A method for quantifying mitochondrial genome rearrangements based on homology analysis and region weighting

Abstract

The present invention belongs to the technical field of molecular biology and bioinformatics, and relates to a mitochondrial genome rearrangement quantification method based on homology analysis and regional weighting. The method first inputs a reference sequence and n sequences to be quantified; manually aligns multiple sequences, aligns the reference sequence with the n sequences to be quantified; then compares the reference sequence, and partitions the n sequences to be quantified from the control region to the replication start point; then compares and judges the genes at the same position of the sequence to be quantified and the reference sequence, and performs weighted scoring according to the judgment result to obtain the scores of all genes in each sequence to be quantified; finally, the rearrangement quantification score RS of each sequence to be quantified of the species and the single gene rearrangement frequency RF of each gene are calculated according to the scores of all genes. The method of the present invention can accurately identify and count almost all situations in real data, ensure the comprehensiveness and accuracy of data analysis, and its quantification results can better infer the homology relationship of species.

Description

Technical Field

[0001] The invention belongs to the technical field of molecular biology and bioinformatics, and more specifically, relates to a mitochondrial genome rearrangement quantification method based on homology analysis and regional weighting. Background Art

[0002] Quantifying the structural features of the mitochondrial genome is crucial for us to gain a deeper understanding of its complexity. Accurately quantifying and interpreting tissue diversity will help us discover the laws and patterns of biological evolution. Currently, the most commonly used method for quantifying mitochondrial genome rearrangement is qMGR (quantifying mitochondrial genome rearrangement). This method regards a single gene as a structural unit and globally and equivalently quantifies multiple rearrangement features in a taxonomic unit by calculating the changes in its two adjacent genes. In addition to scoring the rearrangement quantification score (RS) of a specific taxonomic group, this method can also calculate the rearrangement frequency (RF) of a single gene in the mitochondrial genome, so that we can detect conserved gene clusters and high-frequency rearrangement gene clusters within a taxonomic unit. However, since its scoring strategy is based on the changes in two adjacent genes, its scoring results will be affected by the status of the two adjacent genes, which may lead to a high or low score. Specifically, the scoring deviation can be divided into the following four types: (1) For a gene, if it itself has not changed, but its two adjacent genes have changed, the gene score will be high. (2) When traversing each gene sequence to be quantified, this method will search for each corresponding gene in the benchmark sequence and add the score to the benchmark gene. If a gene does not exist in the benchmark sequence, the gene cannot be retrieved in the gene sequence to be quantified, which will cause the benchmark sequence score to be too high. (3) If the gene cluster undergoes overall displacement, the gene scores in the gene cluster will be too low. (4) This method cannot assign corresponding weights to genes that have undergone long-distance displacement, resulting in a low score. These four situations jointly affect the accuracy of the existing method qMGR results, and will also lead to deviations in our judgment of conservative gene clusters and high-frequency rearrangement gene clusters.

[0003] Patent document with announcement number CN117133351B discloses an optimized mitochondrial gene rearrangement quantification method, which mainly adopts a partitioning array to segment the gene sequence and uses a sliding window to calculate the score of each gene, allowing different weights to be given to gene clusters with different rearrangement intensities. However, it only solves the problem of the fourth case of the existing method qMGR. The problem of errors in the results caused by the scoring strategy in the first three cases has not been solved. Moreover, it is not quantified based on the homology of the gene sequence and cannot accurately describe the diversity of the genome structure. Summary of the invention

[0004] In view of the deficiencies in the above-mentioned prior art, the present invention provides a mitochondrial genome rearrangement quantification method based on homology analysis and regional weighting, named MitoQ, which aims to quantify the diversity of genomic organization in animal mitochondrial genomes. Based on the homology of gene order, this method partitions gene clusters and assigns different weights to cross-region genes, so as to more accurately describe the diversity characteristics of genome structure. In the test of vertebrate mitochondrial genome data, the method MitoQ of the present invention showed better accuracy and interpretability than the existing method qMGR. This method not only significantly improves the comparability of genomes, but also can more accurately reveal the differences in the mitochondrial genome structures of different groups. At the same time, it also has important scientific value in discovering the evolutionary laws of genomes, exploring genome functions and studying molecular evolution processes. More importantly, the method MitoQ of the present invention has opened up a new perspective for genomic research and biological evolution research that is different from traditional sequence evolution, and provides new ideas and methods for research in related fields.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows.

[0006] A method for quantifying mitochondrial genome rearrangement based on homology analysis and region weighting comprises the following steps:

[0007] Step S1, input a reference sequence and n sequences to be quantized;

[0008] Step S2, manual multiple sequence alignment, aligning the reference sequence with the n sequences to be quantified;

[0009] Step S3, partitioning the n sequences to be quantified from the control region to the replication start point in comparison with the reference sequence;

[0010] Step S4, set the initial value of the rearrangement score of the gene at the i-th position in the sequence to be quantified to 0, the initial value of the weight to 0, compare the genes at the same position of the sequence to be quantified and the reference sequence, if they are equal, then the rearrangement score of the gene at the i-th position in the sequence to be quantified is counted as 0; if they are not equal, then it is counted as 1, and the preliminary rearrangement score of the gene at the i-th position in the sequence to be quantified is obtained. ; Then make the following two judgments:

[0011] Step S41, the region of the gene at the i-th position in the sequence to be quantified in the sequence to be quantified and the reference sequence is judged, and a weight is assigned , if weights are given in the same area The value is m1. If the weight is given in the adjacent area The value is m2, if it crosses regions, it is given a weight The value is m3, and m1<m2<m3, the weight is combined with the calculated preliminary rearrangement score of the gene at the i-th position in the sequence to be quantified Multiply to get the rearranged fraction;

[0012] Step S42, determine whether the gene at the i-th position in the sequence to be quantified has been reversed. If reversed, add 1 point to the rearrangement score, otherwise it remains unchanged, and the final rearrangement score of the gene at the i-th position in the sequence to be quantified is obtained. ;

[0013] Step S5, calculate the final rearrangement score of each gene in each sequence to be quantified according to steps S4 to S42, and finally add the final rearrangement scores of all genes in each sequence to be quantified to obtain the rearrangement quantification score RS of each sequence to be quantified, and add the scores of each identical gene in all sequences to be quantified to obtain the single gene rearrangement frequency RF of each gene.

[0014] Among them, in step S2, the specific method of manually aligning the reference sequence with the n sequences to be quantified is: fill the number of missing genes in the gene strings of the reference sequence and the n sequences to be quantified by filling the gaps, align the genes in the vertical direction, and make the length of all gene strings in the genome consistent with the length of the gene string in the longest sequence. By manually aligning the reference sequence and the sequence to be quantified, it is ensured that they are correctly aligned, providing an accurate basis for subsequent analysis.

[0015] In step S3, the partitioning method based on the control region and the replication start point is a partitioning strategy adopted based on biological considerations and after multiple experimental analyses. The number of the divided regions is greater than or equal to 2.

[0016] Among them, in step S4, by comparing the position of each gene and taking into account the regional position, the displacement of the gene is accurately quantified, which can ensure that genes with short-distance displacement and long-distance displacement are distinguished and further given corresponding weights.

[0017] Among them, in step S42, a reversal judgment mechanism is introduced to judge the reversal of the gene. Reversal is more common in gene rearrangement, so a score is also given to the reversal event, which can further accurately quantify the degree of gene rearrangement.

[0018] Among them, the final rearrangement score of the gene at the i-th position in the sequence to be quantified is The calculation formula is as follows:

[0019] ;

[0020] in, is the preliminary rearrangement score of the gene at the i-th position in the sequence to be quantified, is the weight of the gene at the i-th position in the sequence to be quantified;

[0021] The weight of the gene at the i-th position in the sequence to be quantified The formula of the assignment rule is as follows:

[0022] ;

[0023] The preliminary rearrangement score of the gene at the i-th position in the sequence to be quantified The scoring rule formula is as follows:

[0024] .

[0025] The method MitoQ of the present invention solves four major problems existing in the existing method qMGR, and has been applied to analyze a comprehensive data set in previous mitochondrial genome studies. Through pairwise gene order comparison and regional weighting technology, the method MitoQ of the present invention accurately captures the dynamic changes in genome organization. In order to further verify its performance advantages, we compared the results produced by the method MitoQ of the present invention with the results of the existing method qMGR in detail, and used charts to intuitively show the application effects of the two methods on the same data set. Through comparison, we found that due to the limitations of the scoring rules of the existing method qMGR, the adjacent genes that are common in all taxa affect the scoring scores. In particular, in the analysis of bird data, in addition to being affected by adjacent genes, single genes that do not exist in the benchmark sequence are also counted as higher scores, and the overall displacement of gene clusters leads to low scores of genes in the cluster. This is also more serious, which significantly leads to a large deviation in the results. Specifically, due to the defects in the scoring mechanism of the existing method qMGR for missing genes, the scores are falsely high, and the overall displacement of gene clusters cannot be correctly scored, resulting in lower scores of genes in the cluster than expected. In addition, except for birds, the other four groups were unable to accurately score genes that underwent long-distance displacement, which resulted in too low scores to varying degrees. The MitoQ method of the present invention does not have the above problems.

[0026] Beneficial effects of the present invention:

[0027] (1) The MitoQ method of the present invention has fewer constraints on the mitochondrial genome input data, so it can accurately identify and count almost all situations in real data, including but not limited to gene rearrangement, gene duplication or loss, and allows for small changes in gene content, ensuring the comprehensiveness and accuracy of data analysis.

[0028] (2) The MitoQ method of the present invention is a homology quantification method based on gene sequence. Its quantification results can better infer the homology relationship of species and provide a more reliable scientific basis for biological research.

[0029] (3) The MitoQ method of the present invention introduces the idea of ​​regional weighting, which can assign different weights to genes that undergo long-distance displacement. This design not only makes the results more consistent with biological principles, but also improves the sensitivity and explanatory power of the method to genome dynamics.

[0030] The method of the present invention solves all the problems existing in the existing method qMGR and quantifies based on the homology of gene sequences, which can more accurately describe the diversity of genome structure. This method not only enhances the comparability of genomes, but also can more accurately show the differences in the structures of mitochondrial genomes of different groups. It has important scientific value in revealing the evolutionary laws of genomes, exploring genome functions, and studying molecular evolution processes. It also provides a perspective different from sequence evolution for genomic research and biological evolution research, and provides new ideas and methods for research in related fields. For example, the genome rearrangement quantification method of the present invention can be applied in the following major fields:

[0031] (1) Genetic disease research: Some genetic diseases are related to genomic structural variations. The application of rearrangement quantification methods can detect these abnormal structural changes and help understand the mechanism of the disease.

[0032] (2) Evolutionary genomics: By comparing the differences in genome structure between different species or groups, we can infer the evolutionary history and relationships of species or groups.

[0033] (3) Genome engineering: Synthetic biology uses genome rearrangement technology to transform the genome, providing new possibilities for applications such as biopharmaceuticals and bioenergy.

[0034] (4) Population genetics research field: Analysis of large-scale population genome structural variation helps to explore human genetic diversity and ethnic differences.

[0035] (5) Agricultural genomics: Research on the structural variation of crop and livestock genomes can provide a basis for variety improvement and genetic breeding. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Flow chart of the mitochondrial genome rearrangement quantification method based on homology analysis and region weighting in an embodiment of the present invention.

[0037] Figure 2 The figure is a comparison chart of the single gene rearrangement frequency (RF) results of the mitochondrial genome of mammals using the method of the present invention and the existing method.

[0038] Figure 3 The figure is a comparison chart of the single gene rearrangement frequency (RF) results of the amphibian mitochondrial genome by the method of the present invention and the existing method.

[0039] Figure 4 The figure is a comparison chart of the single gene rearrangement frequency (RF) results of the mitochondrial genome of avian animals using the method of the present invention and the existing method.

[0040] Figure 5 This is a comparison chart of the single gene rearrangement frequency (RF) results of the reptile mitochondrial genome using the method of the present invention and the existing method.

[0041] Figure 6 This is a comparison chart of the single gene rearrangement frequency (RF) results of the mitochondrial genome of fish animals using the method of the present invention and the existing method. DETAILED DESCRIPTION

[0042] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and therefore, the present invention is not limited to the limitations of the specific embodiments disclosed below.

[0043] like Figure 1 A method for quantifying mitochondrial genome rearrangement based on homology analysis and region weighting is shown, comprising the following steps:

[0044] Step S1: input a reference sequence and n sequences to be quantized.

[0045] Step S2, manual multiple sequence alignment, aligning the reference sequence with the n sequences to be quantified. In gene sequences, due to rearrangement events, genes may be duplicated or deleted, so the number of genes in a gene string is different. For subsequent homology analysis, multiple sequence alignment is required. Multiple sequence alignment is to align genes in the vertical direction by filling in gaps. After alignment, the length of all gene strings in the genome is the same, which is helpful for further homology analysis and subsequent regional division.

[0046] Step S3, partition the n sequences to be quantified from the control region to the replication start point in comparison with the reference sequence. The region between the control region and the replication start point is a specific region on the DNA, which starts from the control element regulating gene expression and ends at the place where DNA replication starts. There are multiple control regions and replication start points in a gene sequence. The division of this region is based on the biological function and sequence characteristics of the region; the division of the number of regions can be adjusted by preset parameters; the number of regions divided by the sequence to be quantified is the same as the number of regions divided by the reference sequence, except that the genes contained in the region are different due to gene rearrangement. The specific operation of partitioning is to store the position information of the corresponding region, and different regions correspond to different array subscripts, and the region and the corresponding position are judged by the array subscript in the subsequent. For a region, its storage information includes: the region number of the region and its starting position and end position. The region number is sorted in a sequential numbering manner, which is used to judge whether the relative positions between regions are adjacent in the subsequent determination. For example, the input data contains 100 gene sequences, each of which contains 37 genes. These 100 gene sequences are divided into three regions, namely gene region 1, gene region 2 and gene region 3. The corresponding region numbers of the three regions are 1, 2 and 3, and the position information is [1, 15], [16, 27] and [28, 37] respectively.

[0047] Step S4, assuming that the initial value of the rearrangement score of the gene at the i-th position in the sequence to be quantified is 0, the initial value of the weight is 0, and the genes at the same position in the sequence to be quantified and the reference sequence are compared. If they are equal, the preliminary rearrangement score of the gene at the i-th position in the sequence to be quantified is calculated as 0; if they are not equal, it is calculated as 1; the preliminary rearrangement score of the gene at the i-th position in the sequence to be quantified is calculated as The scoring rule formula is as follows:

[0048] ;

[0049] Then make the following two judgments:

[0050] Step S41, the region of the gene at the i-th position in the sequence to be quantified in the sequence to be quantified and the reference sequence is judged, and a weight is assigned , if weights are given in the same area The value is m1. In this embodiment, m1=1. If the weight is given in the adjacent area The value is m2. In this embodiment, m2=2. If it crosses regions, a weight is given. The value of is m3, in this embodiment, m3=3, and the weight is combined with the calculated preliminary rearrangement score of the gene at the i-th position in the sequence to be quantified The weight distribution in this embodiment is designed after multiple experimental comparisons. This design can better reflect the real biological evolution process; the weight of the gene at the i-th position in the sequence to be quantified is The formula of the assignment rule is as follows:

[0051] ;

[0052] Step S42, determine whether the gene at the i-th position in the sequence to be quantified has been reversed. If reversed, add 1 point to the rearrangement score, otherwise it remains unchanged, and the final rearrangement score of the gene at the i-th position in the sequence to be quantified is obtained. The genes in the genome are stored as strings, and a symbol ± (positive sign can be omitted) is added in front of the string to indicate the direction of the gene in the chromosome. For example, (F, -12S, W, A, -N), where the judgment of the reversal is based on the ± in front of the string. The final rearrangement score of the gene at the i-th position in the sequence to be quantified The calculation formula is as follows:

[0053] .

[0054] Step S5, after calculating the final rearrangement score for each gene in each sequence to be quantified according to the above steps, for each species (i.e., each sequence to be quantified), the final rearrangement scores of all genes contained in each sequence to be quantified are added together to obtain the rearrangement quantification score RS of the sequence to be quantified, and the final rearrangement scores of the genes with the same name in all sequences to be quantified are added together to obtain the single gene rearrangement frequency RF of each gene.

[0055] All mitochondrial genome data information of different types of species were obtained from the mitochondrial genome public database, and comparative experiments were performed on the quantitative analysis of mitochondrial gene rearrangement using the method MitoQ of the present invention and the existing method qMGR. All sequence data analyzed in the data set study used in this embodiment were downloaded from the NCBI organelle database (https: / / www.ncbi.nlm.nih.gov / genome / organelle / ) and the MitoZoa website (http: / / srv00.recas.ba.infn.it / mitozoa / ). Since there are a lot of duplications in the data and there are many completely identical gene sequences, it is necessary to first use the Java language to clean and deduplicate the downloaded data packets. The comparative test results are shown in Figure 2. Figure 2~Figure 6 shown.

[0056] Figure 2The figure is a comparison of the single gene rearrangement frequency (RF) results of the mitochondrial genome of mammals by the method of the present invention and the existing method. The results show that the method MitoQ of the present invention not only shows the gene inversion well, but also does not give any score to the genes that have not changed. However, since the existing method qMGR scores the adjacent genes on both sides of the gene, it may cause the gene itself to not change but to be incorrectly scored because of the changes in the genes on both sides, such as gene M, gene nad2, gene cox1, gene D, gene atp6, gene cox3, gene G, gene nad6, gene cob and gene F. These genes in the data set have not been rearranged themselves, and their positions have not changed. The corresponding single gene rearrangement frequency score should be 0, but the existing method qMGR has given them an incorrect score. At the same time, for genes Q, gene N and gene E that have undergone long-distance displacement, their rearrangement degree is high and should be given a higher score. The method MitoQ of the present invention accurately quantifies the rearrangement degree by regional weighting, which makes up for the shortcomings of the existing method qMGR in this regard, thereby more reasonably statistically quantifying the results.

[0057] Figure 3 The figure is a comparison chart of the single gene rearrangement frequency (RF) results of the amphibian mitochondrial genome using the method of the present invention and the existing method. Figure 3 As shown, the genes nad2, gene A, gene C, gene cox1, gene D, gene K, gene atp8, gene atp6, gene nad4 and gene F in the data set did not change but were incorrectly scored by the existing method qMGR. In the data set, gene L1 and gene nad5 underwent long-distance displacement, and the results showed that the existing method qMGR underestimated the changes in gene L1 and gene nad5; when nad6-E-cob in the data set shifted as a whole, the existing method qMGR did not score the rearrangement of gene E, resulting in a low score for the frequency of single gene rearrangement of gene E, while the method MitoQ of the present invention scored correctly.

[0058] Figure 4 The figure is a comparison of the single gene rearrangement frequency (RF) results of the mitochondrial genome of avian animals by the method of the present invention and the existing method. The results show that the genes I, M, W, N, C, cox1, cox2, nad4, S1, F, V and L2 in the data set have not changed but are incorrectly scored by the existing method qMGR. In the mitochondrial genome of avian animals, genes P and E have undergone cross-regional displacement. The method MitoQ of the present invention assigns weights to cross-regional displacements for more accurate quantitative analysis.

[0059] Figure 5The figure is a comparison of the single gene rearrangement frequency (RF) of the mitochondrial genome of reptiles by the method of the present invention and the existing method. The results show that the existing method qMGR has high quantification scores for genes nad1, gene M, gene W, gene cox1, gene cox2, gene nad4, gene L1, gene nad5, gene F and gene rrnL. As with the problem of mammals, the existing method qMGR not only scores genes that have undergone rearrangement, but also incorrectly scores genes that have not changed due to the influence of changes in adjacent genes. In terms of long-distance displacement (such as gene P and gene L2), the method MitoQ of the present invention embodies the reasonable quantification of long-distance gene displacement.

[0060] Figure 6 The figure is a comparison of the results of single gene rearrangement frequency (RF) of the mitochondrial genome of fish animals by the method of the present invention and the existing method. The existing method qMGR has high quantitative scores for genes nad1, gene atp8, gene nad3 and gene nad5. Like the problem of mammals, the existing method qMGR not only scores genes that have undergone rearrangement, but also incorrectly scores genes that have not changed due to the influence of changes in adjacent genes. In addition, the gene rearrangement of this group is characterized by long-distance displacement of genes Q, gene A, gene Y, gene S2, gene K, gene E and gene L2. The method MitoQ of the present invention embodies the reasonable quantification of long-distance displacement of genes by assigning weights.

[0061] Through experiments on 5549 vertebrate mitochondrial genomes, we demonstrated that the method MitoQ provided by the present invention outperforms existing methods in terms of accuracy and interpretability.

Claims

1. A method for quantifying mitochondrial genome rearrangement based on homology analysis and region weighting, characterized in that: The following steps are involved: Step S1, input a reference sequence and n sequences to be quantized; Step S2, manual multiple sequence alignment, fill in the gaps to make up the number of missing genes in the gene strings of the reference sequence and the n sequences to be quantified, align the genes in the vertical direction, so that the length of all gene strings in the genome is consistent with the length of the gene string in the longest sequence; Step S3, partitioning the n sequences to be quantified from the control region to the replication start point in comparison with the reference sequence; Step S4, assuming that the initial value of the rearrangement score of the gene at the i-th position in the sequence to be quantified is 0, and the initial value of the weight is 0, and the genes at the same position of the sequence to be quantified and the reference sequence are compared. If they are equal, the rearrangement score of the gene at the i-th position in the sequence to be quantified is counted as 0; If they are not equal, they are scored as 1, and the preliminary rearrangement score of the gene at the i-th position in the sequence to be quantified is obtained. ; Then make the following two judgments: Step S41, the region of the gene at the i-th position in the sequence to be quantified in the sequence to be quantified and the reference sequence is judged, and a weight is assigned , if weights are given in the same area The value is m1. If the weight is given in the adjacent area The value is m2, if it crosses regions, it is given a weight The value is m3, and m1<m2<m3, the weight is combined with the calculated preliminary rearrangement score of the gene at the i-th position in the sequence to be quantified Multiply to get the rearranged fraction; Step S42, determine whether the gene at the i-th position in the sequence to be quantified has been reversed. If reversed, add 1 point to the rearrangement score, otherwise it remains unchanged, and the final rearrangement score of the gene at the i-th position in the sequence to be quantified is obtained. ; Step S5, calculate the final rearrangement score of each gene in each sequence to be quantified according to steps S4 to S42, and finally add the final rearrangement scores of all genes in each sequence to be quantified to obtain the rearrangement quantification score RS of each sequence to be quantified, and add the scores of each identical gene in all sequences to be quantified to obtain the single gene rearrangement frequency RF of each gene.

2. The method for quantifying mitochondrial genome rearrangement based on homology analysis and regional weighting according to claim 1, characterized in that: In the step S3, the number of regions divided according to the control area and the replication start point is greater than or equal to 2.

3. The method for quantifying mitochondrial genome rearrangement based on homology analysis and regional weighting according to claim 1, characterized in that: The weight of the gene at the i-th position in the sequence to be quantified The formula of the assignment rule is as follows: 。

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