Method and system for reconstructing data of DNA storage based on non-redundant de bruijn graph
By using a non-redundant de Bruijn graph-based method, the error problem in data reconstruction in DNA storage was solved, achieving efficient and accurate data recovery under high error rates.
Patent Information
- Application Number
- CN202410768990.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-14
- Publication Date
- 2026-11-03
- Estimated Expiration
- 2044-06-14
AI Technical Summary
Due to limitations in biotechnology, errors are inevitable during the writing and reading of data in DNA storage, leading to data corruption and loss, and making it difficult to efficiently reconstruct the original data.
A non-redundant de Brouin graph-based approach is adopted, which constructs cluster subgraphs, bundle search graphs, and non-redundant de Brouin graphs to screen nodes with strong correlation to the backbone sequence, perform base alignment and error correction, and obtain consensus sequences through multi-sequence alignment, thereby reducing redundant information and improving data recovery rate.
Even with high error rates, it can accurately reconstruct DNA sequences, reducing the probability of non-same cluster sequences appearing in non-redundant de Brouin diagrams, and improving the accuracy of assembly results and data recovery rate.
Smart Images
Figure CN118782143B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of DNA storage technology, and more specifically to a method and system for reconstructing DNA storage data based on non-redundant de Bruin diagrams. Background Technology
[0002] DNA storage involves encoding data into DNA sequences, writing the data through synthesis, reading the DNA sequences using high-throughput sequencing technology, and then decoding to retrieve the data. Research at Harvard University's Church in the United States has explored the possibility of embedding the textual information of an entire book into DNA molecules, thus sparking a surge in interest in DNA storage technology. To store more data, Goldman et al. used Shannon information encoding and file segmentation techniques to convert data files into DNA sequences, and then precisely reconstructed the original files using DNA synthesis and sequencing technologies. To represent the metadata of the files, Banal et al. encapsulated the encoded DNA sequences in silicon capsules, using specific barcode and fluorescence sequencing techniques to directly select the desired set of files, achieving highly sensitive and selective access to large-scale molecular datasets.
[0003] Currently, the key steps in DNA storage are data writing and reading. In the data writing stage, the core task is to map binary data into DNA sequences according to efficient encoding rules. In the data reading stage, the original data is fully recovered by decoding the amplified and sequenced sequences. However, due to the inherent limitations of biotechnology, unavoidable errors occur during DNA storage, leading to data corruption and loss. Therefore, the challenges of DNA storage include not only how to write data into DNA, but also how to efficiently reconstruct the original data from the DNA sequence. Summary of the Invention
[0004] The purpose of this invention is to propose a method and system for reconstructing DNA stored data based on non-redundant de Bruijn diagrams, which controls the redundant information stored to a relatively low level, and can still ensure the data recovery rate and reconstruct the data well even when the error rate is high.
[0005] According to a first aspect of the present disclosure, a method for reconstructing DNA stored data based on a non-redundant de Bruijn diagram is provided, comprising the following steps:
[0006] The Lewinstein distance between DNA sequences is obtained, and DNA sequences with a Lewinstein distance less than or equal to a set threshold are grouped into the same cluster subgraph. Thus, several DNA sequences are divided into multiple cluster subgraphs.
[0007] In the clustering subgraph, each DNA sequence is regarded as a node. The nodes with the smallest Levenstein distance between DNA sequences are connected by edges. The node with the largest degree represents the backbone sequence in the clustering subgraph.
[0008] The backbone sequence is used as a template sequence for aligning sequencing data, and other DNA sequences are used to construct a bundle search map based on the relative positions of each base in the backbone sequence.
[0009] In the beam search graph, error correction and selection of the best preferred path are performed according to the beam search algorithm, and the DNA sequences involved in the best preferred path are subjected to multiple sequence alignment to obtain the consensus sequence.
[0010] The DNA sequence of each cluster subgraph is divided into subsequence k-mer and a non-redundant de Brouin graph is constructed using a sliding window. In this graph, there are k-1 nodes with the same base connected by edges, and the frequency between nodes is recorded as the weight of the edge.
[0011] Delete nodes connected to edges with weights below a set threshold to reduce the number of subsequent path selection decisions;
[0012] Path selection is performed based on node information in the non-redundant de Bruin graph and the consensus sequence.
[0013] The sequence after path selection is the reconstructed sequence.
[0014] According to a second aspect of the present disclosure, a DNA storage data reconstruction system based on a non-redundant de Bruijn diagram is provided, comprising:
[0015] The clustering subgraph acquisition module obtains the Lewinstein distance between DNA sequences and assigns DNA sequences with a Lewinstein distance less than or equal to a set threshold to the same clustering subgraph. Thus, several DNA sequences are assigned to multiple clustering subgraphs.
[0016] The backbone sequence acquisition module treats each DNA sequence as a node in the clustering subgraph, connects the nodes with the minimum Lewinstein distance between DNA sequences, and the node with the largest degree represents the backbone sequence in the clustering subgraph.
[0017] The bundle search map acquisition module uses the backbone sequence as a template sequence for sequencing data alignment, and constructs bundle search maps for other DNA sequences based on the relative positions of each base in the backbone sequence.
[0018] The consensus sequence acquisition module performs error correction and filters the best preferred path in the bundle search graph according to the bundle search algorithm, and performs multi-sequence alignment on the sequences involved in the best preferred path to obtain the consensus sequence.
[0019] The non-redundant de Brouin graph acquisition module divides the DNA sequence of each cluster subgraph into subsequence k-mer and constructs a non-redundant de Brouin graph using a sliding window. In this graph, there are k-1 nodes with the same base connected by edges, and the frequency between nodes is recorded as the edge weight.
[0020] The deletion module deletes nodes connected to edges with weights below a set threshold to reduce the number of subsequent path selection checks.
[0021] The path selection module selects paths based on node information in the non-redundant de Bruin graph and the consensus sequence.
[0022] The reconstruction module determines the reconstructed sequence based on the selected path.
[0023] According to a third aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the memory, wherein the processor executes the program to implement the described DNA storage data reconstruction method based on non-redundant de Bruijn diagrams.
[0024] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the described method for reconstructing DNA storage data based on a non-redundant de Bruin diagram.
[0025] The advantages of the above technical solutions adopted in this invention compared with the prior art are as follows:
[0026] 1. This invention groups DNA sequences with high similarity into the same cluster subgraph and filters nodes with weak correlation to the backbone sequence. A beam search graph is used to align and initially correct bases in the sequences, identifying candidate paths with high accuracy. Finally, multiple sequence alignment is used to obtain a consensus sequence with high accuracy.
[0027] 2. It reduces the likelihood of the same k-mer appearing in the same non-redundant de Brouin graph from different cluster sequences, resolves path selection ambiguity through consensus sequences, and ensures the accuracy of the assembly results.
[0028] 3. Without adding redundant check bits, it can also accurately correct various errors, restore the original data, and achieve accurate sequence reconstruction. Attached Figure Description
[0029] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.
[0030] Figure 1The flowchart shows a DNA storage data reconstruction method based on a non-redundant de Brouin graph.
[0031] Figure 2 This is a schematic diagram comparing the reconstruction rates of BSDBG and DBGPS under a mixed error rate in the embodiment;
[0032] Figure 3 This is a schematic diagram comparing the data recovery rates of BSDBG and DBGPS under a mixed error rate in the example. Specific implementation methods
[0033] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.
[0034] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0035] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0036] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.
[0037] Example 1:
[0038] This embodiment provides a method for reconstructing DNA stored data based on a non-redundant de Bruin diagram, including the following steps:
[0039] Step 1: Obtain the Lewinstein distance between DNA sequences, and group DNA sequences with a Lewinstein distance less than or equal to a set threshold into the same cluster subgraph. Thus, several DNA sequences are divided into multiple cluster subgraphs.
[0040] For example, assuming the threshold is 3, dist(A,B) represents the Levenstein distance between sequence A and sequence B, and sequence A is ATGGCCATTGTA, sequence B is ATGGCTATTGTA, sequence C is ATGGCCATTGCA, and sequence D is ATGGCAATTGTA.
[0041] Since dist(A,B)=1, dist(A,C)=1, dist(A,D)=2, dist(B,C)=2, dist(B,D)=3, and dist(C,D)=3 are all within the threshold of 3, they are classified into the same cluster subgraph.
[0042] Step 2: In the clustering subgraph, each DNA sequence is regarded as a node. Connect the nodes with the minimum Levenstein distance between DNA sequences. The node with the largest degree represents the backbone sequence in the clustering subgraph.
[0043] For example, the distance between sequence A and sequence B is the smallest, 1, so we connect sequence A and sequence B first. The distance between sequence A and sequence C is also 1, so we connect sequence A and sequence C. The distance between sequence A and sequence D is 2, but since A has already been connected to B and C, and the distance between D and B is 3, we can choose whether to connect A and D. Because node A has the largest degree (3 edges), A can be considered as the backbone sequence in this clustering subgraph.
[0044] Step 3: Use the backbone sequence as the template sequence for sequencing data alignment, and construct a bundle search map for other DNA sequences based on the relative positions of each base in the backbone sequence;
[0045] In one implementation: each base in the backbone sequence is treated as a node, and adjacent bases are connected to form adjacent nodes with a weight of 1; the bases represented by the adjacent nodes of the maximum degree node are added step by step, and they are matched according to their positions in the backbone sequence, with the weight of the edge of the matched adjacent node gradually increased by 1.
[0046] It should be noted that when constructing a bundle search map based on the relative positions of bases in the backbone sequence for other DNA sequences, three possible errors exist: substitution errors, deletion errors, and insertion errors; different error types are represented by the following formulas:
[0047] a. The replaced node is v' re_i ,but:
[0048] w(v i-1 , v' re_i )+=1 and w(v' re_i , v i+1 ,)+=1 (1)
[0049] b. The node to be deleted is v' de_i ,but:
[0050] w(v i-1 , v i+1 )+=1 (2)
[0051] c. The inserted node is v i ' ns_i ,but:
[0052] w(v i-1 , v i ' ns_i ) +=1 and w(v i ' ns_i , v i ) +=1 (3)
[0053] d. For any k ≠ i, if v k and v k+1 If no error occurs, then:
[0054] w(v k , v k+1 ) +=1 (4)
[0055] Where i represents the index of the location where the error occurred, and v i This represents the node with index i in the original sequence, w(v i ,v i+1 ) represents node v i to node v i+1 The weights.
[0056] Step 4: In the beam search graph, perform error correction and select the best preferred path according to the beam search algorithm, and perform multiple sequence alignment on the DNA sequences involved in the best preferred path to obtain the consensus sequence;
[0057] In the beam search algorithm, B is called the beam width. When B is 1, BS is equivalent to best-first search. When applying BS to the beam search graph, starting from the specified starting node, B empty paths are initialized as the output set Y = [y1, y2, ..., y]. B ], where y i It is the i-th path. Let v represent the t-th base in path i; in each step, each path is extended by adding a base; for each node v in the graph xThis yields all its successor nodes as v. x path y b Extended path score S(v) x |y b ):
[0058]
[0059] Among them, W(y) b ) represents path y b Path weights; express to v x The weight of the edges.
[0060] Using the path generation method described above, multiple candidate paths are generated in the beam search graph. Each path represents a pre-corrected sequence, which helps improve the accuracy of multi-sequence alignment. Finally, the generated candidate paths are corrected using a multi-sequence alignment tool to obtain the consensus sequence.
[0061] Step 5: Divide the DNA sequence of each cluster subgraph into subsequence k-mer and construct a non-redundant de Brouin graph using a sliding window. In this graph, there are k-1 nodes with the same base connected by edges, and the frequency between nodes is recorded as the edge weight.
[0062] Specifically, a fixed value k is chosen to represent the length of each subsequence. Starting from the beginning of the DNA sequence, a subsequence of length k, i.e., a k-mer, is extracted. Then, a sliding motion is performed with a fixed step size of 1, repeating this process until the entire DNA sequence is covered. Each node represents a k-mer, and each edge represents the relationship between two adjacent k-mers. Nodes with k-1 identical bases are connected by edges, and the frequency between nodes is recorded as the edge weight.
[0063] Step 6: Delete nodes connected to edges with weights below a set threshold to reduce the number of subsequent path selection checks;
[0064] By setting appropriate thresholds, these low-frequency k-mers that may mislead the assembly process can be filtered out, thereby improving the accuracy of assembly.
[0065] Step 7: Select a path based on the node information in the non-redundant de Bruin graph and the consensus sequence;
[0066] Specifically, if the current node has only one successor node, that node is directly selected as the next node; if the current node has multiple successor nodes, the next node is selected based on the position of the k-mer in the consensus sequence and the frequency of the successor nodes: find the k-mer corresponding to the position in the consensus sequence based on the position of the current node, check whether the k-mer of the current node is the same as the k-mer of the consensus sequence, if they are the same, select the next k-mer node corresponding to the k-mer in the consensus sequence as the successor node; if they are different, select the node with the largest outgoing edge weight as the successor node.
[0067] Step 8: After path selection, the assembled sequence is obtained, which is the reconstructed sequence.
[0068] like Figure 2 and Figure 3 As shown, experimental results demonstrate that the method of this embodiment outperforms the experimental results of other algorithms. In the figure, BSDBG represents the experimental results of this invention.
[0069] Example 2:
[0070] This embodiment provides a DNA storage data reconstruction system based on a non-redundant de Bruin diagram, including:
[0071] The clustering subgraph acquisition module obtains the Lewinstein distance between DNA sequences and assigns DNA sequences with a Lewinstein distance less than or equal to a set threshold to the same clustering subgraph. Thus, several DNA sequences are assigned to multiple clustering subgraphs.
[0072] The backbone sequence acquisition module treats each DNA sequence as a node in the clustering subgraph, connects the nodes with the minimum Lewinstein distance between DNA sequences, and the node with the largest degree represents the backbone sequence in the clustering subgraph.
[0073] The bundle search map acquisition module uses the backbone sequence as a template sequence for sequencing data alignment, and constructs bundle search maps for other DNA sequences based on the relative positions of each base in the backbone sequence.
[0074] The consensus sequence acquisition module performs error correction and filters the best preferred path in the bundle search graph according to the bundle search algorithm, and performs multi-sequence alignment on the sequences involved in the best preferred path to obtain the consensus sequence.
[0075] The non-redundant de Brouin graph acquisition module divides the DNA sequence of each cluster subgraph into subsequence k-mer and constructs a non-redundant de Brouin graph using a sliding window. In this graph, there are k-1 nodes with the same base connected by edges, and the frequency between nodes is recorded as the edge weight.
[0076] The deletion module deletes nodes connected to edges with weights below a set threshold to reduce the number of subsequent path selection checks.
[0077] The path selection module selects paths based on node information in the non-redundant de Bruin graph and the consensus sequence.
[0078] The reconstruction module determines the reconstructed sequence based on the selected path.
[0079] Example 3:
[0080] An electronic device includes a memory, a processor, and a computer program stored in the memory and running thereon, wherein the processor, when executing the program, implements the aforementioned method for reconstructing DNA stored data based on a non-redundant de Bruijn diagram, comprising:
[0081] The Lewinstein distance between DNA sequences is obtained, and DNA sequences with a Lewinstein distance less than or equal to a set threshold are grouped into the same cluster subgraph. Thus, several DNA sequences are divided into multiple cluster subgraphs.
[0082] In the clustering subgraph, each DNA sequence is regarded as a node. The nodes with the smallest Levenstein distance between DNA sequences are connected by edges. The node with the largest degree represents the backbone sequence in the clustering subgraph.
[0083] The backbone sequence is used as a template sequence for aligning sequencing data, and other DNA sequences are used to construct a bundle search map based on the relative positions of each base in the backbone sequence.
[0084] In the beam search graph, error correction and selection of the best preferred path are performed according to the beam search algorithm, and the DNA sequences involved in the best preferred path are subjected to multiple sequence alignment to obtain the consensus sequence.
[0085] The DNA sequence of each cluster subgraph is divided into subsequence k-mer and a non-redundant de Brouin graph is constructed using a sliding window. In this graph, there are k-1 nodes with the same base connected by edges, and the frequency between nodes is recorded as the weight of the edge.
[0086] Delete nodes connected to edges with weights below a set threshold to reduce the number of subsequent path selection decisions;
[0087] Path selection is performed based on node information in the non-redundant de Bruin graph and the consensus sequence.
[0088] The sequence after path selection is the reconstructed sequence.
[0089] Example 4:
[0090] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for reconstructing DNA stored data based on a non-redundant de Bruijn diagram, comprising:
[0091] The Lewinstein distance between DNA sequences is obtained, and DNA sequences with a Lewinstein distance less than or equal to a set threshold are grouped into the same cluster subgraph. Thus, several DNA sequences are divided into multiple cluster subgraphs.
[0092] In the clustering subgraph, each DNA sequence is regarded as a node. The nodes with the smallest Levenstein distance between DNA sequences are connected by edges. The node with the largest degree represents the backbone sequence in the clustering subgraph.
[0093] The backbone sequence is used as a template sequence for aligning sequencing data, and other DNA sequences are used to construct a bundle search map based on the relative positions of each base in the backbone sequence.
[0094] In the beam search graph, error correction and selection of the best preferred path are performed according to the beam search algorithm, and the DNA sequences involved in the best preferred path are subjected to multiple sequence alignment to obtain the consensus sequence.
[0095] The DNA sequence of each cluster subgraph is divided into subsequence k-mer and a non-redundant de Brouin graph is constructed using a sliding window. In this graph, there are k-1 nodes with the same base connected by edges, and the frequency between nodes is recorded as the weight of the edge.
[0096] Delete nodes connected to edges with weights below a set threshold to reduce the number of subsequent path selection decisions;
[0097] Path selection is performed based on node information in the non-redundant de Bruin graph and the consensus sequence.
[0098] The sequence after path selection is the reconstructed sequence.
[0099] This invention employs optimized strategies in cluster preprocessing, multiple sequence alignment, and path selection to keep the stored redundant information at a relatively low level. Even with a high error rate, it can still guarantee a high data recovery rate and reconstruct the data effectively.
[0100] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, which can then be stored in a storage device for execution by a computer device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. This disclosure is not limited to any particular combination of hardware and software.
[0101] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
[0102] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.
Claims
1. A method for reconstructing DNA storage data based on non-redundant de Brouine diagrams, characterized in that, Includes the following steps: The Lewinstein distance between DNA sequences is obtained, and DNA sequences with a Lewinstein distance less than or equal to a set threshold are grouped into the same cluster subgraph. Thus, several DNA sequences are divided into multiple cluster subgraphs. In the clustering subgraph, each DNA sequence is regarded as a node. The nodes with the smallest Levenstein distance between DNA sequences are connected by edges. The node with the largest degree represents the backbone sequence in the clustering subgraph. The backbone sequence is used as a template sequence for aligning sequencing data, and other DNA sequences are used to construct a bundle search map based on the relative positions of each base in the backbone sequence. In the beam search graph, error correction and selection of the best preferred path are performed according to the beam search algorithm, and the DNA sequences involved in the best preferred path are subjected to multiple sequence alignment to obtain the consensus sequence. The DNA sequence of each cluster subgraph is divided into subsequence k-mer and a non-redundant de Brouin graph is constructed using a sliding window. In this graph, there are k-1 nodes with the same base connected by edges, and the frequency between nodes is recorded as the weight of the edge. Delete nodes connected to edges with weights below a set threshold to reduce the number of subsequent path selection decisions; Path selection is performed based on node information in the non-redundant de Bruin graph and the consensus sequence. The sequence after path selection is the reconstructed sequence.
2. The DNA storage data reconstruction method based on non-redundant de Bruin diagrams according to claim 1, characterized in that, The backbone sequence is used as a template sequence for sequencing data alignment. Specifically, each base in the backbone sequence is treated as a node, and adjacent bases are connected to form adjacent nodes with a weight of 1. The bases represented by the adjacent nodes of the node with the highest degree are added step by step, and they are matched according to their positions in the backbone sequence. The weight of the edge of the matched adjacent node is gradually increased by 1.
3. The DNA storage data reconstruction method based on non-redundant de Brouine diagrams according to claim 1, characterized in that, When constructing a bundle search map based on the relative positions of bases in the backbone sequence for other DNA sequences, three possible errors exist: substitution errors, deletion errors, and insertion errors; different error types are represented by the following formulas: a. The replaced node is v' re_i ,but: w(v i-1 ,v' re_i )+=1and w(v' re_i ,v i+1 ,)+=1(1) b. The node to be deleted is v' de_i ,but: w(v i-1 ,v i+1 )+=1(2) c. The inserted node is v i ' ns_i ,but: w(v i-1 ,v i ' ns_i )+=1and w(v i ' ns_i ,v i )+=1(3) d. For any k ≠ i, if v k and v k+1 If no error occurs, then: w(v k ,v k+1 )+=1(4) Where i represents the index of the location where the error occurred, and v i This represents the node with index i in the original sequence, w(v i ,v i+1 ) represents node v i to node v i+1 The weights.
4. The DNA storage data reconstruction method based on non-redundant de Bruin diagrams according to claim 1, characterized in that, In the beam search algorithm, starting from a specified starting node, B empty paths are initialized as the output set Y = [y1, y2, ..., y]. B ], where y i It is the i-th path, y i t Let v represent the t-th base in path i; in each step, each path is extended by adding a base; for each node v in the graph x This yields all its successor nodes as v. x path y b Extended path score S(v) x |y b ).
5. The DNA storage data reconstruction method based on non-redundant de Bruin diagrams according to claim 4, characterized in that, Extended path score S(v) x |y b The method to obtain ) is: Among them, W(y) b ) represents path y b Path weights; express to v x The weight of the edges.
6. The DNA storage data reconstruction method based on non-redundant de Brouine diagrams according to claim 1, characterized in that, The DNA sequence of each cluster subgraph is segmented into subsequence k-mers and a non-redundant de Brouin graph is constructed using a sliding window. Specifically, a fixed value k is selected to represent the length of each subsequence. First, starting from the beginning of the DNA sequence, a subsequence of length k, i.e., a k-mer, is extracted. Then, the sliding window is used with a fixed step size of 1, and this process is repeated until the entire DNA sequence is covered. Each node represents a k-mer, and each edge represents the relationship between two adjacent k-mers. Nodes with k-1 identical bases are connected by an edge, and the frequency between nodes is recorded as the edge weight.
7. The DNA storage data reconstruction method based on non-redundant de Brouine diagrams according to claim 1, characterized in that, Path selection is performed based on node information in the non-redundant de Bruin graph and the consensus sequence. Specifically, if the current node has only one successor node, the current node is directly selected as the next node. If the current node has multiple successor nodes, the next node is selected by considering the position of k-mer in the consensus sequence and the frequency of successor nodes.
8. The DNA storage data reconstruction method based on non-redundant de Bruin diagrams according to claim 7, characterized in that, If the current node has multiple successor nodes, find the k-mer corresponding to the position in the consensus sequence based on the current node's position, check if the current node's k-mer is the same as the consensus sequence's k-mer. If they are the same, select the next k-mer node corresponding to the consensus sequence's k-mer as the successor node; if they are different, select the node with the largest outgoing edge weight as the successor node.
9. A DNA storage data reconstruction system based on non-redundant de Brouine diagrams, characterized in that, include: The clustering subgraph acquisition module obtains the Lewinstein distance between DNA sequences and assigns DNA sequences with a Lewinstein distance less than or equal to a set threshold to the same clustering subgraph. Thus, several DNA sequences are assigned to multiple clustering subgraphs. The backbone sequence acquisition module treats each DNA sequence as a node in the clustering subgraph, connects the nodes with the minimum Lewinstein distance between DNA sequences, and the node with the largest degree represents the backbone sequence in the clustering subgraph. The bundle search map acquisition module uses the backbone sequence as a template sequence for sequencing data alignment, and constructs bundle search maps for other DNA sequences based on the relative positions of each base in the backbone sequence. The consensus sequence acquisition module performs error correction and filters the best preferred path in the bundle search graph according to the bundle search algorithm, and performs multi-sequence alignment on the sequences involved in the best preferred path to obtain the consensus sequence. The non-redundant de Brouin graph acquisition module divides the DNA sequence of each cluster subgraph into subsequence k-mer and constructs a non-redundant de Brouin graph using a sliding window. In this graph, there are k-1 nodes with the same base connected by edges, and the frequency between nodes is recorded as the edge weight. The deletion module deletes nodes connected to edges with weights below a set threshold to reduce the number of subsequent path selection checks. The path selection module selects paths based on node information in the non-redundant de Bruin graph and the consensus sequence. The reconstruction module determines the reconstructed sequence based on the selected path.