Identification method based on Hedou12 soybean genome structure variation

Abstract

The invention provides an identification method based on Hedou12 soybean genome structure variation, and relates to the technical field of gene variation identification, the method comprises the following steps: based on genome sequencing data of Hedou12 soybeans and reference genome sequencing data of Williams82 soybeans, determining an inconsistent double-end read set and a split read set corresponding to the Hedou12 soybeans; constructing at least one genome structure variation diagram corresponding to the Hedou12 soybeans on the basis of each inconsistent double-end read and each split read; and identifying the event type of the genome structure variation event represented by each genome structure variation graph, and generating a genome structure variation set file corresponding to the Hedou12 soybeans. According to the method, diversity and complexity of genome variation are revealed through the genome structure variation graph, so that the types of complex combination variation in the genome and the mutual relation between the genome variation can be identified.

Description

Technical Field

[0001] The present invention relates to the technical field of gene variation identification, and in particular to an identification method based on Hedou12 soybean genome structural variation. Background Art

[0002] In modern genomics research, analysis of genomic structural variation in Hedou12 soybean plays a key role in soybean improvement and variety optimization.

[0003] Analyzing the genomic structural variation of Hedou12 soybean is a complex process. At present, when the genomic structural variation of Hedou12 soybean occurs, it is impossible to accurately identify the type of variation event and the relationship between the genomic structural variations.

[0004] Therefore, how to identify the event types of Hedou12 soybean genome structural variation and analyze the relationship between genome structural variations is a problem that needs to be solved urgently. Summary of the invention

[0005] In view of the problems existing in the prior art, an embodiment of the present invention provides a method for identifying structural variation based on the Hedou12 soybean genome.

[0006] The present invention provides a method for identifying structural variation of Hedou12 soybean genome, comprising:

[0007] Based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean are determined;

[0008] The inconsistent double-end read set includes a plurality of inconsistent double-end reads, and the positions, directions and distances of the two ends of the inconsistent double-end reads in the genome sequencing data and the reference genome sequencing data are different; the split read set includes a plurality of split reads, the first part of the split read is aligned with the first region of the reference genome, the second part of the split read is aligned with the second region of the reference genome, and the distance between the second region and the first region is greater than a first preset threshold;

[0009] Based on each of the inconsistent double-end reads and each of the split reads, at least one genomic structural variation graph corresponding to the Hedou12 soybean is constructed; each genomic structural variation graph represents a genomic structural variation event corresponding to the Hedou12 soybean, the genomic structural variation graph includes at least two vertices and an edge connecting the two vertices, the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or the split read;

[0010] The event type of the genome structure variation event represented by each genome structure variation map is identified, and a genome structure variation collection file corresponding to the Hedou12 soybean is generated.

[0011] Optionally, constructing at least one genomic structural variation map corresponding to the Hedou12 soybean based on each of the inconsistent double-end reads and each of the split reads comprises:

[0012] The inconsistent double-end reads or split reads whose similarity reaches the second preset threshold are used as the vertices and two adjacent vertices are connected by the edges to generate the genome structure variation graph; the edges are provided with weight values, and the weight values ​​are used to characterize the probability that the two adjacent vertices belong to the same genome structure variation event;

[0013] The weight value is calculated by the following formula (1):

[0014]

[0015] Among them, E ij Indicates that the read fragments i and j corresponding to the two vertices support the same genomic structural variation event; d ij represents the difference in insertion length between the read fragments i and j on the reference genome sequencing data; h represents the depth of alignment of the read fragments i and j to the reference genome sequencing data; P(E ij |D ij =d ij , H = h) means that the difference in insertion length is d ij and the weight value when the depth is h; P(D ij =d ij |E ij , H=h) means that in E ij The insertion length difference is d ij The conditional probability of h for d ij The influence of P(E ij|H=h) means that when the depth is h, E ij Prior probability of occurrence; P(D ij =d ij |H=h) means that when the depth is h, the insertion length difference is d ij The marginal probability of .

[0016] Optionally, the determining of the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean based on the genome sequencing data of the Hedou12 soybean and the reference genome sequencing data of the Williams82 soybean includes:

[0017] Aligning the genome sequencing data to the reference genome sequencing data to obtain the inconsistent double-end read set and the consistent double-end read set; the consistent double-end read set includes a plurality of consistent double-end reads, and the two ends of the consistent double-end reads have the same position, direction and distance in the genome sequencing data and the reference genome sequencing data;

[0018] The inconsistent double-end read set and the unaligned reads in the consistent double-end read set are aligned to the reference genome sequencing data to obtain the split read set.

[0019] Optionally, after constructing at least one genome structure variation map corresponding to the Hedou12 soybean, the method further comprises:

[0020] For each genome structure variation graph, based on the observed data of the inconsistent double-end reads or the split reads corresponding to each of the vertices, a maximum likelihood estimation function corresponding to the genome structure variation graph is constructed; the estimated value corresponding to the maximum likelihood estimation function represents the authenticity of the genome structure variation graph;

[0021] The observed data include the alignment quality, coverage depth and number of reads supporting the variation of the inconsistent double-end reads or the split reads aligned to the reference genome sequencing data;

[0022] The genome structure variation graphs whose estimated values ​​are lower than a third preset threshold are removed to obtain at least one genome structure variation graph after authenticity detection;

[0023] The maximum likelihood estimation function is expressed by the following formula (2):

[0024] L(q, d, n|θ) = f(q|θ q )f(d|θ d )f(n|θn ) (2)

[0025] Where q represents the alignment quality; d represents the coverage depth; n represents the number of reads supporting the variation; L(q, d, n|θ) represents the estimated value; f(q|θ q ) represents the probability distribution function of the alignment quality, with parameter θ q ; f(d|θ d ) represents the probability distribution function of the coverage depth, and the parameter is θ d ; f(n|θ n ) represents the probability distribution function of the number of reads supporting the variation, with parameter θ n .

[0026] Optionally, after obtaining at least one authenticity-tested genome structure variation map, the method further comprises:

[0027] Based on the LP-rounding algorithm, identifying the false positive genome structure variation graphs detected incorrectly in the genome structure variation graphs after the authenticity detection;

[0028] The false positive genome structure variation map is removed.

[0029] Optionally, before determining the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, the method further comprises:

[0030] Performing data quality control analysis on the genome sequencing data and the reference genome sequencing data; the data quality control analysis is used to screen and filter the genome sequencing data and the reference genome sequencing data.

[0031] The present invention also provides an identification device based on Hedou12 soybean genome structural variation, comprising:

[0032] A determination module, for determining the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean based on the genome sequencing data of the Hedou12 soybean and the reference genome sequencing data of the Williams82 soybean;

[0033] The inconsistent double-end read set includes a plurality of inconsistent double-end reads, and the positions, directions and distances of the two ends of the inconsistent double-end reads in the genome sequencing data and the reference genome sequencing data are different; the split read set includes a plurality of split reads, the first part of the split read is aligned with the first region of the reference genome, the second part of the split read is aligned with the second region of the reference genome, and the distance between the second region and the first region is greater than a first preset threshold;

[0034] A first construction module is used to construct at least one genomic structural variation graph corresponding to the Hedou12 soybean based on each of the inconsistent double-end reads and each of the split reads; each genomic structural variation graph represents a genomic structural variation event corresponding to the Hedou12 soybean, the genomic structural variation graph includes at least two vertices and an edge connecting the two vertices, the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or the split read;

[0035] The first identification module is used to identify the event type of the genome structure variation event represented by each genome structure variation map, and generate a genome structure variation set file corresponding to the Hedou12 soybean.

[0036] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the identification method based on the structural variation of the Hedou12 soybean genome as described in any one of the above is implemented.

[0037] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the computer program implements any of the above-mentioned identification methods based on Hedou12 soybean genome structural variation.

[0038] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned identification methods based on Hedou12 soybean genome structural variation.

[0039] The invention provides a method for identifying Hedou12 soybean genome structural variation. First, based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, the inconsistent double-end read set and the split read set corresponding to Hedou12 soybean are determined; the split read can reveal a large range of genome rearrangements, such as translocation and insertion or deletion of large fragments, while the inconsistent double-end read helps to locate the precise breakpoint of the variation, thereby providing direct variation evidence for subsequent genome structural variation analysis. Secondly, based on each inconsistent double-end read and each split read, at least one genome structural variation graph corresponding to Hedou12 soybean is constructed; wherein each genome structural variation graph represents a genome structural variation event corresponding to Hedou12 soybean, and the genome structural variation graph includes at least two vertices and an edge connecting the two vertices, and the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or split read; through the above-mentioned genome structural variation graph, it can be revealed that certain positions of the genome may have complex combination variations. That is, through this graph method, the complex combination variation in the genome can be clearly observed, and complex or subtle variation patterns that are difficult to discover with traditional methods can be identified; and the genome structure variation graph can reveal the relationship between genome variations, for example, whether certain variations tend to appear in a specific region of the genome, or whether there is a certain degree of correlation between different types of variations. Finally, by identifying the event type of the genome structure variation event represented by each genome structure variation graph, the genome structure variation collection file corresponding to Hedou12 soybean is generated, which reveals the diverse complexity of genome variation, the event type of complex combination variation within the genome, and the relationship between genome variations. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or 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 work.

[0041] Figure 1 This is one of the flow charts of the method for identifying the structural variation of the Hedou12 soybean genome provided by the present invention;

[0042] Figure 2 This is the second flow chart of the method for identifying the structural variation of the Hedou12 soybean genome provided by the present invention;

[0043] Figure 3 It is a schematic diagram of the structure of the identification device based on the Hedou12 soybean genome structural variation provided by the present invention;

[0044] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0046] In order to facilitate a clearer understanding of the embodiments of the present application, some relevant knowledge is first introduced as follows.

[0047] The present invention systematically illustrates how to identify the event types of genomic structural variation of the diploid Hedou12 soybean through one or more embodiments, wherein the types of variation events are, for example, insertion, deletion, inversion, and the like.

[0048] It should be noted that the analysis of the genomic structural variation of Hedou12 soybean provides a theoretical basis for the genetic improvement and variety optimization of diploid soybean, which in turn helps to achieve the following goals:

[0049] (1) Structural variation identification: Systematically identify and analyze various structural variations in the diploid soybean Hedou12 variety, including insertions, deletions, and flips, to understand their potential effects on phenotype and adaptability.

[0050] (2) Revealing the genetic mechanism: In-depth exploration of the genetic basis of structural variation in diploid soybeans, identification of key genes related to stress resistance and high yield characteristics, and providing targets for subsequent genetic improvement.

[0051] (3) Data support and verification: Based on the analysis results, a scientific basis is provided for diploid soybean variety improvement, and the proposed genetic mechanism is verified through experiments to ensure the reliability and practicality of the research results.

[0052] (4) Agricultural biotechnology development: Promote the development of related biotechnology and innovation in diploid soybean related fields to improve the quality and yield of overall crops, thereby laying a solid foundation for the sustainable development of agriculture.

[0053] In summary, the identification method based on Hedou12 soybean genome structural variation provided by the present invention plays a key role in the agricultural field.

[0054] In the current soybean genomics research, it is particularly important to accurately identify and analyze the structural variation of the Hedou12 soybean genome. In particular, complex combined variation, such as insertion, deletion and inversion contained in deletion, as well as deletion, inversion and tandem duplication contained in insertion, faces great challenges in the accurate detection of these variations.

[0055] In order to solve this problem, the present invention proposes a method for identifying structural variation of Hedou12 soybean genome, which aims to improve the accuracy and efficiency of detecting structural variation of Hedou12 soybean genome by combining graph theory, Bayesian reasoning, maximum likelihood estimation and LP-rounding algorithms. This new complex combination variation identification algorithm is of great significance for understanding the occurrence mechanism of soybean genetic diseases, promoting the improvement of soybean varieties and promoting the development of soybean biotechnology.

[0056] Combine the following Figure 1 to Figure 2 The identification method based on the Hedou12 soybean genome structural variation provided by the present invention is described in detail. Figure 1 This is one of the flow charts of the method for identifying Hedou12 soybean genome structural variation provided by the present invention, see Figure 1 As shown, the method includes steps 101 to 103, wherein:

[0057] Step 101. Based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, determine the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean; wherein the inconsistent double-end read set includes multiple inconsistent double-end reads, and the two ends of the inconsistent double-end read have different positions, directions and distances in the genome sequencing data and the reference genome sequencing data; the split read set includes multiple split reads, the first part of the split read is aligned with the first region of the reference genome, the second part of the split read is aligned with the second region of the reference genome, and the distance between the second region and the first region is greater than a first preset threshold.

[0058] First of all, it should be noted that the execution subject of the present invention can be any electronic device that can identify the structural variation of the Hedou12 soybean genome, for example, it can be any one of a smart phone, a desktop computer, a laptop computer, etc.

[0059] In the embodiments of the present invention, the applications of discordant paired-end reads, concordant paired-end reads and split reads in genomic variation research are crucial.

[0060] The following describes the discordant paired-end reads, consistent paired-end reads, and split reads:

[0061] (1) Consistent paired-end reads: These are reads whose two ends match the reference genome according to the sequencing direction and distance. These reads indicate that the sample genome is highly consistent with the reference genome in this region and are usually not used to directly detect structural variations, but they are necessary to confirm the stability and integrity of most regions of the genome.

[0062] (2) Discordant double-end reads: This means that the position, direction or distance of the two ends on the reference genome is different from that of the sequenced read. This type of inconsistent double-end read is caused by structural variations such as insertion, deletion, inversion or translocation. For example, if the distance between the two ends of a read on the reference genome is much greater than the distance of the insertion, it indicates a potential deletion event.

[0063] (3) Split read: refers to a part of the read (i.e., the first part mentioned above) that can be aligned with a region of the reference genome (i.e., the first region mentioned above), while the other part (i.e., the second part mentioned above) is aligned with another distant region (i.e., the second region mentioned above). This situation occurs at the point where the genome is broken or rearranged. Therefore, split reads are an important clue for detecting such fine structural variations.

[0064] In the present embodiment, discordant paired-end reads and split reads are selected as the objects of studying genomic variation because these two types of reads are used to provide direct evidence of structural variation. Split reads can reveal large-scale genomic rearrangements, such as translocations and large fragment insertions or deletions, while discordant paired-end reads help locate the precise breakpoints of the variation, which is crucial for understanding the precise mechanism of variation and potential biological impact.

[0065] Step 102: construct at least one genomic structure variation graph corresponding to the Hedou12 soybean based on each of the inconsistent double-end reads and each of the split reads; each genomic structure variation graph represents a genomic structure variation event corresponding to the Hedou12 soybean, and the genomic structure variation graph includes at least two vertices and an edge connecting the two vertices, and the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or the split read.

[0066] Traditional linear alignment methods cannot accurately reveal the diversity and complexity of the genome. Therefore, the present invention analyzes the clustering of complex combination variations within the genome by constructing a "genome structural variation map".

[0067] When constructing the genomic structural variation graph, vertices are connected by edges, and edges are added based on the similarity between reads. Among them, similarity is determined by comparing the difference in insert length, the depth of alignment to the genome, and the left-right alignment distance of the read. For example, if two reads have similar insert lengths and their alignment positions on the genome are close, then there may be an edge between them. The relationship between the reads corresponding to each vertex in the graph indicates that there may be complex combinatorial variation at certain locations in the genome, which is a key clue when studying genomic structural variation.

[0068] It should be noted that a genome structure variation map represents a genome structure variation event, for example, a genome structure variation map is used to represent a variation event of insertion, deletion, inversion or translocation. There are as many genome structure variation maps as there are insertion variation events in the genome sequencing data of Hedou12 soybean; the same applies to other variation events.

[0069] By constructing the above-mentioned genome structural variation graph, we can more clearly observe the complex combination variation in the genome and identify those complex or subtle variation patterns that are difficult to find with traditional methods. In addition, the graph model can also help researchers understand the relationship between genomic variations, such as whether certain variations tend to appear in specific areas of the genome, or whether there is a certain degree of correlation between different types of variations.

[0070] Step 103: Identify the event type of the genome structure variation event represented by each genome structure variation graph, and generate a genome structure variation collection file corresponding to the Hedou12 soybean.

[0071] In an embodiment of the present invention, the genome structure variation collection file includes multiple genome structure variation graphs, variation event types corresponding to each variation graph, and read sets corresponding to each vertex of each variation graph. The genome structure variation collection file reveals the diverse complexity of genome variation, the event types of complex combination variation within the genome, and the relationships between genome variations.

[0072] Specifically, for each genome structural variation map, the alignment distance and insertion length of the inconsistent double-end reads and split reads to the reference genome are compared. If the alignment distance is less than the insertion length, the genome structural variation map generated by the aggregation of these inconsistent double-end reads or split reads corresponds to the event type of "insertion structural variation"; if the alignment distance is greater than the insertion length, the map generated by the aggregation of these inconsistent double-end reads or split reads corresponds to the event type of "deletion structural variation"; if the alignment distance and the insertion length are within a range of values, but one end read of the inconsistent double-end read is inverted, while the other end read does not change, it is the event type of "inversion structural variation", and the same applies to split reads.

[0073] The invention provides a method for identifying Hedou12 soybean genome structural variation. First, based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, the inconsistent double-end read set and the split read set corresponding to Hedou12 soybean are determined; the split read can reveal a large range of genome rearrangements, such as translocation and insertion or deletion of large fragments, while the inconsistent double-end read helps to locate the precise breakpoint of the variation, thereby providing direct variation evidence for subsequent genome structural variation analysis. Secondly, based on each inconsistent double-end read and each split read, at least one genome structural variation graph corresponding to Hedou12 soybean is constructed; wherein each genome structural variation graph represents a genome structural variation event corresponding to Hedou12 soybean, and the genome structural variation graph includes at least two vertices and an edge connecting the two vertices, and the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or split read; through the above-mentioned genome structural variation graph, it can be revealed that certain positions of the genome may have complex combination variations. That is, through this graph method, the complex combination variation in the genome can be clearly observed, and complex or subtle variation patterns that are difficult to discover with traditional methods can be identified; and the genome structure variation graph can reveal the relationship between genome variations, for example, whether certain variations tend to appear in a specific region of the genome, or whether there is a certain degree of correlation between different types of variations. Finally, by identifying the event type of the genome structure variation event represented by each genome structure variation graph, the genome structure variation collection file corresponding to Hedou12 soybean is generated, which reveals the diverse complexity of genome variation, the event type of complex combination variation within the genome, and the relationship between genome variations.

[0074] Optionally, before determining the inconsistent double-end read set and split read set corresponding to the Hedou12 soybean based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, the following steps need to be performed:

[0075] Performing data quality control analysis on the genome sequencing data and the reference genome sequencing data; the data quality control analysis is used to screen and filter the genome sequencing data and the reference genome sequencing data.

[0076] First of all, it should be explained that the reference genome sequencing data for Williams82 soybean can be obtained by splicing the second-generation genome sequencing data and the third-generation genome sequencing data corresponding to Williams82 soybean.

[0077] The second-generation genome sequencing data of Williams82 soybean has the advantages of high throughput and low error rate, which is suitable for the detailed assembly of large-scale data. However, the reads of the second-generation genome sequencing data are relatively short, which may lead to the loss of information in complex regions.

[0078] Although the third-generation genome sequencing data of Williams82 soybean provides more comprehensive genome coverage due to its long reads, its relatively high error rate is a challenge that needs to be overcome.

[0079] Therefore, the present invention improves the accuracy and completeness of the Williams82 reference genome by splicing the second-generation genome sequencing data and the third-generation genome sequencing data corresponding to Williams82 soybean.

[0080] In practical applications, in order to ensure the high quality and reliability of the genome sequencing data of diploid Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, data quality control analysis is first required. This is achieved through the following steps:

[0081] 1. Preliminary quality assessment

[0082] 1.1. Use the FastQC tool to perform quality assessment on genome sequencing data and reference genome sequencing data (also known as raw data), including: calculating the sequence length distribution of each read, assessing the base quality distribution, generating a quality score graph, calculating the GC content, and generating a GC content distribution graph.

[0083] 1.2. Record key indicators of quality reports, including low-quality read ratio, repeat sequence ratio, etc.

[0084] 2. Define quality control thresholds

[0085] 2.1、Set parameters:

[0086] For example: set the minimum Phred quality score (Q_threshold) = 20 and the minimum read length (L_threshold) = 50 bases.

[0087] 3. Filter low-quality reads

[0088] Traverse each read in the data set to perform a quality check, including: calculating the average Phred quality score of each read. If the average quality score is less than Q_threshold, mark the read as a low-quality sample and record the discarded read data and proportion.

[0089] 4. Remove the adapter sequence

[0090] 4.1. Use the Cutadapt tool to trim the adapter sequence, including: inputting the original FASTQ data, setting the adapter sequence information and trimming parameters, performing adapter trimming, and generating a new FASTQ file.

[0091] 4.2. Traverse each read, including: checking the adapter sequence in the trimmed read, and if the adapter sequence still exists, further trim the read to remove the adapter sequence.

[0092] 5. Remove short reads

[0093] Traverse the filtered data set, including: Check the length of each read. If the length of the read is less than L_threshold, mark the read as a short read and discard it, and count the number and proportion of short reads.

[0094] 6. Remove duplicate reads

[0095] 6.1. Use Picard to identify duplicate reads, including: Input the filtered FASTQ file. Perform deduplication to identify and mark duplicate reads.

[0096] 6.2. Generate a deduplicated FASTQ file and record deduplication statistics (such as the number of duplicate reads).

[0097] 7. Conduct quality re-inspection

[0098] Use FastQC to recheck the quality of the filtered data set, including: generating a new quality report, comparing the quality report with the initial data, and confirming the filtering effect.

[0099] 8. Record filtering parameters and processes

[0100] 8.1. Create a log file to record all filtering parameters and steps, including: quality control threshold settings; number of low-quality reads, number of short reads, and deduplication statistics.

[0101] 8.2. Ensure that records are traceable to facilitate subsequent analysis and verification.

[0102] 9. Return high-quality filtered sequencing data

[0103] 9.1. Output the final high-quality filtered FASTQ file.

[0104] 9.2. Ensure that relevant quality reports and log files are saved for subsequent research.

[0105] Optionally, after performing data quality control analysis on the genome sequencing data and the reference genome sequencing data, it is necessary to determine the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean based on the genome sequencing data of the Hedou12 soybean and the reference genome sequencing data of the Williams82 soybean, specifically including the following steps:

[0106] Step 1), aligning the genome sequencing data to the reference genome sequencing data to obtain the inconsistent double-end read set and the consistent double-end read set; the consistent double-end read set includes multiple consistent double-end reads, and the two ends of the consistent double-end reads have the same position, direction and distance in the genome sequencing data and the reference genome sequencing data.

[0107] In practical applications, the genome sequencing data of Hedou12 soybean was aligned to the reference genome sequencing data of Williams82 soybean using BWA software to obtain the inconsistent double-end read set D1 and the consistent double-end read set D2.

[0108] Step 2), aligning the inconsistent double-end read set and the unaligned reads in the consistent double-end read set to the reference genome sequencing data to obtain the split read set.

[0109] Specifically, the unaligned reads in D1 and D2 were aligned to the reference genome sequencing data of Williams82 using the mrsFast software to obtain the split read set, which can be expressed as

[0110] In the above implementation, the split read set can reveal a large range of genomic rearrangements, and the discordant double-end read set helps to locate the precise breakpoint of the variation. The above read set is used as a research object to provide data support for subsequent analysis of variation mechanisms and potential biological impacts.

[0111] Optionally, constructing at least one genomic structural variation map corresponding to the Hedou12 soybean based on each of the inconsistent double-end reads and each of the split reads can be specifically achieved by the following steps:

[0112] The inconsistent double-end reads or the split reads whose similarity reaches the second preset threshold are used as the vertices and two adjacent vertices are connected by the edges to generate the genomic structure variation graph; the edges are provided with weight values, and the weight values ​​are used to characterize the probability that the two adjacent vertices belong to the same genomic structure variation event.

[0113] In an embodiment of the present invention, a genome structural variation graph represents a structural variation in the genome; wherein the vertices of the genome structural variation graph represent inconsistent double-end reads or split reads in the genome, and these reads indicate that complex combinatorial variations may exist at certain positions in the genome.

[0114] In the actual process of constructing the genome structure variation graph, in order to more accurately reflect the association strength between reads, it is necessary to add a weight to the edge to measure the importance between two vertices.

[0115] The weight is calculated using a Bayesian probability model, which takes into account the difference in insert length, the depth of alignment to the reference genome sequencing data, and the left-right alignment distance of the read, thereby assigning a probability weight to each edge. The weight value of this weight represents the probability that two inconsistent double-end reads or split reads belong to the same structural variation event.

[0116] The weight value is calculated by the following formula (1):

[0117]

[0118] Among them, E ij Indicates that the read fragments i and j corresponding to the two vertices support the same genomic structural variation event; d ij represents the difference in insertion length between the read fragments i and j on the reference genome sequencing data; h represents the depth of alignment of the read fragments i and j to the reference genome sequencing data; P(E ij |D ij =d ij , H = h) means that the difference in insertion length is d ij and the weight value when the depth is h; P(D ij =d ij |E ij , H=h) means that in E ij The insertion length difference is d ij The conditional probability of h for d ij The influence of P(E ij |H=h) means that when the depth is h, Eij Prior probability of occurrence; P(D ij =d ij |H=h) means that when the depth is h, the insertion length difference is d ij The marginal probability of .

[0119] By using the above-mentioned method of constructing a genome structural variation map, complex combined variations in the genome can be observed more clearly, and complex or subtle variation patterns that are difficult to detect with traditional methods can be identified. In addition, the genome structural variation map can also help researchers understand the relationship between genomic variations, such as whether certain variations tend to appear in specific regions of the genome, or whether there is a certain degree of correlation between different types of variations.

[0120] Optionally, after constructing at least one genome structure variation map corresponding to the Hedou12 soybean, the authenticity of each genome structure variation map needs to be tested.

[0121] Specifically, current high-throughput sequencing technology has greatly promoted the study of genomic structural variation, but how to accurately determine the authenticity of genomic structural variation maps remains a challenge. This is mainly because second-generation high-throughput sequencing data are often accompanied by complex noise backgrounds, such as sequencing errors and alignment problems caused by repeated sequences.

[0122] Therefore, the present invention adopts the maximum likelihood estimation method to define the existence of variation as a probability problem to determine whether the variation events represented by the variation graphs of each genomic structure are real. This is specifically achieved by the following steps:

[0123] Step 1), for each genome structure variation graph, based on the observed data of the inconsistent double-end reads or the split reads corresponding to each of the vertices, construct a maximum likelihood estimation function corresponding to the genome structure variation graph; the estimated value corresponding to the maximum likelihood estimation function represents the authenticity of the genome structure variation graph;

[0124] The observed data include the alignment quality, coverage depth, and number of reads supporting the variation of the inconsistent double-end reads or the split reads aligned to the reference genome sequencing data.

[0125] Step 2), removing the genome structure variation graphs whose estimated values ​​are lower than the third preset threshold, to obtain at least one genome structure variation graph after authenticity detection;

[0126] The maximum likelihood estimation function is expressed by the following formula (2):

[0127] L(q, d, n|θ) = f(q|θ q )f(d|θ d )f(n|θ n ) (2)

[0128] Where q represents the alignment quality; d represents the coverage depth; n represents the number of reads supporting the variation; L(q, d, n|θ) represents the estimated value; f(q|θ q ) represents the probability distribution function of the alignment quality, with parameter θ q ; f(d|θ d ) represents the probability distribution function of the coverage depth, and the parameter is θ d ; f(n|θ n ) represents the probability distribution function of the number of reads supporting the variation, with parameter θ n .

[0129] In the above maximum likelihood estimation, the goal is to maximize the value of the likelihood function L(q, d, n|θ) with respect to the model parameter θ, which is expressed by the following formula (3):

[0130]

[0131] The maximum likelihood estimation parameter value θ is calculated through the gradient descent method to estimate the probability that a genomic structural variation map is real.

[0132] It should be noted that, due to the complex and changeable situations when the inconsistent paired-end reads and split reads of Hedou12 soybean are aligned to the Williams82 reference genome sequencing data, when designing the probability distribution function of the alignment quality, it is necessary to consider the actual situation of continuous base mismatches, continuous interval base mismatches, continuous gaps and continuous interval gaps when the inconsistent paired-end and split reads are aligned to the Williams82 reference genome sequencing data. In order to reduce the impact of these factors on structural variation, f(d|θ d ) can be expressed by the following formula (4):

[0133]

[0134] Among them, Q represents a structure variation set V i The average value of the base values ​​of all the inconsistent paired-end reads and split reads in the case of gaps and base mismatches;

[0135] Q can be expressed by the following formula (5):

[0136]

[0137] in, express The length of the alignment at the left end; express The average value of the base values ​​aligned on the left end; express Sequence similarity of the left end alignment; express the length of the alignment on the right side; express The average value of the base values ​​aligned on the right side; express Sequence similarity of the right-hand side alignment.

[0138] Therefore, a structure variation set V i The probability distribution function of the comparison quality can be expressed by the following formula (6):

[0139]

[0140] Optionally, after obtaining at least one authenticity-tested genome structure variation map, each genome structure variation map needs to be subjected to false positive filtering to remove erroneous genome structure variation maps.

[0141] In order to filter out false positive genome structure variation graphs, the present invention adopts a set coverage filtering method. The reason is that the sequencing error during the second generation sequencing and the error introduced in the data preprocessing stage will lead to the ambiguity of the base value, which may affect the accuracy of the result. Secondly, in the reference genome splicing process, the splicing algorithm may introduce errors of repeated fragments and border filling if it handles bulge improperly. In addition, when the structure variation clustering is performed, the comparison software may have errors such as errors in the alignment of double-end reads to different positions and continuous base mismatches when aligning, which will also affect the accuracy of the result.

[0142] Therefore, in order to effectively filter out false positive structural variations, it is necessary to comprehensively consider the above factors and design a weighted set coverage filtering method. The accuracy and reliability of structural variations can be improved by carefully analyzing and processing these potential errors.

[0143] The present invention adopts LP-rounding (Linear Programming-Rounding) to approximate the solution set coverage and filter the false positive genome structure variation graph. It is specifically achieved by the following steps:

[0144] Step 1), based on the LP-rounding algorithm, identifying the false positive genome structure variation graphs with detection errors in the genome structure variation graphs after the authenticity detection;

[0145] Step 2), removing the false positive genome structure variation map.

[0146] In an embodiment of the present invention, the solution process of step 1) above can be specifically divided into the following steps [a]-[d]:

[0147] Step [a], integer programming, is to convert the set cover literal into integer programming, which is expressed by the following formula (7):

[0148]

[0149] Among them, P(V i ) refers to V i The set is the probability of structural variation, which is a set r (Γ=Γ1∪Γ2={V1,...,V k}) is a subset of

[0150] The constraint condition is expressed by the following formula (8):

[0151]

[0152] Among them, x i ∈{0,1}, if x i =1Select V i , otherwise do not select.

[0153] Step [b], relax the integer programming to linear programming, which is expressed by the following formula (9):

[0154]

[0155] Step [c]: For the linear programming in step [b], use the Ellipsoid algorithm to solve and obtain It is expressed by the following formula (10):

[0156]

[0157] Step [d]: Get the solution set It is expressed by the following formula (11):

[0158]

[0159] in, f represents the number of times an element appears in a subset.

[0160] After completing the above four steps [a]-[d], a set of strictly screened genomic structural variation maps can be obtained. These variation maps have effectively removed false positives, thus ensuring their high credibility. This achievement provides a solid foundation for subsequent Hedou12 genomics research and can more accurately understand the structure and function of the genome. In addition, these reliable structural variation data will greatly promote the research and application of Hedou12-related pests and diseases, and help researchers and agricultural practitioners develop more effective prevention and control strategies. This not only deepens our understanding of the Hedou12 genome, but also promotes the development of agricultural biotechnology and provides a favorable tool for meeting future challenges.

[0161] Figure 2 This is the second flow chart of the method for identifying Hedou12 soybean genome structural variation provided by the present invention, see Figure 2 As shown, the method includes steps 201 to 209, wherein:

[0162] Step 201: Perform data quality control analysis on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean; wherein the data quality control analysis is used to screen and filter the genome sequencing data and the reference genome sequencing data.

[0163] Step 202: align the genome sequencing data to the reference genome sequencing data to obtain an inconsistent double-end read set and a consistent double-end read set.

[0164] Step 203: Align the inconsistent double-end read set and the unaligned reads in the consistent double-end read set to the reference genome sequencing data to obtain a split read set.

[0165] Step 204: Use inconsistent double-end reads or split reads whose similarity reaches a second preset threshold as vertices and connect two adjacent vertices with edges to generate a genome structure variation graph; wherein the edges are set with weight values, and the weight values ​​are used to characterize the probability that two adjacent vertices belong to the same genome structure variation event.

[0166] Step 205: For each genome structure variation graph, a maximum likelihood estimation function corresponding to the genome structure variation graph is constructed based on the observed data of inconsistent double-end reads or split reads corresponding to each vertex; the estimated value corresponding to the maximum likelihood estimation function represents the authenticity of the genome structure variation graph.

[0167] Step 206: remove the genome structure variation graphs whose estimated values ​​are lower than the third preset threshold, and obtain at least one genome structure variation graph after authenticity detection.

[0168] Step 207: Based on the LP-rounding algorithm, identify the false positive genome structure variation graphs with detection errors in the genome structure variation graphs after each authenticity detection.

[0169] Step 208: Remove the false positive genome structure variation graph.

[0170] Step 209: Identify the event type of the genome structure variation event represented by each genome structure variation graph, and generate a genome structure variation collection file corresponding to Hedou12 soybean.

[0171] The following is a description of the identification device based on the Hedou12 soybean genome structural variation provided by the present invention. The identification device based on the Hedou12 soybean genome structural variation described below and the identification method based on the Hedou12 soybean genome structural variation described above can be referenced to each other. Figure 3 : is a schematic diagram of the structure of the identification device based on the Hedou12 soybean genome structural variation provided by the present invention, such as Figure 3 As shown, the identification device 300 based on the Hedou12 soybean genome structural variation includes: a determination module 301, a first construction module 302, and a first identification module 303, wherein:

[0172] A determination module 301 is used to determine the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean based on the genome sequencing data of the Hedou12 soybean and the reference genome sequencing data of the Williams82 soybean;

[0173] The inconsistent double-end read set includes a plurality of inconsistent double-end reads, and the positions, directions and distances of the two ends of the inconsistent double-end reads in the genome sequencing data and the reference genome sequencing data are different; the split read set includes a plurality of split reads, the first part of the split read is aligned with the first region of the reference genome, the second part of the split read is aligned with the second region of the reference genome, and the distance between the second region and the first region is greater than a first preset threshold;

[0174] The first construction module 302 is used to construct at least one genomic structural variation graph corresponding to the Hedou12 soybean based on each of the inconsistent double-end reads and each of the split reads; each genomic structural variation graph represents a genomic structural variation event corresponding to the Hedou12 soybean, and the genomic structural variation graph includes at least two vertices and an edge connecting the two vertices, and the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or the split read;

[0175] The first identification module 303 is used to identify the event type of the genome structure variation event represented by each genome structure variation map, and generate a genome structure variation set file corresponding to the Hedou12 soybean.

[0176] The identification device based on the Hedou12 soybean genome structural variation provided by the present invention, firstly, based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, the inconsistent double-end read set and the split read set corresponding to Hedou12 soybean are determined; the split read can reveal a large range of genome rearrangements, such as translocation and insertion or deletion of large fragments, while the inconsistent double-end read is helpful to locate the precise breakpoint of the variation, thereby providing direct variation evidence for subsequent genome structural variation analysis. Secondly, based on each inconsistent double-end read and each split read, at least one genome structural variation graph corresponding to Hedou12 soybean is constructed; wherein, a genome structural variation graph represents a genome structural variation event, and the genome structural variation graph includes at least two vertices and an edge connecting the two vertices, and the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or split read; through the above-mentioned genome structural variation graph, it can be revealed that certain positions of the genome may have complex combination variations. That is, through this graph method, the complex combination variation in the genome can be clearly observed, and complex or subtle variation patterns that are difficult to discover with traditional methods can be identified; and the genome structure variation graph can reveal the relationship between genome variations, for example, whether certain variations tend to appear in a specific region of the genome, or whether there is a certain degree of correlation between different types of variations. Finally, by identifying the event type of the genome structure variation event represented by each genome structure variation graph, the genome structure variation collection file corresponding to Hedou12 soybean is generated, which reveals the diverse complexity of genome variation, the event type of complex combination variation within the genome, and the relationship between genome variations.

[0177] Optionally, the first building module 302 is further used to:

[0178] The inconsistent double-end reads or split reads whose similarity reaches the second preset threshold are used as the vertices and two adjacent vertices are connected by the edges to generate the genome structure variation graph; the edges are provided with weight values, and the weight values ​​are used to characterize the probability that the two adjacent vertices belong to the same genome structure variation event;

[0179] The weight value is calculated by the following formula (1):

[0180]

[0181] Among them, E ij Indicates that the read fragments i and j corresponding to the two vertices support the same genomic structural variation event; d ij represents the difference in insertion length between the read fragments i and j on the reference genome sequencing data; h represents the depth of alignment of the read fragments i and j to the reference genome sequencing data; P(E ij |D ij =d ij , H = h) means that the difference in insertion length is d ij and the weight value when the depth is h; P(D ij =d ij |E ij , H=h) means that in E ij The insertion length difference is d ij The conditional probability of h for d ij The influence of P(E ij |H=h) means that when the depth is h, E ij Prior probability of occurrence; P(D ij =d ij |H=h) means that when the depth is h, the insertion length difference is d ij The marginal probability of .

[0182] Optionally, the determining module 301 is further configured to:

[0183] Aligning the genome sequencing data to the reference genome sequencing data to obtain the inconsistent double-end read set and the consistent double-end read set; the consistent double-end read set includes a plurality of consistent double-end reads, and the two ends of the consistent double-end reads have the same position, direction and distance in the genome sequencing data and the reference genome sequencing data;

[0184] The inconsistent double-end read set and the unaligned reads in the consistent double-end read set are aligned to the reference genome sequencing data to obtain the split read set.

[0185] Optionally, the device further comprises:

[0186] The second construction module is used to construct a maximum likelihood estimation function corresponding to each genome structure variation graph based on the observed data of the inconsistent double-end reads or the split reads corresponding to each vertex; the estimated value corresponding to the maximum likelihood estimation function represents the authenticity of the genome structure variation graph;

[0187] The observed data include the alignment quality, coverage depth and number of reads supporting the variation of the inconsistent double-end reads or the split reads aligned to the reference genome sequencing data;

[0188] A first removal module is used to remove the genome structure variation graphs whose estimated values ​​are lower than a third preset threshold value, so as to obtain at least one genome structure variation graph after authenticity detection;

[0189] The maximum likelihood estimation function is expressed by the following formula (2):

[0190] L(q, d, n|θ) = f(q|θ q )f(d|θ d )f(n|θ n ) (2)

[0191] Where q represents the alignment quality; d represents the coverage depth; n represents the number of reads supporting the variation; L(q, d, n|θ) represents the estimated value; f(q|θ q ) represents the probability distribution function of the alignment quality, with parameter θ q ; f(d|θ d ) represents the probability distribution function of the coverage depth, and the parameter is θ d ; f(n|θ n ) represents the probability distribution function of the number of reads supporting the variation, with parameter θ n .

[0192] Optionally, the device further comprises:

[0193] A second identification module is used to identify false positive genome structure variation graphs with detection errors in each of the genome structure variation graphs after authenticity detection based on an LP-rounding algorithm;

[0194] The second removal module is used to remove the false positive genome structure variation map.

[0195] Optionally, the device further comprises:

[0196] A data quality control analysis module is used to perform data quality control analysis on the genome sequencing data and the reference genome sequencing data; the data quality control analysis is used to screen and filter the genome sequencing data and the reference genome sequencing data.

[0197] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute an identification method based on the structural variation of the Hedou12 soybean genome, the method comprising: based on the genome sequencing data of the Hedou12 soybean and the reference genome sequencing data of the Williams82 soybean, determining the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean; wherein the inconsistent double-end read set includes a plurality of inconsistent double-end reads, the two ends of the inconsistent double-end reads having different positions, directions and distances in the genome sequencing data and the reference genome sequencing data; the split read set includes a plurality of split reads, the first part of the split read is aligned with the first region of the reference genome, and the first part of the split read is aligned with the first region of the reference genome. The second part is aligned with the second region of the reference genome, and the distance between the second region and the first region is greater than a first preset threshold; based on each of the inconsistent double-end reads and each of the split reads, at least one genome structure variation map corresponding to the Hedou12 soybean is constructed; each genome structure variation map represents a genome structure variation event corresponding to the Hedou12 soybean, and the genome structure variation map includes at least two vertices and an edge connecting the two vertices, and the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or the split read; the event type of the genome structure variation event represented by each genome structure variation map is identified, and a genome structure variation collection file corresponding to the Hedou12 soybean is generated.

[0198] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0199] On the other hand, the present invention also provides a computer program product, which includes a computer program, and the computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the identification method based on the Hedou12 soybean genome structural variation provided by the above-mentioned methods, and the method includes: based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, determining the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean; wherein the inconsistent double-end read set includes multiple inconsistent double-end reads, and the two ends of the inconsistent double-end read are different in position, direction and distance in the genome sequencing data and the reference genome sequencing data; the split read set includes multiple split reads, and the split reads The first part of the ad is aligned with the first region of the reference genome, and the second part of the split read is aligned with the second region of the reference genome, and the distance between the second region and the first region is greater than a first preset threshold; based on each of the inconsistent double-end reads and each of the split reads, at least one genome structure variation graph corresponding to the Hedou12 soybean is constructed; each genome structure variation graph represents a genome structure variation event corresponding to the Hedou12 soybean, and the genome structure variation graph includes at least two vertices and an edge connecting the two vertices, and the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or the split read; the event type of the genome structure variation event represented by each genome structure variation graph is identified, and a genome structure variation collection file corresponding to the Hedou12 soybean is generated.

[0200] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the identification method based on the Hedou12 soybean genome structural variation provided by the above-mentioned methods, the method comprising: based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, determining the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean; wherein the inconsistent double-end read set includes a plurality of inconsistent double-end reads, and the two ends of the inconsistent double-end reads are at different positions, directions and distances in the genome sequencing data and the reference genome sequencing data; the split read set includes a plurality of split reads, and the first part of the split read is inconsistent with the reference genome sequencing data. The first region of the reference genome is aligned, the second part of the split read is aligned with the second region of the reference genome, and the distance between the second region and the first region is greater than a first preset threshold; based on each of the inconsistent double-end reads and each of the split reads, at least one genome structure variation graph corresponding to the Hedou12 soybean is constructed; each genome structure variation graph represents a genome structure variation event corresponding to the Hedou12 soybean, the genome structure variation graph comprises at least two vertices and an edge connecting the two vertices, the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or the split read; the event type of the genome structure variation event represented by each genome structure variation graph is identified, and a genome structure variation collection file corresponding to the Hedou12 soybean is generated.

[0201] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0202] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0203] 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 make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying structural variation of Hedou12 soybean genome, characterized in that: include: Based on the genome sequencing data of Hedou12 soybean and the reference genome sequencing data of Williams82 soybean, determining the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean; The inconsistent double-end read set includes a plurality of inconsistent double-end reads, and the positions, directions and distances of the two ends of the inconsistent double-end reads in the genome sequencing data and the reference genome sequencing data are different; the split read set includes a plurality of split reads, the first part of the split read is aligned with the first region of the reference genome, the second part of the split read is aligned with the second region of the reference genome, and the distance between the second region and the first region is greater than a first preset threshold; Based on each of the inconsistent double-end reads and each of the split reads, at least one genomic structural variation graph corresponding to the Hedou12 soybean is constructed; each genomic structural variation graph represents a genomic structural variation event corresponding to the Hedou12 soybean, the genomic structural variation graph includes at least two vertices and an edge connecting the two vertices, the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or the split read; The event type of the genome structure variation event represented by each genome structure variation map is identified, and a genome structure variation collection file corresponding to the Hedou12 soybean is generated.

2. The method for identifying the structural variation of Hedou12 soybean genome according to claim 1, characterized in that: The step of constructing at least one genomic structural variation map corresponding to the Hedou12 soybean based on each of the inconsistent double-end reads and each of the split reads comprises: The inconsistent double-end reads or split reads whose similarity reaches the second preset threshold are used as the vertices and two adjacent vertices are connected by the edges to generate the genome structure variation graph; the edges are provided with weight values, and the weight values ​​are used to characterize the probability that the two adjacent vertices belong to the same genome structure variation event; The weight value is calculated by the following formula (1): Among them, E ij Indicates that the read fragments i and j corresponding to the two vertices support the same genomic structural variation event; d ij represents the difference in insertion length between the read fragments i and j on the reference genome sequencing data; h represents the depth of alignment of the read fragments i and j to the reference genome sequencing data; P(E ij |D ij =d ij , H = h) means that the difference in insertion length is d ij and the weight value when the depth is h; P(D ij =d ij |E ij , H=h) means that in E ij The insertion length difference is d ij The conditional probability of h for d ij The influence of P(E ij |H=h) means that when the depth is h, E ij Prior probability of occurrence; P(D ij =d ij |H=h) means that when the depth is h, the insertion length difference is d ij The marginal probability of .

3. The method for identifying the structural variation of Hedou12 soybean genome according to claim 1, characterized in that: The method of determining the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean based on the genome sequencing data of the Hedou12 soybean and the reference genome sequencing data of the Williams82 soybean includes: Aligning the genome sequencing data to the reference genome sequencing data to obtain the inconsistent double-end read set and the consistent double-end read set; the consistent double-end read set includes a plurality of consistent double-end reads, and the two ends of the consistent double-end reads have the same position, direction and distance in the genome sequencing data and the reference genome sequencing data; The inconsistent double-end read set and the unaligned reads in the consistent double-end read set are aligned to the reference genome sequencing data to obtain the split read set.

4. The method for identifying the structural variation of Hedou12 soybean genome according to any one of claims 1 to 3, characterized in that: After constructing at least one genome structure variation map corresponding to the Hedou12 soybean, the method further comprises: For each genome structure variation graph, based on the observed data of the inconsistent double-end reads or the split reads corresponding to each of the vertices, a maximum likelihood estimation function corresponding to the genome structure variation graph is constructed; the estimated value corresponding to the maximum likelihood estimation function represents the authenticity of the genome structure variation graph; The observed data include the alignment quality, coverage depth and number of reads supporting the variation of the inconsistent double-end reads or the split reads aligned to the reference genome sequencing data; The genome structure variation graphs whose estimated values ​​are lower than a third preset threshold are removed to obtain at least one genome structure variation graph after authenticity detection; The maximum likelihood estimation function is expressed by the following formula (2): L(q, d, n|θ)=f(q|θ q )f(d|θ d )f(n|θ n ) (2) Where q represents the alignment quality; d represents the coverage depth; n represents the number of reads supporting the variation; L(q, d, n|θ) represents the estimated value; f(q|θ q ) represents the probability distribution function of the alignment quality, with parameter θ q ; f(d|θ d ) represents the probability distribution function of the coverage depth, and the parameter is θ d ; f(n|θ n ) represents the probability distribution function of the number of reads supporting the variation, with parameter θ n .

5. The method for identifying Hedou12 soybean genome structural variation according to claim 4, characterized in that: After obtaining at least one authenticity-tested genome structure variation graph, the method further comprises: Based on the LP-rounding algorithm, identifying the false positive genome structure variation graphs detected incorrectly in the genome structure variation graphs after the authenticity detection; The false positive genome structure variation map is removed.

6. The method for identifying structural variation of Hedou12 soybean genome according to any one of claims 1 to 3, characterized in that: Before determining the inconsistent double-end read set and split read set corresponding to the Hedou12 soybean based on the genome sequencing data of the Hedou12 soybean and the reference genome sequencing data of the Williams82 soybean, the method further includes: Performing data quality control analysis on the genome sequencing data and the reference genome sequencing data; the data quality control analysis is used to screen and filter the genome sequencing data and the reference genome sequencing data.

7. A device for identifying structural variation of Hedou12 soybean genome, characterized in that: include: A determination module, for determining the inconsistent double-end read set and the split read set corresponding to the Hedou12 soybean based on the genome sequencing data of the Hedou12 soybean and the reference genome sequencing data of the Williams82 soybean; The inconsistent double-end read set includes a plurality of inconsistent double-end reads, and the positions, directions and distances of the two ends of the inconsistent double-end reads in the genome sequencing data and the reference genome sequencing data are different; the split read set includes a plurality of split reads, the first part of the split read is aligned with the first region of the reference genome, the second part of the split read is aligned with the second region of the reference genome, and the distance between the second region and the first region is greater than a first preset threshold; A first construction module is used to construct at least one genomic structural variation graph corresponding to the Hedou12 soybean based on each of the inconsistent double-end reads and each of the split reads; each genomic structural variation graph represents a genomic structural variation event corresponding to the Hedou12 soybean, the genomic structural variation graph includes at least two vertices and an edge connecting the two vertices, the similarity between the two mutually connected vertices reaches a second preset threshold, and the vertex is the inconsistent double-end read or the split read; The first identification module is used to identify the event type of the genome structure variation event represented by each genome structure variation map, and generate a genome structure variation set file corresponding to the Hedou12 soybean.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for identifying Hedou12 soybean genome structure variation as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for identifying Hedou12 soybean genome structure variation as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for identifying Hedou12 soybean genome structure variation as described in any one of claims 1 to 6 is implemented.

Images