JP2007061092A - Method of determining biospecies - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method for determining biospecies reducing as possible the possibility of wrong determination according to biological feature of the biospecies when the unknown sample contains organism whose biospices not corresponds to any one of the previously determined categories in determining biospecies using pattern recognition. <P>SOLUTION: The method provides specific indefinable threshold value for every known organism and determines to carry out determination of biospecies to an unknown sample by utilizing the indefinable threshold value, wherein the determination is carried out when the determination is "yes". <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a biological species determination method using pattern recognition, and in particular, can be suitably applied to a nucleic acid sequence analysis system using a DNA microarray as a method for analysis. Demonstrate.

  Conventionally, a DNA microarray in which nucleic acid fragments called probes, which are fixed on a substrate made of glass or the like, are widely used for analyzing unknown nucleic acid samples. By using this DNA microarray, an unknown nucleic acid fragment sample (referred to as an unknown sample) is analyzed, and it has also been used in a method for determining which biological species the unknown sample is derived from. This method uses a nucleic acid base pairing reaction called a hybridization reaction.

  What is a hybridization reaction is described below. In most cases, DNA has a double helix structure, and the bond between the two strands is realized by hydrogen bonding between bases. On the other hand, RNA often exists as a single RNA. There are 4 types of bases, ATGC in the case of DNA, and 4 types of AUGC in the case of RNA. The base pairs capable of hydrogen bonding are A-T (U) and G-C pairs. A hybridization reaction means that two nucleic acid molecules in a single-stranded state react with each other under appropriate conditions and bind to one through a base sequence in the nucleic acid.

  Based on this, a conventional method for determining a biological species will be described below. A probe immobilized on a substrate and a nucleic acid fragment having a complementary base sequence capable of forming a base pair with the probe cause a hybridization reaction under appropriate reaction conditions. Can be combined. If the probe immobilized on the substrate has a base sequence corresponding to a specific organism and it can be recognized that a hybridization reaction has occurred and the probe and the nucleic acid fragment are bound, the species corresponding to the nucleic acid fragment will be the probe. It can be determined that it is the same as the corresponding species. That is, the biological species corresponding to the unknown sample can be determined.

  For example, it is possible to optically recognize whether or not a hybridization reaction has occurred by adding a fluorescent substance to a nucleic acid fragment. When fluorescence is generated from the probe immobilized on the substrate, it can be recognized that a hybridization reaction has occurred and a conjugate of the probe and the nucleic acid fragment has been formed. Based on this result, the nucleic acid fragment is determined to be identical to the biological species corresponding to the probe. On the other hand, if no fluorescence is generated from the probe, it is recognized that a hybridization reaction has not occurred and a conjugate of the probe and the nucleic acid fragment has not been formed, and it is determined that the nucleic acid fragment is not a biological species corresponding to the probe. By using this determination method, when one unknown sample is given, it is possible to determine which species correspond to a plurality of species by a single hybridization reaction. That is, a plurality of probes whose corresponding species are known are prepared, and their positions are fixed on the substrate. The DNA microarray thus prepared is allowed to undergo a hybridization reaction with an unknown sample under appropriate reaction conditions. Then, a biological species is specified from the position on the substrate, and it can be determined whether or not it corresponds to the biological species by the presence or absence of fluorescence. That is, the biological species of the unknown sample can be determined by confirming from which position on the substrate the fluorescence is generated.

  However, in practice, as a result of a hybridization reaction between an unknown sample and a probe, a probe corresponding to only one species does not generate fluorescence. In many cases, even if it is known in advance that an unknown sample corresponds to only one species, the hybridization reaction causes fluorescence from other probes apart from the probe for identifying the species. May occur. This is because a nucleic acid molecule may partially bind to another probe via a base sequence contained therein, and is called cross-hybridization. Since this cross-hybridization occurs, it is often impossible to determine a biological species corresponding to an unknown sample with only two pieces of information, that is, the position on the substrate and the presence or absence of fluorescence as described above. For example, when an unknown sample is hybridized with a DNA microarray having probes corresponding to a plurality of species, even if fluorescence is generated from probes corresponding to organisms A and B, the following results are reflected. It is necessary to examine the possibility of being.

  That is, in consideration of the possibility of cross-hybridization, the case where only organism A is included in the unknown sample, the case where only organism B is included, the case where both organism A and organism B are included, and the like are considered. Species contained in cannot be determined.

  As a general trend, the fluorescence intensity generated from a nucleic acid fragment bound to the same probe occurs when it hybridizes and is almost completely bound to the fluorescence intensity produced when cross-hybridized and partially bound to the probe. The fluorescence intensity is stronger. Therefore, when analyzing an unknown sample using a DNA microarray and determining which species the unknown sample is, it is comprehensive from the position information of the probe and the signal intensity information represented by the fluorescence intensity. Should select a method to determine the species.

  The fluorescence intensity after the hybridization reaction between the DNA microarray and the unknown sample can be stored and used in the storage means as vector data ordered by the probe position.

  In JP 2002-533699, the vector data obtained from an unknown sample is analyzed using a DNA microarray, and the known vector data most similar to the vector data obtained from this unknown sample is searched. A method is disclosed. This information processing of retrieving the most similar known vector data is known as pattern recognition and is very common. Pattern recognition is a process of making an observed pattern correspond to one of a plurality of predetermined “categories”. As an example for explaining “category”, in a technical field called OCR (Optical Character Recognition), characters printed or handwritten on paper are pattern-recognized as one pattern. At this time, assuming that the recognition target is a number, “Which of the numbers 0 to 9 is the closest to the character written on the paper?” Is obtained by comparing with known vector data. In the pattern recognition problem, 10 types of numbers from 0 to 9 to be recognized are “categories”.

Usually, in the problem of pattern recognition, the number and types of categories to be recognized are predetermined. For example, in the above example, numbers are 0 to 9, and the number and types of categories are determined in advance, such as about 3000 kanji for Japanese and 26 for the English alphabet.
Special table 2002-533699 gazette

  However, when pattern recognition is performed using vector data obtained as a result of hybridization with a DNA microarray using an unknown nucleic acid fragment sample, the category may not be assumed in advance. Many. For example, in the case where it is determined using a DNA microarray whether or not there are bacteria in an unknown sample, the type of bacteria corresponding to the nucleic acid fragment to be deployed as a probe is determined in advance. However, it is unlikely that organisms that are actually present in the unknown sample will fit within the species corresponding to the probe. This is because the species of the whole species compared to the nine categories from 0 to 9 in the technical field called OCR described above, 26 categories of alphabets A to Z, or about 3000 categories of kanji. Is an overwhelming majority. Therefore, even if the organism species to be determined are limited to bacteria, there are a huge number of categories to be assumed, and it is virtually impossible to predetermine categories for all types of bacteria. Therefore, it is necessary to define categories by limiting the types of organisms assumed to be present in unknown samples to some extent.

  Therefore, there is a problem in applying a conventional method used for character recognition such as OCR as it is to the determination of a biological species. Specifically, if an unknown sample contains an organism in a category that is not set in advance, there is a problem that an erroneous determination is made such that the organism is forced to correspond to a predetermined category. It was.

  The object of the present invention is to determine the biological species using pattern recognition, when an unknown sample contains an organism that does not correspond to any predetermined category, depending on the biological characteristics of the biological species, The object is to reduce the possibility of erroneous determination.

The biological species determination method of the present invention is a biological species determination method that analyzes a substance assumed to contain a substance derived from an organism and determines the type of the corresponding organism.
Analyzing a plurality of known samples whose corresponding species are known by a species analysis method to obtain a plurality of analysis data;
Setting an indeterminable threshold for the species corresponding to the known sample based on the plurality of analysis data obtained from the known sample;
Analyzing an unknown sample whose corresponding species is unknown by the species analysis method to obtain analysis data for identifying the species corresponding to the unknown sample;
Determining a type corresponding to the unknown sample based on the indeterminable threshold, or determining whether it is impossible to determine;
If it is determined to make a determination, determining the species of the unknown sample based on the plurality of analysis data;
It is the biological species determination method which has.

In another aspect of the biological species determination method of the present invention, a species assumed to contain a substance derived from an organism is analyzed by the biological species analysis method to determine the type of the corresponding organism. In the species determination method,
(1) selecting a biological species assumed as a determination result for the unknown sample;
(2) A plurality of image data that are characteristic of the species and can be used for pattern recognition from each of the known samples obtained from a plurality of individuals known to belong to the selected organism. Obtaining an image data group comprising:
(3) selecting image data from the image data group and setting an indeterminate threshold using a relationship with the remaining image data;
(4) obtaining image data from an unknown sample;
(5) determining whether image data from the unknown sample is based on the indeterminable threshold and determining whether or not it is possible to determine the species corresponding to the unknown sample;
(6) When it is determined to perform the determination in (5), a step of determining a biological species using an identification dictionary including the image data group;
It is a biological kind determination method characterized by having.

  The information processing apparatus for biological species determination according to the present invention includes a plurality of analysis data obtained by analyzing a plurality of known samples whose corresponding species are known by a biological analysis method, and the plurality of analysis data. It is determined whether or not the biological species corresponding to the unknown sample can be determined based on the memory storing the indeterminate threshold set based on and the indeterminate threshold stored in the memory, and determined to be determinable In this case, the information processing apparatus for biological species determination includes a processing unit that determines a biological species corresponding to the unknown sample based on a plurality of analysis data stored in the memory.

Another aspect of the information processing apparatus for biological species determination of the present invention is an information processing for determining a corresponding biological species by analyzing a substance assumed to contain a substance derived from an organism. In the apparatus, known sample image data input means for inputting image data characteristic of the biological species obtained by analyzing a plurality of known samples whose corresponding biological species are known;
Unknown sample image data input means for inputting image data obtained by analyzing an unknown sample in the same manner as the known sample;
Storage means for storing the captured image data;
Means for setting an indeterminate threshold for the species corresponding to the known sample based on the plurality of analysis data obtained from the known sample;
Biological species determining means for determining whether or not to determine whether or not to determine image data from an unknown sample based on the indeterminate threshold, and determining the biological species corresponding to the unknown sample ,
Storage means for storing the determination result of the determination means;
An information processing apparatus for determining a species, comprising output means for outputting a determination result stored in the storage means.

The program for determining a biological species of the present invention is a program for causing a computer to execute determination of a biological species corresponding to an unknown sample,
(1) A plurality of pieces of image data corresponding to image data characteristic of the assumed species obtained by analyzing known samples from a plurality of different individuals belonging to the assumed species as a determination result for the unknown sample. Recalling the plurality of known sample image data from the stored storage means;
(2) reading the unknown sample image data from a storage means storing a plurality of image data corresponding to the image data obtained by analyzing the unknown sample in the same manner as the known sample;
(3) selecting one from the known sample image data, and setting a non-determinable threshold using a relationship between the selected one and the remaining image data;
(4) processing the unknown sample image data based on the non-determinable threshold value, and determining a type of organism corresponding to the unknown sample;
(5) storing the determination result obtained in the determination step in a storage means;
(6) A biological type determination program, comprising: a step of outputting a determination result stored in the storage means.

  The recording medium for biological species determination of the present invention is a recording medium in which a program for executing biological species determination by a computer is recorded so as to be readable, and the program is a biological species determination program having the above-described configuration. It is a recording medium characterized by being.

  Another aspect of the biological species determination method of the present invention is based on a plurality of analysis data obtained by analyzing a plurality of known samples whose corresponding biological species are known by a biological analysis method, and the plurality of analysis data. If it is determined that the biological species corresponding to the unknown sample is determinable based on the determinable threshold set, and then it is determined that determination is possible, the plurality of analysis data Is a biological species determination method for determining a biological species corresponding to the unknown sample.

  According to the present invention, when an organism that does not correspond to any predetermined category is included in the unknown sample, it can be determined that determination is impossible, so that an effect of obtaining an appropriate species determination result can be obtained. is there. In addition, since it is possible to set a parameter for performing an indeterminate determination for each category corresponding to the species, there is an effect that an optimum species determination result corresponding to the biological characteristics of the species can be obtained. .

  The biological species determination method according to the present invention includes a method of analyzing vector data and creating an identification dictionary and determining an indeterminate threshold. In the method for judging a biological species according to the present invention, vector data obtained by analyzing a nucleic acid fragment sample (referred to as a known sample) extracted from an organism whose biological species is first known is stored in an external storage means. . The species data of the unknown sample is determined by referring to the vector data obtained by analyzing the known sample like a dictionary. Therefore, for the species determination obtained by analyzing the known sample stored in the external storage means. The total of the vector data is referred to as an identification dictionary.

  Next, using the vector data stored as an identification dictionary, a determination impossible threshold is set. The method for setting the indeterminate threshold will be described in detail later. It is determined whether or not the biological species can be determined by the identification dictionary created by the unknown sample for which the corresponding biological species is to be determined, or cannot be determined (determination is impossible) according to the set determination impossible threshold.

  Hereinafter, the determination method of the present invention will be described in the case where the result of analyzing a known sample and an unknown sample is obtained as image data.

  In selecting a known sample for creating an identification dictionary, first, a type of organism that is assumed to belong to the organism to be determined is selected. For example, when there is a possibility that bacteria are present in an unknown sample and it is desired to perform species determination regarding bacteria, a known species is selected in advance from the bacteria. The selected biological species corresponds to a category in the biological species determination method using pattern recognition. We have already mentioned that it is necessary to limit the category to some extent because the number of species is overwhelmingly large compared to numbers and alphabets.

  Next, a solid of each selected species is prepared, and a nucleic acid fragment sample extracted from the solid of each species is obtained. With this as a known sample, an analysis method for obtaining image data from the known sample and the unknown sample is selected. This analysis method is selected from methods that enable determination of species by pattern recognition. For example, an analysis method for recognizing the obtained image data as vector data using a DNA microarray or the like can be suitably used.

  How to obtain image data using a DNA microarray will be described. A probe is prepared for each species, and is fixed at a predetermined position on the substrate as described above (that is, a position where a probe corresponding to which species is deployed is determined in advance). Yes. For example, by applying a fluorescent substance to the nucleic acid fragment, it is possible to optically recognize whether or not a hybridization reaction has occurred when the DNA microarray and the nucleic acid fragment are reacted under appropriate conditions.

  In the present invention, rather than creating an identification dictionary from image data obtained from one known sample for one category, each category has two or more known samples (two or more different samples of the same organism). An indeterminable threshold is set based on the image data obtained from the individual, and the biological type is determined.

  In addition, if the organism to be determined is a microorganism, it is needless to say that “species” of the microorganism can be selected as the organism type, and the present invention can be applied to various other organisms.

  Hereinafter, an example of the present invention will be described with reference to the drawings.

  FIG. 1 is a flowchart illustrating a processing procedure in an example of the biological species determination method of the present invention. This organism type determination method determines whether there is a substance derived from this species that can identify the target species in an unknown sample, and if so, what kind of organism the organism is derived from. It is a method to do. The rejection in the biological species determination method is to determine that a substance derived from the biological species selected as the target does not exist in the unknown sample. In the following, the present invention will be described mainly with reference to biological species determination using genome analysis such as microorganisms. However, the technique of the present invention can be applied to, for example, a test system using an antigen-antibody reaction. Further, the present invention may be applied to a system that analyzes an individual identification genomic region such as MHC.

  The flow of the biological sample determination process of the unknown sample in the present invention can be broadly divided into a learning phase for creating an identification dictionary using a known sample and a determination phase for determining an unknown sample. In FIG. 1, 101 to 104 are learning phases, and 105 to 108 are determination phases.

  The learning phase will be described below. In step 101, a known sample containing a nucleic acid fragment extracted from an organism with a known organism type is prepared. For example, a solution containing the genome of a fungus whose species is specified corresponds to the known sample. Using this known sample, a series of hybridization reaction experiments 102 are performed to obtain data. Although details will be described later, for example, when a DNA microarray is used, first, a nucleic acid fragment contained in a known sample is amplified by a PCR reaction, and a fluorescent substance is applied. Thereafter, a hybridization reaction is performed with the DNA microarray, and the fluorescence intensity data of each spot is recognized as an image and stored in the external storage means. Based on this image data, the indeterminable threshold setting step 103 and the dictionary creation step 104 create an indeterminate threshold and an identification dictionary, respectively.

  Next, the determination phase will be described. An unknown sample is prepared (step 105), and the hybridization reaction experiment 106 is performed in exactly the same procedure as in step 102. By comparing the image data obtained by the hybridization reaction with the indeterminable threshold and the identification dictionary obtained in the learning phase, the species is determined for the unknown sample (step 107). As a result of the determination 108, “unknown sample corresponds to species A”, “unknown sample contains substances derived from species A to C”, “unknown sample includes species other than species A to Z” A result such as “a substance derived from an organism included in the organism group α exists” or “105 unknown samples cannot be determined (= not determined)” is obtained.

  Two different methods in particular regarding the setting method of the indeterminate threshold during the learning phase described above will be described in detail below.

First, a known sample obtained from the same but different organism is prepared and subjected to a hybridization reaction with a DNA microarray to obtain image data. Any of the following methods can be preferably used for setting the indeterminate threshold.
Method (1) A method in which one of three or more known sample image data is selected and excluded, an identification dictionary is created from the remaining known sample image data, and an indeterminate threshold is set using the identification dictionary.
Method (2) A method for setting an indeterminable threshold using the distances obtained by the pattern recognition algorithm for all two arbitrary combinations selected from image data of three or more known samples.

  First, the method for setting the indeterminable threshold of the above method (1) will be described. A flowchart of the processing procedure of this method is shown in FIG. First, n different biological species (S1 to Sn: n ≧ 2) assumed as biological species determination results for an unknown sample, that is, target categories are selected (step 802). Next, a process for obtaining an indeterminable threshold value specific to each selected target category is performed. Next, as a result of preparing a known sample of the category selected as the target category in step 802 and performing hybridization, image data can be obtained. The image data is stored in an external storage means for creating an identification dictionary. The total of the image data is called learning data. Hereinafter, a method for setting the non-determinable threshold value of (1) will be described by taking a biological species belonging to the target category S1 as an example.

  First, m individuals S1-X (1 ≦ X ≦ m, m ≧ 3) belonging to the target category S1 are prepared. A diffusion fragment is extracted from each prepared individual, and m (m ≧ 3) known samples are obtained. The m known samples and the DNA microarray are hybridized under appropriate conditions to obtain m (m ≧ 3) (Ps1-1 to Ps1-m) image data groups. Next, in learning data division step 803, one of these image data is selected and removed from the learning data. Next, an identification dictionary 806 is created in a dictionary creation step 805 using the remaining m−1 pieces of learning data 804 excluding one image data. This dictionary creation step 805 is created in accordance with the adopted pattern recognition algorithm.

  For the determination of the unknown pattern by pattern recognition, a method selected from known methods can be used. The method for judgment and classification by pattern recognition is, for example, “Statistical Pattern Recognition: A Review” in IEEE Transaction on Pattern Analysis and Machine Learning, Vol. 22, No. 1, January 2000, pp.4-pp.37. "Reviewed by Anil K. Jain, Robert PW Duin, and Jianchan Mao. Specifically, for example, a pattern recognition technique such as a k-Nearest-Neighbor method, a classification tree, a support vector machine, a Bayes identification method, a boosting method, or a neural network can be used.

  For example, if a neural network is adopted as a pattern recognition algorithm, a network weight parameter set is learned as an identification dictionary. For example, if Support Vector Machine is adopted as a pattern recognition algorithm, a representative sample vector called a support vector and its weight are learned as an identification dictionary. In the present invention, learning or learning as an identification dictionary is synonymous with creating an identification dictionary based on learning data.

  Next, one image data excluded from the learning data is determined using the identification dictionary 806 (step 808). Here, for example, it is assumed that the image data Ps1-1 corresponding to the individual S1-1 is excluded from the learning data. At this time, it should be noted that the identification dictionary 806 does not include one image data excluded in step 807. Therefore, for the identification dictionary 806, the individual S1-1 removed in step 807 is an unknown sample. When the determination is performed using the identification dictionary, it is necessary to previously define a norm represented by the Euclidean norm in order to compare the vector data stored in the external storage unit. The case where the Euclidean norm is employed in the biological species determination method of the present invention will be described later. Of course, various common norms may be employed. The determination index 809 is obtained through the above steps (step 809).

  Generally, the determination result of the pattern recognition algorithm is numerical data. For example, it may be a determination probability, a similarity, or simply a distance between vector data. As described above, the determination index 809 means numerical data that is a determination result calculated using a predefined norm.

  In this way, one determination index A1-1 for the target category S1 is obtained using an identification dictionary created by using learning data obtained by removing one image data from learning data among m pieces of image data.

  Next, the image data of the target category S1 that is not removed in the learning data division step 803 is newly selected from S1-X (1 ≦ X ≦ m, m ≧ 3) and removed in the same manner. Here, it is assumed that the image data corresponding to S1-2 is selected. Similar processing is performed to obtain a determination index A1-2 for the target category S1. That is, the above operation is similarly performed on each image data of the target category S1, thereby obtaining a determination index set {A1} including m determination indexes. The judgment index set {A1} is composed of m judgment index elements, A1-1, A-2,... A1-m.

  A determination impossible threshold for the target category S1 can be set from the determination index set {A1} thus obtained. In the above description, the method for obtaining the determination index set has been described with respect to the target category S1. Similarly, a set of determination indices is obtained for each of the target categories other than the target category S1 among the n target categories selected first. As a result, n decision index sets can be obtained.

  FIG. 9 shows an example in which one determination index set is selected from the n determination index sets, and the distribution of the determination index set 810 is displayed as a histogram. When the determination index 809 indicates similarity, for example, the determination impossible threshold is set to α times (α <1) the minimum value of the set, or β times (β> 0) the average value or median value of the set. ). On the other hand, when the determination index of 809 indicates dissimilarity, for example, the determination impossible threshold is set to α times the maximum value of the set (α> 1), or the average value of the set or the β of the median value Or double (β> 0). What value should be set for the indeterminate threshold based on the set of decision indices depends on the type of organism to be examined, the type of analysis method using pattern recognition, the target decision accuracy, etc. Can be selected. As a method for confirming whether or not the setting of the indeterminate threshold value determined in this way is appropriate, there is a method in which a sample that has been previously determined not to be included in the selected target category is used as the unknown sample 105. The unknown species biological species determination process described above with reference to FIG. 1 is executed to test whether or not a result of “undeterminable” is obtained for this unknown sample. Can be confirmed.

Next, a method for setting the indeterminable threshold of method (2) will be described below. FIG. 10 shows another example in which an indeterminate threshold value is set. An indeterminable threshold setting method when the k-Nearest-Neighbor method (especially k = 1) is selected as the pattern recognition algorithm and the Euclidean norm is adopted as the norm will be described below. In this case, assuming that the determination index calculated from the image data obtained by analyzing the unknown sample indicates the dissimilarity, a result of “determination impossible” is obtained when the determination index is larger than a predetermined determination failure threshold. In order to set an indeterminable threshold, first, one target category S1 is selected, and as a result of hybridization of all known samples belonging to S1, image data is obtained and stored in the external storage means. A combination of arbitrary two image data belonging to S1 is selected from the total of the stored image data, and vector data composed of fluorescence intensities ordered by probe positions recognized based on the two image data. The Euclidean distance of is calculated. Subsequently, a combination of two image data not selected above is newly selected, and similarly, the Euclidean distance is calculated based on the two newly selected image data. With such a procedure, the Euclidean distance is calculated based on the respective combinations of the image data groups belonging to S1 and stored in the external storage means. FIG. 7 shows a case where six known samples belonging to the target category are prepared. In this case, the number of Euclidean distances calculated based on the two image data is ₆ C ₂ = 15.

FIG. 10 shows the Euclidean distance calculated based on the combination of all the image data belonging to the target category S1, using a histogram with the determination index as the x-axis. In FIG. 10, there are two mountains in the distance distribution. This means that sample vectors belonging to the category are localized in two regions. As described above, since the property of the target category S1 can be confirmed from the histogram, it is possible to select an appropriate determination impossible threshold setting method for each target category.
For example, a statistical representative value such as an average value or median value of the distance set can be used as the indeterminate threshold value.

  Next, the obtained determination impossible threshold is subjected to the biological sample determination processing of the unknown sample described above with reference to FIG. 1 to determine whether or not the determination impossible threshold set in this way is appropriate. ) Confirm in the same way. A sample that is previously known not to be included in the selected target category is used as the unknown sample 105. This unknown sample is tested to see if a result of “undecidable” is obtained, and it is confirmed whether the setting of the undecidable threshold is correct.

  Next, each processing such as a computer system as an information processing apparatus that can be used in the above-described organism type determination method, a program, and an analysis method using image recognition will be described.

The organism type determination described above can be automated by processing on a computer according to a program created in advance. The information processing apparatus for biological species determination according to the present invention is an information processing apparatus for analyzing a substance assumed to contain a substance derived from a living organism and determining a corresponding biological species. . This information device can be configured using at least the following means.
(1) A known sample image data input means for inputting image data characteristic of the biological species obtained by analyzing a plurality of known samples whose corresponding biological species are known.
(2) Unknown sample image data input means for inputting image data obtained by analyzing an unknown sample in the same manner as the known sample.
(3) Storage means for storing the captured image data.
(4) A means for setting an indeterminable threshold for a species corresponding to the known sample based on the plurality of analysis data obtained from the known sample.
(5) Biological type determination means for processing image data from an unknown sample based on the indeterminable threshold and determining the type of the biological source of the unknown sample.
(6) Storage means for storing the determination result of the determination means.
(7) Output means for outputting the determination result stored in the storage means.

The determination threshold value is preferably set based on a program having the following steps in which image data from three or more individuals is stored in the storage unit.
(A) One image data is selected and excluded from three or more image data, an identification dictionary is created using a plurality of remaining image data, and the image data previously excluded based on the obtained identification dictionary A process of obtaining a judgment index by performing a process of making a judgment and obtaining a judgment index for each image data to obtain a judgment index set composed of three or more judgment indices.
(B) A step of setting an indeterminable threshold value from the determination index set.

Moreover, it is preferable that the determination impossible threshold value is set as follows. That is, the storage means stores image data from three or more individuals. In addition, the number of individuals is 3 or more, image data from these individuals is stored in the storage means, and the determination impossible threshold is set based on a program that executes at least the following steps. preferable.
(A) A step of obtaining a distance set by obtaining a distance between two image data for all combinations of arbitrary two image data selected from the three or more image data.
(B) A step of determining an indeterminate threshold from the distance set.

In addition, the organism type determination program according to the present invention is a program for causing a computer to execute an organism type determination corresponding to an unknown sample, and for executing at least the following steps.
(1) A plurality of pieces of image data corresponding to image data characteristic of the assumed species obtained by analyzing known samples from a plurality of different individuals belonging to the assumed species as a determination result for the unknown sample. Recalling the plurality of known sample image data from the stored storage means;
(2) A step of reading out the unknown sample image data from the storage means storing a plurality of image data corresponding to the image data obtained by analyzing the unknown sample in the same manner as the known sample.
(3) selecting one from the known sample image data, and setting an indeterminable threshold using a relationship between the selected one and the remaining image data.
(4) A step of processing the unknown sample image data based on the indeterminable threshold and determining a kind of organism corresponding to the unknown sample.
(5) A step of storing the determination result obtained in the determination step in a storage means.
(6) A step of outputting the determination result stored in the storage means.

It is preferable that the above-described determination impossible threshold setting step is performed by at least the following steps with the number of individuals set to 3 or more and image data from these individuals stored in the storage means.
(A) One image data is selected and excluded from the three or more image data, an identification dictionary is created using a plurality of remaining image data, and image data is excluded first based on the obtained identification dictionary The process of obtaining the determination index by performing the process for determining the determination index for each image data to obtain a determination index set composed of three or more determination indices.
(B) determining a non-determinable rejection threshold from the determination index set;

In addition, it is preferable that the above-described determination impossible threshold setting step is performed by at least the following steps by setting the number of individuals to 3 or more, storing image data from these individuals in the storage means.
(A) A step of obtaining a distance set by obtaining a distance between two image data for all combinations of arbitrary two image data selected from the three or more image data.
(B) A step of determining an indeterminate threshold from the distance set.

  The storage means stores indeterminable threshold values for each of a large number of known organism types, and the necessary number of organism-derived substances included in the unknown sample is assumed to indicate the presence according to the type of the unknown sample. It is a good idea to add a category selection step to the program. As a result, the number of categories for examining whether or not determination is possible can be effectively reduced, and more efficient determination processing can be performed.

  The program may be stored in a storage unit of a computer system, or may be stored in a recording medium and distributed to users. Further, it may be distributed via a network system.

  FIG. 2 is a block diagram showing an example of the configuration of an information processing apparatus using a computer system that can execute the biological species determination method. This apparatus includes at least an external storage device 201, a central processing unit (CPU) 202, a memory 203, and an input / output device 204. The external storage device 201 holds the program having the above-described configuration for performing biological type determination, and image data as a result of analysis using a hybridization reaction for a known sample and an unknown sample. The external storage device 201 further stores the result of determination using the indeterminate threshold. The central processing unit (CPU) 202 executes a program for biological species determination and controls all devices. The memory 203 temporarily records programs, subroutines, and data used by the central processing unit (CPU) 202. The input / output device 204 interacts with the user. In many cases, the user triggers program execution via this input / output device. Also, the user views the results and controls the program parameters via this input / output device.

  FIG. 3 is a diagram showing the state of hybridization on the DNA microarray. In most cases, DNA has a double helix structure, and the bond between the two strands is realized by hydrogen bonding between bases. On the other hand, RNA often exists as a single RNA. There are four types of bases in the case of DNA, ATGC, and in the case of RNA, AUGC. The base pairs capable of hydrogen bonding are A-T (U) and G-C pairs. In general, a hybridization reaction refers to a state in which single-stranded nucleic acid molecules are partially bound to each other through a partial base sequence in the nucleic acid molecule. In the example shown in FIG. 3, the nucleic acid molecule (probe) attached to the upper substrate in the figure is shorter than the nucleic acid molecule in the lower sample. If the nucleic acid molecule present in the sample contains the base sequence of the probe, this hybridization reaction will be successful, and the nucleic acid molecule in the sample will be trapped in the DNA microarray.

  Next, the overall experimental procedure for obtaining image data using a DNA microarray will be described with reference to FIG. The “sample” 401 is a liquid or an individual that contains or is assumed to contain a target biological substance, for example, a nucleic acid (including a nucleic acid contained in a cell). . For example, when the present invention is applied to identify the causative agent of an infectious disease, blood derived from animals such as humans and livestock, body fluids such as sputum, gastric juice, vaginal secretions, oral mucus, urine and feces The source of the unknown sample 401 is any effluent, microorganisms such as bacteria collected from humans, animals, etc., or any material that may be derived from such microorganisms. In addition, media that may cause contamination by bacteria, such as food poisoning, food subject to contamination, drinking water, and water in the environment such as hot spring water, may be used as a source of unknown samples. In addition, animals and plants such as quarantine at the time of import / export are also subject to this. In the case of a known sample, it is a sample prepared from a microorganism of a known type.

  Next, if necessary, the nucleic acid as the sample 401 is amplified using the “biochemical amplification” method 402. For example, when the present invention is applied to identify the causative bacteria of an infectious disease, a target nucleic acid is amplified by a PCR method using a PCR reaction primer designed for 16s rRNA detection, or a PCR amplified product is used as the original. Further, a PCR reaction or the like is performed for adjustment. Moreover, you may adjust by amplification methods, such as LAMP method other than PCR.

  Thereafter, the amplified sample or 401 sample itself is labeled by various labeling methods for visualization. As the labeling substance, fluorescent substances such as Cy3, Cy5, and Rodamin are usually used. Further, in the experimental procedure of biochemical amplification of 402, a labeled molecule may be mixed.

  The nucleic acid to which the labeled molecule is added is subjected to a hybridization reaction (405) with the DNA microarray 404 in FIG. This situation is as shown in FIG. For example, when the present invention is applied to identify a causative bacterium of an infectious disease, the DNA microarray 404 has a bacterium-specific probe immobilized on a substrate. The probe design of each bacterium is, for example, highly specific to the bacterium compared to the genome part coding 16s rRNA, and has sufficient hybridization sensitivity that does not vary as much as possible with each probe base sequence. Do as you expect. The carrier (substrate) for fixing the DNA microarray probe 404 may be a flat substrate such as a glass substrate, a plastic substrate, or a silicon wafer. Further, even if a three-dimensional structure having irregularities, a spherical shape such as a bead, a rod shape, a string shape, a thread shape or the like is used, the actual scene form and effect of the present invention are not affected.

  Usually, the surface of the substrate is treated so that the probe DNA can be immobilized. In particular, a product having a functional group introduced so that a chemical reaction is possible on the surface is a preferable form in terms of reproducibility because the probe is stably bound in the course of the hybridization reaction. Examples of the probe immobilization method include a method of immobilizing a probe on a substrate using a combination of a maleimide group and a thiol (-SH) group. That is, a thiol (-SH) group is bonded to the end of the nucleic acid probe, and the solid phase surface is treated so as to have a maleimide group, so that the thiol group of the nucleic acid probe supplied to the solid phase surface is fixed. The maleimide group on the phase surface reacts to immobilize the nucleic acid probe. The maleimide group is introduced into the glass substrate by first reacting the glass substrate with an aminosilane coupling agent and then reacting the amino group with an EMCS reagent (N- (6-Maleimidocaproyloxy) succinimide: Dojin). be able to. Introduction of SH groups into DNA can be performed by using 5′-Thiol-Modifier C6 (Glen Research) on an automatic DNA synthesizer. Examples of combinations of functional groups used for immobilization include combinations of epoxy groups (on the solid phase) and amino groups (ends of nucleic acid probes) in addition to the combinations of thiol groups and maleimide groups described above. . Further, surface treatment with various silane coupling agents is also effective, and oligonucleotides into which functional groups capable of reacting with functional groups introduced with the silane coupling agents are used. Furthermore, a method of coating a resin having a functional group can also be used.

  After performing the hybridization reaction, the surface of the DNA microarray 404 is washed, the nucleic acid not bound to the probe is peeled off, and then usually dried, and the fluorescence amount 405 is measured. Then, an excitation light is irradiated onto the substrate of the DNA microarray, and an image (406) obtained by measuring the fluorescence intensity is obtained. This image (406) becomes image data. An example of the image data is shown in FIG. Corresponding to different known samples, different image data (images) are obtained between 601 and 602 in FIG.

  Next, the principle of the DNA microarray in the case of identifying infectious bacteria is shown using FIG. The DNA microarray shown in FIG. 5 is made for the purpose of specifying Staphylococcus aureus, for example. The left column is a treatment sequence derived from a wild strain of S. aureus, and the right column is a treatment sequence derived from a wild strain of Escherichia coli. For example, it can be considered that the flow on the left is a flow for processing blood of a patient infected with S. aureus, and the flow on the right is a flow for processing blood of a patient infected with Escherichia coli.

  Both basically perform the same processing. That is, first, DNA is extracted from, for example, blood or sputum of a bacterially infected patient. In this case, generally, human DNA derived from somatic cells of a patient may be included.

  When the extracted DNA is small, amplification is performed by a method such as PCR. In this case, a fluorescent substance or a substance capable of binding the fluorescent substance is generally mixed as a label. When amplification is not performed, using the extracted DNA, a fluorescent substance or a substance capable of binding the fluorescent substance is mixed as a label while forming a complementary strand, or the directly extracted DNA is directly fluorescent or fluorescent. A substance capable of binding the substance is added as a label.

  Usually, when PCR amplification is performed, for the purpose of identifying the bacteria of an infectious disease, it is common to amplify a part of a base sequence constituting ribosomal RNA so-called 16s rRNA. In this case, the PCR primer for S. aureus on the left and the PCR primer for E. coli on the right are almost the same. More specifically, multiplex PCR is performed using a primer set that can amplify the 16s rRNA coding portion of any fungus.

  If a DNA microarray designed for the purpose of determining S. aureus works correctly, the left hybrid solution will react positively with the spot and the right hybrid solution will react negatively with the spot. In exactly the same way, if a DNA microarray designed for the purpose of determining the presence of E. coli operates correctly, the following reaction occurs. That is, in the left hybrid solution (hybridization solution), the spot reacts negatively, and in the right hybrid solution (hybridization solution), the spot reacts positively.

  Image data can be obtained by measuring the fluorescence intensity from the positively reacted spot and performing the scan image processing shown in FIG. Here, if image data is obtained under the same analysis conditions using samples from different individuals belonging to the same type, if the same fluorescence intensity is always obtained, it can be used as a dictionary. However, in practice, there is a variation in fluorescence intensity, so there is a clear criterion for determining whether image data from an unknown sample should fall within this variation range or not belong to a known category outside that range. It may be difficult to obtain. Furthermore, as shown in the examples described later, cross-hybridization occurs depending on the probe. Therefore, in the present invention, as shown in FIG. 8, a criterion for determining whether or not an unknown sample is determined for each category is based on creation of an identification dictionary using samples from a number of different individuals belonging to the same type and setting of an indeterminate threshold. It is clear.

  Hereinafter, specific examples of the analytical data acquisition method that can be used in the biological species determination method of the present invention will be given. The present invention is not limited to the identification of the causative bacteria of infectious diseases described below, but may be used for determination of human constitution such as MHC, and analysis of DNA and RNA related to diseases such as cancer.

Example 1
<Preparation of probe DNA>
The following nucleic acid sequence (In) (n is a number) was designed as a probe for detecting Enterobacter cloacae. Specifically, the probe base sequence shown below was selected from the genome part coding 16s rRNA. These probe base sequence groups are designed to be highly specific for the bacteria and to be expected to have sufficient and sensitivity that is as “variable” as possible with each probe base sequence.
I-1: CAgAgAgCTTgCTCTCgggTgA
I-2: gggAggAAggTgTTgTggTTAATAAC
I-3: ggTgTTgTggTTAATAACCACAgCAA
I-4: gCggTCTgTCAAgTCggATgTg
I-5 ： ATTCgAAACTggCAggCTAgAgTCT
I-6: TAACCACAgCAATTgACgTTACCCg
I-7: gCAATTgACgTTACCCgCAgAAgA
After synthesizing the above probe, a thiol group was introduced into the 5 ′ end of the nucleic acid according to a conventional method as a functional group for immobilization on the DNA microarray. After introduction of the functional group, it was purified and lyophilized. The freeze-dried internal standard probe was stored in a -30 ° C freezer.

On the other hand, Staphylococcus aureus (An), Staphylococcus epidermidis (Bn), Escherichia coli (Cn), Neisseria pneumoniae (Dn), Pseudomonas aeruginosa (En), Serratia fungus (Fn) ), Streptococcus pneumoniae (Gn), Haemophilus influenzae (Hn), and Enterococcus faecalis (Jn) (n is a number), the following probe set was designed by the same method.
A-1: gAACCgCATggTTCAAAAgTgAAAgA
A-2: CACTTATAgATggATCCgCgCTgC
A-3: TgCACATCTTgACggTACCTAATCAg
A-4: CCCCTTAgTgCTgCAgCTAACg
A-5: AATACAAAgggCAgCgAAACCgC
A-6: CCggTggAgTAACCTTTTAggAgCT
A-7: TAACCTTTTAggAgCTAgCCgTCgA
A-8: TTTAggAgCTAgCCgTCgAAggT
A-9: TAgCCgTCgAAggTgggACAAAT
B-1: gAACAgACgAggAgCTTgCTCC
B-2: TAgTgAAAgACggTTTTgCTgTCACT
B-3: TAAgTAACTATgCACgTCTTgACggT
B-4: gACCCCTCTAgAgATAgAgTTTTCCC
B-5: AgTAACCATTTggAgCTAgCCgTC
B-6: gAgCTTgCTCCTCTgACgTTAgC
B-7: AgCCggTggAgTAACCATTTgg
C-1: CTCTTgCCATCggATgTgCCCA
C-2: ATACCTTTgCTCATTgACgTTACCCg
C-3: TTTgCTCATTgACgTTACCCgCAg
C-4: ACTggCAAgCTTgAgTCTCgTAgA
C-5: ATACAAAgAgAAgCgACCTCgCg
C-6: CggACCTCATAAAgTgCgTCgTAgT
C-7: gCggggAggAAgggAgTAAAgTTAAT
D-1: TAgCACAgAgAgCTTgCTCTCgg
D-2: TCATgCCATCAgATgTgCCCAgA
D-3: CggggAggAAggCgATAAggTTAAT
D-4: TTCgATTgACgTTACCCgCAgAAgA
D-5: ggTCTgTCAAgTCggATgTgAAATCC
D-6: gCAggCTAgAgTCTTgTAgAgggg
E-1: TgAgggAgAAAgTgggggATCTTC
E-2: TCAgATgAgCCTAggTCggATTAgC
E-3: gAgCTAgAgTACggTAgAgggTgg
E-4: gTACggTAgAgggTggTggAATTTC
E-5: gACCACCTggACTgATACTgACAC
E-6: TggCCTTgACATgCTgAgAACTTTC
E-7: TTAgTTACCAgCACCTCgggTgg
E-8: TAgTCTAACCgCAAgggggACg
F-1: TAgCACAgggAgCTTgCTCCCT
F-2: AggTggTgAgCTTAATACgCTCATC
F-3: TCATCAATTgACgTTACTCgCAgAAg
F-4: ACTgCATTTgAAACTggCAAgCTAgA
F-5: TTATCCTTTgTTgCAgCTTCggCC
F-6: ACTTTCAgCgAggAggAAggTgg
G-1: AgTAgAACgCTgAAggAggAgCTTg
G-2: CTTgCATCACTACCAgATggACCTg
G-3: TgAgAgTggAAAgTTCACACTgTgAC
G-4: gCTgTggCTTAACCATAgTAggCTTT
G-5: AAgCggCTCTCTggCTTgTAACT
G-6: TAgACCCTTTCCggggTTTAgTgC
G-7: gACggCAAgCTAATCTCTTAAAgCCA
H-1: gCTTgggAATCTggCTTATggAgg
H-2: TgCCATAggATgAgCCCAAgTgg
H-3: CTTgggAATgTACTgACgCTCATgTg
H-4: ggATTgggCTTAgAgCTTggTgC
H-5: TACAgAgggAAgCgAAgCTgCg
H-6: ggCgTTTACCACggTATgATTCATgA
H-7: AATgCCTACCAAgCCTgCgATCT
H-8: TATCggAAgATgAAAgTgCgggACT
J-1: TTCTTTCCTCCCgAgTgCTTgCA
J-2: AACACgTgggTAACCTACCCATCAg
J-3: ATggCATAAgAgTgAAAggCgCTT
J-4: gACCCgCggTgCATTAgCTAgT
J-5: ggACgTTAgTAACTgAACgTCCCCT
J-6: CTCAACCggggAgggTCATTgg
J-7: TTggAgggTTTCCgCCCTTCAg
<Preparation of PCR Primer for sample amplification>
The nucleic acid sequences shown in Table 1 were designed as PCR primers for amplifying 16s rRNA nucleic acid (target nucleic acid) for detection of pathogenic bacteria. Specifically, a probe set that specifically amplifies the 16s rRNA-encoding genomic region, that is, a primer with the same specific melting temperature as possible at both ends of the approximately 1500-base long 16s rRNA coding region did. Multiple primers were designed to simultaneously amplify mutant strains and multiple 16s rRNA coding regions on the genome.

  The primers shown in the table are synthesized and purified by high performance liquid chromatography (HPLC), and 3 kinds of Forward Primer and 3 kinds of Reverse Primer are mixed so that each Primer concentration is 10 pmol / μl final concentration. Dissolved in TE buffer.

<Enterobacter # cloacae Genome DNA (model specimen) extraction>
(Preparation of microorganism culture & Genome DNA extraction)
First, the Enterobacter cloacae standard strain was cultured according to a conventional method. 1.0 ml (OD600 = 0.7) of this microorganism culture solution was collected in a 1.5 ml capacity microtube, and the cells were collected by centrifugation (8500 rpm, 5 min, 4 ° C.). After discarding the supernatant, 300 μl of Enzyme Buffer (50 mM Tris-HCl: pH 8.0, 25 mM EDTA) was added and resuspended using a mixer. The resuspended bacterial solution was recovered again by centrifugation (8500 rpm, 5 min, 4 ° C.). After discarding the upper fine particles, the following enzyme solution was added to the collected cells and resuspended using a mixer.
Lysozyme: 50 μl (20 mg / ml in Enzyme Buffer)
N-Acetylmuramidase SG: 50 μl (0.2 mg / ml in Enzyme Buffer)
Next, the bacterial solution resuspended by adding the enzyme solution was allowed to stand for 30 minutes in a 37 ° C. incubator to lyse the cell wall.

(Genome extraction)
Genome DNA extraction of the following microorganisms was performed using a nucleic acid purification kit (MagExtractor-Genome-: manufactured by TOYOBO). Specifically, first, 750 μl of the dissolving / adsorbing solution and 40 μl of magnetic beads were added to the pretreated microorganism suspension and vigorously stirred for 10 minutes using a tube mixer (step 1). Next, the microtube was set on a separation stand (Magical Trapper), left to stand for 30 seconds, the magnetic particles were collected on the wall of the tube, and the upper fine was discarded while being set on the stand (step 2). Next, 900 μl of the washing solution was added, and the mixture was stirred for about 5 seconds with a mixer and re-suspended (step 3). Next, a microtube was set on a separation stand (Magical Trapper), left still for 30 seconds, magnetic particles were collected on the wall surface of the tube, and the upper fine was discarded while being set on the stand (step 4). Steps 3 and 4 were repeated to perform the second washing (Step 5), and then 900 μl of 70% ethanol was added and stirred for about 5 seconds with a mixer to re-suspend (Step 6). Next, the microtube was set on a separation stand (Magical Trapper), left to stand for 30 seconds, the magnetic particles were collected on the wall of the tube, and the upper fine was discarded while being set on the stand (step 7). Steps 6 and 7 were repeated and the second washing with 70% ethanol (step 8) was performed. Then, 100 μl of pure water was added to the collected magnetic particles, and the mixture was stirred for 10 minutes with a tube mixer.

  Next, the microtube was set on a separation stand (Magical Trapper), allowed to stand for 30 seconds to collect the magnetic particles on the wall of the tube, and the supernatant was collected in a new tube while being set on the stand.

(Examination of recovered Genome DNA)
Genome DNA of the recovered microorganism (Enterobacter cloacae strain) was subjected to agarose electrophoresis and absorbance measurement at 260/280 nm in accordance with conventional methods, and the quality (contamination amount of low-molecular nucleic acid, degree of degradation) and recovery amount were tested. . In this example, about 10 μg of Genome DNA was recovered, and Genome DNA degradation and rRNA contamination were not observed. The recovered Genome DNA was dissolved in TE buffer so as to have a final concentration of 50 ng / μl and used in the following steps.

<Production of DNA microarray>
[1] Cleaning of glass substrate A synthetic quartz glass substrate (size: 25 mm x 75 mm x 1 mm, manufactured by Iiyama Special Glass Co., Ltd.) was placed in a heat-resistant and alkali-resistant rack and immersed in a cleaning solution for ultrasonic cleaning adjusted to a predetermined concentration. After soaking in the cleaning solution overnight, ultrasonic cleaning was performed for 20 minutes. Subsequently, the substrate was taken out, rinsed lightly with pure water, and then ultrasonically cleaned in ultrapure water for 20 minutes. Next, the substrate was immersed in a 1N sodium hydroxide aqueous solution heated to 80 ° C. for 10 minutes. Pure water cleaning and ultrapure water cleaning were performed again to prepare a quartz glass substrate for a DNA chip.

[2] Surface treatment A silane coupling agent KBM-603 (manufactured by Shin-Etsu Silicone) was dissolved in pure water to a concentration of 1% and stirred at room temperature for 2 hours. Subsequently, the previously cleaned glass substrate was immersed in an aqueous silane coupling agent solution and allowed to stand at room temperature for 20 minutes. The glass substrate was pulled up and the surface was lightly washed with pure water, and then nitrogen gas was blown onto both sides of the substrate to dry it. Next, the dried substrate was baked in an oven heated to 120 ° C. for 1 hour to complete the coupling agent treatment, and amino groups were introduced onto the substrate surface. Next, N-maleimidocaproyloxy succinimide manufactured by Dojindo Laboratories was dissolved in a 1: 1 mixed solvent of dimethyl sulfoxide and ethanol so that the final concentration was 0.3 mg / ml to prepare an EMCS solution. . Hereinafter, N- (maleimidocaproyloxy) succinimido is abbreviated as EMCS. The glass substrate after baking was allowed to cool and immersed in the prepared EMCS solution for 2 hours at room temperature. By this treatment, the amino group introduced to the surface by the silane coupling agent and the succinimide group of EMCS reacted, and the maleimide group was introduced to the glass substrate surface. The glass substrate pulled up from the EMCS solution was washed with the above-mentioned mixed solvent in which MCS was dissolved, further washed with ethanol, and then dried in a nitrogen gas atmosphere.

[3] Probe DNA
The prepared microorganism detection probe was dissolved in pure water, dispensed to a final concentration (at the time of ink dissolution) of 10 μM, and then freeze-dried to remove moisture.

[4] DNA ejection by BJ printer and binding to substrate An aqueous solution containing glycerin 7.5 wt%, thiodiglycol 7.5 wt%, urea 7.5 wt%, and acetylenol EH (manufactured by Kawaken Fine Chemical Co., Ltd.) 1.0 wt% was prepared. Subsequently, the seven types of probes (Table 1) prepared previously were dissolved in the above mixed solvent so as to have a specified concentration. The obtained DNA solution was filled in an ink tank for a bubble jet printer (trade name: BJF-850 manufactured by Canon Inc.) and mounted on a print head.

  The bubble jet printer used here is modified so that printing on a flat plate is possible. The bubble jet printer can spot a DNA solution of about 5 picoliters at a pitch of about 120 micrometers by inputting a print pattern according to a predetermined file creation method. Subsequently, using this modified bubble jet printer, a printing operation was performed on one glass substrate to produce an array. After confirming that printing was performed reliably, the sample was left in a humidified chamber for 30 minutes to react the maleimide group on the surface of the glass substrate with the thiol group at the end of the nucleic acid probe.

[5] Cleaning
After the reaction for 30 minutes, the DNA solution remaining on the surface was washed away with 10 mM phosphate buffer (pH 7.0) containing 100 mM NaCl to obtain a DNA microarray in which single-stranded DNA was immobilized on the surface of the glass substrate.

<Amplification and labeling of specimen (PCR amplification & incorporation of fluorescent label)>
Amplification of microbial DNA as a specimen and labeling reaction are shown below.
Premix PCR reagent (TAKARA ExTaq): 25μl
Template Genome DNA: 2μl (100ng)
Forward Primer mix: 2μl (20 pmol/tube each)
Reverse Primer mix: 2μl (20 pmol/tube each)
Cy-3 dUTP (1mM): 2μl (2nmol / tube)
H ₂ 0: 17 μl
(Total: 50μl)
The reaction solution having the above composition was subjected to an amplification reaction using a commercially available thermal cycler according to the following protocol.
(Step 1) 95 ° C, 10 min.
(Step 2) 92 ° C, 45 sec.
(Step 3) 55 ° C, 45 sec.
(Step 4) 72 ° C, 45 sec.
(Step 5) 72 ° C, 10 min.
(Steps 2-4 were repeated 35 times.)
After completion of the reaction, Primer was removed using a purification column (QIAGEN QIAquick PCR Purification Kit), and then the amplification product was quantified to obtain a labeled sample.

<Hybridization>
Detection reaction was performed using the DNA microarray prepared in <Preparation of DNA microarray> and the labeled sample prepared in <Amplification and labeling of specimen (PCR amplification & incorporation of fluorescent label)>.

(Blocking of DNA microarray)
BSA (bovine serum albumin Fraction V: manufactured by Sigma) was dissolved in 100 mM NaCl / 10 mM Phosphate Buffer so as to be 1 wt%. The DNA microarray prepared in <Preparation of DNA microarray> was immersed in this solution at room temperature for 2 hours for blocking. After completion of blocking, washing was performed with a 2 × SSC solution containing 0.1 wt% SDS (sodium dodecyl sulfate) (NaCl 300 mM, sodium citrate (trisodium citrate dihydrate, C6H5Na3 · 2H2O) 30 mM, pH 7.0). Then, after rinsing with pure water, draining was performed with a spin dryer.

(Hybridization)
The drained DNA microarray was set in a hybridization apparatus (Genomic Solutions Inc. Hybridization Station), and a hybridization reaction was performed with the following hybridization solution and conditions.

<Hybridization solution>
6 x SSPE / 10% Form amide / Target (2nd PCR Products total amount)
(6xSSPE: NaCl 900mM, NaH2PO4 ・ H2O 60mM, EDTA 6mM, pH 7.4)
<Hybridization conditions>
65 ° C, 3 min → 92 ° C, 2 min → 45 ° C, 3 hr → Wash, 2 x SSC / 0.1% SDS, 25 ° C → Wash, 2 x SSC, 20 ° C → (Rinse with H ₂ O: Manual) → Spin dry
<Detection of microorganisms (fluorescence measurement)>
After completion of the hybridization reaction, the DNA microarray was subjected to fluorescence measurement using a fluorescence detector for DNA microarray (Axon, GenePix 4000B).

An example of an image as image data obtained as a result is shown in FIG. In FIG. 6, the probe having a higher fluorescence intensity is shown in a darker color. Reference numeral 601 denotes an image obtained by reacting a DNA microarray with a sample containing the genome of Staphylococcus aureus. Reference numeral 602 denotes an example of an image obtained by reacting a sample containing the genome of E. coli. The alphabet written on the left of the figure is the probe sequence alphabet. Each of A to J is a probe designed to specifically bind to each of the following bacteria.
(A) Staphylococcus aureus.
(B) Staphylococcus epidermidis.
(C) E. coli.
(D) Neisseria pneumoniae.
(E) Pseudomonas aeruginosa.
(F) Serratia bacteria.
(G) Streptococcus pneumoniae.
(H) Haemophilus influenzae.
(I) Enterobacter cloacae.
(J) Enterococcus faecalis.

  Ideally, only the probe in row A of 601 has high fluorescence intensity, and only the probe in row C of 602 has high fluorescence intensity. The ideal result of 601 is the same as the example of the experimental result shown in FIG.

  However, as shown in FIG. 6, it is not actually ideal. That is, a so-called “cross-hybridization reaction” occurs, and in the case of 601, the probes in rows other than A also have high fluorescence intensity, and in the case of 602, the probes in rows other than C also have high fluorescence intensity. Furthermore, in the case of 602, there is also a probe with weak fluorescence intensity even in the C row.

  FIG. 7 illustrates this situation with a system of three probes. Experiments on six known samples for each bacterium using DNA microarrays with three types of probes: S. aureus, S. epiderimidis, and E. coli Yes. In general, when there are N probes, the experimental data is an N-dimensional vector. In the case of FIG. 6, since there are 72 probes in total, the 72-dimensional vector, and in the case of FIG. 7, since there are three probes, the three-dimensional vector is the experimental data.

  In the lower figure of FIG. 7, 6 types of samples (= 18 data in total) for each of the three bacteria are plotted in three-dimensional coordinates. As shown in the figure, when the three probes are ideally very specific probes for each of the three bacteria, the vector data is concentrated around the respective axes as shown in the lower part of FIG. However, data fluctuations exist and do not concentrate on one point. In the example of FIG. 7, the size of the data existence range of each of the three bacteria is different, and in descending order, E. coli, S. epiderimidis, S. aureus. It has become.

  Further, a determination index set is derived for each bacterium according to the method shown in FIGS. 1 and 8 described above, an indeterminate threshold is set, and the bacterium that is an unknown sample supply source is determined using the set indeterminate threshold. It is possible to determine whether or not to perform the determination.

Example 2
The experimental data of DNA microarray of Klebsiella pneumoniae and Serratia bacteria are shown below. In addition, as a probe, the thing for the detection of the following each bacteria is used.
S. aureus (An).
S. epidermidis (Bn).
E. coli (Cn), K. pneumoniae (Dn).
P. aeruginosa (En).
S. marcescenes (Fn).
S. neumoiae (Gn).
H. influenzae (Hn).
Enterobacter cloacae (In).
And E. faecelis (Jn).

  Note that n in the above parentheses is n = 1 to 6 described above. Eventually, the total number of probes is 10 × 6 = 60.

  First, experimental data of DNA microarrays for 10 different samples of Klebsiella pneumoniae are shown in FIGS. In each figure, the probes A-1, A-2,... To J-5, J-6 are arranged in order from left to right. As shown in the drawing, the experimental data of the DNA microarray is obtained as 60 fluorescence luminance values, that is, 60-dimensional vectors. First, in order to define the distance between arbitrary vectors, normalization of “dividing each element of the vector by the norm of the vector” is performed. In formula

In the expression, vector x is the original vector, and vector y is the normalized vector.
The normalized vector always has a norm of 1. Here, the norm (Euclidean norm) of the n-dimensional vector x is defined by the following equation.

  Then, the distance between two normalized vectors (vector a and vector b) is defined by the following equation.

In this embodiment, the distance definition of the k-th nearest neighbor matching algorithm is as described above. FIG. 21 shows a histogram obtained by calculating a distance between each set of 10 samples. The number of data is ₁₀ C ₂ = 45. From this figure, assuming that the k-th nearest neighbor algorithm is applied to Klebsiella pneumoniae, the determination impossible threshold value is 0.057, which is the maximum value, as one candidate. You may use a value of 1.5 times or 2 times with a little margin.

  Next, experimental data of 10 samples of Serratia bacteria are shown in FIGS. FIG. 32 shows a histogram obtained by calculating the distance between two arbitrary samples of 10 samples using the normalization and distance calculation performed for Klebsiella pneumoniae. It can be seen that the shape of P. pneumoniae histogram and the distribution shape are completely different. The fact that there are two large mountains is assumed that there are two clusters in ten vectors. In fact, it can be seen that there are roughly two types of patterns even when looking at the fluorescence luminance graph of the 10 samples shown above. From this figure, if it is determined by applying the k-th nearest neighbor algorithm to Klebsiella pneumoniae, the rejection value is 0.090, which is the maximum value of the first peak.

It is a figure which shows an example of the biological kind determination method of this invention. It is a block diagram which shows the structure of the information processing apparatus for performing the biological kind determination method of this invention. It is a figure explaining hybridization reaction. 2 shows an experimental procedure using a DNA microarray. It is an experimental procedure of a DNA microarray for determining an infectious disease. It is an example of the image which consists of the fluorescence intensity after hybridization reaction. It is an example of distribution of vector data. It is a figure explaining a determination impossible value setting step. It is an example of distribution of a determination index set. It is an example of a distance set of two arbitrary samples in the same category. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a figure which shows the experimental data of the DNA microarray with respect to the Klebsiella pneumoniae sample. It is a histogram about the distance of every 1 set between 10 Klebsiella pneumoniae samples. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. It is a figure which shows the experimental data of the DNA microarray with respect to a Serratia microbe sample. FIG. 6 is a histogram for any set of distances between 10 Serratia samples.

Claims (16)

In a biological species determination method for analyzing a substance assumed to contain a substance derived from a living organism and determining the type of the corresponding organism,
Analyzing a plurality of known samples whose corresponding species are known by a species analysis method to obtain a plurality of analysis data;
Setting an indeterminable threshold for the species corresponding to the known sample based on the plurality of analysis data obtained from the known sample;
Analyzing an unknown sample whose corresponding species is unknown by the species analysis method to obtain analysis data for identifying the species corresponding to the unknown sample;
Determining a type corresponding to the unknown sample based on the indeterminable threshold, or determining whether it is impossible to determine;
If it is determined to make a determination, determining the species of the unknown sample based on the plurality of analysis data;
A method for determining species.   The indeterminate threshold is an identification obtained by excluding arbitrary analysis data from a total of a plurality of analysis data stored in the storage means obtained from the known sample in one species and learning based on the remaining analysis data The biological species determination method according to claim 1, wherein a dictionary is created, the excluded analysis data is determined based on the identification dictionary, a determination index is derived, and the determination index is set based on the determination index. In a biological species determination method for analyzing a substance assumed to contain a substance derived from a biological organism using a biological species analysis method and determining a corresponding biological species,
(1) selecting a biological species assumed as a determination result for the unknown sample;
(2) A plurality of image data that are characteristic of the species and can be used for pattern recognition from each of the known samples obtained from a plurality of individuals known to belong to the selected organism. Obtaining an image data group comprising:
(3) selecting image data from the image data group and setting an indeterminate threshold using a relationship with the remaining image data;
(4) obtaining image data from an unknown sample;
(5) determining whether image data from the unknown sample is based on the indeterminable threshold and determining whether or not it is possible to determine the species corresponding to the unknown sample;
(6) When it is determined to perform the determination in (5), a step of determining a biological species using an identification dictionary including the image data group;
A method for determining a kind of organism, comprising: Setting of the indeterminate threshold is
(1) selecting three or more different individuals as the individual and obtaining an image data group composed of the obtained three or more image data;
(2) Select and exclude one image data from the image data group, create a dictionary using a plurality of remaining image data, and determine the previously excluded image data based on the obtained dictionary Performing a process for obtaining a decision index for each image data to obtain a decision index set consisting of m decision indices;
(3) determining an indeterminable threshold from the set of determination indices;
The organism type determination method according to claim 3, wherein the method is performed by a method comprising: Setting of the indeterminate threshold is
(1) selecting three or more different individuals as the individual and obtaining an image data group composed of the obtained three or more image data;
(2) obtaining a distance set by obtaining a distance between two image data for all combinations of arbitrary two image data selected from the image data group; and
(3) determining an indeterminate threshold from the distance set;
The biological species determination method according to claim 3, wherein the biological species determination method is performed by a method comprising:   A known or unknown sample is immobilized on a probe-immobilized carrier on which a probe capable of specifically binding to a target nucleic acid having a base sequence characteristic of the selected organism is positioned and immobilized on a substrate. The biological species determination method according to claim 3, wherein the nucleic acid sample is reacted, and the target nucleic acid / probe conjugate formed on the substrate is optically detected to obtain image data.   The determination method according to claim 6, wherein the optical detection of the conjugate is performed using fluorescence from a fluorescent label imparted to the conjugate.   A memory storing a plurality of analysis data obtained by analyzing a plurality of known samples of which corresponding species are known by a biological analysis method, and an indeterminate threshold set based on the plurality of analysis data; Based on the indeterminable threshold value stored in the memory, it is determined whether or not the biological species corresponding to the unknown sample can be determined. And a processing unit for determining a biological species corresponding to the unknown sample based on the information processing apparatus for biological species determination. Analyzing substances that are assumed to contain substances derived from living organisms, and analyzing multiple known samples with known corresponding biological species in an information processing device for determining the corresponding biological species A known sample image data input means for inputting image data characteristic of the biological species obtained as described above,
Unknown sample image data input means for inputting image data obtained by analyzing an unknown sample in the same manner as the known sample;
Storage means for storing the captured image data;
Means for setting an indeterminate threshold for the species corresponding to the known sample based on the plurality of analysis data obtained from the known sample;
Biological species determining means for determining whether or not to determine whether or not to determine image data from an unknown sample based on the indeterminate threshold, and determining the biological species corresponding to the unknown sample ,
Storage means for storing the determination result of the determination means;
An information processing apparatus for biological species determination, comprising: output means for outputting a determination result stored in the storage means. Setting of the indeterminate threshold is
The individual is 3 or more, image data from these individuals is stored in the storage means,
(A) One image data is selected and excluded from the three or more image data, an identification dictionary is created using a plurality of remaining image data, and image data is excluded first based on the obtained identification dictionary A process for obtaining a judgment index by performing a process for each image data to obtain a judgment index set composed of three or more judgment indices;
(B) setting a non-determinable threshold from the set of determination indices;
The information processing apparatus according to claim 9, wherein the information processing apparatus is executed based on a program including: The setting of the indeterminate threshold is 3 or more for the individual, and image data from these individuals is stored in the storage means,
(A) obtaining a distance set by obtaining a distance between two image data for all combinations of arbitrary two image data selected from the three or more image data; and
(B) determining an indeterminate threshold from the distance set;
The information processing apparatus according to claim 9, which is executed based on a program having A program for causing a computer to determine a species corresponding to an unknown sample,
(1) A plurality of pieces of image data corresponding to image data characteristic of the assumed species obtained by analyzing known samples from a plurality of different individuals belonging to the assumed species as a determination result for the unknown sample. Recalling the plurality of known sample image data from the stored storage means;
(2) reading the unknown sample image data from a storage means storing a plurality of image data corresponding to the image data obtained by analyzing the unknown sample in the same manner as the known sample;
(3) selecting one from the known sample image data, and setting a non-determinable threshold using a relationship between the selected one and the remaining image data;
(4) processing the unknown sample image data based on the non-determinable threshold value, and determining a type of organism corresponding to the unknown sample;
(5) storing the determination result obtained in the determination step in a storage means;
(6) A biological type determination program, comprising: a step of outputting a determination result stored in the storage means. Setting of the indeterminate threshold is
The individual is 3 or more, and image data from these individuals is stored in the storage means. (A) One image data is selected and excluded from the three or more image data, and the remaining plurality An identification dictionary is created using image data, and a process for obtaining a determination index by determining image data previously excluded based on the obtained identification dictionary is performed for each image data, and includes three or more determination indexes. Obtaining a decision index set;
(B) determining an indeterminable threshold from the set of decision indices;
The biological species determination program according to claim 10, comprising: Setting of the indeterminate threshold is
The number of individuals is three or more, and image data from these individuals is stored in the storage means. (A) For all combinations of arbitrary two image data selected from the three or more image data, 2 Obtaining a distance set between two image data and obtaining a distance set;
(B) determining an indeterminate threshold from the distance set;
The biological species determination program according to claim 11, comprising: A recording medium in which a program for executing a species determination by a computer is recorded in a readable manner,
A recording medium, wherein the program is the program according to claim 11.   The determination is impossible using a plurality of analysis data obtained by analyzing a plurality of known samples whose corresponding species are known by a biological analysis method, and an indeterminate threshold set based on the plurality of analysis data. After determining whether or not the species corresponding to the unknown sample can be determined based on the threshold value, if it is determined that the species can be determined, determining the species corresponding to the unknown sample based on the plurality of analysis data Species determination method.

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