JP2002063175A - Method for detecting disease-related snp - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for efficiently retrieving SNP the relevance of which with disease is high, while storing large amounts of data related with the SNP according to the analysis of all the genome arrays, based on the recent development in the human genome plane. SOLUTION: An SNP, having relevance with an already known or unknown disease, related with an arbitrary gene array, is detected from the gene array, and when plural SNP are present in the gene array, the SNP are sequenced according to the relevance with the disease in this method for detecting the disease related SNP in genes. This method comprises a step (a) for grouping plural basic arrays, according to array similarity, a step (b) for preparing the alignment of the arrays belonging to the same group, a step (c) for selecting the SNP (monobasic multiple type) candidates from the alignment, a step (d) for selecting the SNP on a protein coating area to bring about deformation of amino acid from the selected SNP candidates, and a step (e) for confirming the position relation of the selected SNP accompanying the amino acid change with the motif array and/or the relation of the SNP with the stereoscopic structure.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to a method for detecting a disease-related SNP, and more particularly to a method for detecting a known or unknown gene related to a gene sequence from any gene sequence using various databases and computer programs. An object of the present invention is to detect SNPs related to the disease of the present invention, and to rank in relation to the disease particularly when a plurality of SNPs are present in the gene sequence.

[0002]

2. Description of the Related Art Genome research has been based on the identification of disease genes in monogenic diseases, and has been described as "Common Dise
ase) ”to identify disease susceptibility genes for polygenic diseases. The so-called lifestyle-related diseases, such as diabetes and hypertension, are `` common diseases '', and the onset of the disease is caused by the interaction of multiple low-penetration-related genes and multiple environmental factors.
The two abnormalities are characterized by a weak effect and no onset. Such pathogenesis-related gene abnormalities are also present in normal individuals as polymorphic mutations (Variant), and it is thought that the higher the frequency, the higher the prevalence in patients and the greater the degree of onset.

[0003] Among the polymorphisms of this onset-related gene, SNP (single nucleotide polymorphism) has attracted attention. SNP means a mutation of only one base in the DNA base sequence. Such mutations are distributed almost uniformly on the genome, appear once every 300 to 1,000 base pairs, and are said to differ in location and frequency for each population such as race or disease. Among these SNPs, the SNP contained in the protein coding region in the exon part itself is a mutation that can cause disease, and therefore the analysis of SNPs present in the protein coding region is being performed to search for disease susceptibility genes .

[0004]

However, at present, it is difficult to immediately discuss the relationship with a disease even if the SNP on the nucleotide sequence is clarified. On the other hand, with the development of the Human Genome Project in recent years, a large amount of SNP data has been accumulated along with analysis of the entire genome sequence. As research on the relationship between discovered SNPs and diseases is beginning to begin, there is a need for a method of efficiently searching for SNPs highly relevant to diseases, along with data processing capabilities.

[0005]

MEANS TO SOLVE THE PROBLEMS The present inventors first elucidate the mechanism of a disease by examining SNPs that cause amino acid mutations in the three-dimensional structure of a protein in a mutation involving an amino acid change in a protein encoded by a gene. (Literature names: Toshio Furuya, Yasufumi Murakami: Diversity analysis of SNPs and genomes, Experimental Medicine Supplement “Genome Function Research Protocol” (edited by Gozo Tsujimoto, Toshio Tanaka,
p. 130-137, Youtosha, Tokyo (2000)).

[0006] As one example, high levels of homocysteine in blood are known to be a risk factor for hypertension, and this is related to reduced methylenetetrahydrofolate reductase (MTHFR) function. (Guenther, BD et al., Nature Struct. Biol., 6:
359-365, 1999). The genetic factor that raises blood homocysteine concentration is that Ala222 of human MTHFR is mutated to Val (A222
V, C677 → T in the base sequence). E. coli
The results of X-ray structural analysis of MTHFR were revealed, and human MTHFR
Approximately 33% homology with E. coli allowed the elucidation of the mechanism based on the three-dimensional structure of E. coli MTHFR.

[0007] Further, when the mutation of the insulin receptor in a diabetic patient was examined, Gly in the middle of the motif Gly-X-Gly-XX-Gly which is important for the kinase activity in the intracellular domain of the insulin receptor was found. (Odawara, M. et al., Science, 245: 66-68, 198).
9). X-ray structural analysis results have been reported for the three-dimensional structure of the intracellular domain of the human insulin receptor in a complex with an ATP analog (Hubbard, SR et al., EMBO J., 16: 5572-55).
81, 1997), and mapping the mutation site on the three-dimensional structure, G996V (G3V in the intracellular domain analyzed by X-ray structure)
It can be seen that the mutation site (corresponding to 6V) is located near the site where ATP binds to the receptor. On computer graphics, it can be seen that when this G996V amino acid substitution is made, the Gly side chain contacts the ATP analog. Therefore, it is considered that in diabetic patients who have undergone the G996V mutation, receptor kinase activity is reduced, and intracellular signaling is not functioning normally.

[0008] Thus, SNP causing amino acid mutation
By mapping information into a three-dimensional structure, the association with a disease and the responsiveness to a drug become clearer. In other words, SNPs that significantly alter the function of proteins and SNPs present at sites where drug molecules bind are expected to be more important than other SNPs. Therefore, results in amino acid changes
SNP is thought to affect the function of the protein, the site important for activity, and the distance from the ligand binding site is measured on the three-dimensional structure of the protein, or
By examining whether or not it is on or near the motif sequence, disease-related SNPs or drug-responsive SNPs
It is thought that it is possible to distinguish between SNPs that do not. The present invention is intended to rank SNPs related to these.

That is, the present invention provides the following (1) to (1)
2) is provided. (1) the following (a) to (e); (a) the step of grouping a plurality of base sequences by sequence similarity; (b) the step of preparing an alignment of sequences belonging to the same group; and (c) the alignment Selecting a base of the SNP candidate, (d) selecting an SNP present on the protein coding region and causing an amino acid mutation from the selected SNP candidate, and (e) selecting the selected SNP. Confirming the positional relationship between the SNP and the motif sequence and / or the relationship between the SNP and the three-dimensional structure.

(2) The following (a) to (e): (a) a step of grouping a plurality of base sequences by sequence similarity, (b) a step of preparing an alignment of sequences belonging to the same group, (c) A) selecting SNP candidate bases on the protein coding region from the alignment, (d) selecting an SNP that causes an amino acid mutation from the selected SNP candidates, and (e) selecting the SNP and motif Confirming the positional relationship with the sequence and / or the relationship with the three-dimensional structure.

(3) The plurality of base sequences are genomic DNs
The method according to the above (1) or (2), which is obtained from A, cDNA and / or EST database. (4) The method according to (1) or (2), wherein the target gene is specified prior to the step (a). (5) detecting a plurality of SNPs contained in the same gene by the method according to any one of (1) to (4),
A method of predicting ranking in association with a disease.

(6) The following (a) to (e): (a) a step of grouping a plurality of base sequences by sequence similarity, (b) a step of preparing an alignment of sequences belonging to the same group, (c) A) selecting SNP candidate bases from the alignment, and (d) selecting SNPs that are on the protein coding region and cause amino acid mutation from the selected SNP candidates.
And (e) confirming the positional relationship between the selected SNP and the motif sequence and / or the relationship with the three-dimensional structure. A computer recording a program for causing the computer to execute a method for detecting a disease-related SNP in a gene. A readable recording medium.

(7) The following (a) to (e): (a) a step of grouping a plurality of base sequences by sequence similarity, (b) a step of preparing an alignment of sequences belonging to the same group, (c) A) selecting SNP candidate bases on the protein coding region from the alignment, (d) selecting an SNP that causes an amino acid mutation from the selected SNP candidates, and (e) selecting the SNP and motif A computer-readable recording medium recording a program for causing a computer to execute a method for detecting a disease-related SNP in a gene, the method including a step of confirming a positional relationship with a sequence and / or a relationship with a three-dimensional structure.

(8) The above-mentioned step (a) is performed using genomic DNA, cDN
The above (6) or (7), which groups a plurality of base sequences obtained from the A and / or EST database.
A recording medium according to claim 1.

(9) The following (a) to (e); (a) means for grouping a plurality of base sequences by sequence similarity; (b) means for preparing an alignment of sequences belonging to the same group; A) means for selecting bases of SNP candidates from the alignment, (d) means for selecting, from the selected SNP candidates, SNPs which are on the protein coding region and which cause amino acid mutation, and (e) A system for detecting a disease-related SNP in a gene, comprising: means for confirming a positional relationship between a selected SNP and a motif sequence and / or a relationship with a three-dimensional structure.

(10) The following (a) to (e): (a) means for grouping a plurality of base sequences by sequence similarity, (b) means for preparing an alignment of sequences belonging to the same group, (c) A) means for selecting bases of SNP candidates on the protein coding region from the alignment, (d) means for selecting SNPs that cause amino acid mutations from the selected SNP candidates, and (e) selected SNPs and motifs A system for detecting a disease-related SNP in a gene, comprising: means for confirming a positional relationship with a sequence and / or a relationship with a three-dimensional structure.

(11) The means (a) is genomic DNA, c
(9) or (1), which is a means for grouping a plurality of base sequences obtained from the DNA and / or EST database.
The system according to 0). (12) further comprising means for specifying a target gene;
The system according to (9) or (10).

[0018]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The method according to the present invention will be described below step by step. The present invention detects an SNP associated with a known or unknown disease associated with the gene sequence from an arbitrary gene sequence, particularly when a plurality of SNPs are present in the gene sequence. Is intended to rank in relation to the disease. Therefore, the application of the present invention is not limited to a specific gene, and the method of the present invention may be started from the step of processing a large amount of nucleotide sequence information registered in a database.
In some cases, the gene whose detection is desired is determined in advance. Thus, in this case, the method of the invention starts with the identification of the gene of interest. The target gene is specified by its name, base sequence, amino acid sequence and the like. In addition, the arbitrary information may include the origin (animal species, tissue name, etc.), the name of the disease to which the gene is related, the relationship between the expression status of the gene and the disease, and the like.

When the base sequence of the target gene has been obtained, base sequences having similarity to the base sequence of the target gene can be grouped using this sequence.
The base sequence may be a sense sequence, an antisense sequence complementary thereto, or a gene fragment having a length of 31 or more nucleotides. When the amino acid sequence has been obtained, it can be converted into the nucleotide sequence of the gene encoding the amino acid sequence, and can be used to group nucleotide sequences derived from the target gene.
Alternatively, if the name of the gene is known, GenBank (http://www.ncbi.nlm.nih.gov/GenBank/i
The nucleotide sequence can be obtained using a database such as ndex.html).

If the disease associated with the gene of interest is unknown, OMIM (http://www.ncbi.nlm.nih.gov/omim/)
By searching a disease-related gene database such as that described above, it is possible to know the name of a potentially related disease.
Also, when the target gene is not specified in advance,
By searching the above database for the nucleotide sequence obtained in the following step, a potentially relevant disease name can be known. However, this step is optional, and before and after any step of the method of the invention,
Alternatively, it can be performed independently of the method of the present invention.

(A ) Grouping of base sequences Based on information such as a gene database, a specific organism (Homo s
For example, exon parts, cDNA sequences, and / or ESTs of the genome are grouped (clustered) by sequence similarity. In order to detect SNPs by the method of the present invention, at least 5 base sequences are required for this grouping and subsequent alignment preparation. In this case, if the gene has been specified first, only the base sequence having similarity to the gene may be selected and grouped, but if the gene has not been specified at this stage, a plurality of grouping processes will be performed. May be performed in parallel, and the subsequent processing may be performed for each group.

[0022] Gene databases that are currently available that can be used in the method of the present invention include:
Genomic DNA Database (NCBI's Human Genome Resource
s http://www.ncbi.nlm.nih.gov/genome/guide/), cDNA database (NCBI's Unigene Resources,
http://www.ncbi.nlm.nih.gov/UniGene/), EST database (Expressed Sequence Tags database of NCBI, db)
EST, http://www.ncbi.nlm.nih.gov/dbEST/)
It can be easily accessed via the Internet. It is also possible to use any private gene database and sequences collected independently by experiments and the like.
It can be used alone or in combination with the above database.

A grouping process is performed from these databases and independently collected data. In the case of a genomic database, cDNAs reconstructed by estimating exons and introns by means such as Genscan and linking exon portions are used. An array can be obtained. In the case of a cDNA database or an EST database corresponding to a cDNA fragment, the registered sequences can be used as they are. In this way, if the group of base sequences to be searched is first limited to those of the protein coding region, the SNs in the protein coding region in the following step (d) will be described.
The operation of selecting P can be omitted, which is efficient. The grouping of these sequences obtained from the database can be performed based on the similarity between the sequences, and specifically, can be performed using a sequence similarity search program such as blast or D2 cluster.

(B) Alignment of sequences belonging to the same group
Preparation placement of then arranged as the matching portion of the sequence with each other to form a group overlap. For the preparation of the multiple alignment based on the sequence similarity, known software may be used, and specifically, Phrap (Universal
ity of Washington) and TIGR Assembler. The prepared multiple alignment is displayed on a computer display screen (FIG. 3) and can also be printed.

In the above step (a), grouping based on sequence similarity is performed. In this step (b),
Furthermore, for example, when observing the overlap of similar sequences over a width of 31 bases, if each individual sequence matches at least 90% of the base positions with the other remaining sequences, that sequence is selected for SNP candidate selection. If it is adopted as a base sequence, the accuracy of the determination of the priority order is improved, and the determination can be performed efficiently.

(C) SNP candidate bases from alignment
Selection By comparing and examining the alignments obtained in the above step (b), differences between the sequences can be known. This operation can be performed manually, but this step can also be performed using a computer program in order to quickly and efficiently process a large amount of sequence information. In the method of the present invention, for example, SNP candidate bases that meet the conditions are selected by setting the following conditions.

(I) If five or more sequences are employed in one set width, the number of appearances of each base is compared for the 16th residue. (Ii) If the number of bases with the second largest number of appearances is two or more, base groups at that position are mutually SNP candidates. (Iii) When the two types of bases have the same number and have the largest number of appearances, the number of bases with the largest number of appearances is 2
If the number of bases is greater than or equal to
Make it a candidate. As described above, all operations such as a method of searching for an SNP candidate from the EST database can be performed on a computer.

(D) Selection of SNPs SNP candidates obtained from a database and SNPs obtained from experimentally obtained sequences (determined SNPs, hereinafter referred to as SNP candidates and defined SNPs)
Are collectively referred to as SNPs), which are SNPs on the protein coding region and cause amino acid mutation. In other words, if the SNP is not in the protein coding region, or if the SNP in the protein coding region does not cause amino acid mutation, the actually expressed protein is identical to the wild-type sequence. Therefore, it can be said that there is no association with the disease.

Specifically, when all the sequences constituting the same group are translated into a protein based on the protein translation frame of the specific sequence in which the information of the protein coding region is described, the base group of SNP When amino acid residues that differ from each other occur, SN with amino acid changes
P. By going through this step, only about 20 to 30% of the SNP candidates selected in step (c) above are selected (according to the tally as of April 2000 performed by the present inventors, mouse , And in humans 32% were selected), further increasing the efficiency, as the number of steps in the later stages and the actual clinical examination of the association with the disease is greatly reduced.

(E) SNPs with selected amino acid changes
Relationship between the motif sequence and / or the three-dimensional structure
Confirmation of the relationship The base sequence containing the SNP candidate selected in the above step (d) is subjected to a motif database (Prosite (http: // w
ww.expasy.ch/prosite), Blocks (http: //www.blocks.fh
crc.org/), Prints (http://www.sanger.ac.uk/Software
/ Pfam /, etc.) to search for a motif sequence contained in the sequence. At this time, it is determined whether or not the SNP with the amino acid change is in the motif sequence or in the vicinity of the motif sequence. If the SNP is in the motif sequence, it is necessary to visually identify the amino acid residue. Can be displayed on a computer display in a color different from that of the residue and printed.

From the positional relationship between the SNP and the motif sequence that is clarified at this stage, it is expected that the SNP is highly related to a disease. That is, for example, when the SNP is on the motif sequence, rank A (highly relevant), when it is on 5 amino acids before and after the motif sequence, rank B (moderately relevant), otherwise, rank C (related) The priority can be assigned to the three stages of “Small”.

In another embodiment, a three-dimensional structure database (PDB (Protein D
ata Bank, http://www.rcsb.org/pdb/)) and examine the similarity with the sequence registered in the database. If a sequence with 30% or more similarity is found, It can be determined that prediction is possible (homology modeling method). If there are multiple proteins with 30% or more sequence similarity, the one with the highest similarity may be used, but this does not preclude examining the second and third proteins with similarity. .

When a three-dimensional structure can be predicted by homology modeling, amino acid residues corresponding to SNPs are mapped on the three-dimensional structure. At this time, similarly to the above, the motif database may be searched for the motif sequence, and the motif sequence may be displayed. In the case where a plurality of SNPs are selected, ranking is performed, for example, when the SNP-derived amino acid residue contacts the active site or motif sequence of the protein within 5 mm of any one of all non-hydrogen atoms of the non-hydrogen atom. The highest A rank can be set. When the SNP is on the motif sequence in the positional relationship with the motif sequence, it is naturally preferable to set the highest A rank. Similarly, when an active site or motif sequence of a protein exists within 10 ° of all SNP-derived amino acid residues from all non-hydrogen atoms, it can be ranked as the second B rank. The other C ranks can be set to other ranks.

If the three-dimensional structure cannot be predicted, from the positional relationship with the motif sequence, for example, in the same manner as described above, for example, when the sequence is on the motif sequence, the A position, the 5 amino acids before and after the motif sequence are the B position, Can be C rank. The priorities obtained as described above can be displayed in different colors according to the priorities on a computer display or on a printed sheet so that the priorities can be easily identified visually.

Another aspect of the present invention provides the following (a)
To (e); (a) grouping a plurality of base sequences by sequence similarity, (b) preparing an alignment of sequences belonging to the same group, and (c) selecting SNP candidate bases from the alignment. (D) selecting, from the selected candidate SNPs, an SNP that is present on the protein coding region and that results in an amino acid mutation; and (e) selecting the SNP with the selected amino acid change and a motif sequence. A computer-readable recording medium that stores a program for causing a computer to execute a method for detecting a disease-related SNP in a gene, the method including a step of confirming a positional relationship with and / or a relationship with a three-dimensional structure.

Further, another embodiment of the present invention provides the following (a)
To (e); (a) grouping a plurality of base sequences by sequence similarity, (b) preparing an alignment of sequences belonging to the same group, (c) SNP candidates on the protein coding region from the alignment. (D) selecting an SNP that causes an amino acid mutation from the selected SNP candidates, and (e) selecting a positional relationship between the selected SNP with amino acid change and the motif sequence and / or Alternatively, the present invention is a computer-readable recording medium in which a program for causing a computer to execute a method for detecting a disease-related SNP in a gene, including a step of confirming a relationship with a three-dimensional structure, is recorded.

The recording medium to which the present invention can be applied may be any recording medium commonly used in the art.
Although not particularly limited, examples include a hard disk, a CD-ROM, a CD-R / RW, a ROM, a RAM, a DVD, and an MO. The recording medium according to the present invention can be used for causing a computer to execute the above-described method of the present invention. A program for causing a computer to execute the method of the present invention is not specified, but is configured to be executed according to the flowchart shown in FIG. That is, first, the base sequence database is searched (10
1) and then grouping based on sequence similarity (1
02). Conditions for sequence similarity for the grouping process can be appropriately selected. Here, when a gene is specified in advance, the above operation is performed on the gene.

Next, alignments of sequences belonging to the same group are prepared (103), and SNP candidates are selected by comparing the sequences of the prepared alignments (10).
4). Here, the condition of whether or not to be selected as an SNP candidate can be appropriately set, for example, as described above,
For example, when observing the overlap of similar sequences with a width of 31 bases, if each sequence matches at least 90% base positions with the rest of the sequence, that sequence is used as the target base sequence for SNP candidate selection. Can be set to be adopted.

Next, from the selected SNP candidates, SNPs which are present on the protein coding region and cause amino acid mutation are selected (105). Alternatively, if only SNPs existing in the protein coding region are selected in advance, this step (105) can be performed more efficiently. With respect to one or a plurality of SNPs selected as described above, the positional relationship with the motif sequence and / or the relationship with the three-dimensional structure is confirmed (106).

Still another embodiment of the present invention provides the following (a) to
(E); (a) means for grouping a plurality of base sequences by sequence similarity, (b) means for preparing an alignment of sequences belonging to the same group, (c) means for selecting SNP candidate bases from the alignment, (D) means for selecting, from the selected SNP candidates, SNPs present on the protein coding region and which cause amino acid mutation, and (e) a SNP with the selected amino acid change and a motif sequence A disease-related SNP in a gene, comprising:
Is a detection system.

Still another embodiment of the present invention provides the following (a) to
(E); (a) means for grouping a plurality of base sequences based on sequence similarity, (b) means for preparing an alignment of sequences belonging to the same group, (c) means for determining SNP candidates on the protein coding region from the alignment. A means for selecting a base, (d) a means for selecting an SNP that causes an amino acid mutation from the selected SNP candidates, and (e) a positional relationship between the SNP with the selected amino acid change and a motif sequence and / or A system for detecting a disease-related SNP in a gene, comprising a means for confirming a relationship with a three-dimensional structure.

FIG. 2 shows the hardware configuration of this system. The main control unit 1 executes the system according to the present invention,
Recording medium 2, for example, hard disk, CD-ROM, CD-R / R
The system is controlled in accordance with programs recorded in W, ROM, RAM, DVD, MO, and the like. The recording medium 2 is a program for executing the method of the present invention, that is, it is obtained by accessing the Internet or the like via the communication unit 7, or is stored in the recording medium 2 in genomic DNA, cDNA and / or A program for grouping a plurality of base sequences obtained from the EST database by sequence similarity, a program for creating sequence alignment, a program for selecting SNP candidates and bases of SNP candidates that cause amino acid mutation, With a related motif sequence and / or three-dimensional structure obtained by accessing the Internet or the like via the communication unit 7 or by searching a motif database and / or three-dimensional structure database stored in the recording medium 2. Program to check the relationship,
At each stage, the data input from the input device 4 and the obtained result are displayed on the display device 5, and a program for outputting from the output device 6, and the data and the result are recorded. The input device 4 is a keyboard, a mouse, and the like, and is used for inputting conditions necessary for executing the system of the present invention. The display device 5 is typically a display, and the user can proceed while confirming the execution of the method according to the present invention. The output device 6 is a printer or the like that can output a result or the like obtained by executing the system of the present invention as needed. The communication unit 7 is used to access an external database or the like via the Internet or the like to execute the system of the present invention. The above input / output is controlled by the input / output control unit 3.

The system of the present invention can further comprise means for specifying a target gene. This means queries (searches) the base sequence database for the name or base sequence of the gene of interest recorded on the recording medium 2 via the communication unit 7, and controls the gene The information related to the specification is displayed on the display device 5 and output from the output device 6 as necessary.

As described above, according to the present invention, when there are a plurality of SNPs highly relevant to a disease, the SNPs can be efficiently detected by prioritizing the relevance to the disease, and clinically. The correlation with the obtained data can be efficiently studied. In addition, by examining the presence or absence of the SNP detected by the present invention using a sample obtained from a subject, it is possible to diagnose the risk of suffering from a disease of the subject before the onset, and so-called tailor-made medicine is more easily performed. Can be performed. Further, it is possible to specify a therapeutic target for the disease and to design and develop a novel drug, for example, an antisense nucleic acid against a detected disease-related SNP.

[0045]

The present invention will be described in more detail with reference to the following examples, but the present invention is not limited to the following examples.

Example 1 Detection of Disease-Related SNP in Nucleoside Phosphorylase (SEQ ID NO: 1, OMIM Accession Number: 164050) (1) Grouping and Alignment of Nucleotide Sequence Nucleoside phosphorylase was selected as a target for detecting disease-related SNP. For nucleoside phosphorylase-related diseases, ataxia with deoxytaxia with cell-mediated immunodeficiency (ataxia with de
ficient cellular immunity) (FIG. 5). Nucleoside phosphorylase is available from UniGene
Database (http://www.ncbi.nlm.nih.gov/UniGene
Hs.75514 is given as the identification number in /). According to the cluster information of UniGene Hs.75514, NCBI (http: //www.ncbi.nlm.nih.go
v /) from the nr and Hs.seq.all data populations. These sequences were aligned by the phrap program to create a consensus sequence. (FIG. 3).

(2) Selection of SNP When observing the aligned sequence group with a width of 31 bases, if each sequence matches 28 or more bases with the other remaining sequences, the sequence is selected as a base for SNP selection. Adopted as an array. When five or more sequences were adopted within the set 31 base width, the number of appearances of each base was compared for the 16th base. If the number of bases having the second largest number of occurrences is 2 or more, the base groups at that position were regarded as SNPs. Two
When the types of bases are the same and the number of occurrences is the largest, if the number of the bases with the largest number of occurrences is 2 or more, the base groups at that position are regarded as SNP. Bases recognized as SNPs are shown in red uppercase letters so that they can be easily identified visually (FIG. 3). At this stage, in the nucleotide sequence (SEQ ID NO: 1), 24 SNPs from positions 170 to 1399 were selected (FIG. 4). The types of bases found in each SNP were as shown in FIG.

(3) Selection of SNPs with Amino Acid Changes Regarding the 24 SNPs obtained above, whether or not they are on the protein coding region, and if they are on the coding region, the translated SNP It was examined whether the amino acid sequence causes mutation. Specifically, based on the protein coding region information of accession number NM # 000270 (GenBank accession number), all SNPs present in the region were translated into amino acids, and changes as amino acids were examined. At this stage, base numbers 170 and 26
Six SNPs at 1, 281, 439, 771 and 805 were found to be in the coding region, of which four SNPs at bases 261, 439, 771 and 805 resulted in amino acid mutations. These correspond to amino acid residue positions of residues 51, 110, 221, and 232 in the amino acid sequence of nucleoside phosphorylase (SEQ ID NO: 2), respectively.
Mutation to Asn, mutation from Thr to Pro, and mutation from Gly to Glu occurred (FIG. 5).

(4) Confirmation of the positional relationship between the selected SNP and the motif sequence and / or the relationship with the three-dimensional structure As a motif sequence found in the amino acid sequence of the nucleoside phosphorylase shown in SEQ ID NO: 2 from the motif database Blocks, 25 to 38, 10 in SEQ ID NO: 2
BL0124 corresponding to positions 7 to 137 and 189 to 230, respectively
0A (SEQ ID NO: 5), BL01240B (SEQ ID NO: 6), and BL01
240C (SEQ ID NO: 7) was searched (FIG. 5), and the four SNs
It became clear that two of 110 and 221 amino acid residues among the amino acid residues corresponding to P were on the motif sequence.

In order to easily identify the positional relationship between the SNP and the motif sequence, the SNPs on the motif sequence are shown in red, the SNPs at the 5 residues before and after it are shown in pink, and the SNPs in other positions are shown in blue. (FIG. 5). The nucleotide sequence of nucleotide phosphorylase was searched against the sequence registered in the three-dimensional structure database PDB, and the sequence similarity was 100%.
The three-dimensional structure of 1ULA (PDB registration number) was displayed on a display, and the motif sequence shown in the shape of a band was shown in the same color as above so that the positions of the four SNPs could be easily identified (Fig. 5). As a result, the first 24
Of the SNPs, SNPs at bases 439 and 771 are most highly associated with the disease (ataxia with cellular immune deficiency), followed by SNPs at bases 805 and 261 Gender was predicted.

Example 2 Detection of Disease-Related SNP in Proteinase 3 (SEQ ID NO: 3, OMIM Accession No .: 177020) In the same manner as in Example 1, SEQ ID NO: 3 found on GenBank (the amino acid sequence is SEQ ID NO: 4) )
Disease-related SNPs were detected. Disease-related gene database O
A MIM search revealed that the gene of SEQ ID NO: 3 was proteinase 3. It should be noted that no disease due to mutation, deletion or the like of proteinase 3 was registered in the OMIM database. Proteinase 3 is a serine protease and is known to have Asp, His, and Ser as active sites.

In the same manner as in Example 1, grouping and alignment of nucleotide sequences, selection of SNP candidate bases, selection of SNPs with amino acid changes, positional relationship between the selected SNPs and the motif sequence and / or conformation Each step of confirming the relationship was performed. As a result, as a motif sequence found in the amino acid sequence of proteinase 3 shown in SEQ ID NO: 4, residues Nos. 56 to 72, 197 to 220 in SEQ ID NO.
And BL00134A (SEQ ID NO: 8), BL00134B (SEQ ID NO: 9), and BL00134C (SEQ ID NO: 10) corresponding to positions 230 to 243, respectively. Not found. The active sites His and Ser were on the motif sequence, respectively.

In this example, it was clarified that an SNP at amino acid residue 119 was present as an SNP with amino acid mutation, and the mutation turned Ile into Val. this
In order to investigate the relationship between SNP and three-dimensional structure, proteinase 3 (1FU
In the three-dimensional structure of J), the active site and this SNP are shown by a space-filling model, the active site part is represented by magenta, and Ile119Val with SNP is shown in blue (FIG. 6). As a result, it was found that Ile119Val was not present on the motif sequence, but was present close to the active site in terms of three-dimensional structure, and this SNP was used in diseases caused by mutation, deletion, etc. of proteinase 3. The possibility of association was suggested.

[0054]

As described in detail above, the present invention can prioritize SNPs obtained from a database in relation to disease. According to the present invention, the time and effort of accumulating in-depth research on each SNP can be saved, and disease-related SNPs can be efficiently detected,
It can be used for prevention and treatment of a disease associated with the detected SNP.

[0055]

[Sequence List] SEQUENCE LISTING <110> PharmaDesign, Inc. <120> A method for detecting disease-related SNP <130> P00-0487 <160> 10 <170> PatentIn Ver. 2.0 <210> 1 <211> 1418 < 212> DNA <213> Homo sapiens <220> <221> CDS <222> (110) .. (976) <400> gga tac acc tat gaa gat tat aag aac act gca gaa tgg ctt ctg tct 166 Gly Tyr Thr Tyr Glu Asp Tyr Lys Asn Thr Ala Glu Trp Leu Leu Ser 5 10 15 cat act aag cac cga cct caa gtt gca ata atc tgt ggt tct gga tta 214 His Thr Lys His Arg Pro Gln Val Ala Ile Ile Cys Gly Ser Gly Leu 20 25 30 35 gga ggt ctg act gat aaa tta act cag gcc cag atc ttt gac tac agt 262 Gly Gly Leu Thr Asp Lys Leu Thr Gln Ala Gln Ile Phe Asp Tyr Ser 40 45 50 gaa atc ccc aac ttt cct cga agt aca gtg cca ggt cat gct ggc cga 310 Glu Ile Pro Asn Phe Pro Arg Ser Thr Val Pro Gly His Ala Gly Arg 55 60 65 ctg gtg ttt ggg ttc ctg aat ggc agg gcc tgt gtg atg atg cag ggc 358 Leu Val Phe Gly Phe Leu Asn Gly Arg Ala Cys Val Met Met Gln Gly 70 75 80 agg ttc cac atg tat gaa ggg tac cca ctc tgg aag gtg aca ttc cca 406 Arg Phe His Met Gly Tyr Pro Leu Trp Lys Val Thr Phe Pro 85 90 95 gtg agg gtt ttc cac ctt ctg ggt gtg gac acc ctg gta gtc acc aat 454 Val Arg Val Phe His Leu Leu Gly Val Asp Thr Leu Val Val Thr Asn 100 105 110 115 gca gca gga ggg ctg aac ccc aag ttt gag gtt gga gat atc atg ctg 502 Ala Ala Gly Gly Leu Asn Pro Lys Phe Glu Val Gly Asp Ile Met Leu 120 125 130 atc cgt gac cat atc aac cta cct ggt ttc agt ggt cag aac cct ctc 550 Ile Arg Asp His Ile Asn Leu Pro Gly Phe Ser Gly Gln Asn Pro Leu 135 140 145 aga ggg ccc aat gat gaa agg ttt gga gat cgt ttc cct gcc atg tct 598 Arg Gly Pro Asn Asp Glu Arg Phe Gly Asp Arg Phe Pro Ala Met Ser 150 155 160 gat gcc tac gac cgg act atg agg cag agg gct ctc agt acc tgg aaa 646 Asp Ala Tyr Asp Arg Thr Met Arg Gln Arg Ala Leu Ser Thr Trp Lys 165 170 175 caa atg ggg gag caa cgt gag c ta cag gaa ggc acc tat gtg atg gtg 694 Gln Met Gly Glu Gln Arg Glu Leu Gln Glu Gly Thr Tyr Val Met Val 180 185 190 195 gca ggc ccc agc ttt gag act gtg gca gaa tgt cgt gtg ctg cag aag 742 Ala Ser Phe Glu Thr Val Ala Glu Cys Arg Val Leu Gln Lys 200 205 210 ctg gga gca gac gct gtt ggc atg agt aca gta cca gaa gtt atc gtt 790 Leu Gly Ala Asp Ala Val Gly Met Ser Thr Val Pro Glu Val Ile Val 215 220 225 gca cgg cac tgt gga ctt cga gtc ttt ggc ttc tca ctc atc act aac 838 Ala Arg His Cys Gly Leu Arg Val Phe Gly Phe Ser Leu Ile Thr Asn 230 235 240 aag gtc atc atg gat tat gaa agc ctg gag agc aac cat gaa gaa 886 Lys Val Ile Met Asp Tyr Glu Ser Leu Glu Lys Ala Asn His Glu Glu 245 250 255 gtc tta gca gct ggc aaa caa gct gca cag aaa ttg gaa cag ttt gtc 934 Val Leu Ala Ala Gly Lys Gln Ala Gln Lys Leu Glu Gln Phe Val 260 265 270 275 tcc att ctt atg gcc agc att cca ctc cct gac aaa gcc agt 976 Ser Ile Leu Met Ala Ser Ile Pro Leu Pro Asp Lys Ala Ser 280 285 tgacctgcct tggagtcgtc tggctcc cacacaagac ccaagtagct gctaccttct 1036 ttggcccctt gctggagtca tgtgcctctg tccttaggtt gtagcagaaa ggaaaagatt 1096 cctgtccttc acctttccca ctttcttcta ccagaccctt ctggtgccag atcctcttct 1156 caaagctggg attacaggtg tgagcatagt gagaccttgg cgctacaaaa taaagctgtt 1216 ctcattcctg ttctttctta cacaagagct ggagcccgtg ccctaccaca catctgtgga 1276 gatgcccagg atttgactcg ggccttagaa ctttgcatag cagctgctac tagctctttg 1336 agataataca ttccgagggg ctcagttctg ccttatctaa atcaccagag accaaacaag 1396 gactaatcca atacctcttg ga 1418 <210> 2 <211> 289 <212> PRT <213> Homo sapiens <400> 2 Met Glu Asn Gly Tyr Thr Tyr Glu Asp Tyr Lys Asn Thr Ala Glu Trp 1 5 10 15 Leu Leu Ser His Thr Lys His Arg Pro Gln Val Ala Ile Ile Cys Gly 20 25 30 Ser Gly Leu Gly Gly Leu Thr Asp Lys Leu Thr Gln Ala Gln Ile Phe 35 40 45 Asp Tyr Ser Glu Ile Pro Asn Phe Pro Arg Ser Thr Val Pro Gly His 50 55 60 Ala Gly Arg Leu Val Phe Gly Phe Leu Asn Gly Arg Ala Cys Val Met 65 70 75 80 Met Gln Gly Arg Phe His Met Tyr Glu Gly Tyr Pro Leu Trp Lys Val 85 90 95 Thr Phe Pro Val Arg Val Phe His Leu Leu Gly Val Asp Thr Leu Val 100 105 110 Val Thr Asn Ala Ala Gly Gly Leu Asn Pro Lys Phe Glu Val Gly Asp 115 120 125 Ile Met Leu Ile Arg Asp His Ile Asn Leu Pro Gly Phe Ser Gly Gln 130 135 140 Asn Pro Leu Arg Gly Pro Asn Asp Glu Arg Phe Gly Asp Arg Phe Pro 145 150 155 160 Ala Met Ser Asp Ala Tyr Asp Arg Thr Met Arg Gln Arg Ala Leu Ser 165 170 175 Thr Trp Lys Gln Met Gly Glu Gln Arg Glu Leu Gln Glu Gly Thr Tyr 180 185 190 Val Met Val Ala Gly Pro Ser Phe Glu Thr Val Ala Glu Cys Arg Val 195 200 205 Leu Gln Lys Leu Gly Ala Asp Ala Val Gly Met Ser Thr Val Pro Glu 210 215 220 Val Ile Val Ala Arg His Cys Gly Leu Arg Val Phe Gly Phe Ser Leu 225 230 235 240 Ile Thr Asn Lys Val Ile Met Asp Tyr Glu Ser Leu Glu Lys Ala Asn 245 250 255 His Glu Glu Val Leu Ala Ala Gly Lys Gln Ala Ala Gln Lys Leu Glu 260 265 270 Gln Phe Val Ser Ile Leu Met Ala Ser Ile Pro Leu Pro Asp Lys Ala 275 280 285 Ser <210> 3 <211> 903 <212> DNA <213> Homo sapiens <220> < 221> CDS <222> (49) .. (816) <40 0> 3 gattggctat aagaggagct tgatcgtggg tgcaccctgg accccacc atg gct cac 57 Met Ala His 1 cgg ccc ccc agc cct gcc ctg gcg tcc gtg ctg ctg gcc ttg ctg ctg 105 Arg Pro Pro Ser Pro Ala Leu Leu Aula Seru 10 15 agc ggt gct gcc cga gct gcg gag atc gtg ggc ggg cac gag gcg cag 153 Ser Gly Ala Ala Arg Ala Ala Glu Ile Val Gly Gly His Glu Ala Gln 20 25 30 35 cca cac tcc cgg ccc tac atg gcc tcc ctg cag atg cgg ggg aac ccg 201 Pro His Ser Arg Pro Tyr Met Ala Ser Leu Gln Met Arg Gly Asn Pro 40 45 50 ggc agc cac ttc tgc gga ggc acc ttg atc cac ccc agc ttc gtg ctg 249 Gly Ser His Phe Cys Gly Gly Thr Leu Ile His Pro Ser Phe Val Leu 55 60 65 acg gcc ccg cac tgc ctg cgg gac ata ccc cag cgc ctg gtg aac gtg 297 Thr Ala Pro His Cys Leu Arg Asp Ile Pro Gln Arg Leu Val Asn Val 70 75 80 gtg ctc gga gcc cac aac gtg cgg acg cag gag ccc acc cag cag cac 345 Val Leu Gly Ala His Asn Val Arg Thr Gln Glu Pro Thr Gln Gln His 85 90 95 ttc tcg gtg gct cag gtg ttt ctg aac aac tac gac gcg gag aac aaa 3 93 Phe Ser Val Ala Gln Val Phe Leu Asn Asn Tyr Asp Ala Glu Asn Lys 100 105 110 115 ctg aac gac att ctc ctc atc cag ctg agc agc cca gcc aac ctc agt 441 Leu Asn Asp Ile Leu Leu Ile Gln Leu Ser Ser Ala Asn Leu Ser 120 125 130 gcg tcc gtc acc tca gtc cag ctg cca cag cag gac cag cca gtg ccc 489 Ala Ser Val Thr Ser Val Gln Leu Pro Gln Gln Asp Gln Pro Val Pro 135 140 145 cac ggc acc cag tgc ctg gcc atg ggc tgg ggc cgc gtg ggt gcc cac 537 His Gly Thr Gln Cys Leu Ala Met Gly Trp Gly Arg Val Gly Ala His 150 155 160 gac ccc cca gcc cag gtc ctg cag gag ctc aat gtc acc gtg gtg Apro Pro Gln Val Leu Gln Glu Leu Asn Val Thr Val Val Thr 165 170 175 ttc ttc tgc cgg cca cat aac att tgc act ttc gtc cct cgc cgc aag 633 Phe Phe Cys Arg Pro His Asn Ile Cys Thr Phe Val Pro Arg Arg Lys 180 185 190 195 gcc ggc atc tgc ttc gga gac tca ggt ggc ccc ctg atc tgt gat ggc 681 Ala Gly Ile Cys Phe Gly Asp Ser Gly Gly Pro Leu Ile Cys Asp Gly 200 205 210 atc atc caa gga ata gac tcc ttc gtg atc t gt gcc acc cgc 729 Ile Ile Gln Gly Ile Asp Ser Phe Val Ile Trp Gly Cys Ala Thr Arg 215 220 225 ctt ttc cct gac ttc ttc acg cgg gta gcc ctc tac gtg gac tgg atc 777 Leu Phe Pro Asp Phe Phe Thrg Arg Ala Leu Tyr Val Asp Trp Ile 230 235 240 cgt tct acg ctg cgc cgt gtg gag gcc aag ggc cgc ccc tgaaccgccc 826 Arg Ser Thr Leu Arg Arg Val Glu Ala Lys Gly Arg Pro 245 250 255 ctcccacagc gctggccggg accccgagcc tggctccaaa ccctcgaggc ggatctttgg 886 acagaagcag ctcttgt 903 <210> 4 <211> 256 <212> PRT <213> Homo sapiens <400> 4 Met Ala His Arg Pro Pro Ser Pro Ala Leu Ala Ser Val Leu Leu Ala 1 5 10 15 Leu Leu Leu Ser Gly Ala Ala Arg Ala Ala Glu Ile Val Gly Gly His 20 25 30 Glu Ala Gln Pro His Ser Arg Pro Tyr Met Ala Ser Leu Gln Met Arg 35 40 45 Gly Asn Pro Gly Ser His Phe Cys Gly Gly Thr Leu Ile His Pro Ser 50 55 60 Phe Val Leu Thr Ala Pro His Cys Leu Arg Asp Ile Pro Gln Arg Leu 65 70 75 80 Val Asn Val Val Leu Gly Ala His Asn Val Arg Thr Gln Glu Pro Thr 85 90 95 Gln Gln His Phe Ser Val Ala Gln Val Phe Leu Asn Asn Tyr Asp Ala 100 105 110 Glu Asn Lys Leu Asn Asp Ile Leu Leu Ile Gln Leu Ser Ser Pro Ala 115 120 125 Asn Leu Ser Ala Ser Val Thr Ser Val Gln Leu Pro Gln Gln Asp Gln 130 135 140 Pro Val Pro His Gly Thr Gln Cys Leu Ala Met Gly Trp Gly Arg Val 145 150 155 160 Gly Ala His Asp Pro Pro Ala Gln Val Leu Gln Glu Leu Asn Val Thr 165 170 175 Val Val Thr Thr Phe Phe Cys Arg Pro His Asn Ile Cys Thr Phe Val Pro 180 185 190 Arg Arg Lys Ala Gly Ile Cys Phe Gly Asp Ser Gly Gly Pro Leu Ile 195 200 205 Cys Asp Gly Ile Ile Gln Gly Ile Asp Ser Phe Val Ile Trp Gly Cys 210 215 220 Ala Thr Arg Leu Phe Pro Asp Phe Phe Thr Arg Val Ala Leu Tyr Val 225 230 235 240 Asp Trp Ile Arg Ser Thr Leu Arg Arg Val Glu Ala Lys Gly Arg Pro 245 250 255 <210> 5 <211> 14 <212> PRT <213> Homo sapiens < 400> 5 Pro Gln Val Ala Ile Ile Cys Gly Ser Gly Leu Gly Gly Leu 1 5 10 <210> 6 <211> 31 <212> PRT <213> Homo sapiens <400> 6 Gly Val Asp Thr Leu Val Val Thr Asn Ala Ala Gly Gly Leu Asn Pro 1 5 10 15 Lys Phe Glu Val Gly Asp Il e Met Leu Ile Arg Asp His Ile Asn 20 25 30 <210> 7 <211> 42 <212> PRT <213> Homo sapiens <400> 7 Glu Gly Thr Tyr Val Met Val Ala Gly Pro Ser Phe Glu Thr Val Ala 1 5 10 15 Glu Cys Arg Val Leu Gln Lys Leu Gly Ala Asp Ala Val Gly Met Ser 20 25 30 Thr Val Pro Glu Val Ile Val Ala Arg His 35 40 <210> 8 <211> 17 <212> PRT <213> Homo sapiens <400> 8 Cys Gly Gly Thr Leu Ile His Pro Ser Phe Val Leu Thr Ala Ala His 1 5 10 15 Cys <210> 9 <211> 24 <212> PRT <213> Homo sapiens <400> 9 Gly Ile Cys Phe Gly Asp Ser Gly Gly Pro Leu Ile Cys Asp Gly Ile 1 5 10 15 Ile Gln Gly Ile Asp Ser Phe Val 20 <210> 10 <211> 14 <212> PRT <213> Homo sapiens <400> 10 Pro Asp Phe Phe Thr Arg Val Ala Leu Tyr Val Asp Trp Ile 1 5 10

[Brief description of the drawings]

FIG. 1 is a flowchart illustrating a program for causing a computer to execute the method of the present invention.

FIG. 2 shows a hardware configuration of the system of the present invention.

FIG. 3 shows an example of an alignment of base sequences belonging to the same group and SNP candidate bases found on the alignment.

FIG. 4 shows the location of SNPs detected in nucleoside phosphorylases and a list of SNPs that define SNPs that result in amino acid changes in the coding region.

FIG. 5 shows the positions of SNPs that cause amino acid changes in nucleoside phosphorylase, their relation to motif sequences and three-dimensional structures, and disease-related information.

FIG. 6 shows the positions of SNPs that cause amino acid changes in proteinase 3, and their relationship with motif sequences and three-dimensional structures.

 ──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Toshiyuki Saito 2-10-1 Miyamoto, Funabashi-shi, Chiba F-term (reference) 4B024 AA01 AA11 AA20 CA03 CA04 FA02 HA12 5B075 ND02 NR12 PP02 PP03 PQ02 PQ03 PR06 QM08 UU19

Claims (12)

[Claims] 1. The following (a) to (e): (a) a step of grouping a plurality of base sequences by sequence similarity, (b) a step of preparing an alignment of sequences belonging to the same group, (c) Selecting a base of a candidate SNP (single nucleotide polymorphism) from the alignment, (d) selecting an SNP that is an SNP on the protein coding region and causes an amino acid mutation from the selected SNP candidate And (e) confirming the positional relationship between the selected SNP and the motif sequence and / or the relationship with the three-dimensional structure. 2. The following (a) to (e): (a) a step of grouping a plurality of base sequences by sequence similarity, (b) a step of preparing an alignment of sequences belonging to the same group, and (c) Selecting the bases of the SNP candidates on the protein coding region from the alignment, (d) selecting the SNP that causes an amino acid mutation from the selected SNP candidates, and (e) selecting the SNP and the motif sequence Confirming the positional relationship with and / or the three-dimensional structure. 3. The method according to claim 1, wherein the plurality of base sequences are genomic DNA or cDNA.
And / or obtained from the EST database,
The method according to claim 1. 4. The method according to claim 1, wherein a gene of interest is specified prior to the step (a). 5. A method for detecting a plurality of SNPs contained in the same gene by the method according to any one of claims 1 to 4, and predicting a rank in relation to a disease. 6. The following (a) to (e): (a) grouping a plurality of base sequences by sequence similarity, (b) preparing an alignment of sequences belonging to the same group, (c) Selecting the bases of the SNP candidates from the alignment, (d) selecting, from the selected SNP candidates, an SNP that is on the protein coding region and that causes an amino acid mutation, and (e) selecting Confirming the positional relationship between the obtained SNP and the motif sequence and / or the relationship with the three-dimensional structure. A computer-readable recording medium recording a program for causing a computer to execute a method for detecting a disease-related SNP in a gene. . 7. The following (a) to (e): (a) a step of grouping a plurality of base sequences by sequence similarity, (b) a step of preparing an alignment of sequences belonging to the same group, and (c) Selecting the bases of the SNP candidates on the protein coding region from the alignment, (d) selecting the SNP that causes an amino acid mutation from the selected SNP candidates, and (e) selecting the SNP and the motif sequence A computer-readable recording medium storing a program for causing a computer to execute a method for detecting a disease-related SNP in a gene, the method including a step of confirming a positional relationship between the gene and a three-dimensional structure. 8. The recording medium according to claim 6, wherein the step (a) groups a plurality of base sequences obtained from a genomic DNA, cDNA and / or EST database. 9. The following (a) to (e): (a) means for grouping a plurality of base sequences by sequence similarity, (b) means for preparing an alignment of sequences belonging to the same group, (c) Means for selecting SNP candidate bases from the alignment, (d) means for selecting, from the selected SNP candidates, SNPs which are on the protein coding region and which cause amino acid mutation, and (e) selection Means for confirming the positional relationship between the identified SNP and the motif sequence and / or the relationship with the three-dimensional structure. 10. The following (a) to (e): (a) means for grouping a plurality of base sequences by sequence similarity, (b) means for preparing an alignment of sequences belonging to the same group, (c) A means for selecting a candidate SNP base on the protein coding region from the alignment, (d) a means for selecting an SNP causing an amino acid mutation from the selected SNP candidates, and (e) a selected SNP and a motif sequence And a means for confirming the positional relationship with and / or the three-dimensional structure. 11. The system according to claim 9, wherein the means (a) is a means for grouping a plurality of base sequences obtained from a genomic DNA, a cDNA, and / or an EST database. 12. The system according to claim 9, further comprising means for specifying a target gene.