Genome splicing method and device based on Williams82 soybeans

Abstract

The invention provides a genome splicing method and device based on Williams82 soybeans, and relates to the technical field of gene splicing.The method comprises the steps that error basic groups in initial third-generation genome sequencing data are corrected based on second-generation genome sequencing data of the Williams82 soybeans, and target third-generation genome sequencing data are obtained; splicing the second generation genome sequencing data to generate a contigs sequence set; the contigs sequence set and the target third generation genome sequencing data are subjected to genome splicing, and a genome splicing set scaftig of the Williams82 soybeans is generated. In the method, the second generation genome sequencing data and the target third generation genome sequencing data are spliced by combining the advantages of the second generation genome sequencing data and the target third generation genome sequencing data, so that the genome splicing set which is low in error rate and has the characteristic of long read length is obtained, and the accuracy and integrity of splicing the Williams82 soybean genome are improved.

Description

Technical Field

[0001] The invention relates to the technical field of gene splicing, and in particular to a genome splicing method and device based on Williams82 soybean. Background Art

[0002] The Williams 82 soybean genome is one of the reference genomes widely used in soybean research. The quality of its genome assembly is crucial for the analysis of soybean genome structure and variation.

[0003] In the related art, the Williams82 genome is usually spliced ​​based on the second-generation genome sequencing data. However, the read length of the second-generation genome sequencing data is relatively short, which may lead to the loss of information in complex regions, thereby causing certain incompleteness and errors in genome splicing.

[0004] Therefore, how to improve the accuracy and completeness of Williams82 soybean genome assembly is an urgent problem to be solved. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a genome splicing method and device based on Williams82 soybean.

[0006] The present invention provides a genome assembly method based on Williams82 soybean, comprising:

[0007] Based on the second-generation genome sequencing data of the Williams82 soybean, the erroneous bases in the initial third-generation genome sequencing data are corrected to obtain target third-generation genome sequencing data; the base accuracy of the second-generation genome sequencing data is higher than the base accuracy of the initial third-generation genome sequencing data;

[0008] Splicing the second-generation genome sequencing data to generate a contig sequence set;

[0009] The contigs sequence set is genome spliced ​​with the target third-generation genome sequencing data to generate the Williams82 soybean genome splicing set scaftig.

[0010] Optionally, the second-generation genome sequencing data based on the Williams82 soybean is used to correct erroneous bases in the initial third-generation genome sequencing data to obtain target third-generation genome sequencing data, including:

[0011] Step A: for each first read length in the initial third-generation genome sequencing data, the first read is divided according to the length of the second read in the second-generation genome sequencing data to obtain a string set; the length of each string in the string set is the same as the length of the second read;

[0012] Step B: for each character string in the character string set, calculating the edit distance between the character string and the first read; when the edit distance reaches a first threshold, taking the first read as a read to be corrected;

[0013] Step C: performing base comparison between each character string and the read to be corrected, counting the number of successful comparisons between each base in each character string and the read to be corrected and the total number of comparisons; when the ratio of the number of successful comparisons to the total number of comparisons reaches a second threshold, correcting the read to be corrected to obtain a corrected read;

[0014] Step D: looping through steps AC to obtain the target third-generation genome sequencing data, wherein the target third-generation genome sequencing data includes a plurality of corrected reads.

[0015] Optionally, the step of splicing the second-generation genome sequencing data to generate a contig sequence set includes:

[0016] Decomposing each second read in the second-generation genome sequencing data into a plurality of fixed-length nucleotide strings k-mers; wherein the length of each k-mer is k;

[0017] Constructing an initial De Bruijn graph based on each k-mer; wherein each node in the initial De Bruijn graph is a k-mer, and the edges are overlaps between adjacent k-mers;

[0018] For each node in the initial De Bruijn graph, remove the node whose first coverage is less than the coverage threshold; wherein the first coverage is determined based on the sequencing depth, sequencing read length and evaluation of sequencing quality of the second-generation genome sequencing data, and the coverage threshold is determined based on the Poisson statistical method;

[0019] Based on each of the nodes, identifying at least one linear path in the initial De Bruijn graph; wherein the node corresponding to the starting point of each linear path has only one input, and the node corresponding to the end point has only one output;

[0020] For each linear path, merge all nodes on the linear path to generate a target node, where the target node represents the linear path;

[0021] Based on each of the target nodes, generating an optimized De Bruijn graph;

[0022] Extracting at least one of an Eulerian path and a Hamiltonian path from the optimized De Bruijn graph;

[0023] Based on at least one of the Eulerian path and the Hamiltonian path, the contigs sequence set is generated.

[0024] Optionally, after generating the optimized De Bruijn graph, the method further includes:

[0025] Detecting whether there is a bubble path in the optimized De Bruijn graph based on a depth-first search (DFS) algorithm, where the bubble path is a branch path that branches and re-merges in the optimized De Bruijn graph;

[0026] In the case where there are multiple bubble paths in the optimized De Bruijn graph, based on at least one of the second coverage of each target node in each bubble path and the sequence length corresponding to the bubble path, determine the bubble paths that need to be removed in each bubble path;

[0027] The bubble path that needs to be removed is removed.

[0028] Optionally, the contigs sequence set is subjected to genome splicing with the target third-generation genome sequencing data to generate the Williams82 soybean genome splicing set scaftig, comprising:

[0029] Segmenting the contigs sequence set and the target third-generation genome sequencing data to obtain a plurality of first gene sequence fragments corresponding to the contigs sequence set and a plurality of second gene sequence fragments corresponding to the target third-generation genome sequencing data;

[0030] Constructing a string graph based on the overlapping relationship between each of the first gene sequence fragments and the second gene sequence fragments; the string graph is used to represent the gene sequence structure after each of the first gene sequence fragments and each of the second gene sequence fragments are spliced;

[0031] The gaps in the string graph are filled using at least one corrected read in the target third-generation genome sequencing data to generate the scaftig.

[0032] Optionally, before correcting the erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of the Williams82 soybean, the method further comprises:

[0033] Performing data quality control analysis on the second-generation genome sequencing data and the initial third-generation genome sequencing data; the data quality control analysis is used to screen and filter the second-generation genome sequencing data and the initial third-generation genome sequencing data.

[0034] The present invention also provides a genome splicing device based on Williams82 soybean, comprising:

[0035] A correction module, used for correcting erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of the Williams82 soybean to obtain target third-generation genome sequencing data; the base accuracy of the second-generation genome sequencing data is higher than the base accuracy of the initial third-generation genome sequencing data;

[0036] A generation module, used for splicing the second-generation genome sequencing data to generate a contig sequence set;

[0037] The splicing module is used to perform genome splicing on the contigs sequence set and the target third-generation genome sequencing data to generate the genome splicing set scaftig of the Williams82 soybean.

[0038] 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 genome assembly method based on Williams82 soybean as described above is implemented.

[0039] 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 genome assembly method based on Williams82 soybean as described in any one of the above is implemented.

[0040] 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 methods for genome assembly based on Williams82 soybean.

[0041] The genome splicing method and device based on Williams82 soybean provided by the present invention provide more comprehensive genome coverage because the initial third-generation genome sequencing data of Williams82 soybean has a long read length, which can better solve the splicing problem of complex genome regions, but also has a relatively high error rate. Therefore, the present invention first corrects the erroneous bases in the initial third-generation genome sequencing data with a high error rate based on the high-precision second-generation genome sequencing data of Williams82 soybean to obtain the target third-generation genome sequencing data, and then splices the second-generation genome sequencing data to generate a contig sequence set, and performs genome splicing on the contig sequence set and the target third-generation genome sequencing data to generate a genome splicing set scaftig of Williams82 soybean. Through the above method, the advantages of the second-generation genome sequencing data and the target third-generation genome sequencing data are combined to splice the two, effectively making up for the shortcomings of a single sequencing technology, and obtaining a genome splicing set scaftig with a low error rate and long read length, which improves the accuracy and completeness of the splicing of the Williams82 soybean genome, and can more accurately restore the structure of the Williams82 soybean genome, laying a solid foundation for the subsequent identification and analysis of complex combinatorial variations. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] 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.

[0043] Figure 1 It is a schematic diagram of the process of the genome assembly method based on Williams82 soybean provided by the present invention;

[0044] Figure 2 It is a schematic diagram of data quality control analysis provided by the present invention;

[0045] Figure 3 It is a schematic diagram of the structure of the genome splicing device based on Williams82 soybean provided by the present invention;

[0046] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] In order to facilitate a clearer understanding of the embodiments of the present application, some relevant knowledge is first introduced as follows.

[0049] In soybean genome research, the Williams82 soybean genome is an important reference genome, and its splicing quality has a decisive influence on genetic research and gene function analysis. In related technologies, the splicing of the Williams82 genome is mainly based on second-generation sequencing data, but it has certain incompleteness and errors. With the rapid development of biotechnology, third-generation sequencing data (such as PacBio and Oxford Nanopore) have attracted widespread attention because they provide longer read lengths and can better solve the splicing problems of complex genomic regions.

[0050] That is to say, 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 splicing 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.

[0051] 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.

[0052] In order to improve the accuracy and completeness of the splicing of the Williams82 genome, the present invention adopts a method of integrating second-generation and third-generation genome sequencing data. By comprehensively utilizing different types of gene sequencing data, the structure of the genome can be restored more accurately, laying a solid foundation for the subsequent identification and analysis of complex combined variations. In addition, the research results will lay a solid foundation for the in-depth understanding of the soybean genome structure, genome function annotation and its application in molecular breeding. This research will not only promote the development of genomics theory, but also provide scientific support for soybean quality improvement and breeding decisions.

[0053] Combine the following Figure 1 to Figure 2 The genome assembly method based on Williams82 soybean provided by the present invention is described in detail. Figure 1 is a schematic diagram of the genome assembly method based on Williams82 soybean provided by the present invention, see Figure 1 As shown, the method includes steps 101 to 103, wherein:

[0054] Step 101: Correct the erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of Williams82 soybean to obtain target third-generation genome sequencing data; wherein the base accuracy of the second-generation genome sequencing data is higher than the base accuracy of the initial third-generation genome sequencing data.

[0055] First of all, it should be noted that the execution subject of the present invention can be any electronic device that can realize the splicing of the Williams82 soybean genome, for example, it can be any one of a smart phone, a desktop computer, a laptop computer, etc.

[0056] In an embodiment of the present invention, since the error rate of the initial third-generation genome sequencing data is relatively high, it is necessary to use high-precision second-generation genome sequencing data to correct errors in the initial third-generation genome sequencing data, thereby improving the overall accuracy and completeness of genome splicing.

[0057] Step 102: splice the second-generation genome sequencing data to generate a contig sequence set.

[0058] In the embodiment of the present invention, since the reads of the second-generation genome sequencing data are shorter, in order to better splice with the target third-generation genome sequencing data, it is necessary to splice the reads in the second-generation genome sequencing data to generate a contig sequence set. Among them, the contig sequence set is a longer sequence formed by splicing the reads in the second-generation genome sequencing data.

[0059] Step 103: perform genome splicing on the contigs sequence set and the target third-generation genome sequencing data to generate a genome splicing set scaftig of the Williams82 soybean.

[0060] Scaftig refers to a data structure used to represent genome assembly results.

[0061] The genome splicing method based on Williams82 soybean provided by the present invention provides more comprehensive genome coverage because the initial third-generation genome sequencing data of Williams82 soybean has a long read length, which can better solve the splicing problem of complex genome regions, but also has a relatively high error rate. Therefore, the present invention first corrects the erroneous bases in the initial third-generation genome sequencing data with a high error rate based on the high-precision second-generation genome sequencing data of Williams82 soybean to obtain the target third-generation genome sequencing data, and then splices the second-generation genome sequencing data to generate a contig sequence set, and performs genome splicing on the contig sequence set and the target third-generation genome sequencing data to generate a genome splicing set scaftig of Williams82 soybean. Through the above method, the advantages of the second-generation genome sequencing data and the target third-generation genome sequencing data are combined to splice the two, effectively making up for the shortcomings of a single sequencing technology, and obtaining a genome splicing set scaftig with a low error rate and long read length, which improves the accuracy and completeness of the splicing of the Williams82 soybean genome, and can more accurately restore the structure of the Williams82 soybean genome, laying a solid foundation for the subsequent identification and analysis of complex combinatorial variations.

[0062] Optionally, before correcting the erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of the Williams82 soybean, the following steps need to be performed:

[0063] Performing data quality control analysis on the second-generation genome sequencing data and the initial third-generation genome sequencing data; the data quality control analysis is used to screen and filter the second-generation genome sequencing data and the initial third-generation genome sequencing data.

[0064] Specifically, it is first necessary to obtain the second-generation genome sequencing data and the initial third-generation genome sequencing data; in actual applications, the sequencing depth selected for the second-generation genome sequencing data is 30X, and the data volume is 69Gbp; the sequencing depth selected for the initial third-generation genome sequencing data is 30X, and the data volume is 30Gbp.

[0065] Data quality control analysis is performed on the second-generation genome sequencing data and the initial third-generation genome sequencing data. Its purpose is to screen and filter the above genome sequencing data to eliminate the impact of uncertain factors in the sequencing process on the data.

[0066] The data quality control analysis specifically includes: using Cutadapt software to remove the adapters during sequencing, using FastQC software to perform quality control on the sequencing data, using Trimmomatic software to perform quality control on the sequencing data, and then using FastQC software to perform quality control again. Figure 2 As shown, Figure 2 It is a schematic diagram of data quality control analysis provided by the present invention.

[0067] See also Figure 2 As shown, step 1, using Cutadapt software to remove the connector of the fastq file of Williams82 sequencing data, including: removing the left end connector, removing the right end connector, removing reads less than the m value, and double-end filtering.

[0068] Step 2: Use FastQC software to perform quality detection on Williams82 sequencing data. The detection contents include: base quality at each position, average quality of each base read, proportion distribution of bases at each position, content of base N at each position, frequency distribution of sequence length, frequency distribution of read duplication, adapter content and k-mer content.

[0069] Step 3: Determine whether to perform quality control on the Williams82 sequencing data; if so, execute step 4; if not, directly obtain the Williams82 quality control data.

[0070] Step 4: Use Trimmomatic software to perform quality control on sequencing data, including: removing the sliding window with an average base value lower than the threshold from the 5' of the read, removing bases with a base value lower than the threshold from the beginning of the read, removing bases with a base value lower than the threshold from the end of the read, removing some bases from the end of the read to make the read instruction length complete, removing the read if the length of the read after trimming is lower than the threshold, and removing the read if the average base value of the read is lower than the threshold.

[0071] Optionally, in order to correct base errors in the initial third-generation genome sequencing data, it is necessary to correct the erroneous bases in each long read sequence using the edit distance on the initial third-generation genome sequencing data after quality control analysis.

[0072] Specifically, the second-generation genome sequencing data based on the Williams82 soybean is used to correct the erroneous bases in the initial third-generation genome sequencing data to obtain the target third-generation genome sequencing data, which can be specifically achieved by the following steps:

[0073] Step A: For each first read length in the initial third-generation genome sequencing data, the first read is divided according to the length of the second read in the second-generation genome sequencing data to obtain a string set; the length of each string in the string set is the same as the length of the second read.

[0074] The initial third-generation genome sequencing data includes multiple first reads, the second-generation genome sequencing data includes multiple second reads, and the target third-generation genome sequencing data includes multiple corrected reads; let the set of multiple first reads be L, the set of multiple second reads be S, and the set of multiple corrected reads be R.

[0075] In step A above, we need to take a first read from the set L (using L j ), a sliding window is used to divide the first read into different strings with the same length as the second read in S, and then a string set C is obtained, where C includes multiple strings.

[0076] Step B: for each character string in the character string set, calculating the edit distance between the character string and the first read; when the edit distance reaches a first threshold, taking the first read as a read to be corrected.

[0077] The edit distance is calculated by the following formula (1):

[0078]

[0079] Among them, d ij represents the edit distance, E ij represents the process of decomposing subproblems using dynamic programming, W represents the weights of insertion, deletion, and replacement operations, and E i0 Indicates that the string C i The edit distance of the first i bases converted to an empty string, E ij Indicates that the empty string C i Convert to L j The edit distance of the first j bases, E i-1,j-1 Indicates that the string C i The first i-1 bases in the sequence are converted to L j The edit distance of the first j-1 bases in the i-1,j Indicates that the string C i The first i-1 bases in the sequence are converted to L j The edit distance of the first j bases in i,j-1 Indicates that the string C i The first i bases in the sequence are converted to L jThe edit distance of the first j-1 bases in the sequence, k represents the index variable, h represents the base position currently processed or represents the index used for weight calculation, and u represents the string C i The length of v is L j The length of i represents the string C currently being processed. i The base index in L is v, which represents the base currently being processed. j The base index in .

[0080] The parameters in the above formula (1) are further explained below through Table 1:

[0081] Table 1

[0082]

[0083]

[0084] In practical applications, the first threshold is set to 10, for example. i indicates) and L j If the edit distance between them is less than 10, then L j As the read to be corrected, it is added to the set H.

[0085] Step C: performing base comparison between each character string and the read to be corrected, counting the number of successful comparisons between each base in each character string and the read to be corrected and the total number of comparisons; when the ratio of the number of successful comparisons to the total number of comparisons reaches a second threshold, correcting the read to be corrected to obtain a corrected read.

[0086] In the above step C, the reads to be corrected in set H are matched to C using a pattern matching algorithm. i , Statistics C i The number of times each base is aligned and the total number of reads. Similarly, the number of successful read alignments and the total number of alignments corresponding to each base in each string in set C are counted. If a base is supported by more than 80% (i.e., the second threshold) of reads in set H, the read to be corrected is corrected; otherwise, it is not corrected.

[0087] Step D: looping through steps AC to obtain the target third-generation genome sequencing data, wherein the target third-generation genome sequencing data includes a plurality of corrected reads.

[0088] In this embodiment, steps AC are executed in a loop until all first read bases in the set L are modified, and a set R of completely corrected erroneous bases is output, wherein R includes a plurality of corrected reads.

[0089] Optionally, the second-generation genome sequencing data is spliced ​​to generate a contig sequence set, which is specifically achieved by the following steps:

[0090] Step 1), decomposing each second read in the second-generation genome sequencing data into a plurality of fixed-length nucleotide strings k-mer; wherein the length of each k-mer is k.

[0091] First of all, it should be noted that before executing step 1), the length of the k-mer needs to be selected. Specifically:

[0092] a) Initialization parameters: Set the minimum value of k to 20bp and the maximum value to 59bp.

[0093] b) Calculate the expected coverage, which is calculated by the following formula (2):

[0094]

[0095] Among them, E represents coverage, C represents the sequencing depth of the second-generation genome sequencing data, L represents the sequencing read length of the second-generation genome sequencing data, and k represents the length of k-mer.

[0096] c) Calculate the existence probability of k-mer, which is calculated by the following formula (3):

[0097] P k =Q k (3)

[0098] Among them, P k represents the probability of existence of k-mer when the length of k-mer is k, Q k It represents the probability of the average sequencing quality of the second-generation genome sequencing data when the length of k-mer is k.

[0099] It should be noted that the existence probability P of k-mer needs to be combined with the coverage E k Adjustment is made, which is specifically achieved through the following formula (4):

[0100]

[0101] Among them, P represents the adjusted P k .

[0102] d) Select the best k, including:

[0103] Iterate between the minimum and maximum k, and choose the value of k that maximizes P.

[0104] If, at each k value, it is greater than a preset threshold (eg, 0.95), it is the optimal k. If the adjusted P value exceeds the preset threshold at a particular k value, then this k value is the optimal choice.

[0105] After the length k of the k-mer is determined through the above steps a) to d), each second read in the second-generation genome sequencing data is decomposed into a plurality of k-mers with a length of k.

[0106] Step 2) constructing an initial De Bruijn graph based on each k-mer; wherein each node in the initial De Bruijn graph is a k-mer, and the edges are overlaps between adjacent k-mers.

[0107] Step 3), for each node in the initial De Bruijn graph, remove the node whose first coverage is less than the coverage threshold; wherein the first coverage is determined based on the sequencing depth, sequencing read length and evaluation of sequencing quality of the second-generation genome sequencing data, and the coverage threshold is determined based on the Poisson statistical method.

[0108] The purpose of step 3) above is to remove the wrong k-mer. In practical applications, it is first necessary to calculate the first coverage of each node in the initial DeBruijn graph, and use the Poisson statistical method to determine the coverage threshold T. Then, traverse the first coverage list corresponding to the k-mer, if the first coverage of a k-mer is less than T, remove the k-mer, and return the filtered k-mer list.

[0109] The first coverage is calculated by the above formula (2).

[0110] Step 4), based on each of the nodes, identify at least one linear path in the initial De Bruijn graph; wherein the node corresponding to the starting point of each linear path has only one input, and the node corresponding to the end point has only one output.

[0111] Step 5), for each linear path, merge all nodes on the linear path to generate a target node, where the target node represents the linear path;

[0112] Step 6), based on each of the target nodes, generate an optimized De Bruijn graph.

[0113] The purpose of the above steps 4) to 6) is to simplify the structure of the initial De Bruijn graph and generate an optimized De Bruijn graph.

[0114] Specifically, the first step is to identify the linear path:

[0115] In the initial De Bruijn graph, each node (denoted by N) is traversed to find a node with only one input as the starting point of the linear path and a node with only one output as the end point of the linear path.

[0116] Step 2: Path merging:

[0117] For each linear path, all nodes and edges on the path are merged into a single node. A new merged node (i.e., the target node mentioned in step 5) is generated to represent the entire sequence from the starting point to the end point.

[0118] Step 3: Update the graph structure:

[0119] Update the connectivity of the initial De Bruijn graph to reflect the merge result.

[0120] Step 4: Return the merged optimized De Bruijn graph.

[0121] Step 7), extracting at least one of an Eulerian path and a Hamiltonian path from the optimized De Bruijn graph.

[0122] Step 8), based on at least one of the Eulerian pathway and the Hamiltonian pathway, generate the contigs sequence set.

[0123] The purpose of the above steps 7) and 8) is to construct the final contigs. Specifically, the Eulerian path is extracted from the optimized DeBruijn graph. If the graph is simple, the Hamiltonian path is extracted to assemble the contigs. Then the remaining graph nodes and edges are assembled into the final contigs along the path sequence.

[0124] Optionally, after generating the optimized De Bruijn graph, if the optimized De Bruijn graph has a bubble structure, the following steps [1] to [3] need to be performed:

[0125] Step [1]: Detect whether there is a bubble path in the optimized De Bruijn graph based on a depth-first search (DFS) algorithm, where the bubble path is a branch path that branches and re-merges in the optimized De Bruijn graph.

[0126] Specifically, the DFS algorithm is used to traverse from the starting node of the optimized De Bruijn graph, and the path is recorded when a branch is detected. When the path is re-merged, the branch is identified as a bubble.

[0127] Step [2], when there are multiple bubble paths in the optimized De Bruijn graph, determine the bubble paths that need to be removed in each bubble path based on at least one of the second coverage of each target node in each bubble path and the sequence length corresponding to the bubble path.

[0128] Specifically, the second coverage, sequence length or other characteristics of each target node in the branch path (ie, bubble path) are compared, and a more reliable bubble path is selected based on the second coverage and quality score.

[0129] Step [3], removing the bubble path that needs to be removed.

[0130] Specifically, less credible bubble paths are removed from the optimized De Bruijn graph, and the inlets and outlets of the bubbles are connected.

[0131] Optionally, after obtaining the contigs sequence set and the target third-generation genome sequencing data after error correction, the contigs sequence set and the target third-generation genome sequencing data need to be genome spliced ​​to generate a genome splicing set scaftig of Williams82 soybean.

[0132] That is, the contigs and corrected reads in the two need to be spliced ​​using a string graph splicing algorithm. This algorithm mainly includes sliding window segmentation of contigs and corrected reads, sting graph construction, string graph simplification and reduction, and gap filling.

[0133] The specific steps include:

[0134] Step 1), segmenting the contigs sequence set and the target third-generation genome sequencing data to obtain a plurality of first gene sequence fragments corresponding to the contigs sequence set and a plurality of second gene sequence fragments corresponding to the target third-generation genome sequencing data.

[0135] In the embodiment of the present invention, the sliding window technology is used to segment the contigs and the corrected reads. In this stage, the algorithm segments the contigs and the corrected reads into multiple shorter sequence fragments, which are convenient for managing and analyzing large-scale sequence data while reducing computational complexity.

[0136] Step 2), constructing a string graph based on the overlapping relationship between each of the first gene sequence fragments and the second gene sequence fragments; the string graph is used to represent the gene sequence structure after each of the first gene sequence fragments and each of the second gene sequence fragments are spliced.

[0137] The above step 2) is the stage of constructing the string graph. Specifically, the segmented sequence fragments (i.e., the first gene sequence fragment and the second gene sequence fragment mentioned above) are vertices, and the overlaps between the fragments are edges. The segmented sequence fragments are used to construct the string graph using the overlapping relationship between the edges.

[0138] After constructing the string graph, the string graph needs to be simplified and reduced. Since the string graph generated in step 2) contains bulge and redundant elements in the graph, a simplification and reduction method is needed to simplify and reduce the generated string graph.

[0139] Step 3) fill the gaps in the string graph using at least one corrected read in the target third-generation genome sequencing data to generate the scaftig.

[0140] In the embodiment of the present invention, after simplifying the string graph, there are still multiple bifurcation sites, at which the graph is broken to generate a scaffold, which results in gaps between scaffolds, which are filled with long reads (i.e., corrected reads).

[0141] It should be noted that the two ends of the long read and the two ends of the scaffold must be identical for at least 100 bp in order to fill the long read and finally generate a scaffold.

[0142] The genome splicing device based on Williams82 soybean provided by the present invention is described below. The genome splicing device based on Williams82 soybean described below and the genome splicing method based on Williams82 soybean described above can be referenced to each other. Figure 3 is a schematic diagram of the structure of the genome splicing device based on Williams82 soybean provided by the present invention, such as Figure 3 As shown, the genome splicing device 300 based on Williams82 soybean includes: a correction module 301, a generation module 302, and a splicing module 303, wherein:

[0143] The correction module 301 is used to correct the erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of the Williams82 soybean to obtain the target third-generation genome sequencing data; the base accuracy of the second-generation genome sequencing data is higher than the base accuracy of the initial third-generation genome sequencing data;

[0144] A generation module 302 is used to splice the second-generation genome sequencing data to generate a contig sequence set;

[0145] The splicing module 303 is used to perform genome splicing on the contigs sequence set and the target third-generation genome sequencing data to generate the genome splicing set scaftig of the Williams82 soybean.

[0146] The genome splicing device based on Williams82 soybean provided by the present invention provides more comprehensive genome coverage because the initial third-generation genome sequencing data of Williams82 soybean has a long read length, which can better solve the splicing problem of complex genome regions, but also has a relatively high error rate. Therefore, the present invention first corrects the erroneous bases in the initial third-generation genome sequencing data with a high error rate based on the high-precision second-generation genome sequencing data of Williams82 soybean to obtain the target third-generation genome sequencing data, and then splices the second-generation genome sequencing data to generate a contig sequence set, and performs genome splicing on the contig sequence set and the target third-generation genome sequencing data to generate a genome splicing set scaftig of Williams82 soybean. Through the above method, the advantages of the second-generation genome sequencing data and the target third-generation genome sequencing data are combined to splice the two, effectively making up for the shortcomings of a single sequencing technology, and obtaining a genome splicing set scaftig with a low error rate and long read length, which improves the accuracy and completeness of the splicing of the Williams82 soybean genome, and can more accurately restore the structure of the Williams82 soybean genome, laying a solid foundation for the subsequent identification and analysis of complex combinatorial variations.

[0147] Optionally, the correction module 301 is further configured to:

[0148] Step A: for each first read length in the initial third-generation genome sequencing data, the first read is divided according to the length of the second read in the second-generation genome sequencing data to obtain a string set; the length of each string in the string set is the same as the length of the second read;

[0149] Step B: for each character string in the character string set, calculating the edit distance between the character string and the first read; when the edit distance reaches a first threshold, taking the first read as a read to be corrected;

[0150] Step C: performing base comparison between each character string and the read to be corrected, counting the number of successful comparisons between each base in each character string and the read to be corrected and the total number of comparisons; when the ratio of the number of successful comparisons to the total number of comparisons reaches a second threshold, correcting the read to be corrected to obtain a corrected read;

[0151] Step D: looping through steps AC to obtain the target third-generation genome sequencing data, wherein the target third-generation genome sequencing data includes a plurality of corrected reads.

[0152] Optionally, the generating module 302 is further configured to:

[0153] Decomposing each second read in the second-generation genome sequencing data into a plurality of fixed-length nucleotide strings k-mers; wherein the length of each k-mer is k;

[0154] Constructing an initial De Bruijn graph based on each k-mer; wherein each node in the initial De Bruijn graph is a k-mer, and the edges are overlaps between adjacent k-mers;

[0155] For each node in the initial De Bruijn graph, remove the node whose first coverage is less than the coverage threshold; wherein the first coverage is determined based on the sequencing depth, sequencing read length and evaluation of sequencing quality of the second-generation genome sequencing data, and the coverage threshold is determined based on the Poisson statistical method;

[0156] Based on each of the nodes, identifying at least one linear path in the initial De Bruijn graph; wherein the node corresponding to the starting point of each linear path has only one input, and the node corresponding to the end point has only one output;

[0157] For each linear path, merge all nodes on the linear path to generate a target node, where the target node represents the linear path;

[0158] Based on each of the target nodes, generating an optimized De Bruijn graph;

[0159] Extracting at least one of an Eulerian path and a Hamiltonian path from the optimized De Bruijn graph;

[0160] Based on at least one of the Eulerian path and the Hamiltonian path, the contigs sequence set is generated.

[0161] Optionally, the device further comprises:

[0162] A detection module, used for detecting whether there is a bubble path in the optimized De Bruijn graph based on a depth-first search DFS algorithm, wherein the bubble path is a branch path that branches and re-merges in the optimized De Bruijn graph;

[0163] A determination module, configured to determine, when there are multiple bubble paths in the optimized De Bruijn graph, a bubble path to be removed from each bubble path based on at least one of the second coverage of each target node in each bubble path and the sequence length corresponding to the bubble path;

[0164] The removal module is used to remove the bubble path that needs to be removed.

[0165] Optionally, the splicing module 303 is further used to:

[0166] Segmenting the contigs sequence set and the target third-generation genome sequencing data to obtain a plurality of first gene sequence fragments corresponding to the contigs sequence set and a plurality of second gene sequence fragments corresponding to the target third-generation genome sequencing data;

[0167] Constructing a string graph based on the overlapping relationship between each of the first gene sequence fragments and the second gene sequence fragments; the string graph is used to represent the gene sequence structure after each of the first gene sequence fragments and each of the second gene sequence fragments are spliced;

[0168] The gaps in the string graph are filled using at least one corrected read in the target third-generation genome sequencing data to generate the scaftig.

[0169] Optionally, the device further comprises:

[0170] A data quality control analysis module is used to perform data quality control analysis on the second-generation genome sequencing data and the initial third-generation genome sequencing data; the data quality control analysis is used to screen and filter the second-generation genome sequencing data and the initial third-generation genome sequencing data.

[0171] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4As 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 a genome assembly method based on Williams82 soybean, the method comprising: correcting the erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of Williams82 soybean to obtain the target third-generation genome sequencing data; the base accuracy of the second-generation genome sequencing data is higher than the base accuracy of the initial third-generation genome sequencing data; the second-generation genome sequencing data is assembled to generate a contig sequence set; the contig sequence set is assembled with the target third-generation genome sequencing data to generate a genome assembly set scaftig of the Williams82 soybean.

[0172] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is 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, and the computer software product is stored in a storage medium, including a number of 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: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0173] On the other hand, the present invention also provides a computer program product, which includes a computer program. 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 genome splicing method based on Williams82 soybean provided by the above methods, and the method includes: correcting the erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of the Williams82 soybean to obtain target third-generation genome sequencing data; the base accuracy of the second-generation genome sequencing data is higher than the base accuracy of the initial third-generation genome sequencing data; splicing the second-generation genome sequencing data to generate a contig sequence set; and performing genome splicing on the contig sequence set and the target third-generation genome sequencing data to generate a genome splicing set scaftig of the Williams82 soybean.

[0174] 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 genome splicing method based on Williams82 soybean provided by the above-mentioned methods, the method comprising: correcting the erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of the Williams82 soybean to obtain target third-generation genome sequencing data; the base accuracy of the second-generation genome sequencing data is higher than the base accuracy of the initial third-generation genome sequencing data; splicing the second-generation genome sequencing data to generate a set of overlapping group contigs sequences; and performing genome splicing on the contigs sequence set and the target third-generation genome sequencing data to generate a genome splicing set scaftig of the Williams82 soybean.

[0175] 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.

[0176] 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.

[0177] 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 genome assembly method based on Williams82 soybean, characterized in that: include: Based on the second-generation genome sequencing data of the Williams82 soybean, the erroneous bases in the initial third-generation genome sequencing data are corrected to obtain target third-generation genome sequencing data; the base accuracy of the second-generation genome sequencing data is higher than the base accuracy of the initial third-generation genome sequencing data; Splicing the second-generation genome sequencing data to generate a contig sequence set; The contigs sequence set is genome spliced ​​with the target third-generation genome sequencing data to generate the Williams82 soybean genome splicing set scaftig.

2. The genome assembly method based on Williams82 soybean according to claim 1, characterized in that: The method of correcting the erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of the Williams82 soybean to obtain the target third-generation genome sequencing data comprises: Step A: for each first read length in the initial third-generation genome sequencing data, the first read is divided according to the length of the second read in the second-generation genome sequencing data to obtain a string set; the length of each string in the string set is the same as the length of the second read; Step B: for each character string in the character string set, calculating the edit distance between the character string and the first read; when the edit distance reaches a first threshold, taking the first read as a read to be corrected; Step C: performing base comparison between each character string and the read to be corrected, counting the number of successful comparisons between each base in each character string and the read to be corrected and the total number of comparisons; when the ratio of the number of successful comparisons to the total number of comparisons reaches a second threshold, correcting the read to be corrected to obtain a corrected read; Step D: looping through steps AC to obtain the target third-generation genome sequencing data, wherein the target third-generation genome sequencing data includes a plurality of corrected reads.

3. The genome assembly method based on Williams82 soybean according to claim 1 or 2, characterized in that: The step of splicing the second generation genome sequencing data to generate a contig sequence set includes: Decomposing each second read in the second generation genome sequencing data into a plurality of fixed-length nucleotide strings k-mers; wherein the length of each k-mer is k; Constructing an initial De Bruijn graph based on each k-mer; wherein each node in the initial De Bruijn graph is a k-mer, and the edges are overlaps between adjacent k-mers; For each node in the initial De Bruijn graph, remove the node whose first coverage is less than the coverage threshold; wherein the first coverage is determined based on the sequencing depth, sequencing read length and evaluation of sequencing quality of the second-generation genome sequencing data, and the coverage threshold is determined based on the Poisson statistical method; Based on each of the nodes, identifying at least one linear path in the initial De Bruijn graph; wherein the node corresponding to the starting point of each linear path has only one input, and the node corresponding to the end point has only one output; For each linear path, merge all nodes on the linear path to generate a target node, where the target node represents the linear path; Based on each of the target nodes, generating an optimized De Bruijn graph; Extracting at least one of an Eulerian path and a Hamiltonian path from the optimized De Bruijn graph; Based on at least one of the Eulerian path and the Hamiltonian path, the contigs sequence set is generated.

4. The genome assembly method based on Williams82 soybean according to claim 3, characterized in that: After generating the optimized De Bruijn graph, the method further includes: Detecting whether there is a bubble path in the optimized De Bruijn graph based on a depth-first search (DFS) algorithm, where the bubble path is a branch path that branches and re-merges in the optimized De Bruijn graph; In the case where there are multiple bubble paths in the optimized De Bruijn graph, based on at least one of the second coverage of each target node in each bubble path and the sequence length corresponding to the bubble path, determine the bubble paths that need to be removed in each bubble path; The bubble path that needs to be removed is removed.

5. The genome assembly method based on Williams82 soybean according to claim 1 or 2, characterized in that: The contigs sequence set is subjected to genome splicing with the target third-generation genome sequencing data to generate the Williams82 soybean genome splicing set scaftig, comprising: Segmenting the contigs sequence set and the target third-generation genome sequencing data to obtain a plurality of first gene sequence fragments corresponding to the contigs sequence set and a plurality of second gene sequence fragments corresponding to the target third-generation genome sequencing data; Constructing a string graph based on the overlapping relationship between each of the first gene sequence fragments and the second gene sequence fragments; the string graph is used to represent the gene sequence structure after each of the first gene sequence fragments and each of the second gene sequence fragments are spliced; The gaps in the string graph are filled using at least one corrected read in the target third-generation genome sequencing data to generate the scaftig.

6. The method for genome assembly based on Williams82 soybean according to claim 1 or 2, characterized in that: Before correcting the erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of the Williams82 soybean, the method further comprises: Performing data quality control analysis on the second-generation genome sequencing data and the initial third-generation genome sequencing data; the data quality control analysis is used to screen and filter the second-generation genome sequencing data and the initial third-generation genome sequencing data.

7. A genome splicing device based on Williams82 soybean, characterized in that: include: A correction module, used for correcting erroneous bases in the initial third-generation genome sequencing data based on the second-generation genome sequencing data of the Williams82 soybean to obtain target third-generation genome sequencing data; the base accuracy of the second-generation genome sequencing data is higher than the base accuracy of the initial third-generation genome sequencing data; A generation module, used for splicing the second-generation genome sequencing data to generate a contig sequence set; The splicing module is used to perform genome splicing on the contigs sequence set and the target third-generation genome sequencing data to generate the genome splicing set scaftig of the Williams82 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 genome assembly method based on Williams82 soybean is implemented as described in any one of claims 1 to 6.

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 genome assembly method based on Williams82 soybean is implemented as claimed in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the genome assembly method based on Williams82 soybean is implemented as claimed in any one of claims 1 to 6.

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