JP4477894B2 - Genetic analysis system - Google Patents

Description

  The present invention relates to a system for identifying individual differences at the gene level for tailor-made medicine using a computer. In particular, by expressing expression experiment data on the genome well, it is possible to easily grasp the expression status of neighboring genes on the chromosome, and specifically to detect genes that can be related candidates with strong individual differences. It relates to a gene analysis system that can be used.

There are various methods for identifying individual differences at the genetic level for tailor-made medicine. Using SNPs of candidate genes related to diseases, drug sensitivity, etc., genotypes are obtained for each patient, and patients are grouped based on the obtained data. At the same time, various cell movements in the same patient group are obtained by expression experiments and associated with each genotype and phenotype.

In particular, an expression experiment is analyzed in order to identify a gene that actually moves by an experiment using expression experiment data. Specifically, genes are clustered using gene expression patterns, and genes having specific expression patterns are identified.

FIG. 18 shows the processing status of conventionally used expression analysis software. You can cluster the genes used in the expression experiment, but you do not know where the clustered genes are on the genome. For example, it is not possible here to see whether genes that are clustered and have similar expression patterns are close to each other on the genome or separated from each other.

Therefore, even if the gene group can be identified by the above expression analysis software, the present situation is that if you do not search the genome database each time, you will not know the location of these genes on the chromosome and the comprehensive information related to the genes. .

FIG. 19 is an example in which a gene group used in an expression experiment is searched using a genome database, and the position where the target gene exists on the chromosome can be confirmed. The part enclosed with ○ is the searched gene, and there are 6 places on all chromosomes. However, exhaustive information (annotations) around the gene is further followed by links and extracted.

In the conventional gene analysis method described above, after searching for expression patterns and clustering by pattern using expression analysis software, it is necessary to separately perform a comprehensive search of gene locations and gene information. It was very time consuming.

Even if the relationship between the positions of genes scattered on the genome and the expression pattern is automatically determined and the results can be listed as numerical values, the information is overlooked only by the numerical information.

Therefore, even if a complete database of expression information and genome information is completed, it is impossible to proceed with research on gene function determination and relevance to diseases, therapeutic drugs, etc. only by computer automatic judgment. The process of determining the function of a gene while viewing and confirming it is essential.

For example, a relationship between a gene position and an expression pattern may be displayed graphically in a format including comprehensive gene information, thereby finding a connection with a biological phenomenon that cannot be found by a computer.

The present invention was created to solve the above-described problems, and collectively extracts gene groups identified by expression analysis, genomic positions of those genes, and comprehensive information related to the genes. A gene analysis system with a graphic display interface that makes it easy for researchers to discover relationships with various categories such as gene function, diseases, and therapeutic drugs, making full use of their intuition and knowledge It is intended to provide.

In order to achieve the above object, the invention according to claim 1 is a gene analysis system that reads gene information and expression data obtained in an expression experiment and synthesizes them with internal data. In the process of reading the data into the internal database, for each probe used in the expression experiment, the genome position specifying means for specifying the gene used by each probe and the position of this gene on the genome are specified by the genome position specifying means. A registration means for storing data by associating position information and information on genes, a first selection means for selecting a range of genes to be browsed on a chromosome, and a gene range selected by the first selection means Extraction means for extracting a first gene region in which expression data exists in the registration means; and a gene range selected by the first selection means The first display means for displaying the annotations, and the positional relationship between the gene range in which the annotations are displayed by the first display means and the first gene regions are compared on a gene basis based on the data of the registration means. Second display means for displaying together with expression data, second selection means for selecting a second gene region from within the first gene region based on the display on the second display means, and the registration Data is obtained from the means, each gene of the second gene region is arranged in a column or row, and the experimental conditions of the expression experiment are arranged in a row or column, and information on the expression value for each experimental condition of each gene A gene analysis system comprising a third display means for displaying .

The invention according to claim 2 is the gene analysis system according to claim 1, wherein the genome position specifying means uses any key of Locus ID, gene name, gene symbol, UniGene ID, nm ID, and Accession ID. External data search means for searching genome position information from external information published by using the first internal data for searching internal data using an Accession ID key when data cannot be detected by the external data search means Nucleic acid or amino acid sequence if no data can be detected by the search means and any of the above means, or if any key of Locus ID, gene name, gene symbol, UniGene ID, nm ID, Accession ID cannot be obtained A gene analysis system comprising: second internal data search means for searching internal data based on information.

The invention according to claim 3 is the gene analysis system according to claim 1 or 2 , wherein the second display means includes a gene range in which an annotation is displayed and the first gene region. It is a gene analysis system characterized by contrasting the positional relationship by coloring each.

According to the present invention, various annotations on chromosomes required for genome research and expression experiment data can be referred to at the same time, so it is possible to search for characteristic gene expression patterns and genomes around the searched genes. It is possible to refer to information comprehensively.

In addition, the positional relationship between the gene region of the annotation display and the gene region related to the expression data can be displayed by comparing them in gene units, or by coloring, the phenomenon can be visually appealed, and the intuition and knowledge of researchers can be expressed. You can make full use of it.

  Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 shows a flowchart for registering and displaying information on genes used in the expression experiment of the present invention.

The gene expression level is data on the amount of mRNA produced based on the DNA base sequence of the region where the gene is encoded, and the gene expression level changes when the gene performs its function. And in order to analyze the function of a gene based on the expression level of this gene, the expression level is detected by experiment in each facility.

Expression data obtained in these expression experiments is accumulated in an external database or the like, for example, as shown in FIG. Experiment category such as cell name, sample, drug, disease, patient, etc., management ID for each facility, Locus ID corresponding to each gene probe set (Gene Symbol in the figure), Accession ID used for each gene probe set ( It consists of the ID) assigned to each entry sequence used in nucleic acid and amino acid sequence databases, and expression values such as Fold_change, P-Value, and Ratio. Although not shown, there are cases where data such as genetic names, gene symbols, nucleic acids or amino acid sequences are registered. Locus ID is used by NCBI (National Biotechnology Center, USA).

Data is imported from such an external database into the gene analysis system of the present invention. The items necessary for reading the expression data are the expression value and a gene for specifying the gene for each expression value. Name, gene symbol, Accession ID, Locus ID, nucleic acid or amino acid sequence.

FIG. 3 schematically shows the process of importing these data into the internal database on the gene analysis system. When described with reference to the flowchart of FIG. 1, first, the data as shown in FIG. The stored external database 1 is read into the internal database 2 on the gene analysis system of the present invention (S1). Next, for each probe set, the gene on the genome used for each probe set and the position of the gene on the chromosome are specified (S2) and stored in the internal database 2 (S3). The data stored in the internal database is merged with other required genome information and distributed by the browser 3 via the Internet or an intranet network (S4).

Various necessary table structures are prepared in the internal database 2, and FIG. 4 shows a table structure for taking in the expression data and registering each information. It has a relational database structure, and a table for each chromosome number (chr (n) _expData), a table of experimental categories (Category) such as cells, samples, drugs, diseases, and patients, and the location of the gene on the chromosome. It consists of a Locus ID and its position information and a correspondence table (geneAcc) of the accession ID of the probe set.

Where category_name is the category name, locus is the Locus ID, gsymbol is the gene symbol or gene name, txStart is the start position of the chromosome, txEnd is the end position of the chromosome, chrom is the chromosome number, accession is the Accession ID, category_id is the ID of the Category table , Gaid is the geneAcc table ID, fchange is fold_change (expression value), dfchange is the expression value, pvalue is the p-value (expression value), dpvalue is the expression value, and tcourse is the time course.

Further, the internal database 2 has not only the table of FIG. 4 but also a table structure as shown in FIG. The table shown in FIG. 5 stores data mined by taking in data from public information or public databases in advance. Each annotation table in the figure means an annotation table with Accession ID. EstHuman table, mouse, rat, bostaurus, susscrofa, etc. EST, mrna table, cds table, unigene table, sts table, snp table, user's own It consists of an annotation table.

In addition, each accession table stores peripheral information on genes unique to the various tables. Table 1 is created in each of these annotation tables, and each annotation table and a table including an Accession ID (Table 1) are linked by annot_id (annotation table ID) of Table 1. Table 1 is composed mainly of the elements shown in FIG. 5, where name is the accession ID, chrom is the chromosome number, chromStart is the chromosome start position, and chromEnd is the chromosome end position.

Next, the process performed in step S2 of FIG. 1 when external data is taken into the internal database 2 will be described in detail. When importing the expression data and gene list corresponding to each probe set used in the expression experiment into the internal database 2, it is necessary to specify the gene name and the position on the chromosome from the input data, and the processing was shown. This is shown in FIG.

The key for specifying the location on the chromosome uses the Locus Link ID, Accession ID, gene name, gene symbol, UnGene ID, nm ID, nucleic acid, amino acid sequence, etc. in the input data. The column in which data of these items is stored or in which field of the database is stored in advance in the internal database, and the process of FIG. 6 is advanced automatically or manually.

First, Locus Link ID, Accession ID, gene name, gene symbol, UnGene ID, nm ID, nucleic acid sequence, or amino acid sequence data are extracted from the search key field (T1). Any one of them can be searched. UniGene is part of the public genome database managed by NCBI and is the ID of the EST sequence managed for each gene. Nm ID is also the database ID of mRNA managed by NCBI, and Locus ID is a database of various databases such as UniGene, mRNA, literature, and disease related to the location information of chromosomes managed by NCBI. This is the database ID in which the link information is listed.

If the key (Locus Link ID, Accession ID, gene name, gene symbol, UnGene ID, nm ID) described in the Locus Link database can be extracted (T2YES), the Locus Link database or an equivalent external database If there is matching data (T3 YES), the position on the chromosome is identified from the Locus Link database or an equivalent external database (T4), and the position information is registered in the internal database table shown in FIG. (T5).

On the other hand, if no search key of Locus Link ID, Accession ID, gene name, gene symbol, UniGene ID, and nm ID exists at T2 (NO), proceed to T12, and at T3, the Locus Link database or equivalent external If no search key data exists in the database (NO), the process proceeds to T6.

At T6, it is confirmed whether Accession ID data exists in the search key extracted from the input data at T1, and if Accession ID data is present (T6 YES), it is stored in the internal database as shown in FIG. Search for the relevant data using the Accession ID data. If there is matching data as a result of the search (T7 YES), the position on the chromosome is specified from the information shown in FIG. 5 (T8), and the position information is registered in the table shown in FIG. 4 (T5).

On the other hand, if the Accession ID data does not exist (T6 NO), the process proceeds to T12. If the Accession ID data matching the internal database does not exist (T7 NO), the process proceeds to T9.

At T9, NCBI (Entrez system) is searched via the Internet using a search key other than Accession ID data to obtain a nucleic acid or amino acid sequence (T9), and the obtained sequence is linked to an internal database or internal database. The homology search is performed on the external amino acid database (T10), the result of the homology search is used to determine the position on the chromosome (T11), and it is registered in the table of FIG. 4 in the internal database (T5).

On the other hand, at T12, nucleic acid or amino acid sequence data in the input data at T1 is acquired, and homology search is performed on the internal database or an external amino acid database linked to the internal database using the acquired sequence (T10), Using the result of the homology search, the position on the chromosome is determined (T11) and registered in the table of FIG. 4 in the internal database (T5).

Next, the genome display unit of the gene analysis system of the present invention will be described.
The main screen 11 of the genome display unit shown in FIG. 7 includes a gene display unit 12 that displays genes on the genome, an annotation display unit 13 that displays other annotations, an expression data display unit 14 that displays expression data, and the like. ing. In this way, the necessary genome annotation and the gene subjected to expression analysis are displayed simultaneously. In the example of FIG. 7, chromosome information of the human genome is shown. 15 is a chromosome, 16 is a chromosome number, and currently the first chromosome is selected. Of the first chromosome, the annotation is displayed in the portion selected at 17. 18 is a gene display in which chromosome 15 is displayed more finely, and 19 is a state in which an annotation display portion is selected as in the case of 17.

Necessary annotations are displayed in the lower half of the main screen 11. The required annotations are selected from the items indicated by 32 on the selection screen as shown in FIG. 8, and the display methods are displayed in detail (Full) and coarse display (Dense) in 31 display method items. , Hide, etc. are selected, and the annotation color is determined by selecting 33 Colors and displayed. The portion 34 corresponds to the expression data portion. In this example, Expression, testexp1, and testexp2 are displayed as expression data in FIG. Expression, testexp1, and testexp2 are units of different experiments, and can be freely selected and added when registering expression data.

Usually, the relationship between the gene and the probe set is as shown in FIG. Of the gene 41 display, the thick part is an exon and the thin part is an intron. On the other hand, each probe of the probe set 42 is designed as a part of a gene as shown in the figure, and expression data is generated for each probe. Originally, the expression data is originally mapped at the same position as each of these probes. However, in order to appeal the phenomenon more visually, the annotation display is performed as indicated by the broken lines 20 and 21 in FIG. The positional relationship between the gene region and the gene region related to the expression data is expressed by comparing in gene units.

In addition, for the gene with the probe set used for the expression experiment when displaying, the same region as the gene region is displayed in the same color, or the average of the expression experiment value is converted into color and displayed, Furthermore, it can be made visually easy to understand. In the example of FIG. 7, the data obtained by the expression experiment with the gene 20 indicated by Genes is expressed by a single color (for example, red) designated by the Color item 33 in FIG. ing.

As described above, the probe set used in the gene is a part of the gene region, but it is easier to understand by looking at the gene unit. The details of the expression data are further expressed by the following method centering on such a main screen.

In the detailed display, the gene data in the range selected in FIG. 7 is displayed as a list as shown in FIG. The vertical axis 51 is a cell, a drug that stimulates the cell under experimental conditions, a sample, and other conditions, and a large number of experimental data is created in a series of experiments using the same gene. The horizontal axis represents the range of genes selected in FIG. 53 is the actual expression value, and the vertical axis is the result of multiple experiments under 51 experimental conditions such as the time course. When there are multiple probe sets in the gene with the same horizontal axis, the display order is displayed divided by the number of probe sets in the order of Accession ID or the order of genome positions within the selected gene range. is there.

Hereinafter, description will be given according to the flowchart of the detailed display method of FIG. First, a necessary position on a chromosome having a gene to be viewed on the main screen is selected (search may be used) (D1). Read the genomic position information of the selected range, obtain the corresponding gene from the table containing the expression data (Chr (n) _expData table created for each chromosome in Fig. 4 in the example) (D2), on the main screen In addition to the annotation specified in FIG. 8, the gene having the expression data specified in FIG. 8 is displayed according to the color display specification in FIG. 8 (D3). Again, the gene for which the details of the expression data are to be browsed is reselected (D4).

The reselected range information is further acquired, and the Accession ID of the sequence corresponding to the probe set and the gene including them are acquired from the table geneAcc table of FIG. 4 (D5). Similarly, information about expression values such as Accession IDs corresponding to each probe set and fold change and P value corresponding to them is acquired from the chr (n) _expData table (D6). The detailed display screen displays various experimental conditions such as columns for genes and rows for cells, drugs, samples, etc. as one block (part corresponding to 53 shown in FIG. 10) (D7). At this time, if a plurality of probe sets exist on the same gene, the horizontal axis of the block (the part corresponding to 53 shown in FIG. 10 is equally divided by the number of probe sets. The vertical axis in the block is This is displayed together with expression values such as fold change and P value in a fine experimental unit such as a time course (D8) A graph 54 shown in FIG.

By the way, registered genes and expression data can be easily understood by the above display method, but a data search method is also necessary. By performing a successful search together with genomic information, it is possible to comprehensively browse the relationship between the inductive expression of genes, the relationship between gene networks, and genomic information such as SNPs.

The feature of the search is that the expression data and genome annotation can be searched simultaneously. For example, by searching for data with a specific range of expression values, change rates of expression values in the time course, and patterns of changes, including conditions such as cells and drugs that were experimented with a gene containing a specific SNP is there. Conventionally, these search methods have been used individually, but genome information and expression information are searched at the same time, and by mapping and displaying them on the same screen on the same screen, it is possible to grasp the induction expression phenomenon that has been difficult to understand conventionally. Convenient for. In particular, it is meaningful that genes located in the vicinity of each chromosome are expressed with each other.

Examples are shown below. FIG. 12 shows an example of search condition setting. Reference numeral 61 denotes a project selection, which is a search target among the expression experiment projects. At 62, the expression data is narrowed down by the position, gene name, cell, etc. on the chromosome. In this example, it is referred to as a cell, but also includes experimental conditions such as drugs and samples in addition to cells.

Furthermore, it is more effective to search within a range of positions on the chromosome in order to confirm the induction of nearby genes. In particular, when the main gene is found, searching for data with a specific expression pattern or expression value within a certain range from that gene is the same as searching for expression experiment data as in the past. This is impossible if the genome database is searched separately.

63 is a search for expression data, and sets the upper and lower limits of expression values (data values used in expression analysis such as Fold_change, Ratio, P-Value). Next, at 64, a condition for searching for the change rate of the expression data value is set. For example, the upper limit and the lower limit of the change rate of the expression value are determined. Comparison of data is performed at a time course. In this example, the rate of change of the expression value is more effective when combined with the change pattern of the expression. 65 is a title for browsing the search result later because the search is executed in the background process because the search process takes time.

The expression data after the search is grouped by the following method. The searched expression data obtains the position of the genome, and confirms whether the expression data of the genes located before and after that match the search conditions. If there is a match, the expression data of the next located gene is also checked if it matches the search condition, and the process is repeated until no match is found. Since these expression data are expressed in the same region, they are grouped together. Similarly, a group is also created based on expression data in the same band region of the genome.

FIG. 13 shows a display example in which these grouped expression data are displayed in a list and are easily understood. Reference numerals 71 and 72 are samples of this example, which are examples collectively displayed in cell units. Each group or each expression data is linked 73 to display in the genome viewer, and the corresponding location can be displayed in the viewer so that confirmation of the induced expression of the gene is obvious at a glance, Annotations can also be confirmed.

Next, the effect of displaying the document data mining information will be described. The effect of displaying the expression information on the genome database was explained more effectively because the result of the actual experiment can be directly related to the genome information. However, it is true that information on genes is not only information on the results of actual experiments, but also on literature information that has already been studied worldwide, but the amount of information is extremely large. Due to the increasing number of people, it is almost impossible to read the literature one by one and collect information related to genes. However, there is much information that can be obtained from literature information, and in particular, researched results such as information more important than expression experiments, such as the relationship between genes and diseases, have already been reported. In particular, there have been reports on genes related to diseases, and as a result of expression experiments on the diseases, there were many expressions.

Therefore, instead of the expression information described in the present invention, the strength of the relevance to the gene calculated by using the literature information mining method disclosed in Japanese Patent Application Laid-Open No. 2003-44481 invented here is expressed here. This is a method of displaying by replacing with a value.

For example, even if the relationship between genes and diseases is considered, there are multiple diseases related to one gene. In other words, when expressed in terms of disease units, the ranking of the relevance of genes related to a specific disease can be obtained by the method of JP-A-2003-44481. When the strength of relevance is the strength of relevance, the value is regarded as the expression value of each gene, and expressed in the same way as in the present invention, how many genes are related to a specific disease or therapeutic unit The result of research is clear at a glance based on the genome.

In order to perform such display, genes related to each disease, compound, therapeutic agent, or both are ranked in advance, and a number indicating the strength of the association is regarded as an expression value and displayed as a map. As shown in the flowchart of FIG. 14, first, conditions such as a category corresponding to 51 of FIG. 10 are determined for diseases, compounds, and both diseases and compounds are common (K1). In the case of disease at the next treatment stage of K2, genes related to a specific disease are ranked.

When ranking is performed, a gene list related to a specific disease can be acquired as shown in FIG. The table in FIG. 15 shows a search for genes related to “Hypertension (hypertension)”. The Freq and Ratio values in FIG. 15 are regarded as expression values, normalized, position information of each gene is obtained, and the values are stored in an internal database. Here, Rank is a ranking of genes related to hypertension, Gene Symbol is a gene symbol, and Gene Name corresponds to the gene symbol and is a commonly used gene name. Freq is a rational fraction of a gene that appeared in the literature at the same time as Hypertension. The number of documents in which only Hypertension appears in the document and the Ratio are the ratios of the Freq values.

Next, display is performed in the same manner as described above. In the example of FIG. 16, a therapeutic agent is displayed in the category in order to obtain an association between a therapeutic agent related to hypertension and a gene. Reference numeral 81 denotes a therapeutic drug category, 82-related genes, and 83 a disease-related strength in color. Reference numeral 84 denotes an example in which Freq and Ratio are displayed as numerical values. In this example, the category indicated by the time course in the expression information is not used.

Moreover, the category is a disease, and it is more effective as a therapeutic drug. Further, it is also effective to display the result of the time series data display method shown in the method of JP-A-2003-44481 as shown in FIG.

As described above, various information can be mined by simultaneously displaying comprehensive information of genes and expression information. For example, considering the study of individual differences (drug sensitivity) of side effects when a therapeutic drug is administered, the patient group is divided into several categories according to the strength of drug sensitivity when a therapeutic drug is administered. Study the obtained cluster of genes and their relationship with individual genes, and identify polymorphic information such as SNPs that differ from individual genes that affect drug sensitivity. Using the identified polymorphism information, the drug sensitivity is examined in advance before the drug is administered to the patient, and treatment without side effects is performed.

In the course of this research, we used a group of patients divided into several categories to study the cluster of genes obtained in gene expression experiments and the relationship with individual genes, and to identify genes that affect drug sensitivity. In a part for specifying different polymorphism information for each individual, it is possible to display at a time polymorphic information classified as genes scattered around the genome, information on the periphery thereof, and expression experiment data.

As described above, displaying such information at a time makes it easy to identify the polymorphism classification and expression information, polymorphism information, genes, and surrounding conditions of the patient group with strong drug sensitivity described above. . Furthermore, the gene movement caused by the stimulation of drug administration is not caused by a single gene but a plurality of genes are simultaneously involved. Research efficiency is greatly improved because it is possible to search and display the state of induced expression caused by multiple genes simultaneously.

As described in the examples, by using this display method as a result display of document data mining, it is possible to find on a chromosome of a gene related to a specific disease, compound, or gene that has been difficult to find by a conventional search alone. You can also grasp the location information at the same time.

Therefore, the relationship between the strength of the gene related to the disease and the annotation of the gene, the information around the gene, and polymorphic information such as SNP in the public database becomes clear at a glance, and the above-mentioned drug sensitivity obtained at the research facility By simultaneously displaying categorized patient groups, polymorphism information such as their respective genotypes, and the above-mentioned expression information, research themes, literature, and genome information can be listed, so researchers' knowledge and intuition are efficient. Since it can be extracted well, the research efficiency is much better than numerically viewing the results of computer data mining.

It is a figure which shows the process until the data registration and display of the gene analysis system of this invention. It is a figure which shows the structural example of the external data input into the gene analysis system of this invention. It is a schematic diagram which shows the process until the data registration and display of the gene analysis system of this invention. It is a figure which shows an example of the database structure which the gene analysis system of this invention has. It is a figure which shows an example of the database structure which the gene analysis system of this invention has. It is a figure which shows the process which pinpoints the position on the genome of the gene used for expression experiment. It is a figure which shows the genome display main screen of the gene-analysis system of this invention. It is a figure which shows the screen which selects the display format of FIG. It is a figure which shows the relationship between a gene and a probe set. It is a figure which shows the detailed display of expression data. It is a figure which shows the detailed display method of expression data. It is a figure which shows an example of search condition setting. It is a figure which shows the example which displayed the list of the grouped expression data. It is a figure which shows the method of performing a detailed display using the mining method of literature information. It is a figure which shows the ranking list example of the gene relevant to a hypertension disease. It is a figure which shows the relationship between the therapeutic agent relevant to a hypertension, and a gene. It is a figure which shows transition of the number of gene articles relevant to a hypertension. It is a figure which shows the example of a process by the conventional expression analysis software. It is a figure which shows the conventional example which searched the gene group used by the expression experiment using the database.

Explanation of symbols

1 External database 2 Internal database 3 Browser

Claims (3)

In a gene analysis system that reads gene information and expression data obtained in expression experiments and synthesizes them with internal data,
In the process of reading the information and expression data obtained in the expression experiment into an internal database, for each probe used in the expression experiment, a genome position specifying means for specifying the gene used by each probe and the position of the gene on the genome When,
Registration means for storing data by linking the position information specified by the genome position specifying means and information on the gene;
A first selection means capable of selecting a range of genes to be browsed on a chromosome;
An extraction means for extracting a first gene region in which expression data is present in the registration means, out of the gene range selected by the first selection means;
First display means for displaying an annotation for the gene range selected by the first selection means;
Second display means for displaying the positional relationship between the gene range in which the annotation is displayed by the first display means and the first gene region on a gene basis based on the data of the registration means and the expression data; ,
Second selection means for selecting a second gene region from within the first gene region based on the display on the second display means;
Data is acquired from the registration means, and each gene of the second gene region is arranged in a column or row, and the experimental conditions of the expression experiment are arranged in a row or column, and the expression value for each experimental condition of each gene A gene analysis system comprising: a third display means for displaying information . The gene analysis system according to claim 1,
The genome localization means is Locus
External data search means for searching genome location information from external information published using any one of ID, gene name, gene symbol, UniGene ID, nm ID, and Accession ID key,
A first internal data search means for searching internal data using an Accession ID key, when the external data search means cannot detect data;
When data cannot be detected by any of the above means, Locus ID, gene name, gene symbol, UniGene
When any key of ID, nm ID, and Accession ID cannot be obtained, the second internal data search means for searching internal data based on nucleic acid or amino acid sequence information is used. Gene analysis system. In the gene analysis system according to claim 1 or 2,
The gene analysis system characterized in that the second display means compares the positional relationship by coloring the gene range in which the annotation is displayed and the first gene region, respectively .