HIV molecular network construction method based on gene distance analysis and analysis system

Abstract

The invention provides an HIV molecular network construction method and analysis system based on gene distance analysis, and relates to the field of bioinformatics, the system sequentially comprises a data input module, a sequence quality control module, a multi-sequence comparison module, a genetic distance analysis module, a network construction and cluster identification module, a network index calculation module, a visual rendering module, a user cooperative control module and the like; all the modules work cooperatively through preset interfaces, alignment, distance calculation and threshold judgment can be automatically completed on batch HIV nucleic acid sequences, sequence pairs meeting the threshold condition are mapped into a node edge network, propagation clusters are recognized in real time, and topological indexes such as node degree and betweenness centrality are calculated. The system adopts a plug-in architecture, supports algorithm replacement and parameter configuration, allows multiple users to synchronously adjust a threshold value or a screening condition, refreshes a network structure and a statistical result in real time, and outputs a personalized network diagram and an analysis report.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to an HIV molecular network construction method and analysis system based on gene distance analysis. Background Art

[0002] With the rapid development of genome sequencing technology, large-scale pathogen gene sequence data can be quickly obtained. How to effectively analyze these gene sequences has become an important issue in bioinformatics research. In the epidemiology of infectious diseases, constructing molecular transmission networks based on pathogen gene sequence data is an important tool. Molecular transmission networks can intuitively present the transmission pathways of viruses between individuals or groups, identify potential transmission clusters, and thus provide a scientific basis for public health decision-making. However, existing technical solutions still have shortcomings in practical applications: Currently, a variety of bioinformatics software is available for gene sequence analysis, including software for efficient multiple sequence alignment and specialized tools for calculating genetic distances and delineating transmission clusters. However, existing solutions often fail to effectively integrate the steps of sequence quality control, multiple sequence alignment, genetic distance calculation, network construction, and analysis, lacking integrated automated workflows. This necessitates manual intervention and results in low efficiency in the analysis process from raw sequence to transmission network results.

[0003] Some existing systems employ fixed architectures, such as containerized deployment of analysis modules and inter-module communication via sockets. While this architecture enables functional integration, it presents limitations in terms of user interaction, multi-user collaboration, and module expansion. Furthermore, some solutions employ specific strategies for threshold setting, such as calculating genetic distance based on a specific evolutionary model and using the distance threshold at which the number of propagation clusters reaches a maximum as the network construction threshold. This single threshold selection mechanism may not be adaptable to different datasets or research needs, and lacks flexibility.

[0004] Existing technologies output static results after completing network construction. This makes it difficult to customize the network to meet specific needs, such as changing thresholds to regenerate the network, selecting different network metrics for analysis, or adjusting the presentation of the network graph. Furthermore, these systems rarely support collaborative analysis, meaning they cannot support multiple users simultaneously viewing and controlling the network analysis process. Summary of the Invention

[0005] The main purpose of the present invention is to provide a visual display system for HIV molecular transmission network, which can effectively solve the problems in the background technology.

[0006] To achieve the above objectives, the present invention provides a technical solution for visualizing HIV molecular transmission networks, comprising: A data input module is used to receive batches of HIV nucleic acid sequence data and perform analysis; A sequence processing module, used to perform quality control preprocessing on the sequence data and filter out low-quality or duplicate sequences; Multiple sequence alignment module, used to perform multiple sequence alignment on sequences that pass quality control and generate sequence alignment results; A genetic distance analysis module is used to calculate the genetic distance between sequences based on the sequence alignment results, and determine the transmission association between sequences according to a preset genetic distance threshold; The network construction and cluster identification module is used to construct an HIV molecular transmission network by using sequences as nodes and establishing edges based on sequence pairs that meet threshold conditions, thereby identifying transmission clusters in the network; A network index calculation module, used to calculate the network topology index of each node and cluster in the propagation network; A visualization rendering module, used to present the propagation network and its analysis indicators in a graphical interface; The user interaction and collaboration module is used to receive user interactions on analysis parameters and visualization settings, and supports multiple users to collaboratively control and synchronously update analysis results and visualization displays.

[0007] The system realizes an automated analysis process from HIV sequence data input to molecular transmission network construction and visualization through the collaborative work of the above modules.

[0008] Furthermore, the multiple sequence alignment module and genetic distance analysis module adopt a modular plug-in design to allow replacement of different alignment algorithms or genetic distance calculation models, thereby adapting to different data sets or analysis requirements.

[0009] Furthermore, the genetic distance analysis module supports multiple distance calculation model parameter configurations. When the user does not set a threshold, the module can automatically recommend a genetic distance threshold for network construction based on the sequence genetic distance distribution.

[0010] Furthermore, the network indicator calculation module can update the calculation results of the network topology indicators in real time when the user adjusts the analysis parameters or the input data changes, so as to dynamically reflect the changes in the network structure.

[0011] Furthermore, the visualization rendering module provides personalized network diagram presentation options, allowing users to customize the display properties of nodes and edges, network layout, and classify and label nodes according to metadata, and supports exporting the network diagram and analysis results as static images or interactive reports.

[0012] Furthermore, the user interaction and collaboration module includes permission control and synchronization mechanisms for managing different users' modification permissions on analysis parameters in a multi-user collaborative environment and ensuring that the visual views of all user interfaces are updated synchronously with the latest analysis results.

[0013] The present invention also provides a technical solution for constructing an HIV molecular network based on gene distance analysis, comprising the following steps: S1: receiving multiple HIV nucleic acid sequences in batches as input data and performing reading and parsing on the sequences; S2: Perform quality control on the input sequence data, filter out sequences that do not meet the quality requirements, and obtain a set of sequences with qualified quality; S3: Perform multiple sequence alignment on the qualified sequence set to obtain sequence alignment result data; S4: Calculating the genetic distance between any two sequences based on the sequence alignment results; determining the association between the sequences according to a preset genetic distance threshold, and determining that there is a transmission connection between the corresponding sequences when the distance is not greater than the threshold; S5: Representing each sequence as a network node and establishing edges between sequence nodes with transmission links to construct an HIV molecular transmission network; further identifying transmission clusters in the network and obtaining the node sets contained in each cluster; S6: Calculating network topology indicators of the propagation network, including the degree and centrality indicators of each node, and statistical parameters such as the size and density of each propagation cluster; S7: Display the propagation network and its topology analysis results to the user in a visual form, and provide an interactive interface to support the user to browse and personalize the network diagram.

[0014] Furthermore, in S4, when the user does not specify a genetic distance threshold, the genetic distance distribution between sequences is automatically analyzed and a threshold is recommended for the user to select for network construction, thereby avoiding the result limitations caused by using a fixed threshold.

[0015] Furthermore, in S6 and S7, when the user modifies the analysis parameters through the interactive interface, including adjusting the genetic distance threshold or filtering part of the sequence data, S4-S6 are executed to update the propagation network and its indicators in real time, and the updated visualization results are presented to the user.

[0016] Furthermore, the HIV molecular network construction method based on genetic distance analysis supports multi-user collaborative execution, and the above-mentioned S4-S7 operations are jointly triggered and viewed by multiple users through networked clients; when a user adjusts the analysis parameters or visualization settings, the system synchronously updates the result views of all online users to achieve collaborative interactive display of HIV transmission network analysis.

[0017] Compared with the prior art, the present invention has the following advantages: Highly integrated, automated workflow: From sequence quality control and alignment to network construction and visualization, the entire process is automated within a unified system, reducing manual intervention. Users simply provide the sequence and basic parameters to obtain the propagation network and analysis results with a single click, improving analysis efficiency and accuracy.

[0018] Modular and scalable design: The system utilizes a plug-in modular configuration, allowing each functional module to be flexibly replaced or expanded as needed. This design avoids dependency on specific tools or frameworks and avoids a fixed container-socket architecture, enabling smooth integration of new algorithms or third-party libraries to meet evolving research needs.

[0019] Real-time Calculation and Interactive Analysis: This invention supports parameter adjustments during analysis, instantly recalculating networks and metrics. Specifically, the genetic distance threshold can be dynamically adjusted by the user, allowing the system to update the propagation network structure and clustering results in real time and recalculate network metrics. This interactive analysis approach surpasses traditional static processes, allowing users to explore the impact of different parameters on results and gain deeper insights.

[0020] User Collaboration: The system supports multi-user collaboration, allowing multiple researchers to simultaneously participate in analysis and decision-making. Through synchronized interface interaction, any user adjusting analysis settings will see updated results in real time for all collaborating users. This collaborative capability is crucial for team-based epidemiological analysis and is unavailable in existing stand-alone or single-user systems.

[0021] Personalized visualization output: This system offers a wealth of visualization customization options and output methods. Users can customize the network diagram presentation to highlight clusters or key nodes of interest. The system also supports exporting results to multiple formats, facilitating further sharing and application of analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is an overall flow chart of a method for constructing HIV molecular network based on gene distance analysis in the present invention.

[0023] Figure 2 The present invention provides a flowchart for quality control of nucleic acid sequences in a method for constructing HIV molecular networks based on gene distance analysis.

[0024] Figure 3 The present invention provides a flowchart of multiple sequence alignment for a method of constructing an HIV molecular network based on gene distance analysis.

[0025] Figure 4The present invention provides a flowchart of the molecular network construction of HIVTRACE based on the gene distance analysis method of HIV molecular network construction.

[0026] Figure 5 The present invention provides a flowchart for constructing molecular network visualization data of an HIV molecular network construction method based on gene distance analysis.

[0027] Figure 6 The present invention provides a flowchart of the genetic distance analysis between nodes of the HIV molecular network construction method based on genetic distance analysis.

[0028] Figure 7 The present invention provides a flowchart of statistical analysis of clusters in a method for constructing HIV molecular networks based on gene distance analysis.

[0029] Figure 8 The present invention provides a flowchart of network analysis of node attributes in a method for constructing HIV molecular networks based on gene distance analysis. DETAILED DESCRIPTION

[0030] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is intended only to enable those skilled in the art to better understand and implement the subject matter described herein, and is not intended to limit the scope of protection, applicability, or examples set forth in the claims. The functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the present specification. Various examples may omit, replace, or add various processes or components as needed. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described relative to some examples may also be combined in other examples.

[0031] Example 1

[0032] The technical solution provided by the present invention is a system for visualizing HIV molecular transmission networks, comprising: A data input module is used to receive batches of HIV nucleic acid sequence data and perform analysis; A sequence processing module, used to perform quality control preprocessing on the sequence data and filter out low-quality or duplicate sequences; Multiple sequence alignment module, used to perform multiple sequence alignment on sequences that pass quality control and generate sequence alignment results; A genetic distance analysis module is used to calculate the genetic distance between sequences based on the sequence alignment results, and determine the transmission association between sequences according to a preset genetic distance threshold; The network construction and cluster identification module is used to construct an HIV molecular transmission network by using sequences as nodes and establishing edges based on sequence pairs that meet threshold conditions, thereby identifying transmission clusters in the network; A network index calculation module, used to calculate the network topology index of each node and cluster in the propagation network; A visualization rendering module, used to present the propagation network and its analysis indicators in a graphical interface; The user interaction and collaboration module is used to receive user interactions on analysis parameters and visualization settings, and supports multiple users to collaboratively control and synchronously update analysis results and visualization displays.

[0033] The system realizes an automated analysis process from HIV sequence data input to molecular transmission network construction and visualization through the collaborative work of the above modules.

[0034] Furthermore, the multiple sequence alignment module and genetic distance analysis module adopt a modular plug-in design to allow replacement of different alignment algorithms or genetic distance calculation models, thereby adapting to different data sets or analysis requirements.

[0035] Furthermore, the genetic distance analysis module supports multiple distance calculation model parameter configurations. When the user does not set a threshold, the module can automatically recommend a genetic distance threshold for network construction based on the sequence genetic distance distribution.

[0036] Furthermore, the network indicator calculation module can update the calculation results of the network topology indicators in real time when the user adjusts the analysis parameters or the input data changes, so as to dynamically reflect the changes in the network structure.

[0037] Furthermore, the visualization rendering module provides personalized network diagram presentation options, allowing users to customize the display properties of nodes and edges, network layout, and classify and label nodes according to metadata, and supports exporting the network diagram and analysis results as static images or interactive reports.

[0038] Furthermore, the user interaction and collaboration module includes permission control and synchronization mechanisms for managing different users' modification permissions on analysis parameters in a multi-user collaborative environment and ensuring that the visual views of all user interfaces are updated synchronously with the latest analysis results.

[0039] The HIV molecular transmission network visualization system of the present invention utilizes a modular architecture, comprising a data input module, a sequence processing module, a multiple sequence alignment module, a genetic distance analysis module, a network construction and cluster identification module, a network metric calculation module, a visualization rendering module, and a user interaction and collaboration module. Each module can be deployed as a separate physical unit or logically integrated into the same software platform. Modules transfer data via predefined interfaces. For example, the sequence processing module passes cleaned sequence data to the alignment module, which then passes the alignment results to the distance analysis module. Due to its plug-in design, the internal implementation of each module can be flexibly replaced or upgraded. For example, the visualization module can replace the underlying graphics library, and the distance analysis module can add support for new computational models. Users connect to the system through a user interface and can access various functions remotely or locally. When multiple users are connected simultaneously, the collaboration module ensures that all users' views and operations remain synchronized. Thus, parameter changes by one user are broadcast to other users through the collaboration module, triggering the system backend to recalculate and update the results. All user interfaces reflect the changes almost synchronously, achieving a truly collaborative analysis experience.

[0040] Example 2

[0041] The present invention also provides a technical solution for constructing an HIV molecular network based on gene distance analysis, comprising the following steps: S1: receiving multiple HIV nucleic acid sequences in batches as input data and performing reading and parsing on the sequences; S2: Perform quality control on the input sequence data, filter out sequences that do not meet the quality requirements, and obtain a set of sequences with qualified quality; S3: Perform multiple sequence alignment on the qualified sequence set to obtain sequence alignment result data; S4: Calculating the genetic distance between any two sequences based on the sequence alignment results; determining the association between the sequences according to a preset genetic distance threshold, and determining that there is a transmission connection between the corresponding sequences when the distance is not greater than the threshold; S5: Representing each sequence as a network node and establishing edges between sequence nodes with transmission links to construct an HIV molecular transmission network; further identifying transmission clusters in the network and obtaining the node sets contained in each cluster; S6: Calculating network topology indicators of the propagation network, including the degree and centrality indicators of each node, and statistical parameters such as the size and density of each propagation cluster; S7: Display the propagation network and its topology analysis results to the user in a visual form, and provide an interactive interface to support the user to browse and personalize the network diagram.

[0042] Furthermore, in S4, when the user does not specify a genetic distance threshold, the genetic distance distribution between sequences is automatically analyzed and a threshold is recommended for the user to select for network construction, thereby avoiding the result limitations caused by using a fixed threshold.

[0043] Furthermore, in S6 and S7, when the user modifies the analysis parameters through the interactive interface, including adjusting the genetic distance threshold or filtering part of the sequence data, S4-S6 are executed to update the propagation network and its indicators in real time, and the updated visualization results are presented to the user.

[0044] Furthermore, the HIV molecular network construction method based on genetic distance analysis supports multi-user collaborative execution, and the above-mentioned S4-S7 operations are jointly triggered and viewed by multiple users through networked clients; when a user adjusts the analysis parameters or visualization settings, the system synchronously updates the result views of all online users to achieve collaborative interactive display of HIV transmission network analysis.

[0045] The method of the present invention includes but is not limited to the following steps: 1. Batch sequence input: Users import multiple HIV nucleic acid sequences through the data input module, and the system parses the input to generate a sequence list. For example, a user can upload a file containing hundreds of sequences, which the system then splits and reads, assigning a unique identifier to each sequence and preparing it for quality control. If the sequence data is stored in a database, the system can also retrieve the required sequences in batches through a query interface. HIV sequences from different patients, different regions, or different time periods can be input in the same batch, and the system will process them uniformly.

[0046] 2. Sequence quality control: The sequence processing module performs a quality check on each input sequence. Implementation includes the following sub-steps: 1) Check whether the sequence length meets the analysis requirements. Sequences that are too short are marked or removed.

[0047] 2) Count the proportion of uncertain bases or mixed base symbols in the sequence. If it exceeds a preset threshold, the sequence quality is judged to be poor.

[0048] 3) Detect illegal characters or format errors and mark the sequence as invalid if they exist.

[0049] 4) Compare the repetitions between sequences. If identical repetitive sequences are found, only one is retained and the redundancy is removed.

[0050] After the aforementioned quality control screening, the system generates a quality control report and a set of valid sequences that passed the screening. The quality control report includes the inspection results for each sequence for user reference. If a sequence is filtered, the report will indicate reasons such as insufficient sequence length or excessive N bases. High-quality sequences that pass quality control proceed to the next step of analysis. The entire quality control process is completed automatically by the system, and users can also adjust certain quality control parameters as needed to adapt to the characteristics of different experimental data.

[0051] 3. Multiple sequence alignment: The sequence set after quality control is sent to the multiple sequence alignment module. The system calls the built-in alignment algorithm to perform a global alignment of all sequences to align the bases at homologous positions. Since there may be insertions and deletions and high diversity between HIV sequences, the algorithm uses iterative refinement or heuristic methods to ensure that the alignment results are reasonable. The present invention does not limit the name of a specific alignment tool, but supports multiple algorithm implementations through a module interface, such as traditional methods based on dynamic programming or more efficient approximate algorithms. After the alignment is completed, an aligned sequence file or data structure is output. At the same time, a summary of the alignment results is generated, such as the difference ratio between each pair of sequences, etc., which can be used for reference in subsequent steps or for user viewing. The multiple sequence alignment step in the figure ensures that there is a unified reference for subsequent distance calculations and can be replaced with an alignment algorithm that is more suitable for a specific data set when necessary.

[0052] 4. Genetic Distance Calculation and Threshold Application: The alignment results are passed to the Genetic Distance Analysis module, where the system calculates the genetic distance between each pair of sequences. Genetic distance calculation can be performed using user-specified model parameters. For example, common models such as the Kimura two-parameter model and the Jukes-Cantor model can be selected to calculate nucleotide substitution rates. Default configurations for commonly used model parameters for RNA viruses such as HIV are also provided. The calculated pairwise distances are compared against a user-specified threshold. When the distance is less than or equal to the threshold, the two sequences are considered to have a possible direct or indirect transmission relationship. By default, users can set a threshold range based on experience or industry recommendations, such as 1.5% of the genetic distance between sequences as an initial threshold. If the user is unsure of the threshold, the system analyzes the distance distribution and intelligently recommends a candidate threshold range. This flexible threshold determination approach differs from some existing methods that rigidly use the maximum number of clusters as the criterion. Instead, it allows the user and the algorithm to determine the threshold, ensuring that the resulting network is both biologically meaningful and meets the specific analysis objectives.

[0053] 5. Transmission Network Construction and Cluster Identification: Based on the distance comparison results from the previous step, the network construction module begins generating a molecular transmission network. Specifically, the system creates a network node for each sequence and creates edges for sequence pairs that meet threshold conditions. This maps all input sequences into an undirected graph structure, where edges represent possible transmission connections. The system then automatically analyzes this network graph to identify transmission clusters: typically, a cluster is defined as a group of nodes in the network that are directly or indirectly connected by edges. The network construction module uses graph algorithms to find all clusters and calculate basic properties of each cluster, such as the number of nodes and average distance within the cluster. For an HIV transmission network, a cluster may represent a group of related cases. Identifying clusters helps public health officials identify clustered transmission events. The system of the present invention supports user collaboration during network construction and cluster identification: when multiple users are online, they can negotiate thresholds or algorithm parameters, which are then applied uniformly by the system to ensure consistent clustering results for all users. Furthermore, if the threshold is slightly adjusted, the network structure is restructured in real time, and the new clustering results are displayed immediately, allowing users to quickly compare the impact of different thresholds on cluster formation.

[0054] 6. Real-time Calculation of Network Indicators: Once the transmission network is generated and clusters are identified, the network indicator calculation module immediately performs quantitative analysis of the network, calculating various network topology indicators. For each network node, the system calculates its degree to measure the number of direct connections in the transmission process. It further calculates centrality indicators to assess the extent to which the node serves as an intermediate bridge or information pathway within the entire network. For example, a node with high betweenness centrality may be a superspreader or a key transmission relay. For each transmission cluster, the system calculates cluster density to understand the closeness of transmission within the cluster and provides indicators such as cluster diameter to measure the cluster's spread. These calculation results are displayed in conjunction with the network diagram in the user interface, allowing users to view specific indicator values ​​by clicking on a node or cluster. Real-time calculation means that if new sequences are added to the input data or if the user changes the threshold, causing the network structure to change, the system automatically recalculates these indicators and updates the display immediately, ensuring that the analysis results always reflect the current network state.

[0055] 7. Visualization and Customizable Output: The visualization rendering module presents the final network and its analysis results through an interactive interface. The user interface consists of a main network diagram and a sidebar information panel. The network diagram displays the HIV transmission network as nodes and edges. Nodes are typically represented by dots and can be colored or labeled based on metadata. Edges are connected by lines, representing possible transmission paths. Transmission clusters are distinguished on the diagram by color or edge style. For example, edges between clusters are represented by different colors, or the edges of nodes within each cluster are highlighted in different colors, making it easy for users to quickly identify them. The information panel lists statistical metrics for the currently selected node / cluster. For example, when a node is selected, its degree, centrality value, cluster ID, and size are displayed; when a cluster is selected, a list of nodes and overall cluster metrics are displayed. Users can customize the visualization by dragging nodes to adjust the layout, changing the background or color scheme, and zooming in and out for detailed information. The system also provides multiple layout algorithm options for users to switch between to optimize the graphical display.

[0056] Example 3 Those skilled in the art will appreciate that the various embodiments disclosed above may be modified and altered without departing from the essence of the invention. Therefore, the scope of protection of the present invention shall be defined by the appended claims.

[0057] It should be noted that not all steps and units in the above processes are required, and certain steps or units can be omitted according to actual needs. The execution order of each step is not fixed and can be determined as needed. The device structure described in the above embodiments can be a physical structure or a logical structure, that is, some units may be implemented by the same physical entity, or some units may be implemented by multiple physical entities, or may be implemented by certain components in multiple independent devices.

[0058] The specific embodiments set forth above describe exemplary embodiments, but do not represent all embodiments that can be implemented or fall within the scope of protection of the claims. The term "exemplary" used throughout this specification means "used as an example, instance, or illustration" and does not mean "preferred" or "having advantages" over other embodiments. For the purpose of providing an understanding of the described technology, the specific embodiments include specific details. However, these technologies can be implemented without these specific details. In some instances, in order to avoid obscuring the concepts of the described embodiments, well-known structures and devices are shown in block diagram form.

[0059] Example 4

[0060] This example demonstrates the complete workflow for multiple HIV nucleic acid sequence data sets, from sequence quality control and multiple sequence alignment to genetic distance network construction and indicator analysis. The entire system is modular, supports parameter configuration and batch processing, and automatically converts input sequences into HIV molecular transmission networks and calculates relevant statistical indicators, offering efficient and flexible technical advantages.

[0061] First, the data input module receives batches of HIV nucleic acid sequences and parses them to generate a sequence list. The system assigns a unique identifier to each sequence, preparing it for quality control. The sequence processing module performs the following quality checks on each input sequence: Length Check: Determines whether the sequence length meets the analysis requirements. If the length is too short, it will be marked as unqualified or rejected. The length threshold can be set by the user, and the default configuration is based on the analysis needs.

[0062] Base quality check: Counts the percentage of uncertain bases or mixed base symbols in the sequence. If the percentage exceeds a preset threshold, the sequence is considered poor quality. This threshold is configurable to accommodate the sequencing accuracy of different experimental data.

[0063] Format validity check: Checks whether the sequence contains illegal characters or format errors. If so, the sequence is marked as invalid. This ensures that the input data format is standardized and avoids errors in subsequent analysis.

[0064] Duplicate sequence detection: Compares the string content of all sequences to identify identical repeating sequences. The system uses a hash table or set structure to store the hash values ​​of existing sequences, allowing for rapid detection of duplicates and removal of redundancy, retaining only one representative sequence for subsequent analysis.

[0065] After the aforementioned quality control screening, the system generates a quality control report and a set of valid sequences that passed the screening. The quality control report records the inspection results for each sequence. If a sequence is filtered out, the report indicates the reasons, such as insufficient sequence length or excessive number of uncertain bases. High-quality sequences that pass quality control proceed to the next step of analysis. The entire quality control process is completed automatically by the system, and users can adjust quality control parameters as needed to adapt to the characteristics of different experimental data.

[0066] Multiple sequence alignment The valid sequence set after quality control is sent to the multiple sequence alignment module for global alignment. The system calls the built-in alignment algorithm to align all sequences according to the homologous base positions. Since there may be insertions and deletions between HIV sequences and the degree of variation is high, iterative refinement and heuristic strategies are used in the implementation of the alignment algorithm to optimize the alignment results. This module is designed in a plug-in manner and supports the replacement of different alignment algorithms: for example, the classic dynamic programming-based algorithm can be used to obtain precise alignment, or a more efficient approximate algorithm can be used to accelerate large-scale sequence alignment. After the alignment is completed, the aligned sequence file or data structure and the summary information of the alignment results are output for reference in subsequent genetic distance calculations or for user review and verification. By providing standardized multiple sequence alignment results, a unified coordinate reference is ensured for subsequent distance calculations; at the same time, due to the modular design, the alignment step can be replaced with an algorithm that is more suitable for a specific data set when necessary, achieving flexible expansion.

[0067] Genetic distance calculation and threshold setting The alignment results are passed to the Genetic Distance Analysis module, which calculates the genetic distance between any two sequences. Genetic distance calculations can be customized based on user-defined nucleotide substitution models, such as the Kimura two-parameter model and the Jukes-Cantor model, among other commonly used evolutionary models. The system provides default model parameter configurations and supports user-defined parameters to meet diverse research needs. The module iterates through the aligned sequence pairs and calculates the distance between each pair. After calculation, the system compares the resulting pairwise distances with a preset genetic distance threshold. If the distance is less than or equal to the threshold, the corresponding sequence pairs are considered to have a direct or indirect transmission link and are considered a possible transmission path within the network. The threshold is typically set by the user based on experience or industry recommendations; for example, a threshold of 1.5% of the genetic distance can be used as an initial association threshold. If the user does not specify a threshold, the system automatically analyzes the global distance distribution and intelligently recommends a candidate threshold range for the user to choose from. Specifically, the code analyzes the distribution of all sequence distance values, for example, identifying breakpoints or gaps in the distance distribution to infer a threshold range that can reasonably separate the network into clusters. This threshold determination method combines the advantages of algorithmic analysis and manual decision-making. Unlike existing technologies that use a single criterion of maximizing the number of clusters, it enhances the flexibility and biological plausibility of threshold selection. All distance calculations and threshold determination processes are performed automatically by the system, and users can dynamically adjust threshold parameters during analysis. The system will re-evaluate sequence associations in real time using the new threshold.

[0068] Network construction and propagation cluster identification Based on the sequence association results output by the Genetic Distance Analysis module, the Network Construction Module begins generating an HIV molecular transmission network. Specifically, the system represents each sequence that passes quality control as a node in the network and creates edges between sequence nodes that meet threshold conditions. This maps all input sequences into an undirected graph structure, where nodes represent viral samples and edges represent possible transmission pathways between samples. The system then automatically analyzes this network graph to identify transmission clusters: typically, a cluster is defined as a group of nodes directly or indirectly connected by edges. The Network Construction Module uses graph algorithms to traverse the entire graph to find all connected subgraphs, each corresponding to a transmission cluster. In implementation, algorithms such as depth-first search, breadth-first search, or union-find can be used to efficiently discover all clusters and record the set of nodes contained in each cluster. For each identified cluster, the system further calculates basic cluster properties, such as the number of nodes and the average genetic distance within the cluster, to provide a summary of the cluster's characteristics. These clusters often correspond to clusters of cases with transmission links, helping public health officials locate clusters of transmission events. The system of the present invention features multi-user collaborative interaction during network construction and cluster identification: when multiple users are online, they can collectively negotiate adjustments to genetic distance thresholds or algorithm parameters. The system then uniformly applies updates to the network construction, ensuring consistent results for all users. If thresholds are adjusted after negotiation, the network structure is reconstructed in real time, and clustering results are updated and presented instantly, allowing users to quickly compare the impact of different thresholds on cluster formation. This modular and collaborative design makes the network construction process both efficient and interactive, adaptable to diverse analysis needs and team collaboration scenarios.

[0069] Network topology indicator analysis Once the transmission network is generated and clusters are identified, the network indicator calculation module immediately performs a quantitative analysis of the network, calculating various network topology indicators. For each network node, the system calculates its degree, that is, the number of edges directly connected to the node, to measure the number of direct transmission connections. The degree calculation is directly based on network adjacency relationships. In implementation, it can be obtained by traversing the adjacency list or set counting number of connections for each node. The system then further calculates the centrality indicator of each node to assess the extent to which the node serves as an intermediate bridge or information pathway in the entire network. For example, this embodiment calculates betweenness centrality as the main centrality indicator: by traversing the shortest paths of all node pairs in the network, the number of shortest paths passing through each node is counted, thereby assessing the importance of the node as a "transit station" in the network. The algorithm uses a calculation method based on shortest path traversal, performing a shortest path search for each node on an undirected graph and accumulating path counts to efficiently obtain the betweenness centrality value of all nodes. Nodes with high betweenness centrality often correspond to super spreaders or key transmission relays, which are of great significance in epidemic transmission networks.

[0070] For each propagation cluster, the system also calculates cluster-level network metrics. For example, cluster density can be calculated to reflect the density of propagation within the cluster, specifically the ratio of the actual number of edges within the cluster to the maximum possible number of edges. Cluster diameter is also calculated, measuring the longest path length between the two most distant nodes within the cluster, thereby characterizing the extent of propagation. In addition, metrics such as cluster size and average path length within the cluster are also calculated to comprehensively describe the cluster's structural characteristics. The calculated node and cluster metrics are linked to the network data structure and provided to the visualization module for display. The system supports real-time metric updates: If new sequences are added, causing changes in the network structure, or if the user adjusts the propagation threshold to filter out nodes and then rebuilds the network in the interactive interface, the system will automatically recalculate and update various network metrics and immediately display the updated results in the interface, ensuring that analysis results always reflect the current network state. This real-time calculation capability, combined with the interactive interface, allows users to dynamically observe changes in network topology metrics under different conditions, greatly improving the timeliness and flexibility of analysis.

[0071] The foregoing description of the present disclosure is provided to enable any person skilled in the art to implement or use the present disclosure. Various modifications to the present disclosure will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. Therefore, the present disclosure is not limited to the examples and designs described herein, but is intended to be consistent with the widest range of principles and novel features disclosed herein.

Claims

1. An HIV molecular analysis system based on genetic distance analysis, characterized by: include: A data input module is used to receive batches of HIV nucleic acid sequence data and perform analysis; A sequence processing module, used to perform quality control preprocessing on the sequence data and filter out low-quality or duplicate sequences; Multiple sequence alignment module, used to perform multiple sequence alignment on sequences that pass quality control and generate sequence alignment results; A genetic distance analysis module is used to calculate the genetic distance between sequences based on the sequence alignment results, and determine the transmission association between sequences according to a preset genetic distance threshold; The network construction and cluster identification module is used to construct an HIV molecular transmission network by using sequences as nodes and establishing edges based on sequence pairs that meet threshold conditions, thereby identifying transmission clusters in the network; A network index calculation module, used to calculate the network topology index of each node and cluster in the propagation network; A visualization rendering module, used to present the propagation network and its analysis indicators in a graphical interface; The user interaction and collaboration module is used to receive user interactions on analysis parameters and visualization settings, and supports collaborative control by multiple users and synchronous updates of analysis results and visualization displays; The system realizes an automated analysis process from HIV sequence data input to molecular transmission network construction and visualization through the collaborative work of the above modules.

2. The HIV molecular analysis system based on genetic distance analysis according to claim 1, characterized in that: The multiple sequence alignment module and genetic distance analysis module adopt a modular plug-in design to allow replacement of different alignment algorithms or genetic distance calculation models, thereby adapting to different data sets or analysis requirements.

3. The HIV molecular analysis system based on genetic distance analysis according to claim 1, characterized in that: The genetic distance analysis module supports multiple distance calculation model parameter configurations. When the user does not set a threshold, the module can automatically recommend a genetic distance threshold for network construction based on the sequence genetic distance distribution.

4. The HIV molecular analysis system based on genetic distance analysis according to claim 1, characterized in that: The network index calculation module can update the calculation results of the network topology index in real time when the user adjusts the analysis parameters or the input data changes, so as to dynamically reflect the changes in the network structure.

5. The HIV molecular analysis system based on genetic distance analysis according to claim 1, characterized in that: The visualization rendering module provides personalized network diagram presentation options, allowing users to customize the display properties of nodes and edges, network layout, and classify and label nodes according to metadata, and supports exporting the network diagram and analysis results as static images or interactive reports.

6. The HIV molecular analysis system based on genetic distance analysis according to claim 1, characterized in that: The user interaction and collaboration module includes permission control and synchronization mechanisms, which are used to manage different users' modification permissions on analysis parameters in a multi-user collaborative environment and ensure that the visual views of all user interfaces are updated synchronously with the latest analysis results.

7. A method for constructing an HIV molecular network based on genetic distance analysis, characterized by: The following steps are involved: S1: receiving multiple HIV nucleic acid sequences in batches as input data and performing reading and parsing on the sequences; S2: Perform quality control on the input sequence data, filter out sequences that do not meet the quality requirements, and obtain a set of sequences with qualified quality; S3: Perform multiple sequence alignment on the qualified sequence set to obtain sequence alignment result data; S4: Calculating the genetic distance between any two sequences based on the sequence alignment results; determining the association between the sequences according to a preset genetic distance threshold, and determining that there is a transmission connection between the corresponding sequences when the distance is not greater than the threshold; S5: Representing each sequence as a network node and establishing edges between sequence nodes with transmission links to construct an HIV molecular transmission network; further identifying transmission clusters in the network and obtaining the node sets contained in each cluster; S6: Calculating network topology indicators of the propagation network, including the degree and centrality indicators of each node, and statistical parameters such as the size and density of each propagation cluster; S7: Display the propagation network and its topology analysis results to the user in a visual form, and provide an interactive interface to support the user to browse and personalize the network diagram.

8. The method for constructing an HIV molecular network based on gene distance analysis according to claim 7, characterized in that: In S4, when the user does not specify a genetic distance threshold, the genetic distance distribution between sequences is automatically analyzed and a threshold is recommended for the user to select for network construction, thereby avoiding the result limitations caused by using a fixed threshold.

9. The method for constructing an HIV molecular network based on gene distance analysis according to claim 7, characterized in that: In S6 and S7, when the user modifies the analysis parameters through the interactive interface, including adjusting the genetic distance threshold or filtering part of the sequence data, S4-S6 are executed to update the propagation network and its indicators in real time, and the updated visualization results are presented to the user.

10. The method for constructing an HIV molecular network based on gene distance analysis according to claim 7, characterized in that: The described HIV molecular network construction method based on genetic distance analysis supports multi-user collaborative execution. The above-mentioned S4-S7 operations are jointly triggered and viewed by multiple users through a networked client. When a user adjusts the analysis parameters or visualization settings, the system synchronously updates the result views of all online users to achieve collaborative interactive display of HIV transmission network analysis.

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