Multiple sequence alignment method for detecting viral gene mutations

Abstract

The present application relates to the technical field of viral gene mutation detection, and specifically to a multiple sequence alignment method for viral gene mutation detection, the method comprising: collecting a viral gene sequence to be detected and a plurality of viral gene alignment sequences; based on an initial preset gap penalty, using a multiple sequence alignment algorithm to obtain an initial score matrix between the viral gene sequence and any one of the viral gene alignment sequences; in the process of backtracking the initial score matrix, dynamically calculating the gap penalty correction results at different positions; based on the gap penalty correction results at different positions between the viral gene sequence and any one of the viral gene alignment sequences in the backtracking process, the initial score matrix is ​​corrected, and the correction results are substituted into the multiple sequence alignment algorithm to perform multiple sequence alignment of viral genes. The present application aims to improve the accuracy of the final alignment result.

Description

Technical Field

[0001] The present application relates to the technical field of viral gene variation detection, and in particular to a multiple sequence alignment method for viral gene variation detection. Background Art

[0002] Multiple Sequence Alignment (MSA) is a basic tool in bioinformatics, which is used to compare three or more biomolecule sequences (usually protein or nucleic acid sequences) to identify similar regions between them and infer their common evolutionary history. In the detection of viral gene mutations, multiple sequence alignment can help researchers identify mutations and natural selection, track viral transmission, design vaccines and drugs, and monitor drug resistance. With the continuous advancement of bioinformatics technology, multiple sequence alignment tools are also constantly being optimized and updated, providing strong support for virology research.

[0003] The basis of multiple sequence comparison is to divide multiple sequences into pairs for comparison, and complete the multiple sequence comparison based on the comparison results of the pairs. In the existing Needleman-Wunsch algorithm, in the process of global comparison of two sequences, the gap penalty is usually set to a fixed negative score that is not greater than the base mismatch score, which results in that a new gap will not be easily inserted during the global comparison, and is not conducive to the extension of multiple consecutive gaps. If the gap penalty is set to a large value, the global comparison result will be more inclined to form a base mismatch rather than inserting one or more gaps, which results in the poor comparison accuracy of many current algorithms during comparison. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a multiple sequence alignment method for detecting viral gene mutations to solve the existing problems.

[0005] The multiple sequence alignment method for viral gene mutation detection of the present application adopts the following technical scheme:

[0006] One embodiment of the present application provides a multiple sequence alignment method for detecting viral gene mutations, the method comprising the following steps:

[0007] Collecting the viral gene sequence to be detected and several viral gene comparison sequences;

[0008] Based on the initial preset gap penalty, a multiple sequence alignment algorithm is used to obtain an initial score matrix between the viral gene sequence and any viral gene alignment sequence; in the process of backtracking the initial score matrix, the gap penalty correction results at different positions are dynamically calculated, specifically:

[0009] Based on the similarity between the matching sequences that have been matched at the current backtracking time, determine the gap penalty correction coefficient at the current backtracking time;

[0010] Based on the continuity of adjacent vacancies at the current backtracking moment and the degree of confusion of the corresponding serial numbers of the unmatched genes in the matched sequences, a vacancy correction factor at the current backtracking moment is determined;

[0011] Determine the vacancy penalty correction factor at the current backtracking time based on the vacancy correction factor and the vacancy penalty correction coefficient at the current backtracking time;

[0012] Obtaining an alignment sequence of the viral gene sequence based on a difference in total scores between the viral gene sequence and any other viral gene alignment sequence;

[0013] Based on the gap penalty correction factor between the viral gene sequence and all other alignment sequences except any one of the viral gene alignment sequences at the current backtracking time, determine the gap penalty correction result between the viral gene sequence and any one of the viral gene alignment sequences at the current backtracking time;

[0014] The initial score matrix is ​​corrected based on the gap penalty correction results of different positions between the viral gene sequence and any viral gene comparison sequence during the backtracking process, and the correction results are substituted into the multiple sequence alignment algorithm to perform multiple sequence alignment of the viral genes.

[0015] Preferably, the calculation formula of the vacancy penalty correction coefficient at the current backtracking time is: ; X represents the vacancy penalty correction coefficient at the current backtracking time, represents an exponential function with a natural constant as the base, le represents the shortest length in the matching sequence, Co represents the matching degree between matching sequences, Indicates the mismatch between matching sequences.

[0016] Preferably, the degree of matching between the matching sequences is the number of genes matched by the matching sequences, and the degree of mismatching between the matching sequences is the number of genes mismatched by the matching sequences.

[0017] Preferably, the method for determining the vacancy correction factor at the current backtracking time includes:

[0018] Based on the clustering results of the scores of the unmatched genes in the initial score matrix, the continuity of the corresponding serial numbers of the scores adjacent to the current backtracking time is determined to determine the vacancy continuity trend at the current backtracking time;

[0019] Based on the degree of disorder of the corresponding sequence numbers of the unmatched genes in the matched sequence that has been matched at the current backtracking moment, the disorder continuity trend at the current backtracking moment is determined;

[0020] Based on the vacancy continuity trend and the chaotic continuity trend at the current lookback time, the vacancy correction factor at the current lookback time is determined; wherein the vacancy correction factor is positively correlated with the chaotic continuity trend and the vacancy continuity trend, respectively.

[0021] Preferably, the calculation expression of the vacancy continuity trend at the current backtracking moment is: ; is the vacancy continuity trend at the current backtracking moment, norm represents the normalization function, is the average score of all elements in the cluster where the gap sequence is located, C represents the length of the gap sequence, and F represents the average length of all gap sequences in the cluster where the gap sequence is located; if there is no gap sequence adjacent to the current backtracking moment, the gap continuity trend at the current backtracking moment is recorded as 1.

[0022] Preferably, the chaotic continuity trend at the current backtracking moment is: the permutation entropy of the differential sequence of the sequence composed of the sequence numbers of the non-matching genes in the matching sequence that has been matched at the current backtracking moment.

[0023] Preferably, the vacancy penalty correction factor at the current backtracking time is determined by a normalized value of the ratio of the vacancy correction factor at the current backtracking time to the vacancy penalty correction coefficient.

[0024] Preferably, the method for determining the comparison sequence of the viral gene sequence comprises:

[0025] A threshold segmentation algorithm is used to obtain a segmentation threshold of the total score between the viral gene sequence and any other viral gene alignment sequence, and the sequence with a total score greater than the segmentation threshold is used as the alignment sequence of the viral gene sequence.

[0026] Preferably, the gap penalty correction result between the viral gene sequence and any viral gene comparison sequence at the current backtracking time includes:

[0027] Calculate the average value of the gap penalty correction factor between the viral gene sequence and all other alignment sequences except any one of the viral gene alignment sequences at the current backtracking time;

[0028] The gap penalty correction factor between the viral gene sequence and any viral gene comparison sequence at the current backtracking time is merged with the average value as the gap penalty correction result between the viral gene sequence and any viral gene comparison sequence at the current backtracking time.

[0029] Preferably, the process of performing multiple sequence alignment on viral genes comprises:

[0030] The gap penalty correction results of different positions in the backtracking process of the viral gene sequence and any viral gene comparison sequence are used as the gap penalty points of the corresponding positions in the initial score matrix to obtain a corrected score matrix;

[0031] The corrected score matrix was substituted into the multiple sequence alignment algorithm to perform multiple sequence alignment of viral genes.

[0032] This application has at least the following beneficial effects:

[0033] The present application analyzes and predicts the penalty correction coefficient of the current backtracking moment by considering the similarity between the virus gene sequence that has been aligned and any virus gene alignment sequence, as well as the matched matching sequence, so as to accurately distinguish between the two possibilities of whether there is a mismatch or a sequence missing between the two sequences; by considering and analyzing the continuity of adjacent vacancies at the current backtracking moment and the degree of confusion of the corresponding serial numbers of the mismatched genes in the matched matching sequence that has been matched, the vacancy correction factor of the current backtracking moment is obtained, which is used to judge the possibility of a vacancy at the current backtracking moment in combination with the degree of distribution confusion of the historical mismatched genes and the gene matching situation at the time adjacent to the current backtracking moment; The above two indicators determine the gap penalty correction factor at the current backtracking moment, and at the same time combine all viral gene alignment sequences that have a large correlation with the viral gene sequence, and the gap penalty correction factor at the current backtracking moment to correct the gap penalty correction result of the viral gene sequence and any viral gene alignment sequence at the current backtracking moment, which is used to combine the gap correction degree between other similar viral gene alignment sequences to affect the determination of the gap penalty correction result; use the gap penalty correction results of all positions in the backtracking process to correct the initial score matrix, and substitute the correction results into the multiple sequence alignment algorithm to perform multiple sequence alignment of viral genes, so that the final alignment result is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A flowchart of a multiple sequence alignment method for detecting viral gene mutations provided in one embodiment of the present application;

[0036] Figure 2 A flowchart for dynamically calculating vacancy penalty correction results at different positions during a backtracking process is provided for one embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the multiple sequence alignment method for viral gene mutation detection proposed in the present application, its specific implementation, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0038] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0039] The specific scheme of the multiple sequence alignment method for detecting viral gene mutations provided by the present application is described in detail below in conjunction with the accompanying drawings.

[0040] An embodiment of the present application provides a multiple sequence alignment method for detecting viral gene mutations.

[0041] Specifically, the following multiple sequence alignment method for detecting viral gene mutations is provided, see Figure 1 , the method comprises the following steps:

[0042] The first step is to collect the viral gene sequence to be detected and several viral gene comparison sequences.

[0043] The viral genes are collected, and the viral gene sequence to be compared and detected is recorded as A0, and the sequence to be compared with the viral gene sequence is recorded as the viral gene comparison sequence.

[0044] The present application aims to solve the problem that a fixed gap penalty is used to construct a score sequence during the alignment process between a viral gene sequence and a viral gene comparison sequence, resulting in low alignment accuracy, which seriously affects the sensitivity and precision of the alignment result. The present application aims to realize real-time correction of the gap penalty during the alignment process with the viral gene sequence.

[0045] Among them, the complete multiple sequence alignment process generally includes three key steps:

[0046] The first step is to calculate the similarity between two sequences;

[0047] The second step is to organize the sequences according to their similarity. A guide tree can be constructed using a tree structure, or a sequence that is most similar to other sequences can be directly selected to construct a central sequence.

[0048] The third step is to complete the double sequence alignment one by one according to the guide tree or central sequence to obtain the multiple sequence alignment result of the entire sequence set.

[0049] The essence of multiple sequence alignment problems is to transform them into two-sequence alignment problems. The strategy for transforming them into two-sequence alignment in this application is the gradual method, and the two-sequence alignment can be calculated by dynamic programming. This embodiment uses the Needleman-Wunsch algorithm to calculate the similarity of two sequences. The Needleman-Wunsch algorithm is a well-known technology and will not be described in detail in this application.

[0050] In the second step, during the backtracking process of the initial score matrix obtained by using the multiple sequence alignment algorithm between the viral gene sequence and any viral gene comparison sequence, the gap penalty correction results at different positions are dynamically calculated.

[0051] When constructing the score matrix in the existing Needleman-Wunsch algorithm, the score calculation method is as follows:

[0052]

[0053] in, represents the matching score between the i-th gene of sequence Q and the j-th gene of sequence P in the score matrix, represents the i-th gene of sequence Q, represents the jth gene of sequence P. When the genes corresponding to the two sequences are the same, 5 points are scored, and when the genes are different, -1 point is scored. Indicates that a gap cannot be matched, that is, when a gene segment is vacant, it is recorded as a gap penalty R, and the initial value of R is -2. The above score is set by the specific implementer.

[0054] The present application first uses a given initial gap penalty R and adopts a global sequence alignment algorithm to obtain an initial score matrix between a viral gene sequence and any viral gene alignment sequence.

[0055] In existing methods, most R values ​​are fixed values, but fixed values ​​will make the global alignment results more inclined to form base mismatches rather than inserting one or more gaps, which leads to low alignment accuracy in many current algorithms, seriously affecting the sensitivity and accuracy of the alignment results. Therefore, this application analyzes the sequences that have been dynamically planned and matched, and dynamically adjusts the value to avoid the above problems and increase the accuracy of gene sequence matching.

[0056] The flowchart of dynamically calculating the vacancy penalty correction results at different positions during the backtracking process is shown in the attached figure. Figure 2 As shown, the gap penalty correction results of different positions are dynamically calculated until the update is completed, and then backtracking is performed according to the corrected results to increase the accuracy and reliability of the comparison results. The specific process is:

[0057] 1. Based on the similarity between the matching sequences that have been matched at the current backtracking time, determine the gap penalty correction coefficient at the current backtracking time.

[0058] First, for the matching sequences that have been matched between the viral gene sequence and any viral gene comparison sequence at the current backtracking moment, the penalty correction coefficient is analyzed according to the similarity between the matching sequences. That is, if the length of the matched partial sequence is longer, the more likely it is that the two sequences are not matched at the current backtracking moment due to the possibility of sequence missing between the two sequences, rather than the mismatch between the two sequences.

[0059] That is, the longer the length of the historical matching sequence of the two sequences is, the higher the similarity of the matching sequences is, and the closer the corresponding gap penalty score is to the gene matching score; conversely, the longer the matching sequence length of the two sequences is, and the stronger the mismatch between the two sequences is, the more likely it is that the reason why the current sequence does not match is that the two sequences do not match, and the corresponding gap penalty score is closer to the gene mismatch score.

[0060] Based on this, based on the similarity between the matching sequences that have been matched at the current backtracking time, the gap penalty correction coefficient at the current backtracking time is calculated, and the expression is: ; X represents the vacancy penalty correction coefficient at the current backtracking time, represents an exponential function with the natural constant e as the base, le represents the shortest length in the matching sequence, for example, when the matching sequences are AAATA and AAAA, le=4, Co represents the matching degree between the matching sequences, Indicates the mismatch between matching sequences.

[0061] The degree of match between matching sequences is the number of genes that the matching sequences match, and the degree of mismatch between matching sequences is the number of genes that the matching sequences do not match.

[0062] It should be understood that the longer the sequence length of the matching sequence between the viral gene sequence and any viral gene comparison sequence, and the larger the ratio of the number of matched genes to the number of unmatched genes in the matching sequence, the smaller the gap penalty correction coefficient at the current backtracking moment.

[0063] 2. Based on the continuity of adjacent vacancies at the current backtracking moment and the degree of confusion of the corresponding serial numbers of the unmatched genes in the matched sequences, determine the vacancy correction factor at the current backtracking moment.

[0064] By analyzing the scores of the matching sequences that have been matched, the matching scores at the current backtracking moment are corrected, and the gap penalties corresponding to all genes that have not been matched are extracted and analyzed, specifically:

[0065] The sequence numbers of the unmatched genes in the matched sequences are obtained to form a gap sequence number sequence, and the distribution and arrangement characteristics of the unmatched gene data are determined by analyzing the periodicity and continuity of these sequence numbers. For example, if the sequence numbers of the two sequences are 1-3000, then according to the backtracking of the score matrix, the sequence numbers of the matched sequences may be 2000-3000, and the gap penalty sequence number is the sequence number of the unmatched gene data in the original sequence.

[0066] The permutation entropy of the differential sequence of the gap sequence is obtained and recorded as the chaotic continuity trend at the current backtracking moment. The greater the chaotic continuity trend, the smaller the regularity of the gaps in the matched sequence before the current backtracking moment, and the greater the possibility that the chaotic continuity trend is a gap, and the gap penalty corresponding to the current sequence should be closer to the score of the inserted gap, rather than the score of the base mismatch.

[0067] At the same time, the scores of the unmatched genes in the initial score matrix are clustered, and the distribution characteristics of different scores of these data at different positions in the sequence are analyzed to obtain the score correction coefficient of the current matching sequence.

[0068] That is, the closer the historical gap penalty is to the base mismatch, the more likely it is that there is a base mismatch at the current backtracking time, and the closer the historical gap penalty is to the base gap, the more likely it is that there is a gap at the current backtracking time, and the score correction coefficient is constructed based on this.

[0069] The DBSCAN clustering algorithm is used to cluster the scores of the mismatched genes in the initial score matrix, and the number of clusters K is obtained. The continuous serial numbers corresponding to the elements in each cluster are obtained to form several continuous gap sequences. For the gap sequences adjacent to the current backtracking moment, the gap continuity trend at the current backtracking moment is calculated based on the average gap score in the gap sequence and the sequence length of the gap sequence. The expression is: ; is the vacancy continuity trend at the current backtracking moment, norm represents the normalization function, is the average score of all elements in the cluster where the gap sequence is located, C is the length of the gap sequence, and F is the average length of all gap sequences in the cluster where the gap sequence is located. If there is no gap sequence adjacent to the current backtracking moment, the gap continuity trend at the current backtracking moment is recorded as 1.

[0070] That is, the higher the average score of the cluster where the vacancy sequence adjacent to the current backtracking moment is located, and the longer the length of the vacancy sequence is, and the closer the length is to the average sequence length in the cluster where it is located, the stronger the vacancy continuity trend at the current backtracking moment is.

[0071] According to the above analysis, based on the vacancy continuity trend and chaotic continuity trend at the current backtracking time, the vacancy correction factor at the current backtracking time is determined. Among them, the vacancy correction factor is positively correlated with the chaotic continuity trend and the vacancy continuity trend respectively.

[0072] It can be understood that a positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. This is determined by actual application and is not specifically limited in this application.

[0073] In one embodiment of the present application, the normalized value of the product of the disorder degree and the vacancy continuity trend is used as the vacancy correction factor at the current backtracking time. In other embodiments of the present application, the sum of the disorder degree and the vacancy continuity trend is used as the vacancy correction factor at the current backtracking time.

[0074] That is, the more irregular the distribution of the matched sequences at the current backtracking moment is, and the stronger the vacancy continuity trend of the adjacent vacancy sequences at the current backtracking moment is, the more likely the current backtracking moment is to be a vacancy sequence, and the larger the corresponding vacancy correction factor is.

[0075] 3. Based on the vacancy correction factor and vacancy penalty correction coefficient at the current backtracking time, determine the vacancy penalty correction factor at the current backtracking time.

[0076] According to the above analysis results, the present application determines the vacancy penalty correction factor at the current backtracking time based on the vacancy correction factor and the vacancy penalty correction coefficient at the current backtracking time. The correction method is: the normalized value of the ratio of the vacancy correction factor at the current backtracking time to the vacancy penalty correction coefficient is used as the vacancy penalty correction factor at the current backtracking time.

[0077] That is, the correction of the gap penalty at the current backtracking moment is comprehensively evaluated from two perspectives: the similarity between the viral gene sequence and the matching sequence that has been matched between any viral gene comparison sequence, and the continuity of the gaps adjacent to the current backtracking moment. The larger the gap correction factor at the current backtracking moment, the stronger the gap continuity trend of the gap sequence adjacent to the current backtracking moment, which means that the current backtracking moment is more likely to be a gap sequence; the smaller the gap penalty correction coefficient at the current backtracking moment, the shorter the sequence length of the matching sequence at the current backtracking moment, and the smaller the ratio of the number of matched genes to the number of unmatched genes in the matching sequence, which means that there are more gaps in the past matched sequences, and the reason why the two sequences do not match is less likely to be the possibility of sequence missing in the two sequences, but the two sequences themselves do not match, that is, the viral gene sequence does not match any viral gene comparison sequence.

[0078] 4. Based on the difference in the total score between the viral gene sequence and any other viral gene alignment sequence, obtain the alignment sequence of the viral gene sequence; based on the gap penalty correction factor between the viral gene sequence and all other alignment sequences except any one of the viral gene alignment sequences at the current backtracking time, determine the gap penalty correction result between the viral gene sequence and any viral gene alignment sequence at the current backtracking time.

[0079] According to the score results of data backtracking in the comparison between different viral gene comparison sequences and viral gene sequences, the score of the viral gene sequence and any viral gene comparison sequence is analyzed. For example, the viral gene sequence A0 has been compared with the viral gene comparison sequences A1, A2, and A3 respectively. The comparison results between A0 and A1, A2, and A3 are used to correct the gap score between the viral gene sequence and any viral gene comparison sequence at the current backtracking moment.

[0080] First, this embodiment uses the Otsu threshold method to perform threshold segmentation on the total score between the viral gene sequence and any other viral gene comparison sequence to obtain a segmentation threshold, and screen out sequences with a total score greater than the segmentation threshold as the comparison sequence of the viral gene sequence. Another embodiment of the present application uses a global threshold method for threshold segmentation. The Otsu threshold method and the global threshold method are both well-known technologies and will not be described in detail.

[0081] Calculate the average value of the gap penalty correction factor between the viral gene sequence and all other alignment sequences except any one of the viral gene alignment sequences at the current backtracking time;

[0082] The gap penalty correction factor between the viral gene sequence and any viral gene comparison sequence at the current backtracking time is merged with the average value as the gap penalty correction result between the viral gene sequence and any viral gene comparison sequence at the current backtracking time.

[0083] It can be understood that fusion can be divided into forward fusion and reverse fusion. This embodiment adopts the forward fusion method. Forward fusion is a fusion method such as addition and multiplication between data. The specific forward fusion method is determined by the implementer according to the actual situation. The application does not impose any special restrictions.

[0084] In one embodiment of the present application, the normalized value of the average value and the sum of the preset adjustment parameters are calculated; the product of the gap penalty correction factor between the viral gene sequence and any viral gene comparison sequence at the current backtracking time and the sum is used as the gap penalty correction result between the viral gene sequence and any viral gene comparison sequence at the current backtracking time. In this embodiment, the preset adjustment parameter is 0.5, and the implementer can set it according to the actual situation.

[0085] In other embodiments of the present application, the calculation result of the preset multiple of the normalized value of the average value is calculated, and the product of the gap penalty correction factor of the viral gene sequence and any viral gene comparison sequence at the current backtracking time and the calculation result is used as the gap penalty correction result of the viral gene sequence and any viral gene comparison sequence at the current backtracking time. Among them, this embodiment takes the preset multiple as 2, and the implementer can set it according to the actual situation.

[0086] That is, the gap penalty correction factor between the viral gene sequence and all other comparison sequences except any one of the viral gene comparison sequences at the current backtracking moment is used to correct the gap penalty correction factor between the viral gene sequence and any viral gene comparison sequence at the current backtracking moment, and is used to affect the gap penalty correction factor between the viral gene sequence and any viral gene comparison sequence at the current backtracking moment according to the correction degree relationship of the gap penalty between the viral gene sequence and other comparison sequences, so as to obtain the gap penalty correction result between the viral gene sequence and any viral gene comparison sequence at the current backtracking moment, and the gap penalty correction result can more accurately adjust the gap penalty in real time in combination with the changes in gene data during the backtracking process, so that the sequence comparison process is no longer limited to a fixed gap penalty, thereby increasing the accuracy of the final comparison result.

[0087] At this point, the gap penalty correction results of different positions of the viral gene sequence and any viral gene comparison sequence in the backtracking process of the backtracking matrix are completed.

[0088] The third step is to correct the initial score matrix based on the gap penalty correction results at different positions between the viral gene sequence and any viral gene comparison sequence during the backtracking process, and substitute the correction results into the multiple sequence alignment algorithm to perform multiple sequence alignment of the viral genes.

[0089] The gap penalty correction results of different positions of the viral gene sequence and any viral gene comparison sequence during the backtracking process are used as the gap penalty points of the corresponding positions of the initial score matrix to obtain a corrected score matrix.

[0090] Substitute the corrected score matrix into the Needleman-Wunsch algorithm to complete the multiple sequence alignment of viral genes, so that more accurate sequence alignment results can be obtained in the process of detecting viral gene mutations, thereby improving the efficiency of viral gene mutation detection.

[0091] In other embodiments of the present application, the MUSCLE algorithm (MUltiple Sequence Comparison by Log-Expectation) may also be used, which is determined by the implementer based on actual conditions.

[0092] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0093] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "including one..." does not exclude the existence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0094] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0095] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A multiple sequence alignment method for detecting viral gene mutations, characterized in that: The method comprises the following steps: Collecting the viral gene sequence to be detected and several viral gene comparison sequences; Based on the initial preset gap penalty, a multiple sequence alignment algorithm is used to obtain an initial score matrix between the viral gene sequence and any viral gene alignment sequence; in the process of backtracking the initial score matrix, the gap penalty correction results at different positions are dynamically calculated, specifically: Based on the similarity between the matching sequences that have been matched at the current backtracking time, determine the gap penalty correction coefficient at the current backtracking time; Based on the continuity of adjacent vacancies at the current backtracking moment and the degree of confusion of the corresponding serial numbers of the unmatched genes in the matched sequences, a vacancy correction factor at the current backtracking moment is determined; Determine the vacancy penalty correction factor at the current backtracking time based on the vacancy correction factor and the vacancy penalty correction coefficient at the current backtracking time; Obtaining an alignment sequence of the viral gene sequence based on a difference in total scores between the viral gene sequence and any other viral gene alignment sequence; Based on the gap penalty correction factor between the viral gene sequence and all other alignment sequences except any one of the viral gene alignment sequences at the current backtracking time, determine the gap penalty correction result between the viral gene sequence and any one of the viral gene alignment sequences at the current backtracking time; The initial score matrix is ​​modified based on the gap penalty correction results of the viral gene sequence and any viral gene comparison sequence at different positions in the backtracking process, and the correction results are substituted into the multiple sequence alignment algorithm to perform multiple sequence alignment of the viral gene; The calculation formula of the vacancy penalty correction coefficient at the current backtracking time is: ; X represents the vacancy penalty correction coefficient at the current backtracking time, represents an exponential function with a natural constant as the base, le represents the shortest length in the matching sequence, Co represents the matching degree between matching sequences, Indicates the mismatch between matching sequences; The method for determining the vacancy correction factor at the current backtracking time includes: Based on the continuity of the serial numbers corresponding to the scores of the mismatched genes in the initial score matrix, the continuity trend of the vacancy at the current backtracking time is determined; Based on the degree of disorder of the corresponding sequence numbers of the unmatched genes in the matched sequence that has been matched at the current backtracking moment, the disorder continuity trend at the current backtracking moment is determined; Based on the vacancy continuity trend and the chaotic continuity trend at the current backtracking time, the vacancy correction factor at the current backtracking time is determined; wherein the vacancy correction factor is positively correlated with the chaotic continuity trend and the vacancy continuity trend respectively; The vacancy penalty correction factor at the current backtracking time is determined by the normalized value of the ratio of the vacancy correction factor at the current backtracking time to the vacancy penalty correction coefficient.

2. The multiple sequence alignment method for detecting viral gene mutations according to claim 1, characterized in that: The degree of matching between the matching sequences is the number of genes that the matching sequences match, and the degree of mismatch between the matching sequences is the number of genes that the matching sequences do not match.

3. The multiple sequence alignment method for detecting viral gene mutations according to claim 1, characterized in that: The calculation expression of the vacancy continuity trend at the current backtracking moment is: ; is the vacancy continuity trend at the current backtracking moment, norm represents the normalization function, is the average score of all elements in the cluster where the gap sequence is located, C represents the length of the gap sequence, and F represents the average length of all gap sequences in the cluster where the gap sequence is located; if there is no gap sequence adjacent to the current backtracking moment, the gap continuity trend at the current backtracking moment is recorded as 1.

4. The multiple sequence alignment method for detecting viral gene mutations according to claim 1, characterized in that: The chaotic continuity trend at the current backtracking moment is: the arrangement entropy of the differential sequence of the sequence composed of the corresponding sequence numbers of the unmatched genes in the matching sequence that has been matched at the current backtracking moment.

5. The multiple sequence alignment method for detecting viral gene mutations according to claim 1, characterized in that: The method for determining the comparison sequence of the viral gene sequence comprises: A threshold segmentation algorithm is used to obtain a segmentation threshold of the total score between the viral gene sequence and any other viral gene alignment sequence, and the sequence with a total score greater than the segmentation threshold is used as the alignment sequence of the viral gene sequence.

6. The multiple sequence alignment method for detecting viral gene mutations according to claim 5, characterized in that: The gap penalty correction result between the viral gene sequence and any viral gene comparison sequence at the current backtracking time includes: Calculate the average value of the gap penalty correction factor between the viral gene sequence and all other alignment sequences except any one of the viral gene alignment sequences at the current backtracking time; The gap penalty correction factor between the viral gene sequence and any viral gene comparison sequence at the current backtracking time is merged with the average value as the gap penalty correction result between the viral gene sequence and any viral gene comparison sequence at the current backtracking time.

7. The multiple sequence alignment method for detecting viral gene mutations according to claim 6, characterized in that: The process of performing multiple sequence alignment on viral genes comprises: The gap penalty correction results of different positions in the backtracking process of the viral gene sequence and any viral gene comparison sequence are used as the gap penalty points of the corresponding positions in the initial score matrix to obtain a corrected score matrix; The corrected score matrix was substituted into the multiple sequence alignment algorithm to perform multiple sequence alignment of viral genes.

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