JP2008021260A - System for identifying rna sequence on genome by mass spectrometry - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device for identifying a minor RNA molecule, especially for identifying an RNA molecule on a genome sequence from RNA molecular weight information by in silico, its method or the like. <P>SOLUTION: An RNA molecule retrieval device marks and identifies on an optional genome sequence an arbitrary RNA molecule, including at least one target RNA fragment, comprises a storing means 10 for storing data relative to the arbitrary genome sequence of an arbitrary species and a DNA decomposition enzyme that can cut the sequence or RNA decomposition enzyme or the data of cut mechanism of both the DNA decomposition enzyme and the RNA decomposition enzyme; an inputting means 20 for reading the target RNA fragment molecular weight, obtained by measuring at least one target RNA segment that can be cut by a decomposition enzyme similar to a decomposition enzyme; and a calculating means for collating at least one target RNA segment molecular weight read with sequence data existing in the storing means 10 and with data relative to the cut mechanism and calculating a candidate region where the target RNA segment exists on the sequence of the storing means 10. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an RNA molecule search apparatus and method for marking and identifying any RNA molecule containing a target RNA fragment on any genome sequence of any biological species, and any RNA containing the target RNA fragment in a computer The present invention relates to a target RNA search program that realizes a function of marking and identifying a molecule on an arbitrary genome sequence of an arbitrary species and a computer-readable recording medium describing the program.

  Recently, due to the discovery of RNA interference and microRNA, new functions that RNAs that do not encode proteins (functional RNAs) are attracting attention. Functional RNA itself is the final product of genes, and these act as functional macromolecules and gradually reveal that they play important roles related to higher life phenomena such as development and differentiation from the regulation of gene expression It is becoming. Recently, there are reports of cases in which functional RNA abnormalities cause disease, and it is necessary to consider not only protein abnormalities but also RNA abnormalities as a cause of diseases. In order to strongly promote functional RNA research, the conventional approach of capturing RNA as “information” is insufficient, and the development of a new methodology for capturing RNA as “molecule” is indispensable.

  However, in the conventional RNA analysis methods, the method of amplifying cDNA by reverse transcription PCR and determining the sequence is the mainstream, but this method can read only the sequence information of RNA. It is insufficient to obtain qualitative information such as post-transcriptional processing and modification of RNA. Also, considering the bias by PCR, the method is by no means a quantitative analysis. The method of labeling with radioisotopes and analyzing the sequence using multiple base sequence-specific ribonucleases (Doniskeller method) and the post-label method of Kuchino et al., Which is an analysis method including modified bases, are also used. All of them are skilled techniques, time consuming and laborious methods, and are not versatile.

  On the other hand, one of the two major ionization methods of biopolymers (MALDI method) invented by Koichi Tanaka of Shimadzu Corporation who won the Nobel Prize in Chemistry contributed greatly to protein research by mass spectrometry. Thereby, the peptide mass fingerprint (PMF) method, which is a mass measurement method for proteins, was established.

  In addition to the progress of mass spectrometry, the background of the dramatic progress in the identification of trace proteins is the enhancement of gene databases by genome analysis. There is no longer any need to sequence the N-terminal or internal peptide for protein identification. Proteins separated by SDS-PAGE are identified by digesting with amino acid residue-specific protease such as trypsin and measuring the mass of the peptide. be able to. In the peptide mass fingerprint (PMF) method, assuming that trypsin is cleaved in silico from the sequence of all proteins derived from the species to be analyzed, peptides cleaved with lysine (K) and arginine (R) are listed. The molecular weight of each peptide is used as a virtual database.

  The database can be identified by searching for the most similar protein with reference to the molecular weight set of the actually analyzed peptide. Since it is unlikely that multiple peptides will fall within the sequence of a single protein, the correct answer rate is high even if not all peptides can be assigned, and the PMF method is now an important technique indispensable for proteomics research. is there.

  Although it has become possible to easily identify a trace amount of protein by the peptide mass fingerprint method, there is no simple method for identifying a trace amount of RNA. Conventionally, RNA molecules are difficult to ionize compared to proteins, and high-sensitivity detection by mass spectrometry of RNA molecules has been difficult, but the present inventors have made it possible to perform high-sensitivity mass analysis of RNA molecules to identify trace RNA molecules. Mass spectrometric data can be obtained. However, RNA molecules cannot be identified from the molecular weight list if the data processing part of the peptide mass fingerprint method is used because the number of types of monomers differs between peptides and RNA and the database to be searched is different. . Therefore, a new method for identifying a minute amount of RNA molecule, particularly for identifying an RNA molecule on a genome sequence in silico from its RNA molecular weight information is desired.

  As a result of earnest efforts so that the identification method equivalent to the peptide mass fingerprint method can be used for RNA molecules, the present inventor paid attention to the molecular weight difference between RNA fragments, and measured molecular weight list and genome sequence database. The present inventors have found an RNA mass fingerprint method (RMF method) that can identify an RNA molecule by evaluating the similarity of a virtual molecular weight list obtained by processing and scoring.

  The present invention (RNA mass fingerprint method) is a method for rapidly identifying RNA genes from a genomic database using molecular weight data of minute amounts of RNA analyzed by high-sensitivity mass spectrometry, based on the difference in molecular weight between RNA fragments. . As shown in FIG. 4, the molecular weights of the fragments obtained when the genomic base sequences of Escherichia coli and yeast were cleaved with a predetermined RNA-degrading enzyme were summarized for each base composition, and the results were sorted in order of molecular weight. The minimum molecular weight difference between fragments when the genomic nucleotide sequence was cut with G was 0.21 (Da) for both E. coli and yeast. Since the mass spectrometer error was 0.1 (Da), it was found that the composition of E. coli and yeast could be reduced by the molecular weight of the fragments.

  On the other hand, when calculating the molecular weight of fragments when the protein is cleaved with trypsin (cleaved with R and K), the molecular weight difference of each composition is often small, and the molecular weight difference of 0.05 (Da) or less exceeds 80%. Therefore, it is very difficult to narrow down the composition only from the molecular weight. As a feature, it can be said that genomic fragments are more easily assigned to the composition from molecular weight than protein. In addition, in higher-species species with large genome sizes such as mice and humans, the combination pattern of the composition increases and it is expected to approach the theoretical base sequence combination, so the difference in molecular weight between compositions up to 35 bases Calculation was performed. The minimum molecular weight difference between fragments is 0.17 (Da), and a high-precision mass spectrometer can narrow down the composition from molecular weight even for higher organisms such as mice and humans with large genome sizes. It turned out to be.

  Accordingly, the present invention provides a storage means (10) for storing data on any genomic sequence of any organism species and a cleavage mechanism of a DNA-degrading enzyme or an RNA-degrading enzyme or both capable of cleaving the sequence. Input means (20) for reading the molecular weight of the target RNA fragment obtained by measuring at least one target RNA fragment that can be cleaved by the same degrading enzyme as the decomposing enzyme, and at least one read target RNA A calculation means (30) for comparing the fragment molecular weight with the sequence data in the storage means (10) and the data relating to the cleavage mechanism, and calculating a candidate region in which the target RNA fragment is present on the sequence of the storage means (10); The present invention relates to an RNA molecule search apparatus for marking and identifying an arbitrary RNA molecule comprising at least one target RNA fragment consisting of:

  The present invention also provides a storage step (10) for storing data on an arbitrary genomic sequence of an arbitrary species and a cleavage mechanism of a DNA-degrading enzyme and / or an RNA-degrading enzyme capable of cleaving the sequence, An input step (20) for reading the molecular weight of the target RNA fragment obtained by measuring at least one target RNA fragment that can be cleaved by the same degrading enzyme as the decomposing enzyme, and the read at least one target RNA A calculation step (30) for comparing the fragment molecular weight with the sequence data in the storage step (10) and the data on the cleavage mechanism, and calculating a candidate region in which the target RNA fragment is present on the sequence in the storage step (10); RNA molecule search method for marking and identifying any RNA molecule comprising at least one RNA fragment of interest on any genome sequence On.

  Furthermore, the present invention provides a memory function for storing in a computer any genome sequence of any organism species, and data relating to the cleavage mechanism of DNA-degrading enzyme or RNA-degrading enzyme or both capable of cleaving the sequence ( 10) an input function (20) for reading the molecular weight of the target RNA fragment obtained by measuring at least one target RNA fragment that can be cleaved by the same degrading enzyme as the decomposing enzyme, and at least one read The calculation function (30) for comparing the molecular weights of two target RNA fragments with the data relating to the sequence and the cleavage mechanism in the memory function (10) and calculating the candidate region where the target RNA fragment is present on the sequence of the memory function (10) An RNA molecule search program for marking and identifying any RNA molecule containing the at least one target RNA fragment on any genome sequence. Ram, or, a computer-readable recording medium according to the program.

  In the present invention, by directly measuring a small amount of RNA using high-sensitivity mass spectrometry without PCR amplification or radioisotope labeling, the RNA gene sequence can be identified in silico from its molecular weight information. RNA contained in a small amount of RNA-protein complex (RNP) present in cells immunoprecipitated with antibodies can be rapidly and quantitatively measured, making it a completely new fundamental technology for RNA-protein interaction analysis. It is expected to contribute greatly to the creation of RNA-protein interaction networks in the future.

  According to the method and apparatus including the program of the present invention, RNA mass spectrometry can be an important basic technology that supports next-generation RNA research, and RMF is indispensable to make use of this technology. . There is a possibility of large-scale deployment involving device manufacturers, bioinformatics industry, drug discovery ventures, national projects, and so on.

[Definition of terms]
In order to make the content of the present invention easier to understand, terms used in the specification are defined here. The “composition” in the present invention is a term representing the kind of base and the number thereof contained in a fragment regardless of the sequence order. For example, the composition of the fragment represented by A1U0C2G1 means that it is a fragment containing 1 residue of adenine, 0 residue of uracil, 2 residues of cytosine and 1 residue of guanine, regardless of the sequence order. It is. Further, the “molecular weight” in the present invention is a measurement object calculated by a known method based on the actual molecular weight or the mass-to-charge ratio (m / z) and the charge (z) which are data obtained from a mass spectrometer. It is a term that represents one of the molecular weights of a substance, and represents the molecular weight or equivalent data.

  The “genomic sequence” in the present invention is a single-stranded RNA sequence corresponding to the double strand of an arbitrary genome of an arbitrary biological species known at the time of filing of the present patent application, for example, a virus having an RNA genome. In this case, the term includes the double-stranded RNA and single-stranded RNA sequences, and the single-stranded DNA genome represents the corresponding RNA sequence. The term "genomic fragment molecular weight" is a term used to describe a genomic fragment formed when the genomic sequence is virtually cleaved according to the cleaving mechanism of any RNA degrading enzyme and / or DNA degrading enzyme that is actually present. The term "genomic fragment composition" is a term representing the molecular weight of a genomic fragment. The term "genomic fragment position" is a term representing the composition of a virtually fragmented genomic sequence fragment. The genome break In the presence term representing the position data indicating where to the, the "number of genomic fragments" is a term that represents the number of genomic fragments of the same genomic fragment composition above the genomic sequence.

In the present invention, the “target RNA” is a term representing a specific RNA molecule to be identified on the genome sequence, particularly a functional RNA molecule, and the “target RNA fragment” is used to obtain the genomic fragment. It is a term that represents the fragment obtained by actually cleaving the target RNA with the same RNase used, and the `` target RNA fragment number '' is a term that represents the number assigned to the cleaved target RNA fragment, `` Target RNA fragment molecular weight '' is a term representing the molecular weight of the target RNA fragment, and `` target RNA fragment composition '' is a term representing a genomic fragment composition having the same molecular weight as the target RNA fragment molecular weight. “Number” is a term representing the number of genomic fragments having the same composition as the target RNA fragment composition above the genome sequence, and “target RNA fragment position” is the target RNA fragment composition above the genome sequence. Is a term representing the position of a genomic fragment having the same composition.
Embodiment of the present invention
An RNA molecule search apparatus for marking and identifying an arbitrary RNA molecule containing at least one target RNA fragment of the present invention on an arbitrary genomic sequence is capable of cleaving an arbitrary genomic sequence of an arbitrary biological species and the sequence. Storage means (10) for storing data relating to the cleavage mechanism of DNA-degrading enzyme or RNA-degrading enzyme or both, and measuring at least one target RNA fragment that can be cleaved by a degrading enzyme similar to the degrading enzyme Input means (20) for reading the molecular weight of the target RNA fragment obtained in this manner, and comparing the read at least one target RNA fragment molecular weight with the sequence data and the data on the cleavage mechanism in the storage means (10), Computation means (30) for computing a candidate area in which a fragment exists on the array of storage means (10).

  The data on the cleaving mechanism of the DNA degrading enzyme and / or the RNA degrading enzyme capable of cleaving the sequence memorized in the memory means (10) is specific to guanine (G) when RNase is taken as an example. Data include RNaseT1 that cleaves, RNaseCL3 that specifically cleaves cytosine (C), RNaseA that specifically cleaves U or C, and RNaseU2 that cleaves A or G specifically. In addition, the storage means (10) stores any data stored in the storage means (10) developed in the storage area (for example, memory) according to data relating to the cleavage mechanism of the degrading enzyme stored in the storage area (for example, on the memory) Data relating to fragments obtained by virtually cutting an arbitrary genome sequence of a biological species can be stored.

  In the present invention, as an example of data relating to a fragment obtained by virtually cutting an arbitrary genome sequence, a set of data including a molecular fragment molecular weight, a genome fragment composition, the number of genome fragments, and a genome fragment position can be cited. In order to save the storage space on the area (for example, memory), as shown in FIG. 2, it may further include a storage means (11) for storing at least two data among the set of data. As another example, as shown in the table E in the figure, data relating to the correction means (22) for correcting the following error can be stored.

  The input means (20) in the present invention is an input means for reading the molecular weight of the target RNA fragment obtained by measuring at least one target RNA fragment that can be cleaved by the same decomposing enzyme as the decomposing enzyme, Here, it is not necessary to actually cleave the target RNA fragment with an RNase. If the molecular weight is known, the molecular weight is directly measured. If the molecular weight is unknown, the molecular weight is directly measured, for example, LC / MS (liquid chromatography). (Graphography / Mass Spectrometry) or MALDI-TOF MS (Matrix Assisted Laser Desorption / Ionization / Time-of-Flight Mass Spectrometer).

  In order to more accurately identify a target RNA fragment, the present invention provides a target obtained by measuring at least one target RNA fragment obtained by actually cleaving the target RNA with a degrading enzyme similar to the above degrading enzyme. An input means (21) for reading the RNA fragment molecular weight can be further included. For example, the molecular weight of each actually cleaved target RNA fragment can be input as the sequence I (n) (n is an integer of 1 or more and indicates the target RNA fragment number).

  The calculation means (30) in the present invention collates the read molecular weight of at least one target RNA fragment with the sequence data in the storage means (10) and the data relating to the cleavage mechanism, and the target RNA fragment is stored in the storage means (10). A calculation means for calculating a candidate region existing on a sequence.For example, the molecular weight of a target RNA fragment is compared with data on a virtually cut genomic sequence fragment stored in a storage region, and the target RNA fragment is Candidate regions existing on the genome sequence can be calculated.

  In order to calculate the candidate region where the target RNA is present on the genome sequence more accurately, the present invention uses at least one of the read target RNA fragment molecular weights in the storage means (10) and / or the storage means (11). After collating with the data, it can further include extraction means (31) for extracting the target RNA fragment composition. Specifically, a genomic fragment composition corresponding to a genomic fragment molecular weight having a molecular weight that matches the molecular weight of the target RNA fragment and in the same set as the genomic fragment molecular weight is defined as a target RNA fragment composition, for example, matrix H (n) (n is The target RNA fragment number is indicated by an integer of 1 or more) and stored in a storage area (including a medium).

  However, even if the molecular weight of the target RNA fragment is measured by LC / MS (liquid chromatography / mass spectrometry) or MALDI-TOF MS (matrix-assisted laser desorption / ionization / time-of-flight mass spectrometer), Various factors cause an error in the molecular weight of the genomic fragment. The reasons are as follows: (1) error due to the shape of the terminal phosphate group of the RNA fragment, (2) error due to an error in the breakdown of the U / C number in the composition, and (3) virtual cleavage actually occurs due to modification. Errors due to absence, (4) errors due to fragments at both ends of the original RNA, (5) errors that cause incorrect mass to be extracted due to the influence of natural isotopes, etc.

  The present invention further includes a correction means (22) for correcting an error with respect to the molecular weight of at least one target RNA fragment read by the input means (21) in order to input a more accurate target RNA molecular weight, and in various cases In addition, it is possible to comprehensively define how to handle molecular weights of target RNA fragments with errors. For example, all the molecular weight changes that can occur in the current RNA base sequence of this patent application are stored and stored in advance in a predetermined database. When the target RNA fragment molecular weight developed in memory and the genomic fragment molecular weight do not match, it is judged that there is a cause of error in the target RNA fragment, and for the target RNA fragment molecular weight that seems to be causing the error, Correct the molecular weight error.

  In the present invention, in order to calculate a candidate region where the target RNA fragment molecule is present on the genome sequence more accurately, the calculation means (30) further stores the obtained target RNA fragment composition in the storage means (10) or the storage. Extraction means (32) for collating with the data of means (11) or both and extracting the number of at least one target RNA fragment on the genome sequence can be included. Specifically, for example, the matrix F (n) (n is 1 or more), where the number of genomic fragments corresponding to the genomic fragment composition corresponding to the target RNA fragment composition and in the same set as the genomic fragment composition is the target RNA fragment number. The target RNA fragment number is indicated by an integer) and stored in the storage area (including the medium).

  In the present invention, in order to calculate a candidate region where the target RNA fragment molecule is present on the genome sequence more accurately, the calculation means (30) further stores the obtained target RNA fragment composition in the storage means (10) or the storage. An extraction means (33) for collating with the data of the means (11) or both and extracting at least one target RNA fragment position on the genome sequence can be further included. Specifically, for example, a matrix L (n) (where n is 1 or more) is defined as a target RNA fragment position corresponding to a genomic fragment composition that matches the target RNA fragment composition. The target RNA fragment number is indicated by an integer) and stored in the storage area (including the medium). Thereby, it is possible to identify a place where the target RNA fragment is likely to exist on the genome.

  According to the present invention, in order to calculate a candidate region where the target RNA fragment molecule is present on the genome sequence more accurately, the calculation means (30) sends a predetermined genome sequence from at least one target RNA fragment position obtained. A scanning means (34) for scanning the genome sequence composition in the frame provided with a predetermined base length in the direction can be further included. The presence of the target RNA fragment composition on the genome sequence indicates that there is a high possibility that the target RNA exists, so a predetermined frame is provided from all target RNA fragment positions on the genome sequence, By scanning the entire genome sequence composition in the frame with the target RNA fragment composition, a frame containing the entire target RNA fragment composition can be detected on the genome sequence.

  The length of the frame in the present invention is not limited. The target RNA base length is preferred. By setting the length of the base sequence of the target RNA as a frame, if the entire target RNA fragment composition is included in the frame, the frame itself is very likely to be the target RNA to be identified, and the target RNA is the genome sequence. Will almost locate the position on the top. In the present invention, it is preferable to determine the length of the frame by measuring the base length of the target RNA by other means such as electrophoresis. In addition, all of the known methods capable of measuring the length of the base sequence of the target RNA at the time of filing this patent application can be used in the present invention.

  In the present invention, in order to digitize the candidate region of the target RNA fragment molecule present on the calculated genome sequence, the calculation means (30) asks the calculation means (30) for at least one control RNA fragment composition that matches the composition in the obtained frame. Based on the number (number of target RNA fragments), a calculation means (35) for calculating the appearance probability of the target RNA fragment in the frame can be further included. In the present invention, a preferred calculation means for calculating the appearance probability includes an appearance frequency ratio method or a binomial distribution method.

The frequency ratio method used in the present invention refers to the total number of RNA fragments obtained by virtually cutting a genome as F _total , and the genome of a given genomic fragment having two or more bases with the largest number of genomic fragments. When the number of fragments is F _{max and the} number of target RNA fragments existing on _the genome sequence is Fn (n is an integer of 1 or more and indicates the target RNA fragment number), the target RNA fragment appears on the genome sequence. In this method, the frequency ratio (P (n)) is calculated by the following formula, and this is used to calculate the score as the occurrence of a composition fragment within a frame.

P (a) = Fa / F _total ÷ F _max / F _total

For example, when RNaseT1 is used as an RNA-degrading enzyme, if the base length reflected in the score is 3 bases or more, the number of fragments having a composition of 3 bases, AOU1C1G1, having the highest appearance frequency among them may be used as F _max. .

  In the present invention, a binomial distribution method can be used in addition to the appearance frequency ratio method. Here, the binomial distribution method is a method using a probability p (p = frequency of appearance of a specific composition / genomic length) that a specific composition appears at an arbitrary point on the genome. The probability that a specific composition appears a specific number of times in a frame is considered to follow a binomial distribution with p as the success probability and the frame length as the number of trials. Here, when the frame length is l, the random variable X follows a binomial distribution, and for B (p, l), that is, from X to B (p, l), p is the theoretical appearance probability of the composition. May be used. A probability derived from such a binomial distribution or a Poisson distribution that approximates the binomial distribution can be used for the calculation of the score as a composition fragment appearance probability within the frame.

  The present invention calculates a score from the appearance probability of the target RNA fragment in the frame in the calculation means (35) in order to express the candidate region of the target RNA existing on the calculated genome sequence with a more specific number. A calculation means (36) can further be included. The present invention adds the log of P (n), which is the appearance probability or ratio of all target RNA fragments in the frame, calculated using the appearance frequency ratio method or binomial distribution method. Or a value obtained by multiplying the product obtained by multiplying P (n), which is the appearance probability or ratio, by the log, is a score indicating the possibility that the target RNA fragment is present in the frame.

  The value of this score is the product of the occurrence probability or ratio (0 <P (n) <1) of the target RNA fragment in the frame, so if there are many target RNA fragments in the frame, the product of positive numbers less than that one If all fragments are in one frame, the product is the minimum. In addition, by taking a minus log (-log) for the product for easy understanding, the appearance frequency of the in-frame control RNA increases as the score value increases. In addition, since taking a minus log (-log) with respect to the appearance frequency P (n) and obtaining a score by addition is exactly the same from a mathematical point of view, the present invention determines the order in calculating the score. There is no limit.

  For example, the probability that a specific composition appears k times in a certain frame is expressed as P [X = k] as Pf, that is, Pf = P [X = k]. Let -log (Pf) be the score for a particular composition. A score can be calculated for each different composition appearing in the frame, and the sum can be used as the frame score.

  Since the frame of the present invention is provided from the position of the target RNA fragment appearing on the genome sequence, it means that there are as many frames as the number of the positions, and one score per frame. Will be calculated. Therefore, it is possible to extract a higher score by rearranging the largest scores in order. When there are a plurality of target RNAs on the genome sequence, the upper score is not limited to one, and there may be a plurality of scores.

  The present invention for marking and identifying an arbitrary RNA molecule containing an RNA fragment of interest on an arbitrary genome sequence is particularly suitable for mammals such as mice and humans, and in particular, an RNA molecule containing an RNA fragment of interest containing a specific RNA of interest is human. Can be identified and identified on the genome.

  In addition, the present invention is configured based on the molecular weight and composition of RNA as a “molecule”. DNA that differs from RNA by only one base is also regarded as a “molecule”, and any DNA molecule including a control DNA fragment according to the present invention can be represented on any genome sequence based on the DNA molecular weight and composition that is not significantly different from the molecular weight and composition of RNA And can be identified. In this case, a DNA degrading enzyme is used instead of the RNA degrading enzyme.

  That is, the present invention provides a DNA molecule searching apparatus and method for marking and identifying an arbitrary DNA molecule containing the target DNA fragment on an arbitrary genome sequence of an arbitrary biological species, and a target DNA fragment using a computer. The present invention relates to a program that realizes a function of marking and identifying an arbitrary DNA molecule contained in an arbitrary genome sequence of an arbitrary species and a computer-readable recording medium describing the program. In the present invention, the description of the present invention relating to the RNA described above can be directly applied to the target DNA fragment.

  The present invention provides not only an RNA molecule search apparatus for marking and identifying an arbitrary RNA molecule containing an RNA fragment of interest on an arbitrary genome sequence of an organism species, but also a search method thereof, and an RNA fragment of interest using a computer. The present invention relates to an RNA molecule search program that realizes a function of marking and identifying an arbitrary RNA molecule including a target on an arbitrary genome sequence of an arbitrary biological species, and a computer-readable recording medium describing the program.

  The present invention also provides a storage means (10), an input, which are various means constituting an RNA molecule search apparatus for marking and identifying an arbitrary RNA molecule containing a target RNA fragment on an arbitrary genome sequence of an arbitrary biological species. Means (20), calculation means (30), storage means (11), input means (21), correction means (22), extraction means (31), extraction means (32), extraction means (33), scanning The means (34), the calculation means (35), and the calculation means (36) can correspond to the respective steps constituting the search method, thereby providing an RNA molecule search method.

  In addition, the present invention provides an RNA molecule search program that realizes a function of marking and identifying an arbitrary RNA molecule containing an RNA fragment of interest on an arbitrary genome sequence of an arbitrary biological species using a computer, and the means described above. It is possible to correspond to each function constituting a computer-readable recording medium describing the program.

  In the present invention, only the embodiment related to the RNA molecule search apparatus has been described. However, the RNA search method and a computer are used to mark and identify any RNA molecule containing the target RNA fragment on any genome sequence of any organism species. The embodiment relating to the RNA molecule search program and the computer-readable recording medium describing the program that realizes the function to be performed can also be read in correspondence with the description of the embodiment relating to the RNA molecule search apparatus. It will be disclosed.

  The most preferred embodiment of the present invention is shown in FIG.

  In the figure, 11 is an arbitrary genomic sequence of any biological species included in the storage means (10) according to the cleavage mechanism of a DNA-degrading enzyme and / or RNA-degrading enzyme that can cleave the genomic sequence. The storage means (11) which virtually cuts and stores at least two pieces of data consisting of the molecular fragment molecular weight, the genome fragment composition, the number of genome fragments and the genome fragment position is shown (see FIG. 2).

  In this embodiment of the present invention, as shown in FIG. 2, an RNA sequence corresponding to an arbitrary genomic sequence of an arbitrary biological species is hypothesized according to a cleavage mechanism of an RNase that specifically degrades RNA. And storing means (11) in which a set of data including the molecular weight of the genomic fragment, the genomic fragment composition, the number of genomic fragments, and the genomic fragment position is stored.

  The genome fragment database 20 shown in FIG. 2 contains at least a known sequence, for example, a genome sequence corresponding to a double-stranded genome sequence (front and back) that can be obtained from a commercially available NCBInr or TrEMBL database. Including a table that virtually cuts in silico on a computer in accordance with the cleavage mechanism of the degrading enzyme and stores the molecular fragment molecular weight, genome fragment composition, genome fragment position, and number of genome fragments as a set of data .

  As shown in Table D (24) shown in FIG. 2, the genome fragment composition, genome fragment molecular weight, genome fragment number, and genome fragment position of the virtually cut genome fragment are stored as a set of data in the same table. Alternatively, as shown in Tables A to C (21, 22 and 23), they may be stored in a separate table as a set with other data centering on the genome fragment composition. In the present invention, in order to keep the capacity small when expanding the data on the memory, as shown in 21, 22 and 23 of FIG. 2, the genome fragment composition, the genome fragment molecular weight, the number of genome fragments, and the genome fragment position are set. Each is preferably stored in a separate table.

  Specific cleavage of all RNases known at the time of filing of the present patent application can be used for virtual RNA sequence cleavage of the present invention. For example, RNaseT1, which specifically cleaves guanine (G), cytosine There are RNaseCL3 that specifically cleaves, RNaseA that specifically cleaves U or C, and RNaseU2 that specifically cleaves A or G. The specifically cleaved RNase used in the present invention is not limited to the above example.

The genome used in the genome fragment database of this embodiment of the present invention is an arbitrary genome of an arbitrary species, and is not particularly limited from E. coli, yeast to various mammals, and humans. In order to explain this embodiment more clearly, E. coli and yeast genomes were used, but the present invention is not limited thereto. Examples of the E. coli genome used here include E. coli K12 MG1655 strain, and examples of the yeast genome include budding yeast Saccharomyces_cerevisiae. In addition, E. coli gene product names include 5S rRNA, 6S RNA, 4.5S RNA, 23S rRNA, 16S rRNA, etc., and budding yeast gene names include snR9, scR1, snR128, snR190, snR14, snR6, etc. In the figure, reference numeral 12 denotes a target RNA obtained by measuring at least one target RNA fragment obtained by actually cleaving with the same decomposing enzyme as the decomposing enzyme contained in the memory means (20). An input means (21) for reading the fragment molecular weight as a matrix I (n) (n is an integer of 1 or more and indicates the target RNA fragment number) is shown (see FIG. 3).

  The purpose of this embodiment is to identify an RNA molecule whose sequence or the like actually exists on the genome sequence, and obtain the target RNA by actually cleaving the target RNA with the same degrading enzyme as the degrading enzyme. A set of data consisting of the molecular weight of the target RNA fragment obtained by measuring at least one target RNA fragment and the target RNA fragment number is read.

  Specifically, a target RNA molecule having an unknown sequence such as 30 in FIG. 3 is cleaved into a target RNA fragment such as 31 with RNaseT1 that specifically cleaves G. Next, measure the molecular weight of all RNA fragments of interest using, for example, LC / MS (liquid chromatography / mass spectrometry) or MALDI-TOF MS (matrix-assisted laser desorption / ionization / time-of-flight mass spectrometer). The target RNA fragment number and the corresponding molecular weight of the target RNA fragment are stored in a storage area (including the medium) in the form of a sequence I (n) (where n is an integer of 1 or more, for example) in Table Y. Store.

  In the figure, reference numerals 13 and 14 denote correction means (22) for correcting an error with respect to the molecular weight of at least one target RNA fragment read by the input means (21). In this embodiment, all the molecular weight changes that can occur with respect to the RNA base sequence as of the present patent application are stored in advance in a predetermined database as shown in Table E (25) of FIG. If the comparison result between the molecular weight of the target RNA fragment developed in memory and the molecular weight of the genomic fragment does not match, it is determined that there is a cause for an error in the target RNA fragment (see 13 in FIG. 1), and this error seems to have occurred. The molecular weight error is corrected for the molecular weight of the target RNA fragment (see 14 in FIG. 1).

  In the figure, reference numeral 15 denotes a state in which at least one read RNA fragment molecular weight included in the calculation means (30) is collated with the data of the storage means (10) and / or the storage means (11). Furthermore, extraction means (31) for extracting the target RNA fragment composition as a matrix H (n) (n is an integer of 1 or more and indicates the target RNA fragment number) is shown.

  In this embodiment, the table Y in FIG. 3 and the table A (21) in FIG. 2 are expanded on a calculation region, for example, a memory, and the target RNA fragment molecular weight is collated with the genome fragment molecular weight. Define the genomic composition having the same molecular weight as the target RNA fragment composition, for example, the target RNA fragment number as a set of data, for example, a matrix H (n) in table Y (n is an integer of 1 or more and the target RNA fragment It is stored in a storage area (including a medium) in the form of a number. By this means, the composition information of each fragment of the target RNA actually cleaved can be known.

  In the figure, 16 indicates that the obtained target RNA fragment composition contained in the calculation means (30) is further collated with the data of the storage means (10) and / or the storage means (11), and the genome sequence The extraction means (32) for extracting the number of at least one target RNA fragment above the matrix F (n) (where n is an integer equal to or greater than 1 and indicates the target RNA fragment number). The target RNA fragment composition is collated with the genomic fragment composition shown in Table C of FIG. 2, and the number of genomic fragments having the same composition as the target RNA fragment composition above the genomic sequence when they match is the target RNA above the genomic sequence. The number of fragments is defined. For example, a target RNA fragment number is stored as a set of data on a memory as a matrix F (n) (n is an integer of 1 or more and indicates a target RNA fragment number).

  In the figure, 17 indicates that the calculation means (30) collates the obtained target RNA fragment composition with the data of the storage means (10) and / or the storage means (11), and is above the genome sequence. The extraction means (33) for extracting at least one target RNA fragment position as L (n) (n is an integer of 1 or more and indicates the target RNA fragment number) is shown. The target RNA fragment composition is collated with the genomic fragment composition in Table B of FIG. 2, and the genome fragment position having the same composition as the target RNA fragment composition above the genomic sequence is defined as the target RNA fragment position when matching. For example, the target RNA fragment number can be stored on the memory as a set of data, for example, as a matrix L (n) (n is an integer of 1 or more and indicates the target RNA fragment number). Thereby, it is possible to identify a place where the target RNA fragment is likely to exist on the genome.

  In the figure, 18 indicates the presence of the target RNA fragment composition in the frame above the genome sequence by the appearance frequency of the RNA fragment composition, and the number F (n of at least one target RNA fragment existing in the frame ), A calculation means (35) for calculating the appearance frequency P (n) of the target RNA fragment in the frame by the binomial distribution method is shown.

  In the figure, 19 is a scan in which the genomic sequence composition in a frame provided with the base length of the target RNA is scanned in a predetermined direction of the genomic sequence from at least one target RNA fragment position obtained by the extraction means (5). Means (34) are shown. The presence of the target RNA fragment composition above the genome sequence indicates that there may be a target RNA molecule around it, so a predetermined frame from all target RNA fragment positions where the target RNA fragment composition exists By scanning the entire genome sequence in the frame, a frame containing the entire target RNA fragment composition can be detected on the genome sequence. See FIG.

  In the figure, reference numeral 21 or 22 denotes calculation means (36) that is included in the calculation means (35) and calculates a score from the appearance probability of the target RNA fragment in a frame. Reference numerals 23 and 24 denote registration in the obtained score list and display of the list (see FIGS. 6 and 7).

Example 1
The present invention, based on the measurement data by LC / MS of a fragment prepared by reacting RNase T1 with purified E. coli 5S ribosomal RNA molecule based on the above description, A1U2C5G1,
A1U2C5G1, contained in A2U1C4G1, A3U1C2G1, A3U0C1G1, A3U0C1G1, A2U1C1G1, A2U1C1G1, A1U0C2G1, A1U0C2G1, A0U1C2G1, A0U1C2G1, A2U0C0G1, A1U1C0G1, A1U1C0G1, A0U0C2G1, part or most of the E. coli 5S ribosomal RNA gene region of the composition corresponding to E. coli (K12 MG1655 strain)
(Downloadable from ftp://ftp.ncbi.nih.gov/genomes/Bacteria/Escherichia_coli_K12/) is the genomic sequence, the above RNA fragment is identified as the target RNA fragment, and its location in the genome is identified. Successful assignment of the existing 5S ribosomal RNA gene. 1 out of 8 genes
One has a different score from the other 7 genes because the composition of the least frequently occurring fragment is different. The results are shown in FIGS. 6 (a) to (c).

Example 2
The present invention, based on the measurement data by LC / MS of a fragment prepared by allowing RNase T1 to act on a purified budding yeast 5S ribosomal RNA molecule based on the above description, A4U4C4G1, A4U3C5G1, A4U1C2G1, A3U1C3G1, A2U4C1G1, A2U3C2G1, A2U2C2G1, A2U2C2G1, A0U3C3G1, A3U1C1G1, A2U2C1G1, A1U3C1G1, A0U3C2G1, A3U1C0G1, A2U2C0G1, A1U1C2G1, A0U1C3G1, A0U2C2G1, A3U0C0G1, A2U1C0G1, A2U1C0G1, A1U1C1G1, A1U0C2G1, A0U1C2G1, A0U1C2G1, A1U1C0G1, corresponding to a part or most of the composition Is found in the 5S ribosomal RNA gene region, budding yeast (downloadable from ftp://ftp.ncbi.nih.gov/genomes/Saccharomyces_cerevisiae) is used as the genome sequence, and the above RNA fragment is used as the target RNA fragment. We identified the position of the 5S ribosomal RNA gene in 6 locations in Saccharomyces cerevisiae and succeeded in assigning it. The results are shown in FIGS. 7 (a) to (c).

The flowchart which shows the means order which identifies object RNA fragment on this invention genomic sequence. The schematic diagram which shows storing a genome fragment molecular weight, a genome fragment composition, the number of genome fragments, and a genome fragment position in a database. The schematic diagram which shows storing the production | generation of an object RNA fragment, and object RNA fragment molecular weight in order of an object RNA fragment number. The graph which shows that the difference in the molecular weight between genome fragments differs from the difference in the molecular weight between peptide fragments. The schematic diagram which scans the genome arrangement | sequence composition in a frame with the frame provided on the genome arrangement | sequence. The table | surface which put in order the score which the target RNA fragment in Example 1 exists on an E. coli genome sequence. The table | surface which put in order the score which the target RNA fragment in Example 1 exists on an E. coli genome sequence. The table | surface which put in order the score which the target RNA fragment in Example 1 exists on an E. coli genome sequence. The table | surface which arranged in order the score in which the object RNA fragment in Example 2 exists on a yeast genome sequence. The table | surface which arranged in order the score in which the object RNA fragment in Example 2 exists on a yeast genome sequence. The table | surface which arranged in order the score in which the object RNA fragment in Example 2 exists on a yeast genome sequence.

Claims (46)

A storage means (10) for storing data on an arbitrary genomic sequence of an arbitrary species and a cleavage mechanism of a DNA degrading enzyme and / or an RNA degrading enzyme capable of cleaving the sequence, the same as the degrading enzyme Input means (20) for reading the molecular weight of the target RNA fragment obtained by measuring at least one target RNA fragment that can be cleaved by a degrading enzyme; and storage means (10) for the read molecular weight of at least one target RNA fragment And at least one target RNA comprising a calculation means (30) for calculating a candidate region in which the target RNA fragment is present on the sequence of the storage means (10). An RNA molecule search device that marks and identifies an arbitrary RNA molecule containing a fragment on an arbitrary genome sequence. The storage means (10) virtually cleaves any genome sequence of any organism species according to a cleaving mechanism of a DNA-degrading enzyme and / or a RNA-degrading enzyme capable of cleaving the genome sequence, and a genome fragment The RNA molecule search according to claim 1, further comprising storage means (11) for storing at least two data among a set of data consisting of molecular weight, genome fragment composition, genome fragment number and genome fragment position. apparatus. Input means (21) for reading the molecular weight of the target RNA fragment obtained by measuring at least one target RNA fragment obtained by actually cleaving the input means (20) with a degrading enzyme similar to the degrading enzyme The RNA molecule search apparatus according to claim 1 or 2, further comprising: The RNA molecule according to claim 3, wherein the input means (20) further comprises a correction means (22) for correcting an error relative to the molecular weight of at least one target RNA fragment read by the input means (21). Search device. After the calculation means (30) collates the read molecular weight of at least one target RNA fragment with the data of the storage means (10) and / or the storage means (11), the composition of at least one target RNA fragment The RNA molecule search device according to claim 1, further comprising an extraction unit (31) for extracting the RNA. The calculation means (30) further collates the obtained RNA fragment composition with the data of the storage means (10) and / or the storage means (11), and then extracts the number of at least one target RNA fragment. 6. The RNA molecule search device according to claim 5, comprising an extraction means (32). The calculation means (30) further collates the obtained target RNA fragment composition with the data of the storage means (10) and / or the storage means (11), and then extracts at least one target RNA fragment position. The RNA molecule search device according to claim 6, further comprising extraction means (33) for performing the operation. The calculating means (30) further comprises scanning means (34) for scanning a genomic sequence composition in a frame provided with a predetermined base length in a predetermined direction on the genome sequence from at least one obtained target RNA fragment position. The RNA molecule search apparatus according to claim 7, comprising: The RNA molecule search apparatus according to claim 8, wherein the scanning means (34) provides the base length of the target RNA as a predetermined base length of the frame. The calculation means (30) calculates the appearance probability of the target RNA fragment in the frame based on the number of at least one control RNA fragment composition (number of target RNA fragments) that matches the obtained composition in the frame. The RNA molecule search device according to claim 8 or 9, further comprising a calculation means (35) for performing the calculation. The RNA molecule search device according to claim 10, wherein the calculating means (35) calculates the appearance probability of the target RNA fragment in the frame by an appearance frequency ratio method. The RNA molecule search device according to claim 10, wherein the calculating means (35) calculates the appearance probability of the target RNA fragment in the frame by binomial distribution method. 13. The RNA molecule search apparatus according to claim 10, wherein the calculation means (35) further includes calculation means (36) for calculating a score based on the appearance probability of the target RNA fragment in a frame. 14. The RNA molecule search apparatus according to claim 1, wherein the arbitrary genome sequence of the arbitrary biological species is a human arbitrary genome sequence. 15. The DNA molecule search apparatus according to claim 1, wherein the target RNA fragment is a DNA fragment. A storage step (10) for storing data on an arbitrary genomic sequence of an arbitrary species and a cleavage mechanism of a DNA-degrading enzyme and / or an RNA-degrading enzyme capable of cleaving the sequence; An input step (20) for reading at least one target RNA fragment molecular weight obtained by measuring at least one target RNA fragment that can be cleaved by a degrading enzyme; and a step for storing at least one read target RNA fragment molecular weight (10) And at least one target RNA comprising: a calculation step (30) in which the target RNA fragment is collated with data relating to the sequence and the cleavage mechanism in (1) and a candidate region in which the target RNA fragment is present on the sequence in the storage step (10) is calculated An RNA molecule search method for identifying and identifying an arbitrary RNA molecule containing a fragment on an arbitrary genome sequence. The memory step (10) virtually cleaves any genome sequence of any organism species according to a cleaving mechanism of a DNA-degrading enzyme and / or a RNA-degrading enzyme capable of cleaving the genome sequence, The RNA molecule search according to claim 16, further comprising a storage step (11) for storing at least two data among a set of data consisting of molecular weight, genome fragment composition, genome fragment number, and genome fragment position. Method. The input step (20) includes an input step (21) for reading the molecular weight of the target RNA fragment obtained by measuring at least one target RNA fragment obtained by actually cleaving with the same degrading enzyme as the decomposing enzyme. The RNA molecule search method according to claim 16, further comprising: 19. The RNA molecule according to claim 18, wherein the input step (20) further includes a correction step (22) for correcting an error with respect to the molecular weight of at least one target RNA fragment read in the input step (21). retrieval method. After the calculation step (30) collates the read molecular weight of at least one target RNA fragment with the data of the storage step (10) and / or the storage step (11), the composition of at least one target RNA fragment 20. The RNA molecule search method according to claim 16, further comprising an extraction step (31) for extracting. After the calculation step (30) collates the obtained target RNA fragment composition with the data of the storage step (10) and / or the storage step (11), the number of at least one target RNA fragment is further extracted. The RNA molecule search method according to claim 20, comprising an extraction step (32). After the calculation step (30) collates the obtained target RNA fragment composition with the data of the storage step (10) and / or the storage step (11), at least one target RNA fragment position is further extracted. The RNA molecule search method according to claim 21, further comprising an extraction step (33). The calculation step (30) further includes a scanning step (34) for scanning a genome sequence composition in a frame provided with a predetermined base length in a predetermined direction on the genome sequence from the obtained position of at least one target RNA fragment. The RNA molecule search method according to claim 22, comprising: The RNA molecule search method according to claim 23, wherein the scanning step (34) provides the base length of the target RNA as a predetermined base length of the frame. The calculation step (30) calculates the appearance probability of the target RNA fragment in the frame based on the number of at least one control RNA fragment composition (number of target RNA fragments) that matches the obtained composition in the frame. The RNA molecule search method according to claim 23 or 24, further comprising a calculating step (35). 26. The RNA molecule search method according to claim 25, wherein the calculating step (35) calculates the appearance probability of the target RNA fragment in the frame by an appearance frequency ratio method. The RNA molecule search method according to claim 25, wherein the calculating step (35) calculates the appearance probability of the target RNA fragment in the frame by a binomial distribution method. 28. The RNA molecule search method according to claim 25 to claim 27, wherein the calculation step (35) further includes a calculation step (36) for calculating a score based on the appearance probability of the target RNA fragment in a frame. 29. The RNA molecule search method according to any one of claims 16 to 28, wherein the arbitrary genomic sequence of the arbitrary biological species is a human arbitrary genomic sequence. 30. The DNA molecule search method according to claim 16, wherein the target RNA fragment is a DNA fragment. A memory function (10) for storing in a computer an arbitrary genomic sequence of an arbitrary species and data on a cleavage mechanism of a DNA degrading enzyme and / or an RNA degrading enzyme capable of cleaving the sequence, the degrading enzyme An input function (20) for reading at least one target RNA fragment molecular weight obtained by measuring at least one target RNA fragment that can be cleaved by the same degrading enzyme, and memorizing at least one read target RNA fragment molecular weight The calculation function (30) for checking the candidate region in which the target RNA fragment is present on the sequence of the memory function (10) is collated with the data on the sequence and the cleavage mechanism in the function (10), An RNA molecule search program for marking and identifying any RNA molecule containing at least one target RNA fragment on any genome sequence. The memory function (10) virtually cleaves any genome sequence of any organism species according to a cleaving mechanism of a DNA-degrading enzyme and / or a RNA-degrading enzyme capable of cleaving a genome sequence, and a genome fragment 32. The RNA molecule search according to claim 31, further comprising a memory function (11) for storing at least two data among a set of data consisting of molecular weight, genome fragment composition, genome fragment number, and genome fragment position. apparatus. The input function (20) has an input function (21) for reading the molecular weight of the target RNA fragment obtained by measuring at least one target RNA fragment obtained by actually cleaving with the same degrading enzyme as the decomposing enzyme. The RNA molecule search program according to any one of claims 31 to 32, further comprising: The RNA molecule according to claim 33, wherein the input function (20) further includes a correction function (22) for correcting an error with respect to the molecular weight of at least one target RNA fragment read by the input function (21). Search program. After the calculation function (30) collates the read molecular weight of at least one target RNA fragment with the data of the memory function (10) and / or the memory function (11), the composition of at least one target RNA fragment The RNA molecule search program according to any one of claims 31 to 34, further comprising an extraction function (31) for extracting. The calculation function (30) further collates the obtained target RNA fragment composition with the data of the memory function (10) and / or the memory function (11), and then extracts the number of at least one target RNA fragment. 36. The RNA molecule search program according to claim 35, comprising an extraction function (32). The calculation function (30) further collates the obtained RNA fragment composition with the data of the memory function (10) and / or the memory function (11), and then extracts at least one target RNA fragment position The RNA molecule search program according to claim 36, further comprising an extraction function (33). The calculation function (30) further includes a scanning function (34) for scanning a genomic sequence composition in a frame provided with a predetermined base length in a predetermined direction on the genome sequence from at least one target RNA fragment position obtained. The RNA molecule search program according to claim 37, comprising: The RNA molecule search program according to claim 38, wherein the scanning function (34) provides the base length of the target RNA as a predetermined base length of the frame. The calculation function (30) calculates the appearance probability of the target RNA fragment in the frame based on the number of at least one control RNA fragment composition (number of target RNA fragments) that matches the obtained composition in the frame. 40. The RNA molecule search program according to claim 38 or 39, further comprising a calculation function (35) for performing the calculation. 41. The RNA molecule search program according to claim 40, wherein the calculation function (35) calculates an appearance probability of the target RNA fragment in a frame by an appearance frequency ratio method. 41. The RNA molecule search program according to claim 40, wherein the calculation function (35) calculates an appearance probability of the target RNA fragment in a frame by a binomial distribution method. 43. The RNA molecule search program according to claim 40, wherein the calculation function (35) further includes a calculation function (36) for calculating a score based on the appearance probability of the target RNA fragment in a frame. 44. The RNA molecule search program according to any one of claims 31 to 43, wherein the arbitrary genomic sequence of the arbitrary biological species is a human arbitrary genomic sequence. 45. The DNA molecule search program according to claim 31, wherein the target RNA fragment is a DNA fragment. The medium which described the program in any one of Claims 31-45.

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