A novel coronavirus genome feature similarity measurement method

Abstract

The present invention discloses a novel coronavirus genome feature similarity measurement method, including: obtaining the novel coronavirus genome to be analyzed and the coronavirus genome infected by other animals for comparison; extracting the viral genome features of each coronavirus genome; calculating the frequency of occurrence of the viral genome features; traversing and searching for the common viral genome features of the novel coronavirus, performing numerical processing and normalization processing; performing fuzzy clustering on the common viral genome features of the novel coronavirus after normalization processing to obtain the novel coronavirus clustering center; calculating the Euclidean distance between the normalized numerical features of the coronavirus infected by other animals and the novel coronavirus clustering center, for quantifying the similarity of the virus, predicting the homology and affinity relationship of the virus according to the quantification results, for quantifying the similarity of the virus, predicting the homology and affinity relationship of the virus according to the quantification results. This method is low in cost, fast in speed, and easy to obtain experimental results.

Description

Technical Field

[0001] The present invention relates to the field of viral genomes, and in particular to a method for measuring similarity of novel coronavirus genome features. Background Art

[0002] At present, traditional medicine mostly uses traditional bioinformatics tools, such as BLAST sequence alignment, to measure genome similarity. However, traditional alignment methods require large investment, are slow, have long cycles, and are difficult to use, and cannot quickly and accurately measure gene similarity. Especially in the case of rapid virus transmission, it is impossible to make quick judgments, timely and effectively analyze virus homology, and provide timely and reliable basis for treatment. Summary of the invention

[0003] The present invention provides a method for measuring the similarity of the genome characteristics of the novel coronavirus, so as to overcome the technical problems of the traditional gene similarity comparison method, such as high investment, slow speed, long cycle and high difficulty.

[0004] In order to achieve the above object, the technical solution of the present invention is:

[0005] A novel coronavirus genome feature similarity measurement method, characterized in that it comprises the following steps:

[0006] Step 1: Obtain the genome of the novel coronavirus to be analyzed and the genome of the coronavirus that has infected other animals as a control;

[0007] Step 2: Extract the genome characteristics of the new coronavirus and the genome characteristics of coronaviruses that have infected other animals;

[0008] Step 3: Calculate the frequency of occurrence of the genomic features of the novel coronavirus and the genomic features of coronaviruses that have infected other animals, and digitize the genomic features of the novel coronavirus and the genomic features of coronaviruses that have infected other animals;

[0009] Step 4: Using the digitized genomic features of the novel coronavirus and the genomic features of coronaviruses infected by other animals, traverse and search for common viral genomic features with the novel coronavirus, and normalize the digitized genomic features of coronaviruses infected by other animals to obtain normalized numerical features of coronaviruses infected by other animals;

[0010] Step 5: normalize the common viral genome features of the novel coronavirus, perform fuzzy clustering on the normalized common viral genome features of the novel coronavirus, and obtain the cluster center of the novel coronavirus;

[0011] Step 6: Calculate the Euclidean distance between the normalized numerical features of coronaviruses that have infected other animals and the cluster center of the new coronavirus to quantify the similarity of the viruses and predict the homology and affinity of the viruses based on the quantification results.

[0012] Furthermore, the specific steps of obtaining the virus cluster center in step 5 are as follows:

[0013] Step 5.1: Initialize the novel coronavirus feature membership matrix u ij ;

[0014] Step 5.2: Based on the common viral genome features of the new coronavirus x j And the novel coronavirus characteristic membership matrix u ij , get the novel coronavirus characteristic cluster center v i ;

[0015] Step 5.3: Cluster centers v according to the characteristics of the new coronavirus i Update the novel coronavirus feature membership matrix u ij ;

[0016] Step 5.4: According to the updated novel coronavirus feature membership matrix u ij and novel coronavirus characteristic cluster center v i Obtain the objective function value and determine the size of the objective function value and the preset value. If the objective function value is less than the preset value, output the novel coronavirus feature cluster center v i If the objective function value is greater than or equal to the preset value, return to step 5.2 to re-obtain the novel coronavirus feature cluster center v i .

[0017] Furthermore, in step 5.1, the novel coronavirus characteristic membership matrix u is initialized ij The specific calculation formula is:

[0018]

[0019] Among them, c represents the number of fuzzy clusters, u ij represents the membership of the i-th novel coronavirus genome sample to the j-th category, and n represents the number of novel coronavirus genome samples.

[0020] Furthermore, in step 5.2, the novel coronavirus characteristic cluster center v is obtained. i The specific calculation formula is:

[0021]

[0022] Where m is a real number greater than 1. Represents the membership matrix uij The membership degree of the j-th feature in belongs to the i-th category.

[0023] Furthermore, in step 5.3, the novel coronavirus characteristic membership matrix u is updated ij The specific calculation formula is:

[0024]

[0025] Among them, v k Represents the kth cluster center.

[0026] Furthermore, in step 5.4, according to the updated novel coronavirus characteristic membership matrix u ij and novel coronavirus characteristic cluster center v i The specific calculation formula for the objective function value is:

[0027]

[0028] Among them, Q is the objective function value.

[0029] Furthermore, the specific calculation formula for calculating the Euclidean distance between the normalized numerical features of other coronaviruses and the cluster center of the new coronavirus in step 6 is:

[0030]

[0031] Among them, Distance is the Euclidean distance between the virus cluster center and the novel coronavirus cluster center.

[0032] x j ′ is the normalized numerical feature of coronaviruses that have infected other animals.

[0033] Beneficial effect: The present invention infers virus homology through gene similarity. First, the frequency of occurrence of coronavirus genome features in gene character sequences is calculated to obtain numerical features of gene sequences; by data normalization, the absolute value relationship of public gene features is processed into a relative value relationship to simplify calculations; after fuzzy mean clustering, the cluster center is calculated to further obtain genome features, and the similarity and homology are judged by calculating the normalized values ​​of other genome features and the Euclidean distance of this cluster center. From the perspective of verification effect, the conclusion is consistent with that of traditional comparison methods. Since the existing similarity and homology comparison amplification is essentially the comparison of fragment pairs, the basic process is: first find all fragment pairs whose matching degree exceeds a certain threshold between the query sequence and the target sequence, and then extend the fragment pairs according to a given similarity threshold to obtain similar fragments of a certain length, and finally give high-scoring fragment pairs, thereby extending the similarity and homology of the sequence judgment. The present invention only needs to obtain the existing virus sequence, and the similarity and homology can be obtained by calculating its Euclidean distance, so only one computer is needed, and this method is low in cost, fast in speed, and easy to obtain experimental results. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0035] Figure 1 This is a flow chart of the novel coronavirus genome feature similarity measurement method of the present invention;

[0036] Figure 2 This is a similarity analysis diagram after applying the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] This embodiment provides a novel coronavirus genome feature similarity measurement method, such as Figure 1 , including the following steps:

[0039] Step 1: Obtain the genome of the novel coronavirus to be analyzed and the genome of coronaviruses infected by other animals for comparison; other animals are specifically chickens, ducks, cattle, and bats; specifically, the coronavirus genomes are obtained using public genome FASTA type data;

[0040] Step 2: Extract the genome characteristics of the new coronavirus and the genome characteristics of coronaviruses that have infected other animals;

[0041] Step 3: Calculate the frequency of occurrence of the genomic features of the novel coronavirus and the genomic features of coronaviruses that have infected other animals, and digitize the genomic features of the novel coronavirus and the genomic features of coronaviruses that have infected other animals;

[0042] Step 4: Using the digitized genomic features of the novel coronavirus and the genomic features of coronaviruses infected by other animals, traverse and search for common viral genomic features with the novel coronavirus, and normalize the digitized genomic features of coronaviruses infected by other animals to obtain normalized numerical features of coronaviruses infected by other animals; by data normalization, the absolute value relationship of the common gene features is processed into a relative value relationship to simplify the calculation;

[0043] Step 5: normalize the common viral genome features of the novel coronavirus, perform fuzzy clustering on the normalized common viral genome features of the novel coronavirus, and obtain the cluster center of the novel coronavirus;

[0044] Step 6: Calculate the Euclidean distance between the normalized numerical features of coronaviruses infected by other animals and the cluster center of the new coronavirus to quantify the similarity of the virus. When the Euclidean distance is multiple groups, take the average value for quantification, and predict the homology and affinity of the virus based on the quantification results.

[0045] In a specific embodiment, the steps of obtaining the virus cluster center in step 5 are as follows:

[0046] Step 5.1: Initialize the novel coronavirus feature membership matrix u ij ;

[0047] Step 5.2: Based on the viral genome feature x j And the novel coronavirus characteristic membership matrix u ij , get the novel coronavirus characteristic cluster center v i ;

[0048] Step 5.3: Cluster centers v according to the characteristics of the new coronavirus i Update the novel coronavirus feature membership matrix u ij ;

[0049] Step 5.4: According to the updated novel coronavirus feature membership matrix u ij and novel coronavirus characteristic cluster center v i Obtain the objective function value, and determine the size of the objective function value and the preset value ε. If the objective function value is less than the preset value ε, output the novel coronavirus feature cluster center v i If the objective function value is greater than or equal to the preset value ε, return to step 5.2 to re-obtain the novel coronavirus feature cluster center v i .

[0050] In a specific embodiment, the novel coronavirus characteristic membership matrix u is initialized in step 5.1. ij The specific calculation formula is:

[0051]

[0052] Among them, c represents the number of fuzzy clusters, u ij represents the membership of the i-th novel coronavirus genome sample to the j-th category, and n represents the number of novel coronavirus genome samples.

[0053] In a specific embodiment, the novel coronavirus characteristic cluster center v is obtained in step 5.2. i The specific calculation formula is:

[0054]

[0055] Where m is a real number greater than 1. Represents the membership matrix u ij The membership degree of the j-th feature in belongs to the i-th category.

[0056] In a specific embodiment, the novel coronavirus characteristic membership matrix u is updated in step 5.3. ij The specific calculation formula is:

[0057]

[0058] Among them, v k Represents the kth cluster center.

[0059] In a specific embodiment, in step 5.4, according to the updated novel coronavirus characteristic membership matrix u ij and novel coronavirus characteristic cluster center v i The specific calculation formula for the objective function value is:

[0060]

[0061] Among them, Q is the objective function value. To further verify the feasibility of this method and the consistency of the conclusion, the clustering parameter c and coefficient m can be traversed within a certain range.

[0062] In a specific embodiment, the specific calculation formula for calculating the Euclidean distance between the normalized numerical features of other coronaviruses and the cluster center of the new coronavirus in step 6 is:

[0063]

[0064] Where Distance is the Euclidean distance between the virus cluster center and the novel coronavirus cluster center, x j ′ is the normalized numerical feature of coronaviruses that have infected other animals. The closer the distance, the higher the similarity and the greater the possibility of homology; conversely, the farther the distance, the lower the similarity and the smaller the possibility of homology.

[0065] Figure 2 This is a similarity analysis result diagram after applying the present invention, specifically the similarity (distance) between the coronaviruses infected by four animals and the new coronavirus; Figure 2 It can be seen that in the process of analyzing the genetic similarity between coronavirus samples that have infected animals and new coronavirus samples, it was found that the coronavirus that had infected bats had the highest genetic similarity with the new coronavirus samples (the smallest Euclidean distance). From this, it can be inferred that the new coronavirus is most likely to have originated from bats.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A novel coronavirus genome feature similarity measurement method, characterized in that: The following steps are involved: Step 1: Obtain the genome of the novel coronavirus to be analyzed and the genome of the coronavirus that has infected other animals as a control; Step 2: Extract the genome characteristics of the new coronavirus and the genome characteristics of coronaviruses that have infected other animals; Step 3: Calculate the frequency of occurrence of the genomic features of the novel coronavirus and the genomic features of coronaviruses that have infected other animals, and digitize the genomic features of the novel coronavirus and the genomic features of coronaviruses that have infected other animals; Step 4: Using the digitized genomic features of the novel coronavirus and the genomic features of coronaviruses infected by other animals, traverse and search for common genomic features of the novel coronavirus, and normalize the digitized genomic features of coronaviruses infected by other animals to obtain normalized numerical features of coronaviruses infected by other animals; Step 5: normalize the common viral genome features of the novel coronavirus, perform fuzzy clustering on the normalized common viral genome features of the novel coronavirus, and obtain the novel coronavirus feature clustering center; The novel coronavirus characteristic cluster center is obtained in step 5, specifically: Step 5.1: Initialize the novel coronavirus feature membership matrix u ij ; In step 5.1, the novel coronavirus characteristic membership matrix u is initialized. ij The specific calculation formula is: Among them, c represents the number of fuzzy clusters, n represents the number of novel coronavirus genome samples; u ij Represents the membership of the j-th novel coronavirus genome sample to the i-th category; Step 5.2: Based on the common viral genome features of the new coronavirus x j And the novel coronavirus characteristic membership matrix u ij , get the novel coronavirus characteristic cluster center v i ; The novel coronavirus characteristic cluster center v is obtained in step 5.2 i The specific calculation formula is: Where m is a real number greater than 1. Represents the membership matrix u ij The membership degree of the jth feature in the ith category; Step 5.3: Cluster centers v according to the characteristics of the new coronavirus i Update the novel coronavirus feature membership matrix u ij ; In step 5.3, the novel coronavirus characteristic membership matrix u is updated ij The specific calculation formula is: Among them, v k The cluster center of the novel coronavirus characteristics representing the kth fuzzy cluster; Step 5.4: According to the updated novel coronavirus feature membership matrix u ij and novel coronavirus characteristic cluster center v i Obtain the objective function value and determine the size of the objective function value and the preset value. If the objective function value is less than the preset value, output the novel coronavirus feature cluster center v i If the objective function value is greater than or equal to the preset value, return to step 5.2 to re-obtain the novel coronavirus feature cluster center v i ; In step 5.4, according to the updated novel coronavirus characteristic membership matrix u ij and novel coronavirus characteristic cluster center v i The specific calculation formula for the objective function value is: Among them, Q is the objective function value; Step 6: Calculate the Euclidean distance between the normalized numerical features of coronaviruses that have infected other animals and the cluster center of the novel coronavirus features to quantify virus similarity and predict the homology and affinity of the viruses based on the quantification results.

2. A novel coronavirus genome feature similarity measurement method as claimed in claim 1, characterized in that: The specific calculation formula for calculating the Euclidean distance between the normalized numerical features of coronaviruses infected by other animals and the cluster center of the new coronavirus features in step 6 is: Distance is the Euclidean distance between the normalized numerical features of coronaviruses infected by other animals and the cluster center of the novel coronavirus features, and x′ j These are normalized numerical features of coronaviruses that have infected other animals.

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