUVENO0O1 SURFACE WINDOW HYFONC801 KIDNNOT01 UVRNOT01 LUNGNOT01 MUSCNOT01 OVIONO0O1

PAMCNOTOI

PmjNORO, for a long time

MARKET SJNTNOT02 SPLNFET01 SPLNNOTO2

STCMNOTOI SYNORABO1 T3LYNOTD1

TESTNOTO THP1NO0O1 THP1PEBO1 THP1PLBO1 U937NOT01 libnameInftamed adenoidAdrenal gland (r) Adrenal gland (T) AML blast eaUs (T) Eone mafnj **

Sone míww (T) Cardlac musele (T) CWn. hamster ovaryComeal stromaFibroblaat, AT 5Fibroblast, AT 30Fibroblast. ATFibroblast, uv 5FlbfOblast. uv 30FibroblastFibroblast. NormalOther Prefix HMC-1HUVEC IFN.TNF.LPSHUVEC eontrolHUVEC ski stearHypolhalamuaKIdnay (T)

Liver (T)

Lung (T)

Skaletal mutde (T) OMduct

Psncreaj, NormalPliuilary (r)

Pllullary (T)

Placenta fimall inteatine (T) Spleenrliver, latoiSpleen (T)

SlomachRheum. synovtumT + B lympfioblastTest »» (T) THP-1 eontrolTHP phorbotTHP-1 phorbot LPSU937, monocytlc leuk number library d I 1 zren 11 y detorlptor rfatea rt flarid 3304 U937NOT01 EHC c T HUMEF1B Elongatlon lador 1-beta 0 · 0 773 3240 EHC c T HUMEFtB Elongation (actor 1-beta 0 370 773 3359 HMC1NOT01 EHC c T HUMEF1B Elongatone lactor 1-beta 0 371 773 • U93 HMC1NOT01 EHCCT HUMEF1B Elongation lactor 1-beta 0 470 773 3359 HMC1NOT01 EHC c T beta 0 327 773 9139 HMC1NOT01 EHC c T HUMEF1B Elongau (actor i-bata 0 375 773

Claims (4)

PATIENT INDIVIDUAL POINTS Figure 1 P96 02047 DISCLOSURE COPIES A method for testing a sample of gene transcripts comprising (i) preparing a first mRNA mixture; (ii) producing cDNA copies of the mRNA; (iii) isolating a representative population of clones transfected with cDNA to produce a first library of biological sequences; (iv) generating a first set of gene transcripts which, in a first set, each of the first gene transcripts characterizes another biological sequence of the first library; (v) processing the first gene transcripts with a programmed computer (in which a database of reference transcript sequences referring to reference biological sequences is stored) to generate a first identified sequence value for each first gene transcript, wherein each identified sequence value is characteristic of sequence annotation and the first gene transcripts; the degree of fit between at least one of the reference transcript sequences; and (vi) processing all identified sequence values to generate first final data values indicating the number of times each first identified sequence value is present in the first library of biological sequences. 2. The method of claim 1, wherein the first library of biological sequences is a library comprising at least 5000 sequences derived from human cell lines. Figure 2 Ρ96 02047 DISCLOSURE COPIES 2 / Figure 3 DISCLOSURE COPIES P96 02047 4/4 Sequence evaluation and database annotation 3/4 Sequence Library Identified sequences 3. The method of claim 1, further comprising (vii) preparing a second mixture of mRNAs from a second human cell line; (viii) producing cDNA copies of the mRNA; (ix) isolating a representative population of clones transfected with cDNA and producing a second library of biological sequences; (x) generating a second set of gene transcripts, the second set of each of the second gene transcripts characterizing another biological sequence of the second library; (xi) processing the first gene transcripts with a programmed computer (in which a database of reference transcript sequences referring to reference biological sequences is stored) to generate a second identified sequence value for each second gene transcript, wherein each second identified sequence value is characterized by sequence annotation; the degree of fit between one of the gene transcripts and at least one of the reference transcript sequences; and (xii) processing all identified sequence values so that the second final data values are ge • ··· ·· · «·« · · · · · ···· ·· «« · · 107 indicate the number of times each second identified sequence value is present in the second library of biological sequences. 4. The method of claim 3, further comprising (xiii) processing the first final data values of step (vi) and the second final data values of step (xii) to obtain ratios of gene transcripts, each of which is the differences in the number of gene transcripts present in the first mRNA mixture and the second mRNA mixture. 5. The method of claim 3, further comprising: (xiv) extracting the first final data values of step (vi) from the second final data values of step (xii) so as to obtain a series of differences; and the first mRNA mixture is obtained from a healthy human and the second mRNA mixture from a sick person. 6. The method of claim 1, wherein the cDNA copies are prepared using random primers. A method for determining the relative frequency of mRNA in a biological sample, comprising (i) isolating a population of mRNA transcripts from the biological sample; (ii) identifying, by a sequence-specific method, the genes that have been transcribed by mRNA transcription; · · · · · · · · · · · · · · · (Iii) determine the number of mRNA transcripts corresponding to each gene, and (iv) the number of mRNA transcripts used to determine the relative frequency of mRNA transcripts within the mRNA transcript population. 8. The method of claim 7, further comprising (v) generating a gene transcript image based on the relative frequency data of the mRNAs within the mRNA transcript population; and (vi) comparing the transcript of step (v) with a biological sample from a biological patient and / or a gene transcript from a normal biological sample. 9. A method for producing a gene transcript image comprising: (i) preparing a mRNA mixture from a biological sample; (ii) producing cDNA copies of the mRNA; (iii) inserting the cDNA into a suitable vector, transfecting the cells of the appropriate host strain with the vector, and growing clones, each clone representing a unique mRNA; (iv) isolating a representative population of at least 5000 recombinant clones; (v) identifying cDNAs amplified from each clone in the population for which sequence specific · · ···· Method 109 is used to identify the gene from which the individual mRNA has been transcribed; (vi) determining the number of times each gene is represented in the clone population (which refers to the relative frequency); and (vii) listing the genes with their relative frequency in order of frequency, to obtain a gene transcript image. The method of claim 9, further comprising (viii) comparing the gene transcript images of step (v) with a sample of a gene derived from a patient and / or a normal biological sample. Agent: DANUBIA Ρ96 02047 DISCLOSURE COPIES collaborator cDNA szoloáltató Number Number Name Name Title Title Phone Phone Fax E-mail i project I Seit-szolqáltató cDNA construct Number Number Name r λ Prep # # Title Treatment relationship Könyvtárkód Phone Management name provider Fax T-Identification Type -1- v-p: j Used mRNA l l Γ Vector I Primer directions Biological source Culture mRNA Preparation View Tum Delivery date Number Number Number Catalog Number # Fabric Source Culture Price Organ Sample Number Sample Number Date of arrival gender PO Date cloning Disease * - Date * Expiration - Primary size Species Cell Density Quantity Background Species Quantity Weight Un amplified titer Pathology Minutes Minutes Amplified titer Disease condition Treatment RNA Extraction host Strain Source Notes mRNA extraction genotypes Sample number -% extraction actin PO amendments Amplified titer Notes the gel Notes Notes Figure 4