JP7196579B2 - Data processor, analyzer, data processing method and program - Google Patents

Description

The present invention relates to a data processing device, an analysis device, a data processing method and a program.

Proposals have been made to calculate the degree of similarity between a plurality of types of bacteria by various methods and display them in an easy-to-understand manner. Non-Patent Document 1 describes a heat map showing an index indicating the homology between the base sequences of genes or the amino acid sequences encoded by the genes for a plurality of species. Non-Patent Document 2 has a graph showing the relationship between the sequence homology of a specific gene between the type strain and other organisms and the ratio of ribosomal proteins commonly observed between the type strain and other organisms is described.

Contreras-Moreira B and Vinuesa P, "get_homologues-est manual" [online], May 16, 2018, University of Leeds and Macquarie University, [searched on September 27, 2018], Internet (URL: http:// eead-csic-compbio.github.io/get_homologues/manual-est/) Teramoto K, Sato H, Sun L, Torimura M, Tao H, Yoshikawa H, Hotta Y, Hosoda A, Tamura H. "Phylogenetic classification of Pseudomonas putida strains by MALDI-MS using ribosomal subunit proteins as biomarkers" Anal Chem, USA ), American Chemical Society, October 16, 2007, Volume 79, Issue 22, pp.8712-8719

When comparing the nucleotide sequences of genes or the amino acid sequences of proteins encoded by the genes as in Non-Patent Document 1, information on the nucleotide sequences of genes in each organism is required in principle. However, gene sequencing is still quite labor intensive, time consuming and costly. In principle, the possibility of base sequence decoding errors and annotation errors cannot be ruled out. The method of Non-Patent Document 2 has the problem that only the homology between each organism and the reference strain is known, and the homology between each organism is not known.

In the data processing device according to a preferred embodiment of the present invention, a target molecule commonly contained in a first sample derived from a first organism and a second sample derived from a second organism is In each mass spectrometry of the sample, the first organism and the second organism are detected based on whether they are detected corresponding to the same m / z calculated from the structure of the target molecule or obtained by past measurement a similarity calculation unit that calculates the similarity between organisms and calculates the similarity for each of a plurality of combinations obtained by selecting two types of organisms from three or more types of organisms; An output image generation unit for generating an output image including a similarity image that maps and displays similarities between the above types of living things, and an output unit for outputting the output image.
In a further preferred embodiment, the similarity calculation unit determines, based on which of a plurality of m/z's corresponding to one type of target molecule, at least one of the target molecules is detected to correspond to the A degree of similarity between the first organism and the second organism is calculated.
In a further preferred embodiment, the output image generating section generates the similarity image in which at least one of hue, brightness and saturation of the portion corresponding to the similarity is different according to the similarity value.
In a further preferred embodiment, the output image generation unit calculates the similarity between the similarity image and the similarity between the three or more types of organisms calculated by a method different from the calculation of the similarity by the similarity calculation unit. generating said output image containing information about and
In a further preferred embodiment, said information is a phylogenetic tree of said three or more species of organisms.
In a further preferred embodiment, the target molecule is a protein, and the degree of similarity calculated by the different method is the degree of similarity calculated based on the homology of nucleotide sequences of nucleic acids corresponding to the protein.
In a further preferred embodiment, the target molecule is a sugar chain, lipid, protein or nucleic acid.
In a further preferred embodiment, the target molecule is a protein or a nucleic acid, and the similarity calculator corresponds to the same m/z derived based on the amino acid sequence or base sequence of the target molecule in the mass spectrometry. Then, the degree of similarity between the first organism and the second organism is calculated based on whether the target molecule is detected.
An analyzer according to a preferred embodiment of the present invention comprises the data processing device described above and a mass spectrometer.
In a data processing method according to a preferred embodiment of the present invention, a target molecule commonly contained in a first sample derived from a first organism and a second sample derived from a second organism is In each mass spectrometry of the sample, the first organism and the second organism are detected based on whether they are detected corresponding to the same m / z calculated from the structure of the target molecule or obtained by past measurement calculating the degree of similarity between organisms, and calculating the degree of similarity for each of a plurality of combinations obtained by selecting two types of organisms from three or more types of organisms; generating an output image including a similarity image that maps and displays the similarity between the organisms; and outputting the output image.
A program according to a preferred embodiment of the present invention, wherein a target molecule commonly contained in a first sample derived from a first organism and a second sample derived from a second organism is In each mass spectrometry, the first organism and the second organism are detected corresponding to the same m / z calculated from the structure of the target molecule or obtained by past measurement A similarity calculation process for calculating the similarity for each of a plurality of combinations obtained by selecting two types of organisms from three or more types of organisms, and calculating the similarity between the three or more It is for causing the processing device to perform an output image generation process for generating an output image including a similarity image that maps and displays similarities between types of living things.

According to the present invention, it is possible to quickly calculate the degree of similarity between a plurality of organisms and display it in an easy-to-understand manner.

FIG. 1 is a conceptual diagram showing the configuration of an analysis device according to one embodiment. FIG. 2 is a table (Table A, Table B and Table C) showing whether peaks corresponding to each ribosomal protein were detected for three types of P. acnes. FIG. 3 is a diagram showing an image showing the degree of similarity between three types of P. acnes. FIG. 4 is a flow chart showing the flow of the analysis method according to one embodiment. FIG. 5 is a diagram showing a table showing the degree of similarity among 23 types of P. acnes and a phylogenetic tree of the P. acnes. FIG. 6 is a conceptual diagram for explaining program provision. FIG. 7 is a mass spectrum of P. acnes obtained in Example.

Embodiments for carrying out the present invention will be described below with reference to the drawings.

-First Embodiment-
In the first embodiment, each of a plurality of samples is a sample derived from a plurality of different organisms, a data processing device that calculates the similarity between these organisms, maps the similarity, and creates an image showing the similarity. An analyzer including is described.

FIG. 1 is a conceptual diagram showing the configuration of the analysis device 1. As shown in FIG. The analysis device 1 includes a measurement section 100 and an information processing section 40 . The information processing section 40 constitutes a data processing device.
Some or all of the functions of the information processing section 40 may be arranged in a computer, server, or the like physically separated from the measurement section 100 .

The measurement unit 100 includes an ionization unit 10 that generates an ionized sample (hereinafter referred to as sample ions S) by matrix-assisted laser desorption ionization (hereinafter referred to as MALDI), an ion acceleration unit 21, and a mass separation unit 22. and a detection unit 30 . The ion acceleration section 21 includes acceleration electrodes 210 . The mass separator 220 includes a time-of-flight mass spectrometer 220 . In FIG. 1, the movement of the sample ions S is schematically indicated by an arrow A1.

The information processing section 40 includes an input section 41 , a communication section 42 , a storage section 43 , an output section 44 and a control section 50 . The control unit 50 includes a device control unit 51 , a data processing unit 52 , an output image generation unit 53 and an output control unit 54 . The data processor 52 includes a mass spectrum generator 521 and a similarity calculator 522 . The flow of the detection signal of the sample ions S output from the detection section 30 of the measurement section 100 is schematically indicated by an arrow A2. Control of the measurement unit 100 by the device control unit 51 is schematically indicated by an arrow A3.

(Regarding the sample)
The sample is not particularly limited as long as it contains target molecules derived from organisms. In order to map and show the degree of similarity (see FIG. 3), 3 or more samples each derived from 3 or more organisms are prepared. Target molecules are selected from molecules commonly contained in three or more types of organisms from which these three or more samples are derived, and are preferably sugar chains, lipids, proteins, or nucleic acids. The organism is preferably a microorganism, more preferably a eubacterium, but is not particularly limited as long as it produces the target molecule.

When the organism is a microorganism, a colony obtained by culturing is collected, a solution containing a matrix (hereinafter referred to as a matrix solution) is added to the cells forming the colony, and the mixture is dropped onto a MALDI sample plate and dried. A sample is prepared. A matrix solution may be added after arranging the cells on the MALDI sample plate. The type of matrix is not particularly limited, but CHCA (α-cyano-4-hydroxycinnamic acid), sinapinic acid, DHB (2,5-dihydroxybenzoic acid), THAP (2,4,6-trihydroxyacetophenone) or DAN (1,5 -diaminonaphtalene) is preferable for accurate mass spectrometry. As a solvent for the matrix solution, an aqueous solution containing several tens of volume % of an organic solvent such as acetonitrile, to which 0 to 3 volume % of trifluoroacetic acid (TFA) is added, or the like can be used.
A sample may be prepared by adding a matrix solution to the extract after extracting the target molecule from the cells contained in the obtained colonies. When preparing samples from organisms other than microorganisms, well-known pretreatment methods and the like can be used as appropriate.

The measurement unit 100 includes a mass spectrometer, ionizes a sample, separates the sample by mass, and detects the ionized sample.

The ionization unit 10 of the measurement unit 100 includes a sample plate holder (not shown) that supports the MALDI sample plate and an ion source that includes a laser device (not shown) that irradiates the MALDI sample plate with a laser. Ionize by irradiation.
The sample ionization method is not particularly limited as long as the target molecules can be ionized, and any ionization method other than the MALDI method, such as the electrospray (ESI) method, can be used.

The ion acceleration section 21 includes an acceleration electrode 210 and accelerates the introduced sample ions S. The accelerated flow of sample ions S is appropriately converged by an ion lens (not shown) or the like and introduced into the mass separator 22 .

The mass separation unit 22 includes a time-of-flight mass spectrometer 220, and separates the sample ions S based on the difference in flight time when each sample ion S flies through the flight tube of the time-of-flight mass spectrometer 220. .
Although a linear time-of-flight mass spectrometer is shown in FIG. 1, it may be a reflectron type, a multi-turn type, or the like. The method of mass spectrometry is not particularly limited as long as the sample ions S can be separated and detected with desired accuracy, and any mass spectrometer such as an ion trap or quadrupole mass filter can be used.

The detection unit 30 includes an ion detector such as a microchannel plate, detects the sample ions S separated by the mass separation unit 22, and generates a detection signal having an intensity corresponding to the amount of the sample ions S incident on the detection unit 30. Output. The detection signal output from the detection section 30 is A/D converted and then stored as measurement data in the storage section 43 of the information processing section 40 (arrow A2).

The input unit 41 of the information processing unit 40 includes an input device such as a mouse, keyboard, various buttons, or a touch panel. The input unit 41 receives information and the like necessary for the measurement of the measurement unit 100 and the processing of the control unit 50 from the user of the analyzer (hereinafter simply referred to as "user"). The communication unit 42 of the information processing unit 40 includes a communication device capable of communicating by wireless or wired connection such as the Internet. The communication unit 42 appropriately transmits and receives data such as information required for measurement by the measurement unit 100 and processing by the control unit 50 .

The storage unit 43 of the information processing unit 40 includes a nonvolatile storage medium. The storage unit 43 stores measurement data, data required for processing by the control unit 50, data obtained by the processing by the control unit 50, programs for the control unit 50 to execute processing, and the like. The output unit 44 of the information processing unit 40 includes a display device such as a liquid crystal monitor, a printer, and the like, and displays the data processed by the data processing unit 52 and the output image generated by the output image generation unit 53. Display it on a device or print it out on a paper medium.

The control unit 50 of the information processing unit 40 is configured including a processor such as a CPU, and functions as an entity that controls the analysis apparatus 1 . The control unit 50 performs various processes by executing programs stored in the storage unit 43 or the like.

The device control section 51 of the control section 50 controls the operation of the measurement section 100 based on the analysis conditions set according to the input from the input section 41 or the like.

A data processing unit 52 of the control unit 50 processes measurement data.

The mass spectrum generation unit 521 of the data processing unit 52 generates data corresponding to the mass spectrum (hereinafter referred to as mass spectrum data) from the measurement data including the intensity of the ions detected by the detection unit 30 and the time of flight of the ions. to generate The mass spectrum generator 521 converts the time of flight into m/z values based on previously obtained calibration data, and generates mass spectrum data indicating the intensity corresponding to each m/z value.

The similarity calculation unit 522 of the data processing unit 52 calculates two masses obtained by mass spectrometry of two samples (hereinafter referred to as “first sample” and “second sample”) among the three or more samples. A degree of similarity between the first and second organisms from which the first and second samples are derived is calculated based on the spectral data. The mass spectra obtained by mass spectrometrically analyzing the first sample and the second sample are called the first mass spectrum and the second mass spectrum, respectively.

The similarity calculation unit 522 refers to the storage unit 43 and acquires multiple m/z values corresponding to multiple target molecules. At least some of the plurality of target molecules have a plurality of variants in three or more organisms (hereinafter referred to as target organisms) whose similarity is to be calculated. The storage unit 43 stores, as reference data, the m/z values when the target molecule and its mutants are detected by mass spectrometry. These m/z values are either pre-calculated from the structure of the target molecule, such as the amino acid sequence of a protein, or obtained from past measurements by the user or others.

In the following, the target molecule is a protein (target protein), the target organism is a plurality of different strains of microorganisms of the same genus and the same species, and a part of the target protein is a plurality of mutants with different amino acid sequences depending on the target organism, that is, the strain (hereinafter referred to as (referred to as the mutant of interest) exists.

Reference data in the storage unit 43 is configured as follows. When the target protein does not have a mutant in the target organism, the target protein and the m/z value corresponding to the target protein are associated and stored. When a plurality of target variants of the target protein exist for the target organism, the target protein, each target variant, and the m/z value corresponding to each target variant are associated and stored.

For example, the target organisms are Acne bacteria A, B and C strains, and the target proteins are ribosomal proteins S15, S19, S08, L09, S18, L27, S17 and L23. Among them, in acne bacteria A, B and C strains, S17 has three different target mutants (hereinafter referred to as S17 (1), S17 (2) and S17 (3)), and L23 has two types different target mutants (hereinafter referred to as L23(1) and L23(2)) are present. These mutants of interest differ in m/z when detected from each other. At this time, in the reference data, the IDs of S15, S19, S08, L09, S18, and L27 in which the target mutant does not exist are associated with the corresponding m/z values. The ID of S17 in which the target mutant exists is associated with the IDs of the target mutants S17(1), S17(2) and S17(3), and S17(1), S17(2) and The IDs of S17(3) are associated with corresponding m/z values. L23 is similar to S17.

The similarity calculator 522 detects peaks corresponding to the m/z of the target protein and target mutant in the mass spectrum obtained by mass spectrometry of the sample derived from each target organism. Specifically, for each m / z of the reference data, if a peak is detected within the allowable range (permissible error) of m / z variation determined based on the accuracy of mass spectrometry, the mass spectrum Assume that there is a protein of interest or variant of interest corresponding to that m/z in the corresponding organism of interest. The detection or non-detection of each target protein or target mutant in the target organism is stored in the storage unit 43 as a binarized value (hereinafter referred to as a detection confirmation value) or the like. be done.

The similarity calculation unit 522 determines whether each target protein is detected within the same m/z-based allowable range in the first mass spectrum and the second mass spectrum, between the first organism and the second organism. Calculate the similarity of

Fig. 2 shows the detection confirmation value when the target protein or target mutant is detected in the mass spectrum obtained by mass spectrometry of each of P. acnes A (P_acnes_A), B (P_acnes_B), and C (P_acnes_C) strains. is 1, and the detection confirmation value is 0 when not detected, and the detection confirmation value is shown in a table for each strain. In FIG. 2, for comparison between the two samples, Table A shows the detection confirmation values for P. acnes A and B strains, Table B shows the detection confirmation values for A and C strains, and B and C strains. are shown in Table C.

As shown in Table A, S15, S19, S08, L09, S18, L27, S17(2) and L23(1) were detected by mass spectrometry in Acne strain A. In P. acnes strain B, mass spectrometry detected S15, S19, S08, L09, S18, L27, S17(3) and L23(2). As shown in Table B, S15, S19, S08, L09, S18, L27, S17(1) and L23(2) were detected by mass spectrometry in P. acnes C strain.

The similarity calculation unit 522 calculates the similarity between the P. acnes A strain and the P. acnes B strain based on whether or not each target protein is detected with the same m/z. That is, if the target protein has a mutation, the degree of similarity is calculated based on whether or not the same mutation is detected.

For P. acnes A and B strains in Table A, the six target proteins S15, S19, S08, L09, S18 and L27 were detected together, but the two target proteins S17 and L23 were detected in strains A and B. different variants have been detected in Therefore, the similarity calculation unit 522 calculates the similarity between P. acnes A strain and B strain by dividing the number of target proteins detected by the same m/z by the total number of target proteins, that is, 6/8 = Calculate 0.75 (or 75%).

Similarly, six target proteins S15, S19, S08, L09, S18 and L27 are detected for acne strains A and C in Table B, but two target proteins S17 and L23 are detected in strain A. and C strains have been detected with different mutations. Therefore, the similarity calculation unit 522 calculates the similarity between P. acnes A strain and C strain as 6/8=0.75 (or 75%).

Furthermore, for P. acnes strains B and C in Table C, the 7 proteins of interest S15, S19, S08, L09, S18, L27 and L23 (with L23(2) being detected together) were both of the same m/ A molecule corresponding to z is detected, but different mutants are detected in the target protein of S17 between the B strain and the C strain. Therefore, the similarity calculation unit 522 calculates the similarity between P. acnes B strain and C strain as 7/8=0.875 (or 87.5%).

The similarity calculation unit 522 calculates similarities as described above for a plurality of combinations (pairs) obtained by selecting two target organisms from three or more types of target organisms. After calculating the similarities for all combinations of target organisms, the similarity calculation unit 522 causes the storage unit 43 to store similarity data in which the similarities between two target organisms and the target organisms are associated with each other.

Based on the similarity data, the output image generation unit 53 generates a similarity image that maps and displays the similarity between the target organisms. The output image generation unit 53 generates an output image output from the output unit 44 including the similarity image and other information such as analysis conditions as appropriate.

FIG. 3 is a diagram showing an example of a similarity image. The similarity image 60 is shown as a table (hereinafter referred to as a similarity table), in which each row and each column corresponds to one type of target organism. In the similarity table, an element E corresponding to a row corresponding to one target organism (assumed to be target organism X) and a column corresponding to another target organism (target organism Y) has the relationship between target organisms X and Y. The degree of similarity (in %) between the two is shown. The similarity table in FIG. 3 shows the similarity between A, B and C strains of P. acnes. For example, the degree of similarity between P. acnes A and B strains is the element Eab (or Eba). In the similarity image 60, the similarity between the same organisms is displayed as 100%.

The output image generator 53 generates a similarity image such that at least one of the hue, brightness, and saturation of the element E corresponding to each similarity in the similarity table differs according to the similarity. In the example of FIG. 3, each element E is hatched differently to schematically show that the hue differs according to the similarity value corresponding to each element E. In the example of FIG.

The output control unit 54 causes the output image generated by the output image generation unit 53 to be output from the output unit 44 . An analyst or the like can see the similarity image or the like output from the output unit 44 and visually grasp how similar the target organisms are to each other in an easy-to-understand manner.

FIG. 4 is a flow chart showing the flow of the data processing method according to this embodiment. In the example of FIG. 2 as well, different strains of microorganisms of the same genus and species (hereinafter referred to as target microorganisms) are used as target organisms, and proteins are used as target molecules, but the present invention is not limited to these conditions. In step S1001, the control unit 50 sets a protein that is commonly expressed as the same molecule or a mutant in a plurality of strains of a target microorganism as a target protein, and sets the mutant as a target mutant. After step S1001 ends, step S1003 is started.

In step S1003, the storage unit 43 acquires the m/z of the target protein and mutants. After step S1003 ends, step S1005 is started. In step S1005, n (n is 3 or more) samples containing the target microorganism are prepared. After step S1005 ends, step S1007 is started.

In step S1007, the measurement unit 100 performs mass spectrometry on a plurality of samples, and the mass spectrum generation unit 521 generates data corresponding to the mass spectrum of each sample. After step S1007 ends, step S1009 starts. In step S1009, the similarity calculation unit 522 calculates the similarity for each pair of the n samples based on whether or not there is a peak in the range based on m/z acquired in step S1003 in the mass spectrum. calculate. After step S1009 ends, step S1011 is started.

In step S1011, the similarity calculation unit 522 generates data corresponding to an n×n similarity table indicating the similarity of each pair. After step S1011 ends, step S1013 starts. In step S1013, the output image generation unit 53 generates image data corresponding to a similarity image showing the degree of similarity and the color corresponding to the value of the degree of similarity in association with each other. After step S1013 ends, step S1015 is started.

In step S1015, the output unit 44 outputs a similarity display image. After step S1015 ends, the process ends.

According to the above-described embodiment, the following effects are obtained.
(1) In the data processing device (information processing unit 40) and data processing method according to the present embodiment, the similarity calculation unit 522 calculates the first sample derived from the first organism and the second sample derived from the second organism. is detected corresponding to the same m/z calculated from the structure of the target molecule or obtained by past measurement in each of the first sample and the second sample. Calculate the degree of similarity between the first organism and the second organism based on whether the The similarity is calculated, the output image generation unit 53 generates an output image including a similarity image that maps and displays the similarity between three or more types of target organisms, and the output unit 44 outputs the output image. do. As a result, it is possible to quickly calculate the degree of similarity between a plurality of organisms using mass spectrometry and display it in an easy-to-understand manner. In addition, even if the base sequence of the gene of the target organism is unknown, by using a ribosomal protein or the like for which the peak m/z has been obtained by mass spectrometry in the past as the target molecule, there is no need to perform gene sequencing or the like. can also calculate the similarity. Furthermore, since analysis is performed using the m/z value corresponding to the target molecule instead of the mass spectrum pattern, it is less susceptible to variations due to sample preparation conditions, equipment models, and the like.

(2) In the data processing apparatus according to the present embodiment, the similarity calculation unit 522 determines which of a plurality of m/z corresponding to one type of target molecule corresponds to at least one target molecule. Based on, the similarity between the target organisms is calculated. This makes it possible to accurately calculate the degree of similarity using information about mutants of the target molecule.

(3) In the data processing device according to the present embodiment, the output image generation unit 53 changes at least one of the hue, brightness, and saturation of the portion corresponding to the similarity (element E, etc.) according to the value of the similarity. A similarity image 60 is generated. This makes it possible to display the degree of similarity between target organisms in a more comprehensible manner.

(4) In the data processing device according to this embodiment, the target molecule is a protein, and the similarity calculation unit 522 calculates the same m/z derived based on the amino acid sequence or base sequence of the target molecule in mass spectrometry. The degree of similarity between target organisms can be calculated based on whether the target molecule is detected corresponding to . This makes it possible to more accurately calculate the degree of similarity based on genetic information obtained in the past. Similarity can be calculated using such genetic information even when the target molecule is a nucleic acid.

(5) The analysis apparatus according to this embodiment includes the data processing apparatus (information processing unit 40) according to this embodiment and a mass spectrometer. As a result, it is possible to quickly calculate the degree of similarity between a plurality of organisms using mass spectrometry and display it in an easy-to-understand manner.

The following modifications are also within the scope of the present invention and can be combined with the above-described embodiments. In the following modified examples, the same reference numerals are used to refer to parts having the same structures and functions as those of the above-described embodiment, and description thereof will be omitted as appropriate.
(Modification 1)
In the above-described embodiment, when the output image generation unit 53 generates an output image including the similarity image 60, the output image includes, in addition to the similarity image 60, a similarity calculation unit such as a phylogenetic tree of the target organism. Information about the degree of similarity between the target organisms calculated by a method different from the method of calculating the degree of similarity by 522 may be displayed.

FIG. 5 is a diagram showing an example of an output image 80 of this modified example. The output image 80 shows a similarity image 60 and a phylogenetic tree image 70 . The similarity image 60 of FIG. 5 displays the similarity of 23 different strains of P. acnes as target organisms. The phylogenetic tree image 70 shows a phylogenetic tree based on the nucleotide sequence of the gene of the target organism or the amino acid sequence of the protein encoded by the gene. An image portion 61 of the similarity image 60 indicates a number corresponding to each strain of P. acnes. An image portion 71 of the phylogenetic tree image 70 shows a scale bar indicating the number of substitutions at the gene locus. Hatching is omitted in the similarity image 60 of FIG.
Note that the output image 80 may be an image showing the similarity image 60 and information including the cluster analysis result based on the similarity of the cellular components of the target organism. The output image 80 may further include the phylogenetic tree image 70 in the image.

In addition to the effects of the above-described embodiment, the following effects are obtained in this modified example.
(1) In the data processing device according to this modified example, the output image generation unit 53 calculates the similarity between the similarity image 60 and the target organism calculated by a method different from the calculation of the similarity by the similarity calculation unit 522. generate an output image containing information about the degree of Since the degree of similarity between target organisms calculated by a plurality of methods is displayed, it is possible to provide more detailed information about the relationship between target organisms.

(2) In the data processing device according to this modified example, the information about the degree of similarity between the target organisms calculated by the different methods is the phylogenetic tree of the target organisms. Accordingly, by comparing the phylogenetic tree with the similarity calculated in the above-described embodiment, it is possible to provide more detailed information about the relationship between the target organisms.

(3) In the data processing device according to this modified example, the target molecule is a protein, and the degree of similarity calculated by the different method is similarity calculated based on the homology of the base sequence of the nucleic acid corresponding to the protein. degree. As a result, the degree of similarity between the mass spectrometry of the target protein and the base sequence of the gene can be calculated from different aspects, and more detailed information about the relationship between the target organisms can be provided.

(Modification 2)
In the above-described embodiments, an example in which the target molecule is a protein has been described, but the target molecule may be sugar chains, lipids, dyes, or nucleic acids. When the target molecule is a lipid, each target molecule can be set based on the difference in the molecular structure of the polar group and the carbon chain length. In this case, the target mutants may be a plurality of molecules having the same number of carbon atoms in chain hydrocarbons but different numbers and positions of double bonds. When the target molecule is a sugar chain, differences in the type, combination or number of monosaccharides that make up the sugar chain, the chain length of the sugar chain, the type or bonding mode of the complex sugar chain, etc. body can be set. The method of setting target molecules and target variants is not particularly limited as long as the target molecules are commonly present in the target organism and a plurality of target variants related to at least some of the target molecules are present in the target organism. In addition, similarity data can be generated by combining at least two analysis results of each cell component such as protein, lipid, pigment, sugar chain, and nucleic acid, and a similarity image can be generated based on the similarity data.

(Modification 3)
A program for realizing the information processing function of the analysis device 1 is recorded in a computer-readable recording medium, and the measurement including the processing by the data processing unit 52 and the output image generation unit 53 described above recorded in this recording medium , data analysis and display processing and control of related processing may be read and executed by the computer system. The term "computer system" used herein includes an OS (Operating System) and peripheral hardware. The term "computer-readable recording medium" refers to portable recording media such as flexible disks, magneto-optical disks, optical disks, memory cards, etc., and storage devices such as hard disks incorporated in computer systems. Furthermore, "computer-readable recording medium" means a medium that dynamically retains a program for a short period of time, like a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line. It may also include something that retains the program for a certain period of time, such as a volatile memory inside a computer system that serves as a server or client in that case. Further, the above program may be for realizing part of the functions described above, or may further realize the above functions by combining with a program already recorded in the computer system. .

When applied to a personal computer (hereinafter referred to as PC) or the like, the above-described control program can be provided through recording media such as CD-ROMs and DVD-ROMs and data signals such as the Internet. FIG. 6 is a diagram showing the situation. The PC 950 receives programs via a CD-ROM 953 . Also, the PC 950 has a connection function with a communication line 951 . A computer 952 is a server computer that provides the above program, and stores the program in a recording medium such as a hard disk. The communication line 951 is a communication line such as the Internet, personal computer communication, or a dedicated communication line. Computer 952 reads the program using the hard disk and transmits the program to PC 950 via communication line 951 . That is, the program is carried by a carrier wave as a data signal and transmitted via the communication line 951 . Thus, the program can be supplied as a computer readable computer program product in various forms such as a recording medium or carrier wave.

As a program for realizing the information processing function described above, a target molecule that is commonly contained in a first sample derived from a first organism and a second sample derived from a second organism is detected in the first sample and the second sample. In each mass spectrometry, based on whether it is detected corresponding to the same m / z calculated from the structure of the target molecule or obtained by past measurement, between the first organism and the second organism A similarity calculation process for calculating the similarity for each of a plurality of combinations obtained by selecting two types of organisms from three or more types of organisms, and a similarity calculation process for calculating the similarity for each of the three or more types of organisms and an output image generating process for generating an output image 80 including a similarity image 60 that maps and displays the similarity between the images. As a result, it is possible to quickly calculate the degree of similarity between a plurality of organisms using mass spectrometry and display it in an easy-to-understand manner.

The present invention is not limited to the contents of the above embodiments. Other aspects conceivable within the scope of the technical idea of the present invention are also included in the scope of the present invention.

Examples according to this embodiment are shown below, but the present invention is not limited to the following examples.

(Example 1)
In Example 1, mass spectrometry was performed on three types of P. acnes, and similarity images were created using eight types of ribosomal proteins as target molecules.

Cells of P.acnes, a resident skin bacterium, were disrupted using zirconia beads (0.5 mm in diameter), and the solution containing cytoplasmic components was centrifuged at 15000 g for 5 minutes. The supernatant after centrifugation was filtered using a filtration filter with a nominal cut-off molecular weight of 100 KDa, and the fraction remaining on the filtration filter was used as a ribosome fraction containing ribosomes. Using sinapinic acid as a matrix, the ribosomal fraction was ionized by MALDI and measured by time-of-flight mass spectrometry.

FIG. 7 is a diagram showing a mass spectrum obtained by mass spectrometric analysis of a ribosome fraction derived from one strain of P. acnes (corresponding to the P. acnes A strain described above). FIG. 7 shows the names of the ribosomal proteins assigned to correspond to the peaks. These assignments were derived from the theory of observed masses of proteins based on amino acid sequence information of ribosomal proteins. In the range indicated by A10, the intensity values are shown to be five times greater than the other ranges for clarity. Mass spectra were similarly obtained for the other two strains (A. acnes B and C strains described above), and peaks derived from ribosomal proteins were assigned.

Target molecules were eight ribosomal proteins S15, S19, S08, L09, S18, L27, S17 and L23. For the target molecule, if a peak is observed in the same m/z value range (allowable error of 200 ppm) as the theoretical value obtained from the amino acid sequence, it is 1, and if not observed, it is 0. These values are shown in Table A in FIG. Shown in Tables B and C. As shown in the above embodiment, the similarity between P. acnes A and B strains is 0.75, the similarity between P. acnes A and C strains is 0.75, and the similarity between P. acnes B and C strains is 0.75. The similarity between is 0.875. Mapping these values yielded the similarity image shown in FIG.

(Example 2)
Twenty-three different strains of P.acnes were subjected to mass spectrometry in the same manner as in Example 1, and similarity images were created. In addition, a phylogenetic tree was created based on genetic analysis. FIG. 5 shows the obtained similarity image and phylogenetic tree.

DESCRIPTION OF SYMBOLS 1... Analyzer, 10... Ionization part, 22... Mass separation part, 30... Detection part, 40... Information processing part, 43... Storage part, 44... Output part, 52... Data processing part, 53... Output image generation part, 60... Similarity image 70... Phylogenetic tree image 80... Output image 100... Measurement unit 521... Mass spectrum generation unit 522... Similarity calculation unit E, Eab, Eba... Elements of similarity table S... sample ion.

Claims (12)

a storage unit that stores m/z values corresponding to a plurality of molecules, which are calculated from molecular structures or obtained by past mass spectrometric measurements;
By referring to the storage unit, target molecules commonly contained in the first sample derived from the first organism and the second sample derived from the second organism are found in each of the first sample and the second sample. Three or more types of operations for calculating the degree of similarity between the first organism and the second organism based on whether they are detected corresponding to the same m/z value in mass spectrometry A similarity calculation unit that executes each of a plurality of combinations obtained by selecting two types of organisms from the organisms,
an output image generation unit that generates an output image including a similarity image that maps and displays the similarity between the three or more types of organisms;
and an output unit that outputs the output image. The data processing device according to claim 1,
The storage unit stores m/z values respectively corresponding to a plurality of different mutants for at least one of the molecules,
The similarity calculation unit refers to the storage unit, based on which of a plurality of m/z corresponding to each of a plurality of different mutants , at least one of the target molecules is detected. and a data processing device for calculating a degree of similarity between the first living thing and the second living thing. 3. In the data processing device according to claim 1 or 2,
The output image generating unit is a data processing device that generates the similarity image in which at least one of hue, brightness and saturation of a portion corresponding to the similarity is different according to the value of the similarity. In the data processing device according to any one of claims 1 to 3,
The output image generation unit includes the similarity image and information about the similarity between the three or more types of organisms calculated by a method different from the calculation of the similarity by the similarity calculation unit. A data processing device for generating said output image. In the data processing device according to claim 4,
The data processing device, wherein the information is a phylogenetic tree of the three or more types of organisms. 6. In the data processing device according to claim 4 or 5,
the molecule of interest is a protein;
The data processing device, wherein the degree of similarity calculated by the different method is a degree of similarity calculated based on the homology of the nucleotide sequences of the nucleic acids corresponding to the protein. In the data processing device according to any one of claims 1 to 5,
The data processing device, wherein the target molecules are sugar chains, lipids, proteins or nucleic acids. In the data processing device according to claim 7,
said molecule of interest is a protein or nucleic acid;
In the mass spectrometry, the similarity calculation unit determines whether the target molecule is detected corresponding to the same m/z derived based on the amino acid sequence or base sequence of the target molecule. A data processing device for calculating a degree of similarity between one organism and the second organism. In the data processing device according to any one of claims 1 to 8,
The similarity is data obtained by dividing the number of target molecules detected at the same m/z between the first sample and the second sample among the target molecules by the number of all the target molecules. processing equipment. a data processing device according to any one of claims 1 to 9 ;
an analyzer comprising a mass spectrometer; a storage unit that stores m/z values corresponding to a plurality of molecules calculated from molecular structures or obtained by past mass spectrometry measurements; a similarity calculation unit; and an output image generation unit. , and an output unit to perform data processing, comprising:
The similarity calculation unit refers to the storage unit, so that target molecules commonly contained in a first sample derived from a first organism and a second sample derived from a second organism are obtained in the first sample and An operation of calculating the degree of similarity between the first organism and the second organism based on whether or not the same m/z value is detected in mass spectrometry of each of the second samples A similarity calculation step for executing each of a plurality of combinations obtained by selecting two types of organisms from three or more types of organisms;
an output image generation step in which the output image generation unit generates an output image including a similarity image that maps and displays the similarity between the three or more types of organisms;
and an output step in which the output unit outputs the output image. derived from the first organism by referring to a storage unit storing a plurality of m/z values respectively corresponding to a plurality of molecules calculated from the structure of the molecule or obtained by past mass spectrometric measurement The target molecule contained in common in the first sample and the second sample derived from the second organism corresponds to the same m/z value in the mass spectrometry of each of the first sample and the second sample The operation of calculating the similarity between the first organism and the second organism based on whether it is detected as a A similarity calculation process to be executed for each of a plurality of combinations;
A program for causing a data processing device to perform an output image generation process for generating an output image including a similarity image that maps and displays the similarity between the three or more kinds of living things.

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