Rapid sequencing and traceability analysis system for input infectious diseases

Abstract

The invention discloses a rapid sequencing and traceability analysis system for input infectious diseases, which relates to the field of customs quarantine and comprises a sample preprocessing unit, a high-throughput sequencing unit, a data processing and quality control unit, a rapid comparison and annotation unit and a traceability analysis unit. According to the input infectious disease rapid sequencing and traceability analysis system, a complete link is formed from sample preprocessing, library construction, data cleaning, pathogen recognition and transmission map construction, module splitting and manual intervention are avoided, and tasks can be automatically completed in large-scale and emergency scenes.

Description

Technical Field

[0001] This specification relates to the field of customs quarantine, and more specifically, this application relates to a rapid sequencing and traceability analysis system for imported infectious diseases. Background Art

[0002] The existing imported pathogen detection and analysis process mainly relies on the following technical paths: 1. Traditional PCR or antigen detection methods: Although they have fast reaction speed and low cost, they can only target known pathogens and cannot cover emerging or mutated pathogens. Their sensitivity and breadth are limited.

[0003] 2. Conventional high-throughput sequencing and manual analysis process: Currently, some research institutions have carried out whole-genome testing based on high-throughput sequencing, but this process usually has the following problems: sample information is poorly structured and lacks semantic coding, which cannot express the source background of the sample; the sequencing result analysis links are scattered and lack acceleration mechanisms, resulting in low overall processing efficiency; pathogen identification and transmission chain construction are separated, making it difficult to support rapid response and epidemic trend judgment; poor systematization, and inability to output decision-making factors such as maps, risks, and node indicators in an integrated manner.

[0004] In addition, in actual scenarios, such as when ports handle a large number of samples per day and have high testing timeliness requirements, the traditional model is difficult to simultaneously take into account timeliness, accuracy, and traceability throughout the entire process. A more systematic, intelligent, and structured technical solution is urgently needed. Summary of the Invention

[0005] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0006] First, this application proposes a rapid sequencing and traceability analysis system for imported infectious diseases, including: A sample pre-processing unit, configured to identify and encode the sample to be tested after sample processing to form pre-processed sample information, wherein the sample processing includes nucleic acid extraction, purification, and quality testing; A high-throughput sequencing unit is used to construct a sequencing library based on the pre-processed sample information and obtain raw sequencing data; The data processing and quality control unit is used to perform quality control, remove adapters, and eliminate host sequences on the raw sequencing data to obtain valid pathogen sequencing data; A rapid alignment and annotation unit, used to align the above-mentioned valid pathogen sequencing data with a preset reference database to obtain alignment and annotation results; The traceability analysis unit is used to construct an evolutionary relationship map between pathogens based on the above comparison and annotation results.

[0007] In a feasible implementation manner, the specific steps of performing identification coding on the sample to be tested after sample processing to form pre-processed sample information include: Generate identity information code segments and time code segments based on the personnel information corresponding to the sample to be tested after the sample processing; Generate environmental data embedded in coding segments based on port passenger flow level information and historical disease levels; Generate an extended coding segment based on the vaccination information and travel itinerary information of the person information corresponding to the sample to be tested; The identity information coding segment, the time coding segment, the environmental data embedded coding segment and the extended coding segment are bound to the sample to be tested after the sample processing to form the pre-processed sample information.

[0008] In a feasible embodiment, the specific steps of constructing a sequencing library based on the pre-processed sample information and obtaining raw sequencing data include: Determine the target library type based on the nucleic acid type information, concentration range information, and target pathogen type information of the pre-treated sample information; After the target library type is determined, a target library corresponding to the preprocessed sample information is established according to the library construction rules corresponding to the target library type; Obtaining concentration information and fragment distribution information of the target library through fluorescence quantification and fragment analysis; Calculate the library quality score based on the above concentration information and the above fragment distribution information; The target library whose quality score meets the preset quality is loaded into the sequencing platform to obtain the raw sequencing data.

[0009] In a feasible embodiment, the specific steps of performing quality control, linker removal, and host sequence elimination on the raw sequencing data to obtain valid pathogen sequencing data include: Perform quality assessment on the raw sequencing data, analyze the base quality distribution, N-base ratio, length distribution, and GC content deviation of the sequencing reads, and filter and trim the low-quality sequencing reads based on the set threshold to obtain quality-filtered sequencing data; Load the adapter sequence database of the corresponding library construction platform, use the adapter recognition algorithm to match and locate the adapter sequences in the quality-filtered sequencing data, and perform the adapter sequence trimming operation to obtain the trimmed sequencing data; The host reference genome database is loaded, the above-mentioned trimmed sequencing data is compared with the host genome, and the sequencing reads derived from the host are identified and removed to obtain the above-mentioned valid pathogen sequencing data.

[0010] In a feasible embodiment, the specific steps of comparing the above-mentioned effective pathogen sequencing data with a preset reference database to obtain comparison and annotation results include: Constructing a preset reference database including multiple known pathogen reference genome sequences, and performing index preprocessing on the preset reference database to form a target index structure, wherein the target index structure is a two-layer index structure consisting of a Bloom filter and a hash table; Extract k-mer fragments from each sequencing read in the above-mentioned valid pathogen sequencing data, perform parallel hash matching operations on the GPU side, compare the above-mentioned k-mer fragments with the above-mentioned target index structure of the above-mentioned preset reference database, and generate a hit score for each pathogen based on the number of hits to obtain a preliminary screening result; Based on the above preliminary screening results and the dynamically restricted high-precision alignment range, the reference sequences of the top-N high-scoring candidate pathogens are selected as targets, and the alignment algorithm is used to perform mapping operations on the above sequencing reads to obtain alignment results; The annotation module is called to quickly map the virulence factors, resistance genes and key functional regions of the sequencing reads of the above-mentioned successful alignment results using the annotation hash table mechanism to annotate them, so as to obtain the above-mentioned alignment and annotation results.

[0011] In a feasible embodiment, the specific steps of constructing an evolutionary relationship map between pathogens based on the comparison and annotation results include: Performing feature extraction on the above alignment and annotation results to obtain a feature vector, wherein the feature vector includes a SNP site list, InDel or amino acid substitution position, and virulence or resistance site label; Constructing enhanced variation distance information between any two samples in the above alignment and annotation results, wherein the enhanced variation distance information includes genotype differences, entry country differences, and entry port information; The enhanced variation distance information is input into the DBSCAN algorithm for unsupervised clustering to obtain clustering results; Construct a minimum spanning tree propagation graph based on the enhanced mutation distance information; The above clustering results are mapped to the above minimum spanning tree propagation graph to construct the evolutionary relationship map among the above pathogens.

[0012] In a feasible implementation, the above-mentioned construction of a minimum spanning tree propagation graph based on the above-mentioned enhanced mutation distance information includes: Based on the enhanced mutation distance information, a graph structure G(V, E) is constructed, where the node V represents a sample, the edge E represents the potential propagation relationship between samples, and the edge weight is the enhanced mutation distance information. The minimum spanning tree algorithm is applied on the above graph structure to construct a minimum spanning tree propagation graph between samples.

[0013] In a feasible embodiment, mapping the clustering results to the minimum spanning tree propagation graph to construct an evolutionary relationship map among the pathogens includes: Write the cluster labels in the above clustering results into the attributes of each node in the above minimum spanning tree propagation graph, so that each node in the graph contains the cluster category information; Traverse the edge structure of the above minimum spanning tree propagation graph to determine whether the cluster labels of the nodes at both ends of the edge are consistent; If they are inconsistent, mark the edge as a cross-cluster propagation path; The edge weights of the above cross-cluster propagation paths are weighted and adjusted to enhance the risk expression capability of propagation jumps in the graph; The output is an enhanced propagation graph structure containing the above cluster labels and cross-cluster connection labels to construct an evolutionary relationship map among the above pathogens.

[0014] In a feasible embodiment, the system further includes a disease transmission path analysis unit, and the disease transmission path analysis unit further includes a construction module, a traversal module, a calculation module and a display module. The above-mentioned construction module is used to call the edge weight information, timestamp information and transmission path connection information in the evolutionary relationship map between the above-mentioned pathogens to construct a transmission time sequence diagram between samples; The traversal module is used to perform a graph traversal operation based on the propagation timing graph to determine a propagation subgraph starting from a specified source sample, wherein the graph traversal operation includes a breadth-first search or a depth-first search; The calculation module is used to analyze the propagation characteristics of the nodes of the propagation subgraph and calculate key propagation node indicators, including node degree, betweenness centrality and propagation path length; The display module is used to display the propagation timing diagram and the node indicators in a visual map.

[0015] In a feasible embodiment, the system further includes a transmission risk assessment unit, which includes a statistical module, an identification module and a risk calculation module; The statistical module is used to count the number and average edge weight of cross-cluster connected edges in the evolutionary relationship graph between the pathogens; The identification module is used to identify high-frequency regions and high-frequency transmission channel nodes with high-frequency cross-cluster connections in the evolutionary relationship map between the pathogens; The risk calculation module is used to generate a propagation risk score according to the number of edges, the average edge weight, the high-frequency area and the high-frequency propagation channel node.

[0016] In summary, the rapid sequencing and traceability analysis system for imported infectious diseases proposed in the embodiment of the present application constitutes a complete link from sample preprocessing, library construction, data cleaning, to pathogen identification and transmission map construction, avoiding module fragmentation and manual intervention, which is conducive to automatically completing tasks in large-scale and emergency scenarios. A dynamic and scalable encoding strategy is introduced in the sample preprocessing stage to convert high-order semantics such as personnel flow, port channels, and travel history into structured labels, effectively supporting subsequent sample priority scheduling and transmission map semantic enhancement. The fast alignment unit combines the Bloom filter and hash table double-layer structure to achieve a second-level response to the initial screening of millions of reads, and improves the alignment efficiency through the Top-N candidate restriction strategy, which is significantly better than the traditional full alignment algorithm. The traceability unit not only constructs a genotype difference map between samples, but also integrates information such as entry country, port channel, etc. to generate an enhanced transmission relationship map, supporting more realistic disease transmission modeling and abnormal jump detection. The system can automatically calculate indicators such as node degree, betweenness centrality, path depth, and display key nodes and chains through interactive transmission maps. This system can also be connected with downstream risk warning platforms and port dispatch systems to achieve data-driven optimization of prevention and control mechanisms, such as high-risk transmission chain alarms and entry inspection priority sorting.

[0017] Other advantages, objectives and features of the present application will be reflected in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings: Figure 1 A structural schematic diagram of a rapid sequencing and traceability analysis system for imported infectious diseases provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only some embodiments of the present application, not all embodiments.

[0020] See also Figure 1 , which is a structural diagram of a rapid sequencing and traceability analysis system for imported infectious diseases provided in an embodiment of the present application, which may specifically include: The sample pre-processing unit 101 is used to identify and encode the sample to be tested after sample processing to form pre-processed sample information, wherein the sample processing includes nucleic acid extraction, purification and quality testing; The high-throughput sequencing unit 102 is used to construct a sequencing library for the pre-processed sample information and obtain raw sequencing data; The data processing and quality control unit 103 is used to perform quality control, remove adapters, and eliminate host sequences on the raw sequencing data to obtain valid pathogen sequencing data; A rapid comparison and annotation unit 104 is used to compare the above-mentioned valid pathogen sequencing data with a preset reference database to obtain comparison and annotation results; The traceability analysis unit 105 is used to construct an evolutionary relationship map between pathogens based on the above comparison and annotation results.

[0021] Exemplarily, the sample preprocessing unit 101 is used to perform standardized preprocessing operations on collected samples. Its functions include extracting and purifying nucleic acids from the samples, and performing quality testing to ensure the accuracy and stability of subsequent sequencing. Following biochemical processing, the system also generates a unique identification code for each sample. This code incorporates information such as the sample's source, the individual's epidemiological background (e.g., travel history, port of entry, and level of traffic flow), and a timestamp to form "preprocessed sample information."

[0022] Based on the nucleic acid type, concentration range, and target pathogen type in the preprocessed sample information, the high-throughput sequencing unit 102 automatically selects a matching library construction strategy and sequencing platform to construct a high-quality sequencing library. Qualified samples are loaded onto a platform (such as Illumina or Nanopore), where the system acquires the corresponding raw sequencing data, providing foundational sequence information for subsequent analysis.

[0023] The data processing and quality control unit 103 performs a series of cleaning processes on the raw sequence data output by the sequencing platform. This process includes sequencing quality control, identification and removal of adapter sequences, and alignment with the host reference genome to eliminate host-derived reads. The final output is "valid pathogen sequencing data," the target information sequence for traceability and identification analysis.

[0024] The rapid alignment and annotation unit 104 is then called upon to efficiently compare the valid pathogen sequencing data against a pre-built pathogen reference database. This module typically combines GPU parallel acceleration technology with a two-layer indexing structure (such as a Bloom filter and a hash table) to initially screen the data for matches and then perform high-precision alignment of the top-N candidate pathogens. Simultaneously, this unit includes a built-in annotation module that functionally annotates the aligned positions, identifying virulence factors, drug-resistance genes, key mutation sites, and more, generating structured "alignment and annotation results."

[0025] Finally, based on the comparison and annotation results, the traceability analysis unit 105 extracts variant signature information, constructs an enhanced propagation distance matrix between samples, and combines a clustering algorithm with a minimum spanning tree algorithm to generate an evolutionary propagation relationship map between samples. This map intuitively displays the propagation paths, degree of variation, and possible traceability directions between different samples, and can be used to support tasks such as entry prevention and control risk assessment, identification of abnormal transmission behavior, and analysis of disease evolution trends.

[0026] In summary, the rapid sequencing and traceability analysis system for imported infectious diseases proposed in the embodiment of the present application constitutes a complete link from sample preprocessing, library construction, data cleaning, to pathogen identification and transmission map construction, avoiding module fragmentation and manual intervention, which is conducive to automatically completing tasks in large-scale and emergency scenarios. A dynamic and scalable encoding strategy is introduced in the sample preprocessing stage to convert high-order semantics such as personnel flow, port channels, and travel history into structured labels, effectively supporting subsequent sample priority scheduling and transmission map semantic enhancement. The fast alignment unit combines the Bloom filter and hash table double-layer structure to achieve a second-level response to the initial screening of millions of reads, and improves the alignment efficiency through the Top-N candidate restriction strategy, which is significantly better than the traditional full alignment algorithm. The traceability unit not only constructs a genotype difference map between samples, but also integrates information such as entry country, port channel, etc. to generate an enhanced transmission relationship map, supporting more realistic disease transmission modeling and abnormal jump detection. The system can automatically calculate indicators such as node degree, betweenness centrality, path depth, and display key nodes and chains through interactive transmission maps. This system can also be connected with downstream risk warning platforms and port dispatch systems to achieve data-driven optimization of prevention and control mechanisms, such as high-risk transmission chain alarms and entry inspection priority sorting.

[0027] In a feasible implementation manner, the specific steps of performing identification coding on the sample to be tested after sample processing to form pre-processed sample information include: Generate identity information code segments and time code segments based on the personnel information corresponding to the sample to be tested after the sample processing; Generate environmental data embedded in coding segments based on port passenger flow level information and historical disease levels; Generate an extended coding segment based on the vaccination information and travel itinerary information of the person information corresponding to the sample to be tested; The identity information coding segment, the time coding segment, the environmental data embedded coding segment and the extended coding segment are bound to the sample to be tested after the sample processing to form the pre-processed sample information.

[0028] For example, the system obtains the personal information corresponding to the sample to be tested, including their ID number or passenger identification number. Based on this information, a hash function is used to generate a unique identity information encoding segment, such as SHA-256, truncated to the first 8 bits as the individual identity identifier.

[0029] At the same time, the sample collection time (accurate to the hour or minute) is obtained and formatted into a timestamp format (such as YYYYMMDDHHmm) to generate a time code segment. The combination of the two can be used to uniquely identify a sample at a specific time point and source.

[0030] For example: Identity information: Passenger ID = ZL2345XH89 hash is 4E91A5C2, sampling time: April 5, 2025 13:26 → time code segment is 02504051326, generating the code prefix: 4E91A5C2-202504051326.

[0031] The system calls the real-time port database to obtain information on the flow level of the port where the sample entered. For example, if port number T3 corresponds to "high flow level," the code is set to H. The system also queries the historical disease database. If there have been confirmed cases at the port in the past seven days, the disease level is "medium," and the code is set to M. These two types of information are combined to generate environmental data embedded in the code segment, for example: HM (High flow + Medium epidemic level).

[0032] The following two types of information are extracted from the health declaration system for incoming personnel: 1. Vaccination information: If three doses of vaccination have been completed, set the code to V3; if not, set it to V0. 2. Travel itinerary information: If you have stayed in a country with a high incidence of disease, such as Italy (IT), in the past 14 days, record the country code; if the itinerary is normal, set it to NA. Splice to form an extended code segment: for example, V3-IT The four segments are concatenated sequentially to form the final sample identification code string: <Identity Code> - <Time Code> - <Environmental Data> - <Extension> → 4E91A5C2-202504051326-HM-V3-IT. The system writes this code into the database and binds it to the sample barcode or electronic tag. This serves as the sample's unique identification ID in subsequent processes and is used for tasks such as scheduling priority sorting and tracking and traceability mapping.

[0033] Through this implementation, the system can achieve multi-dimensional semantic identification of samples, significantly improving the sample priority screening capabilities and intelligent scheduling efficiency in high-concurrency scenarios, while ensuring that the source and characteristics of samples are traceable and analyzable in the subsequent tracing process.

[0034] In a feasible embodiment, the specific steps of constructing a sequencing library based on the pre-processed sample information and obtaining raw sequencing data include: Determine the target library type based on the nucleic acid type information, concentration range information, and target pathogen type information of the pre-treated sample information; After the target library type is determined, a target library corresponding to the preprocessed sample information is established according to the library construction rules corresponding to the target library type; Obtaining concentration information and fragment distribution information of the target library through fluorescence quantification and fragment analysis; Calculate the library quality score based on the above concentration information and the above fragment distribution information; The target library whose quality score meets the preset quality is loaded into the sequencing platform to obtain the raw sequencing data.

[0035] For example, the system automatically completes the entire process of library type determination, library construction, quality testing, and sequencing data collection based on sample metadata input from the sample preprocessing module. Sample metadata includes nucleic acid type information (such as DNA or RNA), concentration range information (e.g., 8-50 ng / μL), and target pathogen type information (such as virus, bacteria, or fungus). The system's built-in library construction rule library automatically matches the appropriate sequencing platform and corresponding library construction plan based on this information.

[0036] For example, if the sample is RNA, the target pathogen is a virus, and high sequencing accuracy is required, the system will match the strand-specific transcriptome library construction solution of the Illumina platform. If the sample is DNA, the goal is full genome coverage, or the traceability task requires high response speed, the full-length fragment library construction solution of the Nanopore platform will be prioritized. For example, if the sample type is RNA with a concentration of 12.4 ng / μL and the target pathogen is a coronavirus, the system will automatically select the TruSeq RNALibrary solution and assign it to the Illumina sequencing platform.

[0037] After the target library type is determined, the system calls the corresponding library construction workflow module based on the matching results to complete the library construction operation. If the Illumina platform library construction solution is selected, the library construction process includes nucleic acid fragmentation (through enzyme digestion or ultrasonic fragmentation), end repair, A-tailing, ligation of sequencing adapters, PCR amplification of the library, and purification and recovery using magnetic beads. If the Nanopore platform library construction solution is selected, the library construction process is simplified to skip fragmentation and directly perform end repair on long DNA chains and ligation of sequencing adapters containing barcode tags. The PCR amplification step is also omitted to preserve the original fragment length information.

[0038] The entire library construction process is completed through an automated pipetting system (such as a liquid handling system), and the library construction batch number, library construction parameters, consumables number and operation process metadata are simultaneously recorded to ensure the traceability and standardization of the process.

[0039] After library construction is complete, the system automatically tests the library quality. This process includes measuring the overall concentration of the library using a fluorescence quantification device (such as Qubit) and obtaining fragment length distribution, main peak fragment size, and peak shape uniformity using a fragment analyzer (such as Bioanalyzer or FragmentAnalyzer). The system inputs these test results into a built-in quality scoring model to calculate the Library Quality Score (LQS), which is used to determine whether the library meets the requirements for use in the analysis.

[0040] Library quality scoring It can be calculated based on the following formula:

[0041]

[0042]

[0043] in, is a penalty term (such as pollution, failure, etc.), A small constant to prevent the denominator from being zero.

[0044] The concentration standard score (range 0-1) indicates the degree of match between the measured library concentration and the optimal concentration recommended by the platform. The calculation formula is as follows:

[0045] in, To measure the concentration, is the recommended concentration. Falling When it is within the interval, it is considered optimal and the score can be directly assigned a value of 1.

[0046] The main peak accuracy score (value range 0-1) indicates how close the length of the main peak fragment of the library is to the target length. The calculation formula is as follows:

[0047] in, is the actual length of the main peak, is the target length, is an adjustment factor that reflects the sensitivity of fragment length deviation to the score.

[0048] The peak width variation score is used to characterize the concentration of the fragment distribution and is defined as the coefficient of variation of the main peak, that is:

[0049] in, is the main peak width (standard deviation), The smaller the CV value, the more concentrated the fragments are and the higher the quality of the library.

[0050] is a regularization term used to avoid the situation where the denominator is zero in the division operation. This is a nonlinear enhancement index used to control the degree of nonlinear enhancement of the overall score. It is suitable for boosting, compressing, or amplifying the score results. It can range from 1.2 to 2.0 and can be adjusted based on data experience.

[0051] Score the concentration standard, is the quality consistency angle. If the concentration is high, the main peak is concentrated and the peak width is small, and the three items are consistent, then , , indicating consistent quality; if an item deviates seriously, the angle increases and the score is suppressed.

[0052] Generally speaking, if the LQS score is higher than 85 points (out of 100), it is considered a qualified library and can enter the sequencing process; if the score is lower than the threshold, the system will automatically mark the sample as "needing to rebuild the library" and return to the library construction process for re-execution.

[0053] For libraries that pass the scoring process, the system loads them onto the corresponding sequencing platform and configures the required parameters to initiate the sequencing task. For example, on Illumina platforms (such as MiSeq or NextSeq), the system sets the sample index tag and read mode (such as PE150). On Nanopore platforms (such as GridION), the library is directly loaded onto the machine, and the system monitors its sequencing status in real time.

[0054] During sequencing, the system continuously records key metrics, including the number of valid reads, error rate, and Q30 ratio. Upon completion, the system exports the raw sequence data file in either FASTQ (containing sequencing reads and quality scores) or BAM (aligned format). Ultimately, the sequencing data is associated with the corresponding sample identifier and uploaded to a central database, providing data support for subsequent data cleaning, pathogen identification, and traceability analysis modules.

[0055] This implementation method realizes the automatic selection of sample-specific library construction strategies, the automated execution of high-throughput library construction processes, the fine scoring control of library quality, and the seamless connection of multi-platform sequencing tasks, significantly improving the processing efficiency, sequencing success rate and data reliability of the entire system in the rapid response to imported infectious diseases.

[0056] In a feasible embodiment, the specific steps of performing quality control, linker removal, and host sequence elimination on the raw sequencing data to obtain valid pathogen sequencing data include: Perform quality assessment on the raw sequencing data, analyze the base quality distribution, N-base ratio, length distribution, and GC content deviation of the sequencing reads, and filter and trim the low-quality sequencing reads based on the set threshold to obtain quality-filtered sequencing data; Load the adapter sequence database of the corresponding library construction platform, use the adapter recognition algorithm to match and locate the adapter sequences in the quality-filtered sequencing data, and perform the adapter sequence trimming operation to obtain the trimmed sequencing data.

[0057] The host reference genome database is loaded, the above-mentioned trimmed sequencing data is compared with the host genome, and the sequencing reads derived from the host are identified and removed to obtain the above-mentioned valid pathogen sequencing data.

[0058] For example, the system receives raw sequencing data from a sequencing platform, typically in FASTQ format. This format contains each sequencing read and its corresponding base quality value (i.e., Phred quality score). The system first performs a comprehensive quality assessment and cleansing on this raw data to ensure data accuracy and validity for downstream analysis.

[0059] During the quality assessment phase, the system mainly analyzes the following indicators: 1. The quality distribution of each base, with particular attention to the ratio of Q20 (quality value ≥ 20) to Q30 (quality value ≥ 30), which is used to reflect the overall sequencing accuracy; 2. The proportion of N bases. N bases represent uncertain base sites. Too high a proportion will affect the credibility. 3. Length distribution of sequencing fragments to determine whether there are abnormally short fragments; 4. GC content deviation: compare with the reference GC composition of the target species to identify possible contamination or library construction bias.

[0060] Based on the analysis, the system implements the following quality filtering strategies: 1. Use a sliding window algorithm to perform local quality trimming on reads. For example, if the average quality of four consecutive bases is less than 20, trimming is triggered. 2. Delete short reads whose length is less than a set threshold (e.g., 50 bp); 3. Remove low-quality reads with N bases accounting for more than 10%; 4. Optional poly-G tail trimming is available, especially for pseudo-tail problems commonly found in Illumina NovaSeq platforms.

[0061] After the above processing, the retained high-quality reads are output as the `clean_qc.fastq` file. At the same time, the system generates and saves the quality control log for subsequent traceability and analysis (this process can be completed through FastQC or self-developed quality control modules).

[0062] After completing quality control, the system enters the adapter identification and removal step. First, based on the library construction platform information recorded during the sample pre-processing stage, the matching adapter sequence database is automatically selected. For example: 1. If the sample uses the Illumina TruSeq library construction solution, call the corresponding TruSeq adapter sequence; 2. If using the Nanopore Native Barcoding solution, load its dedicated adapter sequence; 3. The system also supports specific connector sequence configurations for other platforms (such as MGI, Ion Torrent, etc.).

[0063] During the adapter identification phase, the system uses existing sequence processing tools (such as Cutadapt and Trimmomatic) to perform adapter sequence alignment on the 5' and 3' ends of each read. Matching methods support both exact matches and fuzzy matching strategies with a maximum of 1-2 base mismatches to enhance identification sensitivity. Successfully matched adapter sequences are trimmed, retaining the intermediate, valid target sequence region.

[0064] After processing is completed, the system outputs the reads file `clean_adapter.fastq` after the adapters are removed, and records metadata such as the adapter position and identification adapter type of each read for subsequent traceability and quality control statistics.

[0065] The system then performs a host sequence removal operation on the data after adapter processing. The system first loads the genome sequence of the target host species from the built-in reference database. The system uses efficient alignment algorithm tools such as Bowtie2, BWA, or minimap2 to align the reads in `clean_adapter.fastq` with the reference host genome. The alignment process uses strict parameter restrictions, such as setting alignment homology (identity) greater than 90% and sequence coverage greater than 50% as matching thresholds. All reads that fully or partially match the host sequence are marked as "host origin" and removed from the dataset.

[0066] Finally, reads that were not mapped to the host genome are retained as valid pathogen sequencing data. The system outputs this data as a `clean_hostfree.fastq` file and simultaneously generates a host elimination statistics report. The report includes the proportion of host reads, the number of retained reads, and the elimination rate, which is used for quality traceability and automatic assessment mechanisms.

[0067] Through the above three-stage processing flow, the system can effectively clean the original sequencing data, retain the real, reliable and analytically valuable pathogen sequencing information, and provide a reliable data foundation for subsequent pathogen identification, variant annotation and traceability analysis.

[0068] This implementation significantly improves the effectiveness and specificity of sequencing data through a multi-step quality control and decontamination process, ensuring that the data entering subsequent alignment and annotation modules has high fidelity and low background contamination characteristics, laying a data foundation for accurate pathogen identification and traceability analysis.

[0069] In a feasible embodiment, the specific steps of comparing the above-mentioned effective pathogen sequencing data with a preset reference database to obtain comparison and annotation results include: Constructing a preset reference database including multiple known pathogen reference genome sequences, and performing index preprocessing on the preset reference database to form a target index structure, wherein the target index structure is a two-layer index structure consisting of a Bloom filter and a hash table; Extract k-mer fragments from each sequencing read in the above-mentioned valid pathogen sequencing data, perform parallel hash matching operations on the GPU side, compare the above-mentioned k-mer fragments with the above-mentioned target index structure of the above-mentioned preset reference database, and generate a hit score for each pathogen based on the number of hits to obtain a preliminary screening result; Based on the above preliminary screening results and the dynamically restricted high-precision alignment range, the reference sequences of the top-N high-scoring candidate pathogens are selected as targets, and the alignment algorithm is used to perform mapping operations on the above sequencing reads to obtain alignment results; The annotation module is called to quickly map the virulence factors, resistance genes and key functional regions of the sequencing reads of the above-mentioned successful alignment results using the annotation hash table mechanism to annotate them, so as to obtain the above-mentioned alignment and annotation results.

[0070] For example, the system integrates reference genome sequences of known pathogens from multiple authoritative public databases (such as NCBI RefSeq, GISAID, and VFDB), covering multiple species and strains of viruses, bacteria, fungi, and more. Each reference sequence is accompanied by relevant metadata, including species name, strain number, and country of origin. All reference sequences are standardized in FASTA format and uniformly constructed into a pathogen reference database.

[0071] To improve comparison efficiency and reduce the misidentification rate, the system performs index preprocessing on the reference database and constructs a two-layer index structure: The first layer is a Bloom filter, which generates a fixed-length k-mer set (e.g., k=31) for each reference species and constructs a spatially compressed Bloom filter. This is used to quickly determine whether a k-mer is likely to come from a certain species during the initial screening phase. The second layer is a hash table, which creates a precise hash map for each k-mer, recording its corresponding species ID and location in the reference sequence. This structure supports parallel access on the GPU, facilitating high-speed queries on large-scale data.

[0072] This dual-layer index structure effectively combines space efficiency and query accuracy, significantly reducing redundant comparison calculations while ensuring initial screening speed.

[0073] In the alignment stage, the system first performs a k-mer extraction operation on each read in the valid pathogen sequencing data, and uses a sliding window method to extract all possible fixed-length k-mers (such as 31 bp) with a base step size to form a k-mer vector sequence for each read.

[0074] The system then batch-transferred these k-mer vectors to the GPU, initiating a parallel hash matching process. This matching process consists of two phases: first, a Bloom filter is used to quickly eliminate unlikely reference species; then, a hash table is used to perform an exact match on the remaining k-mers, recording the hit locations and species to which they belong, and counting the number of hits for each reference pathogen.

[0075] Based on the number of hits, the system calculates a pathogen score vector for each read, which indicates the likelihood of its match to different candidate species. The higher the score, the more likely the sample contains the corresponding pathogen sequence.

[0076] Based on this, the system selects the top N candidate pathogens (e.g., the top 3) based on their scores and proceeds to the next high-precision comparison process. This strategy significantly reduces the computing resources required for subsequent comparisons and improves the overall system throughput.

[0077] In the high-precision alignment stage, the system calls efficient alignment algorithms, such as BWA-MEM (Burrows-WheelerAligner - Maximal Exact Matches), to map the reads to be tested to the Top-N reference sequences and generate standard alignment output files (such as SAM or BAM format), recording the alignment position, quality score, mutation information, and other content of each read.

[0078] After completing the precise alignment, the system enters the functional annotation phase. Here, the system invokes a built-in annotation hash table module, which has pre-established mappings between specific sites in the reference sequence and functional regions, including virulence factors, drug-resistance genes, and regions of characteristic variation. The system quickly localizes each successfully aligned read to its corresponding functional region without revisiting a large database.

[0079] For each aligned read, the system can quickly determine whether it falls into a specific functional region, for example: whether it hits a drug-resistant gene (such as blaKPC, mecA), whether it is located in a typical virulence factor region (such as Type III secretion system); whether it contains a specific SNP mutation (such as D614G), etc.

[0080] The annotation results are output in a structured format, such as JSON, for use by downstream modules of the system, such as traceability analysis, cluster construction, and transmission risk assessment.

[0081] The method proposed in this embodiment uses a dual-layer index structure of Bloom filters and hash tables to achieve fast matching and low misrecognition rate in the initial screening process. Based on GPU parallel execution, it greatly improves the initial screening speed for millions of reads. It adopts a dynamic Top-N screening mechanism to effectively avoid invalid comparisons and save computing resources. It uses an annotation hash mapping mechanism to achieve real-time and rapid annotation of key functional areas.

[0082] In a feasible embodiment, the specific steps of constructing an evolutionary relationship map between pathogens based on the comparison and annotation results include: Performing feature extraction on the above alignment and annotation results to obtain a feature vector, wherein the feature vector includes a SNP site list, InDel or amino acid substitution position, and virulence or resistance site label; Constructing enhanced variation distance information between any two samples in the above alignment and annotation results, wherein the enhanced variation distance information includes genotype differences, entry country differences, and entry port information; The enhanced variation distance information is input into the DBSCAN algorithm for unsupervised clustering to obtain clustering results; Construct a minimum spanning tree propagation graph based on the enhanced mutation distance information; The above clustering results are mapped to the above minimum spanning tree propagation graph to construct the evolutionary relationship map among the above pathogens.

[0083] In a feasible implementation, the above-mentioned construction of a minimum spanning tree propagation graph based on the above-mentioned enhanced mutation distance information includes: Based on the enhanced mutation distance information, a graph structure G(V, E) is constructed, where the node V represents a sample, the edge E represents the potential propagation relationship between samples, and the edge weight is the enhanced mutation distance information. The minimum spanning tree algorithm is applied on the above graph structure to construct the above minimum spanning tree propagation graph between samples.

[0084] In a feasible embodiment, mapping the clustering results to the minimum spanning tree propagation graph to construct an evolutionary relationship map among the pathogens includes: Write the cluster labels into the attributes of each node in the minimum spanning tree propagation graph so that each node in the graph contains the cluster category information; Traverse the edge structure of the above minimum spanning tree propagation graph to determine whether the cluster labels of the nodes at both ends of the edge are consistent; If they are inconsistent, mark the edge as a cross-cluster propagation path; The edge weights of the above cross-cluster propagation paths are weighted and adjusted to enhance the risk expression capability of propagation jumps in the graph; The output is an enhanced propagation graph structure containing the above cluster labels and cross-cluster connection labels to construct an evolutionary relationship map among the above pathogens.

[0085] For example, based on sequencing alignment and functional annotation results, the system constructs a pathogen evolutionary relationship map that integrates genetic variation characteristics and epidemiological information to support transmission chain reconstruction and traceability analysis. This process includes five key steps: feature extraction, enhanced distance calculation, unsupervised clustering, transmission map construction, and map output.

[0086] First, the system performs a structured analysis of the alignment and annotation results for each sample, extracts the following key variant information, and constructs a unified feature vector representation: 1. SNP site list: records the single base mutation position (e.g. G23403A) of the sample in the reference sequence compared with the standard strain.

[0087] 2. InDel information: including the starting position, length, and variation type of the insertion or deletion.

[0088] 3. Amino acid substitution information: such as D614G, P681R, etc., marking the characteristic sites where substitutions have occurred at the protein level.

[0089] 4. Functional annotation label: used to mark whether the sample hits virulence factors, drug resistance genes or other key functional regions, usually represented in the form of a sparse binary vector.

[0090] All features are uniformly encoded as structured feature vectors , written as:

[0091] Structured feature vector Used for subsequent sample difference calculation. After feature extraction is completed, the system calculates the difference between any two samples. and Calculate the enhanced variant distance. This distance function takes into account genotype differences, entry country differences, and port information, and is defined as follows:

[0092] in, Hamming distance based on SNP / InDel / functional annotation vector is used to measure the degree of genetic difference. is the country difference item, if the entry country is different, it is 1, if the entry country is the same, it is 0, is the port difference item, different ports are 1, the same ports are 0, , , is the weight factor, which controls the relative contribution of the three indicators and can be set as , the remaining adjustments balance the impact of regional factors, is the nonlinear enhancement index of genetic distance, which is used to amplify the effects of genetic differences, such as , is the distance norm exponent, which controls the overall function shape, Can be used to compress extreme value differences, is the joint structure penalty term, which represents the consistency correction of the propagation path between samples.

[0093]

[0094] in, Represents the propagation similarity direction angle between sample feature vectors; , that is, the standard cosine similarity, , , is the feature vector of the two samples; is the structural penalty coefficient, which controls the incremental penalty when the propagation direction is inconsistent (for example, 0.3~0.5); if the mutation directions of the two samples are highly consistent (that is, the direction angle is small and the cosine value is close to 1), then If the value is close to 0, no penalty is imposed. If the variation directions of the two samples differ significantly (the cosine value is small), it means that the propagation logic is weak, and the overall distance is widened.

[0095] The system constructs a distance matrix based on the enhanced variation distances between all samples, inputs it into the DBSCAN unsupervised clustering algorithm, and sets an appropriate distance threshold. The clustering results are output as multiple similar samples (e.g., Cluster A, Cluster B, etc.), along with some outliers, which are used to identify anomalous samples. Each sample is assigned a cluster label, indicating the structure of the propagation group to which it belongs.

[0096] Based on this, the system further constructs a pathogen transmission graph. First, a weighted undirected graph G(V, E) is constructed, with samples as nodes V and the enhanced distance between each pair as edge weight E. The system then applies Prim's algorithm (Minimum Spanning Tree) to this graph, generating a minimum spanning tree (MST) connecting all samples with the minimum total edge weight. In this tree, each edge represents the most likely transmission path between two samples, with smaller edge weights indicating a higher likelihood of transmission.

[0097] To enhance the graph's ability to represent transmission risk, the system maps the DBSCAN clustering results back to a minimum spanning tree. The cluster label of each sample node is written into the node attribute field of the graph. Simultaneously, the system traverses all edges in the tree. If the cluster labels connecting two nodes are different, the edge is marked as a "cross-cluster transmission edge," indicating potential transmission jumps or multi-source crossover. To enhance risk representation, the system adjusts the edge weights of such edges (for example, increasing the weight by 1.0) to highlight their jump characteristics in transmission path calculation and visualization. Finally, the system outputs the generated transmission graph structure into a standard graph format (such as GraphML or JSON). In the output graph, nodes represent samples and are accompanied by cluster labels, entry country, port information, and mutation characteristics. Edges represent transmission paths and are accompanied by attributes such as enhanced mutation distance and whether they are cross-cluster edges. The graph can be used for tasks such as transmission chain visualization, origin tracing, jump path identification, and transmission risk assessment.

[0098] This implementation method achieves more realistic modeling of transmission distances between samples by integrating genetic variations and epidemiological background information between samples; by combining DBSCAN clustering with the minimum spanning tree, the constructed transmission map has structural rationality, transmission explainability and jump identification capabilities, and is particularly suitable for restoring transnational disease transmission chains, auxiliary evaluation of entry prevention and control strategies, and priority disposal analysis of high-risk samples.

[0099] In a feasible embodiment, the system further includes a disease transmission path analysis unit, and the disease transmission path analysis unit further includes a construction module, a traversal module, a calculation module and a display module. The above-mentioned construction module is used to call the edge weight information, timestamp information and transmission path connection information in the evolutionary relationship map between the above-mentioned pathogens to construct a transmission time sequence diagram between samples; The traversal module is used to perform a graph traversal operation based on the propagation timing graph to determine a propagation subgraph starting from a specified source sample, wherein the graph traversal operation includes a breadth-first search or a depth-first search; The calculation module is used to analyze the propagation characteristics of the nodes of the propagation subgraph and calculate key propagation node indicators, including node degree, betweenness centrality and propagation path length; The display module is used to display the propagation timing diagram and the node indicators in a visual map.

[0100] Exemplarily, the transmission path analysis unit in the system consists of a construction module, a traversal module, a calculation module and a display module, which aims to construct a time-series transmission structure diagram based on the pathogen evolution relationship map, and track and analyze potential transmission chains.

[0101] First, the construction module constructs a propagation time sequence diagram based on the edge weight information in the pathogen evolution relationship map, the timestamp information of sample collection, and the connection relationship of the propagation path between nodes. . In which, each node in the graph Corresponding to a sample obtained by actual sampling; edge Indicates that there is a potential transmission relationship between samples, its direction indicates the order of transmission possibility, and the weight reflects the transmission intensity. It is recorded as a timestamp and is used to constrain the directionality of the edge, making the graph structure conform to the temporal causal logic.

[0102] The weight of the propagation edge comes from the enhanced mutation distance defined in the system In addition, in order to improve the temporal rationality of the propagation modeling, the system introduces a time factor correction term based on the original mutation distance. It can be defined as:

[0103] in, represents the sampling time difference between samples, The weight coefficient for time difference correction.

[0104] The traversal module performs graph traversal operations on the propagation sequence graph to trace the possible propagation paths of any given source sample node. This module supports classic graph traversal algorithms, such as breadth-first search (BFS) and depth-first search (DFS), which can start from the earliest node or the node with the strongest propagation ability and gradually expand its possible influence range. During the traversal process, the system dynamically generates a propagation subgraph starting from the source node. , indicating the upstream and downstream transmission network of the sample in the disease chain.

[0105] The computation module performs propagation feature analysis on the nodes in the propagation subgraph, including but not limited to the following key graph metrics: 1. Node degree: indicates the number of direct connections between a node and other nodes. The out-degree (spread to others) of , the in-degree (spread by others) is , then the total degree of the node is Nodes with higher degrees are likely to be propagation intersections or diffusion points.

[0106] 2. Betweenness Centrality: measures the ability of a node to act as an information transfer hub. The number of shortest paths is recorded as , which passes through the node The number of paths is ,but:

[0107] The higher the index is, the more likely the node is in a "bridge" position in the transmission path and may be a potential key transmission node or super spreader.

[0108] 3. Path Length: The shortest path length from a specified source sample node to any node, used to assess the depth of a sample in the propagation chain or its distance from the source.

[0109] Finally, the system uses a display module to graphically display the transmission map and its analytical indicators. This module supports interactive visualization. In the output transmission map, node colors are used to identify transmission clusters or stages. Node sizes can be dynamically adjusted based on their degree or betweenness centrality. The thickness and direction of edges reflect the intensity and directionality of transmission. The graphical display also supports interactive operations such as clicking on specific nodes, filtering path chains, and expanding cluster structures. This visualization function helps users intuitively understand the pathogen's transmission trajectory, identify key nodes and paths, and provide intuitive data support for disease tracing and the formulation of prevention and control strategies.

[0110] In a feasible embodiment, the system further includes a transmission risk assessment unit, which includes a statistical module, an identification module and a risk calculation module; The statistical module is used to count the number and average edge weight of cross-cluster connected edges in the evolutionary relationship graph between the pathogens; The identification module is used to identify high-frequency regions and high-frequency transmission channel nodes with high-frequency cross-cluster connections in the evolutionary relationship map between the pathogens; The risk calculation module is used to generate a propagation risk score according to the number of edges, the average edge weight, the high-frequency area and the high-frequency propagation channel node.

[0111] Exemplarily, the transmission risk assessment module in the system is used to identify and quantitatively evaluate potential cross-cluster transmission phenomena in the pathogen evolution relationship map, and generate a transmission risk score that can be used for early warning.

[0112] First, the system uses a statistical module to extract all cross-cluster connections in the graph. Cross-cluster connections are edges connecting sample nodes with two different cluster labels, reflecting possible propagation jumps or multi-source convergence paths. These edges do not belong to the regular evolutionary paths within the same cluster and therefore have high risk-indicating value in the transmission chain structure.

[0113] The statistics module performs quantitative analysis on these cross-cluster edges along the following two key dimensions: The number of cross-cluster edges N cross Indicates how many edges connecting different clusters exist in the entire graph structure. The average edge weight of the cross-cluster edge is Represents the average cost of cross-cluster connection edges in terms of the combined distance of genetic variation and geographical paths.

[0114] After completing basic statistics, the identification module further mines potential high-risk areas and key transmission channel nodes from the structural map. Specifically, it includes: Identifying high-frequency transmission areas: The system uses a sliding window algorithm or graph clustering analysis to identify localized areas within the graph where multiple cross-cluster edges converge. These areas often represent transmission path intersections, super-spreading events, or hotspots where multiple sources of cases converge, and are therefore valuable for monitoring.

[0115] Identifying transmission channel hotspot nodes: The system counts the number of cross-cluster edges connected to each sample node. If this number exceeds a set threshold (e.g., three), the node is marked as a "transmission channel hotspot." Such nodes typically represent important distribution hubs, key passenger hubs, or super-spreader samples that have been linked to multiple transmission paths.

[0116] Based on the above statistical and identification results, the system constructs a communication risk score model R, which is used to comprehensively quantify the overall communication complexity and potential risk level in the map.

[0117]

[0118] in, is the number of cross-cluster connection edges; is the average variation distance across cluster edges (including genetic / national / port differences); is the number of high-frequency propagation areas (local high-aggregation cross-cluster areas); is the number of hotspot nodes in the transmission channel (such as super-spreader samples); is the source diversity index, calculated by Shannon entropy, indicating the diversity of samples from different countries / ports. A larger value indicates a more complex transmission path. , , is the risk model coefficient, which is used to adjust the weights of various risk factors and can be obtained through historical disease training.

[0119] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application 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 application.

Claims

1. A rapid sequencing and traceability analysis system for imported infectious diseases, characterized in that: include: A sample pre-processing unit, configured to identify and encode the sample to be tested after sample processing to form pre-processed sample information, wherein the sample processing includes nucleic acid extraction, purification, and quality testing; A high-throughput sequencing unit, used to construct a sequencing library for the pre-processed sample information and obtain raw sequencing data; A data processing and quality control unit is used to perform quality control, remove adapters, and eliminate host sequences on the raw sequencing data to obtain valid pathogen sequencing data; A rapid comparison and annotation unit, used to compare the effective pathogen sequencing data with a preset reference database to obtain comparison and annotation results; The traceability analysis unit is used to construct an evolutionary relationship map between pathogens based on the comparison and annotation results.

2. The imported infectious disease rapid sequencing and traceability analysis system according to claim 1, characterized in that: The specific steps of encoding the sample to be tested after sample processing to form pre-processed sample information include: Generate an identity information code segment and a time code segment according to the personnel information corresponding to the sample to be tested after the sample processing; Generate environmental data embedded in coding segments based on port passenger flow level information and historical disease levels; generating an extended coding segment according to vaccination information and travel itinerary information of the person information corresponding to the sample to be tested; The identity information coding segment, the time coding segment, the environmental data embedded coding segment and the extended coding segment are bound to the sample to be tested after the sample processing to form the pre-processed sample information.

3. The imported infectious disease rapid sequencing and traceability analysis system according to claim 1, characterized in that: The specific steps of constructing a sequencing library based on the pre-processed sample information and obtaining raw sequencing data include: Determining the target library type based on the nucleic acid type information, concentration range information, and target pathogen type information of the pretreated sample information; After the target library type is determined, a target library corresponding to the preprocessed sample information is established according to the library construction rules corresponding to the target library type; Obtaining concentration information and fragment distribution information of the target library through fluorescence quantification and fragment analysis; Calculating a library quality score based on the concentration information and the fragment distribution information; The target library whose library quality score meets the preset quality is loaded into the sequencing platform to obtain the raw sequencing data.

4. The imported infectious disease rapid sequencing and traceability analysis system according to claim 1, characterized in that: The specific steps of performing quality control, linker removal, and host sequence elimination on the raw sequencing data to obtain valid pathogen sequencing data include: Performing quality assessment on the raw sequencing data, analyzing the base quality distribution, N-base ratio, length distribution, and GC content deviation of the sequencing reads, and filtering and tail-trimming low-quality sequencing reads based on a set threshold to obtain quality-filtered sequencing data; Loading the adapter sequence database of the corresponding library construction platform, using the adapter recognition algorithm to match and locate the adapter sequences present in the quality-filtered sequencing data, and performing a trimming operation on the adapter sequences to obtain trimmed sequencing data; The host reference genome database is loaded, the trimmed sequencing data is compared with the host genome, and sequencing reads derived from the host are identified and removed to obtain the valid pathogen sequencing data.

5. The imported infectious disease rapid sequencing and traceability analysis system according to claim 1, characterized in that: The specific steps of comparing the effective pathogen sequencing data with a preset reference database to obtain comparison and annotation results include: Constructing a preset reference database including multiple known pathogen reference genome sequences, and performing index preprocessing on the preset reference database to form a target index structure, wherein the target index structure is a double-layer index structure consisting of a Bloom filter and a hash table; Extracting k-mer fragments from each sequencing read in the valid pathogen sequencing data, performing parallel hash matching operations on the GPU side, aligning the k-mer fragments with the target index structure of the preset reference database, and generating a hit score for each pathogen based on the number of hits to obtain a preliminary screening result; Based on the initial screening results and the dynamically restricted high-precision alignment range, the reference sequences of the top-N high-scoring candidate pathogens are selected as targets, and an alignment algorithm is used to perform a mapping operation on the sequencing reads to obtain alignment results; The annotation module is called to annotate the sequencing reads of the successfully aligned results by quickly mapping virulence factors, resistance genes and key functional regions using an annotation hash table mechanism to obtain the alignment and annotation results.

6. The imported infectious disease rapid sequencing and traceability analysis system according to claim 1, characterized in that: The specific steps of constructing an evolutionary relationship map between pathogens based on the comparison and annotation results include: Performing feature extraction on the alignment and annotation results to obtain a feature vector, wherein the feature vector includes a list of SNP sites, InDel or amino acid substitution positions, and virulence or resistance site labels; Constructing enhanced variation distance information between any two samples in the comparison and annotation results, wherein the enhanced variation distance information includes genotype differences, entry country differences, and entry port information; Inputting the enhanced variation distance information into the DBSCAN algorithm for unsupervised clustering to obtain clustering results; Constructing a minimum spanning tree propagation graph according to the enhanced mutation distance information; The clustering results are mapped to the minimum spanning tree propagation graph to construct an evolutionary relationship map among the pathogens.

7. The imported infectious disease rapid sequencing and traceability analysis system according to claim 6, characterized in that: The constructing of a minimum spanning tree propagation graph according to the enhanced mutation distance information includes: Construct a graph structure G(V,E) based on the enhanced mutation distance information, where the node V represents a sample, the edge E represents the potential propagation relationship between samples, and the edge weight is the enhanced mutation distance information; A minimum spanning tree algorithm is applied on the graph structure to construct the minimum spanning tree propagation graph between samples.

8. The imported infectious disease rapid sequencing and traceability analysis system according to claim 6, characterized in that: Mapping the clustering results to the minimum spanning tree propagation graph to construct an evolutionary relationship map among the pathogens includes: Writing the cluster labels in the clustering results into the attributes of each node in the minimum spanning tree propagation graph so that each node in the graph contains the cluster category information; Traversing the edge structure of the minimum spanning tree propagation graph to determine whether the cluster labels of the nodes at both ends of the edge are consistent; If they are inconsistent, mark the edge as a cross-cluster propagation path; Performing weighted adjustment on the edge weights of the cross-cluster propagation paths to enhance the risk expression capability of propagation jumps in the graph; Output is an enhanced propagation graph structure including the cluster labels and cross-cluster connection marks to construct an evolutionary relationship map among the pathogens.

9. A rapid sequencing and traceability analysis system for imported infectious diseases according to any one of claims 6 to 8, characterized in that: The system also includes a disease transmission path analysis unit, which includes a construction module, a traversal module, a calculation module and a display module. The construction module is used to call the edge weight information, timestamp information and propagation path connection information in the evolutionary relationship map between the pathogens to construct a propagation time sequence diagram between samples; The traversal module is used to perform a graph traversal operation based on the propagation timing graph to determine a propagation subgraph starting from a specified source sample, wherein the graph traversal operation includes a breadth-first search or a depth-first search; The calculation module is used to perform propagation feature analysis on the nodes of the propagation subgraph and calculate key propagation node indicators, wherein the key propagation node indicators include node degree, betweenness centrality and propagation path length; The display module is used to display the propagation timing diagram and the node indicators in a visual map.

10. A rapid sequencing and traceability analysis system for imported infectious diseases according to any one of claims 1 to 8, characterized in that: It also includes a transmission risk assessment unit, which includes a statistical module, an identification module and a risk calculation module; The statistical module is used to count the number and average edge weight of cross-cluster connected edges in the evolutionary relationship graph between the pathogens; The identification module is used to identify high-frequency regions and high-frequency propagation channel nodes with high-frequency cross-cluster connections in the evolutionary relationship map between the pathogens; The risk calculation module is used to generate a propagation risk score according to the number of edges, the average edge weight, the high-frequency area and the high-frequency propagation channel node.

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