JP5007803B2 - Gene clustering apparatus, gene clustering method and program - Google Patents

Description

  The present invention relates to a gene clustering apparatus, a gene clustering method, and a program for clustering a plurality of genes based on sequence similarity.

In order to estimate the function of a gene whose function is unknown, it is known that a technique of evaluating similarity to a known gene and performing clustering based on sequence similarity is effective.
Conventionally, a maximum saving method, a maximum likelihood method, a neighborhood connection method, and the like are used for gene clustering. These methods are common in that a phylogenetic tree is created while directly comparing the sequences of genes to be clustered. As an example using such clustering, a clustering and alignment program disclosed in Non-Patent Document 1 can be cited.

  In the conventional gene clustering method, a phylogenetic tree is created by paying attention to the base sequence of each gene, and estimating the time and context of mutation of each base sequence. However, with these methods, there is a problem that it is not possible to compare things that are significantly different from each other, such as those that are genetically separated or functions that are newly acquired after differentiation. . Conventional clustering is suitable for comparing gene changes that occur in an evolutionary process, that is, genes with relatively little change.

CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice; J.D.Thompson et.al .; Nucleic acids Research, 1994, Vol. 22, No.22 4673-4680.

  As described above, it is difficult to cluster genes that are evolutionarily separated by the method of clustering using all gene sequences as they are, as in the conventional clustering method.

  An object of the present invention is to provide a gene clustering apparatus, a gene clustering method, and a program capable of discovering genes having similar functions even in genes of evolutionarily distant organisms.

The gene clustering apparatus according to the present invention is a gene clustering apparatus that clusters a plurality of genes based on sequence similarity, and includes a motif search unit that searches for a motif sequence included in a gene sequence, and each gene sequence A motif score calculation unit for calculating a similarity score between two arbitrary genes by comparing motif sequences to be calculated, and an intergenic distance for calculating an intergenic distance between any two genes using the similarity score A calculation unit and a clustering processing unit that clusters the plurality of genes based on the inter-gene distance.
In the present invention, gene similarity is analyzed using a motif included in the gene sequence as an index. Since genes that have similar functions even though they are evolutionarily separated often have similar motifs, the present invention can be used to discover functionally similar genes among a wide range of species and to estimate the functions of unknown genes. It is very effective.

  The motif score calculation unit obtains a brute force similarity for all motif sequences included in the sequence of the first gene and all motif sequences included in the sequence of the second gene. The sum of the similarities is preferably a similarity score between the first gene and the second gene.

  The intergene distance calculation unit includes a first vector, a first vector having elements of similarity scores of the second to Nth genes, a second gene, the first, third to Nth It is preferable to calculate the intergenic distance between the first gene and the second gene by calculating the correlation between the elements of the second vector having the gene similarity score as an element.

The gene clustering method according to the present invention is a gene clustering method for clustering a plurality of genes based on sequence similarity, and includes a motif search step for searching a motif sequence included in a gene sequence, and included in each gene sequence A motif score calculating step for calculating a similarity score between two arbitrary genes by comparing the motif sequences to be calculated, and an intergenic distance for calculating an intergenic distance between any two genes using the similarity score A calculation step and a clustering step for clustering the plurality of genes based on the inter-gene distance.
In the present invention, gene similarity is analyzed using a motif included in the gene sequence as an index. Since genes that have similar functions even though they are evolutionarily separated often have similar motifs, the present invention can be used to discover functionally similar genes among a wide range of species and to estimate the functions of unknown genes. It is very effective.

A computer program according to the present invention is a program that causes a computer to function as a gene clustering apparatus that clusters a plurality of genes based on sequence similarity, and a motif search unit that searches for a motif sequence included in a gene sequence; A motif score calculator that calculates the similarity score of any two genes by comparing the motif sequences included in each gene sequence, and the intergenic distance between any two genes using the similarity score And a clustering processing unit that performs clustering of the plurality of genes based on the intergenic distance.
In the present invention, gene similarity is analyzed using a motif included in the gene sequence as an index. Since genes that have similar functions even though they are evolutionarily separated often have similar motifs, the present invention can be used to discover functionally similar genes among a wide range of species and to estimate the functions of unknown genes. It is very effective.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
Embodiment 1 FIG.
FIG. 1 is a block diagram showing a functional configuration of a gene clustering apparatus 10 according to Embodiment 1 of the present invention. As shown in the figure, the gene clustering device 10 includes an input device 11, a user interface unit 12, a data access unit 13, a gene sequence storage unit 14, a score storage unit 15, a motif storage unit 16, a motif search unit 17, and a motif score calculation. A unit 18, an intergene distance calculation unit 19, a clustering processing unit 20, and an output device 21 are provided.

  The gene clustering apparatus 10 is obtained by causing a general-purpose personal computer to execute a predetermined program, for example. The user interface unit 12, the data access unit 13, the motif search unit 17, the motif score calculation unit 18, the intergene distance calculation unit 19, and the clustering processing unit 20 represent modules of operations performed by a computer processor according to a program, These actually constitute a processor of the gene clustering apparatus 10 as a whole.

The gene sequence storage unit 14, the score storage unit 15, and the motif storage unit 16 are storage devices such as a hard disk of the gene clustering device 10.
The input device 11 is input means such as a keyboard, a mouse, and a touch panel, for example, and is used by the user to give processing instructions to the gene clustering device 10 and to input data and parameters. It is also possible to read data from a memory medium or the like via a USB (Universal Serial Bus) interface. The operation through the input device 11 by the user is controlled by the user interface unit 12.
The output device 21 is a display device, a printer, or the like.

Next, the gene clustering process according to the present embodiment will be described.
First, sequence information of a gene group to be clustered is supplied from the gene sequence storage unit 14 to the motif search unit 17 via the data access unit 13. The gene sequence storage unit 14 stores gene sequence information input via the input device 11.

  FIG. 2 is a diagram showing an example of gene groups to be clustered. Here, the gene number of the target gene and its species are shown. The example shown in FIG. 2 is a blast search (threshold 1e-30) for amino acid sequences of rice (Oryza Sativa), Arabidopsis thaliana, and red algae using the corn (Zea mays) ID1 (indeterminate1) gene as a query. ) And shows the hit genes.

Each gene sequence can be referred to, for example, at the following site.
Rice: http://rapdb.lab.nig.ac.jp/ (RAP1)
Arabidopsis: http://mips.gsf.de/proj/thal/db/ (MIPS)
Red algae: http://merolae.biol.su-tokyo.ac.jp/

The ID1 gene has been isolated as a gene that controls flowering in maize, and encodes a transcription factor having a zinc finger.
The method for selecting a gene group is not limited to the above method, and other sequence analysis methods may be used.

  Next, a motif search is executed in the motif search unit 17 for the supplied gene group. A motif is a sequence pattern corresponding to an active site or a functional region in a protein structure. The motif search can be performed using a technique such as MEME (Bailey and Elkan, 1994). FIG. 3 is a diagram illustrating an example of motif data obtained as a result of a motif search performed on the gene group partially shown in FIG. In the figure, the numbered squares correspond to individual motifs. For example, it is understood that the ID1 gene has a motif sequence represented by No. 5, No. 2, No. 3, No. 1, No. 7, No. 6, No. 18. In general, functionally similar genes often have the same motif even if they are genetically separated.

  By performing a motif search, it is possible to obtain information on partial sequences of various sizes, which are considered to contribute to determining the main structure / function, from the sequence of each gene. The obtained motif data is stored in the motif storage unit 16.

Next, the motif score calculation unit 18 compares all the genes to be clustered, and calculates a score representing the degree of similarity in terms of the included motif sequences. For similarity score calculation, PAM (Point-Accepted Mutation, In Margaret O. Dayhoff, editor, Atlas of Protein Sequence and Structure, volume 5, pages 345-352. National Biochemical Research Foundation, Washington DC, 1978) and BLOSUM (Blocs Substitution Matrix, Henikoff and Henikoff (1992; PNAS 89: 10915-10919)) and the like can be used. The score storage unit 15 stores score data used in these methods.
In the present embodiment, score calculation is not performed for regions other than the motif. This means that the part other than the motif is regarded as score 0. Clustering is performed at high speed by narrowing down to the part where the sequence called motif is stored and calculating the score. If there is a further need, add not only the conserved sequence motif but also a function such as secondary structure prediction to extract the structural part that determines α-helix and β-sheet, and score them as motifs By giving, it is possible to perform not only functions but also structure-like clustering.

A similarity score calculation method will be described.
For example, the sequences of motif 1 included in gene 1 and motif 2 included in gene 2 are as follows.
Motif 1: WKCEKCAK
Motif 2: WKCDKCN

Since the first amino acid residue of motif 1 and motif 2 is W, referring to the column of W in the W row of the PAM40 matrix shown in FIG. It can be seen that the second amino acid residue is K for both sequences and the score is 6. When scores are sequentially obtained in this way and added, the scores of motif 1 and motif 2 are as follows.
Score = 13 + 6 + 9 + 3 + 6 + 9 + (− 3) = 43
In this way, a score is obtained for all the motifs included in gene 1 and gene 2 in a round-robin manner. Furthermore, the sum of the scores of all motifs is obtained and used as the similarity score between gene 1 and gene 2. Here, when calculating the optimal score in consideration of deletion and insertion of amino acid residues when comparing motifs, the algorithm Smith-Waterman method (Smith TF, Waterman MS (1981). “Identification of Common Molecular Subsequences”. Journal of Molecular Biology 147: 195-197.).

  FIG. 5 shows a part of the score matrix between genes determined as described above. FIG. 5 shows gene similarity scores for the four genes.

  Next, the intergene distance calculation unit 19 calculates the distance between each gene. The distance between genes can be defined in various ways. In the present invention, the Pearson correlation coefficient is used. This is a method of obtaining data of two arbitrary rows of the matrix shown in FIG. 5 and obtaining a correlation between elements. By using the correlation coefficient, the correlation becomes high for those having a relative motif similarity, and the correlation coefficient does not leave due to the bias of the absolute value. Even if there is a slight difference between a large number of common motifs and a small number of common motifs, the distance can be obtained while correcting with a common scale.

  Next, the clustering processing unit 20 performs clustering using a method such as the Ward method or the group average method using the distance value calculated by the intergene distance calculation unit 19. FIG. 6 shows a dendrogram of the clustering result. FIG. 6 suggests that the maize ID1 gene has a function similar to that of the Os10g0419200 gene. The Os10g0419200 gene encodes zinc finger protein, and the function of Os10g0419200 is given as Zinc finger and C2H2 type family protein, and it can be analogized that it actually has a similar function to ID1.

  Thus, according to the present invention, it is possible to perform a series of analyzes of extraction of motifs and clustering using the presence / absence and similarity of motifs as indices. A motif includes a conserved sequence pattern that is characteristic of a functional domain. By analyzing the motif as an index, genes that are functionally similar can be comparatively analyzed even if they are genetically separated. Analyzes using amino acid sequence substitution rates have existed so far, but methods for comparative analysis using the presence or similarity of motifs as indices have not been established, and functions that have been preserved between organisms in the future It can be used for gene analysis, function estimation of unknown function genes, and the like. Advances in DNA sequencing technology have led to the reading of genomes of a large number of species, and even if they are not necessarily genetically homologous, if functionally similar ones can be found by clustering, unknown genes It is very useful for analyzing the function of a sequence.

  It should be noted that the clustering method according to the present invention is not limited to gene motif information, but a numerical sequence pattern in which structural features are replaced with various index values such as α helix, β sheet, hydrophobic and hydrophilic areas. It can also be used as a target. Further, the gene sequence described in the present invention is a character string itself. Therefore, it is possible to replace the gene sequence as it is with clustering of character sequences. Needless to say, the present invention can be applied to any character information or numerical information sequence. For character strings, there is no problem even if the number of matched characters is used as a score, or a method of simply giving a constant score for each word existing in the dictionary. In the case of a numeric string, it is needless to say that the difference between the numerical values themselves or the square value thereof can be widely applied as a distance.

FIG. 1 is a block diagram showing a functional configuration of a gene clustering apparatus according to Embodiment 1 of the present invention. FIG. 2 is a diagram showing an example of gene groups to be clustered. FIG. 3 is a diagram illustrating an example of a motif obtained by a search. FIG. 4 is a matrix table of PAM40. FIG. 5 is a diagram illustrating an example of a similarity score between genes. FIG. 6 is a dendrogram of gene clustering results.

Explanation of symbols

  10 gene clustering devices, 11 input devices, 12 user interface units, 13 data access units, 14 gene sequence storage units, 15 score storage units, 16 motif storage units, 17 motif search units, 18 motif score calculation units, 19 intergenic distances Calculation unit, 20 Clustering processing unit, 21 Output device

Claims (4)

A gene clustering apparatus for clustering a plurality of genes based on sequence similarity,
A motif search unit for searching a motif sequence included in a gene sequence;
A motif score calculation unit for calculating a similarity score between any two genes by comparing the motif sequences included in each gene sequence;
An intergenic distance calculation unit for calculating an intergenic distance between any two genes using the similarity score;
Based on the genetic distance between Bei example a clustering processing unit which performs clustering of the plurality of genes,
The motif score calculation unit
Similarity is obtained for all motif sequences included in the sequence of the first gene and all motif sequences included in the sequence of the second gene, and the sum of the similarities between the obtained motifs is calculated as the first. A gene clustering apparatus characterized in that a similarity score between the gene of and the second gene is used. The inter-gene distance calculation unit is:
A first vector, a first vector whose elements are similarity scores of the second to Nth genes, a second gene, and a similarity score of the first, third to Nth genes as elements The gene clustering apparatus according to claim 1, wherein an inter-gene distance between the first gene and the second gene is calculated by obtaining a correlation between elements of the second vector to be calculated. A gene clustering method for clustering a plurality of genes based on sequence similarity,
A motif search step for searching for a motif sequence contained in a gene sequence;
A motif score calculation step of calculating a similarity score between any two genes by comparing the motif sequences included in each gene sequence;
An intergenic distance calculating step of calculating an intergenic distance between any two genes using the similarity score;
Based on the genetic distance between Bei example a clustering processing step of performing clustering of the plurality of genes,
In the motif score calculation step,
Similarity is obtained for all motif sequences included in the sequence of the first gene and all motif sequences included in the sequence of the second gene, and the sum of the similarities between the obtained motifs is calculated as the first. A gene clustering method characterized in that a similarity score between the gene of and a second gene is used. Computer
A program that functions as a gene clustering device that clusters a plurality of genes based on sequence similarity,
A motif search unit for searching a motif sequence included in a gene sequence;
A motif score calculation unit for calculating a similarity score between any two genes by comparing the motif sequences included in each gene sequence;
An intergenic distance calculation unit for calculating an intergenic distance between any two genes using the similarity score;
Based on the distance between the genes, function as a clustering processing unit for clustering the plurality of genes ,
The motif score calculation unit
Similarity is obtained for all motif sequences included in the sequence of the first gene and all motif sequences included in the sequence of the second gene, and the sum of the similarities between the obtained motifs is calculated as the first. A program characterized by having a similarity score between the gene of and the second gene .

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