JP2003014612A - SNP scoring system, SNP scoring method, and SNP scoring program - Google Patents

Abstract

(57) [Summary] [Problem] To quickly evaluate analysis results by a high-throughput SNP scoring technology according to the present invention, feed back the evaluation results, mature the analysis technology, and combine it with the SNP scoring technology. Accordingly, the present invention provides an SNP scoring system and an SNP scoring method that have not only speed but also high reliability important for medical diagnosis and the like and can be put to practical use. SOLUTION: A means for setting parameters, a means for performing SNP determination comparison and collation for reference source data (SNP data) and reference destination data (flow cytometry data) for each parameter set by the parameter setting means, Means for displaying the result of the comparison.

Description

Detailed Description of the Invention

[0001]

TECHNICAL FIELD The present invention relates to an SNP (Single Nucleotide Polymorphism) to which magnetic fine particle handling technology and fluorescent particle identification technology are applied.
Regarding the scoring system, in detail, using a genomic DNA fragment of several tens to several kilobases as a template, "a barcoded (fluorescently labeled) detection oligonucleotide containing all combinations and barcoded on magnetic beads ( SN, a SNP scoring system that rapidly analyzes not only known but also unknown SNPs by detecting "specific binding with fluorescently labeled DNA fragments" using a fluorescence excitation flow cytometer, etc.
P-scoring method and SNP scoring program.

[0002]

2. Description of the Related Art As a typical technique that has been put to practical use for SNP analysis, PCR (Polymerase
Single-chain higher-order structure polymorphism (Multiple Fluorescence-based PCR-Si) using chain reaction
ngle Strand Conformation Polymorphism: MF-PCR-SSC
P) Analysis technology and DNA chip (DNA microarray)
There is a mutation analysis technique based on hybridization using.

On the other hand, regarding the image analysis technique, as a conventional technique relating to the present invention, which compares a reference image pattern stored in advance with a read collated image pattern and effectively collates the read image pattern, for example, Japanese Patent Application Laid-Open No. Sho 59-10
Those described in 5175 and JP-A-59-105177 are known.

In these, for example, in the case of determining the authenticity of the seal image of a rubber stamp, the authenticity of the collated image pattern is discriminated by comparing the reference image pattern stored in advance with the read collated image pattern. This is a method for matching read image patterns. The reference image pattern is stored as a histogram in which the number of black pixels for each scanning line is calculated, and the collated image pattern is read and converted into binarized information, and then the number of black pixels for each scanning line is counted to be collated. Create and store a histogram of the image pattern. Relative to the stored histogram of the reference image pattern, the histogram of the collated image pattern is slid for each predetermined scanning line to obtain the difference for each scanning line of both histograms, and the average value of the differences for each sliding operation is calculated. Obtained, with reference to the average value of the difference, the minimum average value of the difference is extracted, the minimum average value of the difference is compared with a preset setting value, and the image pattern to be collated based on the comparison result. Authenticity is determined.

[0005]

However, in the above-mentioned prior art, only the histogram is used for collation, and high reliability cannot be obtained, and it takes a long time to determine the authenticity. Moreover, since the design concept is completely different from that of the present invention, it cannot be applied to SNP analysis based on the fluorescence intensity ratio.

The present invention has been made in order to solve the above-mentioned problems of the prior art, and it promptly evaluates the analysis result by the high throughput SNP scoring technique and feeds back the evaluation result. By maturing analysis technology and combining it with SNP scoring technology, SNP scoring system, SNP scoring method and SNP scoring method with high reliability that is important not only for speed but also for medical diagnosis, etc. The purpose is to provide a SNP scoring program.

[0007]

In order to solve the above problems, the invention according to claim 1 is an SNP scoring system for analyzing SNP fluorescence intensity information from a flow cytometry system, and means for setting parameters. And the reference source data (SNP data) or / and the reference destination data (flow cytometry data) are searched by means of SNP judgment comparison and verification for each parameter set by parameter setting means and SNP judgment comparison and verification And a means for displaying the result of the SNP.
It is a scoring system.

Further, the invention according to claim 2 is an SNP scoring system characterized by having an actual sample database in which a result of judgment comparison and collation and detailed information are recorded. The invention according to claim 3 is the SNP scoring system, wherein the parameter is a base.

According to the first to third aspects of the present invention, with respect to the reference source data and / or the reference destination data, each base is set for each parameter and SNP determination comparison and collation is performed. It is possible to detect whether they are similar to each other and store the detection result.

The invention according to claim 4 is the SNP scoring system, wherein the parameter is a combination of patterns or the number of gradations. Further, the invention according to claim 5 is the SNP scoring system, characterized in that the reference source data and / or the reference destination data is detected by fluorescently labeling the DNA fragment and barcode-coding it.

According to the fourth and fifth aspects of the invention, the SNP is set within the search range in which the bar coded reference data is designated.
Since the search is performed, it is possible to detect the probability of similarity within the specified search range. The invention according to claim 6 is the SNP according to claim 1.
The SNP determination method is characterized in that the SNP determination is performed by comparing and comparing the determination. In the invention according to claim 7, the reference data or //
And an SNP determination method characterized by creating an SNP determination standard based on reference source data.

Further, the invention according to claim 8 is characterized in that the SNP determination standard is created by calculating an average value or a boundary value based on reference destination data or / and reference source data. Is the way. Further, the invention according to claim 9 is characterized in that the SNP criterion is an average value or boundary value of the reference data calculated by the self-learning function.
This is the P determination method. The invention according to claim 10 is the SNP.
The SNP determination method is characterized in that the determination criterion is a sector shape or an ellipse that is the search range of the reference data.

According to the sixth to tenth aspects of the present invention, since the average value or boundary value of the reference data, or the sector shape or ellipse is used as the SNP determination standard, the reliability of the analysis result is improved. The invention according to claim 11 is the SNP determination method, wherein a plurality of SNP determination criteria are created.

According to the eleventh aspect of the invention, since a plurality of SNP criteria are created, the SNP criteria can be diversified. Further, the invention according to claim 12 is the SNP determination method characterized in that the reference destination data is partially compared and collated with the SNP determination.

According to the twelfth aspect of the present invention, since the reference data is partially SNP-determined by comparing and collating, a large amount of SNP strength information can be analyzed at high speed. The invention according to claim 13 creates a predetermined area starting from the maximum point of the reference data in the X-axis and Y-axis directions based on the reference data, and calculates the number of events included in the area. It is an SNP search method characterized by:

According to the thirteenth aspect of the present invention, based on the reference destination data, an area including a predetermined number of reference destination data starting from the maximum point of the data in the X-axis and Y-axis directions is created, Since the number of events included in these areas is calculated, even if the reference data distribution areas overlap,
You can do SNP search.

The invention according to claim 14 is a SNP scoring method for analyzing SNP fluorescence intensity information from a flow cytometry system, which comprises a step of setting a parameter and reference source data and / or reference destination data. , SNP judgment comparison and collation for each parameter set in the parameter setting step, and SNP
And a step of displaying the result of the search performed by the step of determining, comparing, and collating, and the SNP scoring method.

The invention according to claim 15 is a SNP scoring method for analyzing SNP fluorescence intensity information from a flow cytometry system, which comprises a step of setting a parameter and reference source data and / or reference destination data. , SNP judgment comparison and collation for each parameter set in the parameter setting step, and SNP
It is an SNP scoring program for executing an SNP scoring method including a step of displaying a result retrieved by a judgment comparing and collating step.

[0019]

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. First, a first embodiment of the present invention will be described. In this embodiment,
The base is set and the SNP judgment comparison and matching process is performed.

FIG. 1 is a block diagram showing an example of the configuration of an SNP scoring system according to the first embodiment of the present invention. As shown in FIG. 1, the SNP scoring system includes a CPU (central processing unit) 101 that performs various processes and a CPU 1
Program memory 102, which stores the programs required for the processing in 01, and flow cytometry (Fluorescence Cell
A flow cytometry database 103 for storing data from a Sorter (FCS) system (not shown),
An actual sample database 104 that stores the processing results
And an SNP search result display device 106 and an input device 107 such as a pointing device 171 and a keyboard 172.

In the program memory 102, a reference generation program 121 such as a histogram or dot plot, a base designation program 122 as a parameter setting means, a detailed information registration program (base information / date, detailed name information such as disease name, etc. are registered. ) 123, result data reading program (having self-learning function) 124, judgment area designation program (fan / oval) 125, SNP judgment standard diversification program 12
6, an ellipse creation program 127, a partial selection and peak value correction function program 128, a determination area automatic generation program (gauge) 129, and an SNP determination comparison and collation means SNP determination comparison and collation program 130 are stored.

Each of the programs 121 to 130 in the program memory 102 can be interactively operated / executed from a pointing device 171 such as a mouse of the input device 107 and a keyboard 172.

The flow cytometry database 103 stores data such as fluorescence intensity, the number of events and parameter names from the flow cytometry system. The actual sample database 104 stores actual sample data (actual data) that has been processed by the SNP determination comparison / collation program 130. Each processing program described above is created by, for example, Java, Visual C ++, or the like. Therefore, the system can usually be realized by a personal computer or a workstation.

FIG. 2a shows the contents of the flow cytometry database 103, and FIG. 2b shows the actual sample database 1
It is the contents of 04. The contents of the flow cytometry database 103 can be searched with the key specified for each item. FIG. 3a is a screen for setting bases (A, T, G, C) and performing SNP determination comparison and matching processing. This screen appears when you start the SNP scoring system. Figure
3b is a screen in which the base is being set by the pointing device 171, and FIG. 3c is a screen in which the setting is completed.

As shown in FIG. 3b, it is set in association with a parameter (detector) for detecting a base. You can set P1
There are 8 from P8 to P1: SSC (Side Scatter: Side scattered light)
-H, P2: FL1-H, P3: FSC (Forward Scatter: Forward scattered light)-
It corresponds to H, P4: FL2-H, P5: FL3-H, P6: FL3-H, respectively. When you click the "▼" part of the spin button, a drop-down list is displayed, and one base is selected from A, T, C, and G. Here, as an example, bases are associated with P3 to P6. When the “SAVE” button is clicked here, the setting contents can be saved in the actual sample database 104. When the SNP judgment comparison and verification process is completed, a screen as shown in FIG. 3d is displayed.

FIG. 3d is a screen displaying the processing result of the SNP determination comparison / collation program 130. The screen of FIG. 3d is divided into a “match list” display area 200 and an “information” display area 201. The “match list” display area 200 is a list of files that match as a result of the SNP determination comparison and matching process. The parameter 201a in the "information" display area 201 indicates the type of detector used. The “match rate” 201b is
It shows the probability of similarity and is displayed as a percentage (%). Also, "Number of events" 201c
Indicates the frequency of how many times an event has occurred within a predetermined number of times (here, 10000). "Judgment" 20
1d indicates the determination result of the final SNP determination comparison / collation processing. For example, how likely is the similarity, “home” (the same base), or “hetero”.
(Different base).

FIG. 4a is a screen for adding detailed information such as a comment to the result of the SNP determination comparison and collation processing and registering it in the actual sample database 104. This screen is displayed when you click the "Register" button in Figure 3d. SN in Figure 3d
P judgment comparison The information of memo 1 to memo 5 can be added for each group for files on the coincidence list as a result of the collation process. Enter the required memo and click the "Save" button. The registered contents will be displayed in the screen in Fig. 4b.

FIG. 4b is a screen displaying the detailed information added in FIG. 4a. FIG. 5 is a flow chart showing the procedure of the SNP judgment comparison and matching process which is an event driven from the screens of FIGS. 3a to 3d, and FIG. 6 is the SNP judgment comparison and matching process which is an event driven from the screens of FIG. 4a. For the result of
7 is a flowchart showing a processing procedure for adding detailed information and registering it in the actual sample database 104.

First, in step S100, the bases "A", "T",
The “C” and “G” setting screen (Fig. 3a) is displayed. Step S101
Then, set the base displayed in step S100 (Fig. 3
b). After the bases have been set (FIG. 3c), in step S102, a mode for creating a base sequence pattern relating to the set bases is selected. If the "read" button is clicked in step S102, the flow advances to step S103 to enter the existing data selection mode, and the actual sample database 10
The base sequence pattern registered in advance in 4 is read out.

If the "new" button is clicked in step S102, the flow advances to step S104 to enter the new pattern creation mode and create a new base sequence pattern. When the "automatic creation" button is clicked in step S102, the process proceeds to step S105, the data folder mode is entered, and a base sequence pattern is automatically created based on the data stored in the data folder.

When the base sequence pattern is created, in step S106, SNP data as a reference source for performing the SNP determination comparison / collation processing is set. What is SNP data setting?
This is a process of setting the base sequence data (recorded or registered SNP data) created here as a reference source. In step S107, the flow cytometry data that is the reference destination for performing the SNP determination comparison / collation processing is set. The setting of flow cytometry data is a process of setting reference data.

In step S108, the SNP determination comparison and collation processing of the data set in steps S106 and S107 is performed. The SNP determination comparison / collation process is a process for comparing and collating SNP data and flow cytometry data or determining whether or not flow cytometry data is included in a designated area based on SNP data. SN
When the P determination comparison and matching process is completed, the result of the comparison determination is displayed in step S109 (FIG. 3d).

In order to explain the registration of the information addition file in the actual sample database 104, the description will be made with reference to FIG. 6 by leaving FIG. 5 once. Registration means adding attached information to the result of the SNP determination comparison and verification process and registering it in the actual sample database 104.

In step S200, a "registered group" is selected (FIG. 4a). If "Registration Group" is selected, proceed to step S201, switch to text input mode, and make a note of the registration group selected from the screen in Figure 4a.
Enter 1 to 5 and proceed to step S202. "Registration group"
Even if is not selected, the process proceeds to step S202, and notes 1 to 5 are displayed for the displayed registered group (FIG. 4).
b).

In step S203, a comment is input to the memo displayed in step S202 from the screen of FIG. 4a. Up to 250 characters can be entered for a comment. When the comment input is completed, in step S204. Click the "Save" button (Fig. 4a) and proceed to step S205 to register the file with the added information in the actual sample database 104.

In the present embodiment, in order to improve the reliability of SNP data (fluorescence intensity information), SNP determination criteria (region determined to be the relevant nucleotide sequence, or the relevant SNP
It is possible to create an area that is determined to be). To create the SNP criteria, first calculate the average value of all events, and then calculate the boundary value.

FIG. 7 is a flow chart showing the processing procedure for calculating the average value based on the flow cytometry data and creating the SNP criterion. FIG. 8 is a diagram showing normalization of the number of events.

The number of events is normalized because it is necessary to calculate the average value of the subsequent events. Since the fluorescence intensity setting changes depending on the sample preparation conditions, the number of events that need to be adjusted is shown in Figure 8.
, The intermediate value between the number of high intensity events and the number of low intensity events is taken for normalization.

In step S300, the flow cytometry data is read from the flow cytometry database 103. In step S301, it is determined whether all flow cytometry data has been read. If the result of determination is that reading of all files has been completed, the flow proceeds to step S302, and the number of data events is normalized.

After normalizing the number of events, step S3
Go to 03 and calculate the average value of each event. The average value is obtained by adding the fluorescence intensities of the same event and dividing by the total number of files. After calculating the average value, in step S304, it is determined whether the average value of all events has been calculated. If the result of determination is that the average value of all events has been calculated, step S3
In 05, click the “Save” button and proceed to step S306 to display the average value of all events in the actual sample database 10
Register to 4. If the average value of all events has not been calculated yet, the process returns to step S303 to calculate the average value of each event.

FIG. 9a is a diagram showing the flow cytometry data as the SNP judgment standard, and FIG. 9b shows the flow cytometry data of FIG. 9a read n times and the boundary value is newly calculated to determine the SNP judgment standard. FIG. 9 is a diagram showing the created state, and FIG.
c is a diagram showing a state in which the flow cytometry data is read n + m times, the boundary value is newly calculated, and the SNP criterion is created.

In FIG. 9c, new data "*" is included, and the SNP judgment standard is expanded to include these data. As mentioned above, the self-learning function is used to develop the SNP judgment criteria in order to mature the analysis technique. FIG. 10 is a flowchart showing a processing procedure for calculating a boundary value based on flow cytometry data and generating an SNP criterion.

In step S400, flow cytometry data is read from the flow cytometry database 103. In step S401, the SNP determination criterion is created so as to surround all the events extracted up to the nth time. After the creation process of the SNP determination standard, in step S402, it is determined whether all the flow cytometry data have been read. If all files have been read as a result of the determination, step S4
In 03, the "save" button is clicked, the process proceeds to step S404, and the SNP determination criterion is registered in the actual sample database 104. If you have not clicked the "Save" button, exit without changing the registration. If all the files have not been read yet, the process returns to step S400 to continue reading the flow cytometry data.

As described above, according to the present embodiment, by using the SNP determination criteria as described above, it is possible to determine with what probability the similarity or homo / hetero. Therefore, the reliability of the SNP analysis result can be improved.

Next, a second embodiment of the present invention will be described. In the present embodiment, the SNP search is performed within the specified search range for barcoded flow cytometry data.

FIG. 11 shows a fluorescently labeled bar coded DNA fragment. As shown in FIG. 11, bar coding means fluorescent labeling of one of a pair of paired bases immobilized on the magnetic beads 204. The barcoded DNA 205 is sorted by the polarity of the magnetic beads 204. FIG. 12a is a screen for performing parameter, gradation number, and detailed setting processing to perform SNP search within the specified range of bar coded flow cytometry data.

In FIG. 12a, “combination” 206 designates a combination of the types of detectors (barcoded detection oligonucleotides) to be used.
Specify as 1: P2. The “number of gradations” 207 specifies the number of fluorescent colors to be detected, and can be specified in the range of 1 to 6 here. The “detailed setting” 208 is to add a comment for the search in which the specified pattern combination and the number of gradations are specified. The above specified contents are the actual sample database 10
Stored in 4. When the “Read” button 209 is clicked, the data corresponding to the specified content is read from the flow cytometry database 103. Also, "Save"
When the button 210 is clicked, the set data is saved in the actual sample database 104.

FIG. 12b is a screen for adding a comment in the detailed setting process. The search range of flow cytometry data can be selected from two types, fan-shaped and elliptical. Figure 12c
2a is a screen surrounded by a fan shape corresponding to the number of gradations based on the information set in the screen of 2a, and FIG. 12d is a screen in which the search range is surrounded by an ellipse based on the information set in the screen of FIG. 12a. Is. Figure 12e
Is a screen displaying the result of the SNP determination comparison and matching process designated by an ellipse, for example. In Figure 12d, the bases A, T, C, G
12e, the bases are combined and displayed in FIG. 12e.

FIG. 13a is a screen for creating an ellipse. When the "Create" button 211 is clicked from the screen of FIG. 13a, the ellipse creation mode is entered. To create an ellipse, first draw a straight line that is the axis of the ellipse. The straight line is shown in Fig. 13b. Next, the size and angle of a rectangle inscribed by an ellipse of a desired size on a straight line are set. This is shown in FIG. 13c.
FIG. 13d shows how an ellipse inscribed in a rectangle is generated.
Figure 13e shows a stretched ellipse. In order to create a plurality of ellipses, a rotation matrix (affine transformation) is used to rotate the ellipses by a set angle and regenerate the required number. The screen showing this state is shown in FIG. 12d above.

FIG. 14 is a flowchart showing the processing procedure for SNP retrieval of bar coded flow cytometry data in a designated search range, and FIG. 15 is a flowchart showing the processing procedure for ellipse generation. In step S500, a combination of parameters, setting of the number of gradations, and comment setting processing are performed (FIG. 12a). Up to 64 gradations can be set. In step S501, it is determined which of a fan shape process and an ellipse process is to be performed. If the result of determination is that ellipse processing is to be performed, processing proceeds to step S503 and ellipse creation processing is performed.

From this point, the ellipse creation process starts, so
The process of creating an ellipse will be described in detail with reference to FIG. In step S600, a straight line is input (Fig. 13
b). In step S601, it is determined whether a straight line has been input. If a straight line is entered, step S6
Go to 02. If the straight line is not input, the process directly proceeds to step S504. In step S602, the dimensions and angles of the rectangle are set (Fig. 13c). After setting the dimensions and angles of the rectangle, in step S603, an ellipse inscribed in the rectangle generated in step S602 is generated (FIG. 13d). In some cases, stretching processing is performed (Fig. 13e). Then, in step S604, the ellipse generated in step S603 is rotated by an angle by the conversion matrix to regenerate a plurality of ellipses (FIG. 12d).

Returning to FIG. 14 again, the description will be continued. If fan-shaped processing is performed in step S501, the flow advances to step S502 to generate a fan-shaped shape equally divided by the number of gradations set in step S500. In step S504, the data as the reference source for performing the SNP determination comparison and collation is set. In step S505, the flow cytometry data that is the reference destination for performing the SNP determination comparison / collation processing is set. In step S506, the data set in steps S504 and S505 are compared and collated. When the comparison and collation of data is completed, in step S507, the result of the SNP determination comparison and collation processing is displayed in step S506.

FIG. 16 shows the fluorescence intensity (P1, P
FIG. 16 is a diagram showing that the SNP criterion can be diversified by increasing 2). In FIG. 16a, the SNP criterion “Pn” is displayed in the two-dimensional area because there are two types of parameters, P1 and P2. Figure 16b shows the fluorescence intensity (P1, P2, P
The SNP criterion P _{n + 1} obtained in 3) is shown (displayed in a three-dimensional area).

A diversified SNP criterion can be generated by repeating the generation of the SNP criterion for the number of colors. Here, P means a parameter (fluorescence intensity). By increasing the fluorescence intensity in this way, the determination of genes at different parts of the individual,
Alternatively, it becomes possible to simultaneously determine the genes of a plurality of people. In principle, there is no limit to the diversification of SNP criteria.

FIG. 17 shows SN by increasing the fluorescence intensity.
7 is a flowchart showing a processing procedure for diversifying P determination criteria. In step S700, SNP with parameters P1 and P2
A process of generating the determination standard Pn is performed. In step S701, it is determined whether generation for the number of colors has been repeated. If the result of determination is that generation processing for the number of colors has been performed, the processing ends. If generation processing for the number of colors has not been performed yet,
The processes of steps S701 to S702 are repeated to generate SNP determination criteria for the number of colors.

As described above, according to the present embodiment, not only it is possible to judge with what probability the similarity is within the specified search range, but also a large amount of SNP analysis results and high-speed analysis are possible. And the reliability of the analysis result is improved. Subsequently, a third embodiment of the present invention will be described. In the present embodiment, a partial SNP determination comparison and collation process of reference destination data is performed.

FIG. 18a is a range designation screen in the histogram format when partially performing SNP determination comparison and collation processing on the reference data, and FIG. 18b is a range designation screen in the dot plot format. FIG. 19 is a flowchart showing a procedure of a partial SNP determination comparison / collation process of reference destination data.

In step S800, the search range is specified. If the search range is specified, the process proceeds to step S801. If the search range is not specified, the process ends. In step S801, peak value recognition processing of the histogram within the designated range is performed. In step S802, the flow cytometry data that is the reference destination for performing the SNP determination comparison / collation processing is set. In step S803, the judgment and comparison collation of the data set in step S802 is performed. A step S804 displays the result of the judgment comparing and collating in the step S803. As described above, according to the present embodiment, since the search range is partially specified, high-speed analysis of SNP fluorescence intensity information becomes possible.

Finally, a fourth embodiment of the present invention will be described. In the present embodiment, a gauge (one type of SNP determination standard) that is a detection range of an event is automatically created based on flow cytometry data, and determination comparison and matching processing is performed.

FIG. 20 is an explanatory diagram of the SNP determination comparison / verification processing performed by automatically creating a gauge based on the flow cytometry data. As shown in FIG. 20, the gauge is an enlarged value in a predetermined range having local maximum points in the X-axis direction and the Y-axis direction. Figure 21
FIG. 6 is a flowchart showing the procedure of SNP determination comparison / collation processing performed by automatically creating a gauge based on flow cytometry data.

In step S900, a histogram of the X-axis parameter is created. In step S901, the maximum point of the histogram on the X-axis side is detected. If there are multiple equivalence maxima, the one closer to the origin is recognized as valid. In step S902, the number of events included in the gauge range is calculated from the threshold value on the X-axis side. The number of events is calculated by multiplying the total number of events * threshold value.

In step S903, it is determined whether the number of events in the X-axis gauge width exceeds the number of events included in the gauge range obtained in step S902. If the result of determination is that the number of events within the X-axis gauge width exceeds the number of events included within the gauge range, step S905.
Proceed to. Step if the number of events in the X-axis gauge width does not exceed the number of events in the gauge range
The process proceeds to S904, the X-axis gauge width is expanded by the event number addition process, and the process returns to step S903.

In step S905, a histogram of Y-axis parameters is created. In step S906, the maximum point of the histogram on the Y-axis side is detected. If there are multiple equivalence maxima, the one closer to the origin is recognized as valid. In step S907, the number of events included in the gauge range is calculated from the threshold value on the Y-axis side. The number of events is calculated by multiplying the total number of events * threshold value.

In step S908, it is determined whether the number of events in the Y-axis gauge width exceeds the number of events included in the gauge range obtained in step S907. If the result of determination is that the number of events within the Y-axis gauge width exceeds the number of events within the gauge range, step S910.
Proceed to and generate a gauge from the X-axis and Y-axis gauge widths.
When the number of events in the Y-axis gauge width does not exceed the number of events included in the gauge range, the process proceeds to step S909, the Y-axis gauge width is expanded by the event number addition processing, and the process returns to step S908. As described above, according to the present embodiment, even if the distribution areas of the reference destinations overlap, the SNP
Judgment comparison collation processing can be performed.

Although the respective embodiments of the present invention have been described above, the present invention is not limited to the above-mentioned respective embodiments, and various modifications can be made without departing from the spirit of the present invention. It is possible. For example, in each of the above embodiments, as a result of the SNP determination comparison and collation processing, it is determined how likely they are to be similar, and whether they are homo / hetero, but it is not limited to this, and cancer, heart Disease, diabetes,
It may be possible to search and identify a disease gene such as how likely Alzheimer's disease or the like will develop (susceptibility to disease). Furthermore, as a result of the disease gene search,
A guideline for drug discovery that is compatible with the individual gene code, so-called personalized medicine, for example, a drug to be synthesized and a method for synthesizing the drug may be instructed.

Furthermore, in the present embodiment, an example in which the program is stored in the program memory 102 and used is described, but the program may be stored in a computer-readable recording medium. Alternatively, it may be downloaded through a computer network such as the Internet.

[0067]

As described above, according to the present invention, the conversion processing from the fluorescence intensity information obtained by the analysis using the bar-coded magnetic beads into the SNP fluorescence intensity information, the database of these information, and the interactive interface are provided. With the program we have, we can detect and map known and unknown SNPs at high speed and with high reliability.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration example of an SNP scoring system according to a first exemplary embodiment of the present invention.

FIG. 2a shows the contents of the flow cytometry database 103. 2b is the contents of the actual sample database 104.

FIG. 3a is a screen for setting bases (A, T, G, C) and performing SNP determination comparison and matching processing. 3b is a screen in which the base is being set by the pointing device 171. 3c is a setting completion screen. Reference numeral 3d is a screen for displaying the result of the SNP determination comparison / collation process.

FIG. 4a is a screen for adding detailed information to the result of the SNP determination comparing and collating process and registering it in the actual sample database 104. 4b is a screen for displaying the detailed information added in 4a.

FIG. 5 is an event driven from the screen of FIGS.
It is a flow chart which shows the procedure of SNP judgment comparison collation processing.

6 is an event driven SNP from the screen of FIG. 4a
7 is a flowchart showing a processing procedure for adding detailed information to the result of the judgment comparison and matching processing and registering it in the actual sample database 104.

FIG. 7 is a flow chart showing a processing procedure for calculating an average value based on flow cytometry data and creating an SNP criterion.

FIG. 8 is a diagram showing normalization of the number of events.

[Fig. 9] Fig. 9a is a diagram showing flow cytometry data serving as SNP determination criteria. 9b is a diagram showing a state in which the flow cytometry data of 9a is read n times, the boundary value is calculated, and the SNP determination standard is created. FIG. 9c is a diagram showing a state in which flow cytometry data is read n + m times, boundary values are calculated, and SNP determination criteria are created.

FIG. 10 is a flowchart showing a processing procedure for calculating a boundary value based on flow cytometry data and creating an SNP criterion.

FIG. 11 is a DNA fragment barcoded with a fluorescent label.

FIG. 12a is a screen for performing parameter, gradation number, and detailed setting processing to perform SNP search within a specified range of bar coded flow cytometry data. 12
b is a screen for adding a comment in the detailed setting process. 1
2c is a screen surrounded by a fan shape for the number of gradations based on the information set on the screen of 12a. 12d is a diagram in which the search range is surrounded by an ellipse based on the information set on the screen of 12a. 12
e is a screen that displays the result of the SNP determination comparison and matching process designated by an ellipse.

FIG. 13a is a screen for creating an ellipse. 13b is a diagram showing a state in which a straight line is drawn. Reference numeral 13c is a screen for setting the size and angle of a rectangle inscribed with an ellipse of a desired size on a straight line. 13d is a diagram showing how an ellipse inscribed in a rectangle is generated. 13e is a diagram showing a state in which the ellipse is stretched.

FIG. 14 is a flowchart showing a processing procedure for performing SNP search within the specified search range of barcoded flow cytometry data.

FIG. 15 is a flowchart showing a procedure for generating an ellipse.

FIG. 16a is a diagram showing that SNP criteria can be diversified by increasing fluorescence intensity (P1, P2). 16b is SNP obtained by fluorescence intensity (P1, P2, P3)
It is a figure which shows the determination standard Pn + 1.

FIG. 17 is a flowchart showing a processing procedure for diversifying SNP determination criteria by increasing fluorescence intensity.

FIG. 18a is a range specification screen in a histogram format when partially performing SNP determination comparison and collation processing on reference destination data. 18b is a range designation screen in the dot plot format.

FIG. 19 is a flowchart illustrating a procedure of a partial SNP determination comparison / collation process of reference destination data.

FIG. 20 is an explanatory diagram of SNP determination comparison / verification processing performed by automatically creating a gauge based on flow cytometry data.

FIG. 21 is a flow chart showing the procedure of SNP determination comparison and verification processing performed by automatically creating a gauge based on flow cytometry data.

[Explanation of symbols]

101 CPU (Central Processing Unit) 102 Program Memory 103 Flow Cytometry Database 104 Actual Sample Database 106 SNP Search Result Display Device 107 Input Device 121 Reference Generation Program (Histogram / Dot Plot) 122 Base Designation Program 123 Detailed Information Registration Program (Base Information) / Date, disease name) 124 Actual data reading program (self-learning function) 125 Judgment area designation program (fan-shaped / oval) 126 SNP judgment criteria diversification program 127 Oval creation program 128 Partial selection and peak value correction program 129 Judgment area Automatic generation program 130 SNP judgment comparison collation program

─────────────────────────────────────────────────── ─── Continuation of front page (51) Int.Cl. ⁷ Identification code FI theme code (reference) // G01N 21/64 G01N 21/64 F (71) Applicant 591081697 Precision System Science Co., Ltd. Matsudo, Chiba Prefecture Ichikami Hongo 88 (71) Applicant 000233055 Hitachi Software Engineering Co., Ltd. 1-43 Suehiro-cho, Tsurumi-ku, Yokohama-shi, Kanagawa (72) Inventor Mitsuo Itakura 3--18, Kuramoto-cho, Tokushima-shi, Tokushima Tokushima University (72) Inventor Yosuke Takahama 3-18-15 Kuramoto-cho, Tokushima City, Tokushima Prefecture National Tokushima University (72) Inventor Masayuki Machida 1-1-1 East Higashi, Tsukuba, Ibaraki Prefecture Tokoro Tsukuba Center (72) Inventor Toshiki Morita 6-81 Onoue-cho, Naka-ku, Yokohama-shi, Kanagawa Hitachi Sof In-house (72) inventor, Towa Engineering Co., Ltd. Mika Suyama 6-81, Onoue-cho, Naka-ku, Yokohama, Kanagawa Hitachi Software Engineering Co., Ltd. Internal F-term (reference) 2G043 AA04 BA16 CA03 DA02 DA05 EA01 2G045 DA12 DA13 FA36 FA37 FB02 FB07 FB12 GC15 JA01 4B029 AA07 BB20 FA15

Claims (15)

[Claims] 1. A SN from a flow cytometry system.
A SNP scoring system for analyzing P fluorescence intensity information, wherein means for setting parameters and reference source data (SNP data) or / and reference destination data (flow cytometry data) are set by the parameter setting means. An SNP scoring system comprising: means for performing SNP determination comparison / collation for each parameter; and means for displaying the result of the SNP determination comparison / collation. 2. The SNP scoring system according to claim 1, further comprising an actual sample database in which the result of the judgment comparison and collation and the detailed information are stored. 3. The SNP scoring system according to claim 1, wherein the parameter is a base. 4. The SN according to claim 1, wherein the parameter is a combination of patterns or the number of gradations.
P scoring system. 5. The SNP scoring system according to claim 1, wherein the reference source data and / or reference destination data is detected by fluorescently labeling a DNA fragment and barcode-coding it. 6. An SNP determination method, characterized by performing SNP determination by comparing and collating the SNP determination according to claim 1. 7. A SNP determination method, characterized in that, when performing the SNP determination comparison and collation according to claim 1, an SNP determination standard is created based on reference destination data and / or reference source data. 8. The SNP criterion is reference data or /
The SNP determination method according to claim 7, wherein the SNP determination method is created by calculating an average value or a boundary value based on the reference source data. 9. The SNP determination method according to claim 5, wherein the SNP determination criterion is an average value or a boundary value of reference destination data calculated by a self-learning function. 10. The SNP determination method according to claim 7, wherein the SNP determination criterion is a sector shape or an ellipse which is a search range of reference destination data. 11. The SNP determination method according to claim 7, wherein a plurality of the SNP determination criteria are created. 12. A SNP determination method, wherein the reference data according to claim 1, 5, 7, 9 or 10 is partially subjected to SNP determination comparison and collation. 13. A predetermined area is created based on the reference data according to claim 1, 5, 7, 9 or 10 and having a maximum point of the reference data in the X-axis and Y-axis directions as a starting point. An SNP determination method characterized by calculating the number of events included in the area. 14. From a flow cytometry system
A SNP scoring method for analyzing SNP fluorescence intensity information, in which a parameter setting step and SNP determination comparison and collation are performed for each of the parameters set in the parameter setting step with respect to reference source data and / or reference destination data A SNP scoring method, comprising: a step of displaying a result of the search performed by the step of comparing and collating the SNP. 15. From a flow cytometry system
A SNP scoring method for analyzing SNP fluorescence intensity information, wherein a step of setting a parameter and reference source data or / and reference destination data, SNP determination comparison and collation for each parameter set by the step of setting the parameter And a step of displaying a result retrieved by the step of comparing and collating the SNP judgment.
SNP scoring program for running SNP scoring methods.