Method for constructing a visualization platform for molecular regulatory networks and visualization platform

Abstract

The present invention belongs to the technical field of molecular regulatory network analysis, and specifically discloses a method and a visualization platform for constructing a molecular regulatory network visualization platform. The method integrates and standardizes the access to regulatory network data, obtains node set files and edge set files and imports them into a server-side database, extracts parameters of gene subnetworks on the server side, and generates data required for web-side response and visualization. Based on the extracted data, a web-side interface and event response program are developed using computer language, and a web-side and server-side communication interface is developed using PHP language for transmitting parameters to complete the construction of a visualization platform. By adopting this technical solution, a web-based online database provides online interactive analysis and visualization of molecular regulatory networks, and collects and provides very detailed biological information for each node for easy reference.

Description

Technical Field

[0001] The present invention belongs to the technical field of molecular regulatory network analysis, and relates to a method for constructing a visualization platform for a molecular regulatory network and a visualization platform. Background Art

[0002] Cancer is one of the three major diseases that seriously endanger human health in the world, and it is also one of the diseases that endanger the health of Chinese residents. Its annual incidence and mortality account for about 23.7% and 30% of the world respectively. In 2020, there were 19.29 million new cases of cancer and 9.95 million cancer deaths worldwide. In the past 10 years, the survival rate of malignant tumors has shown a gradual upward trend. At present, the 5-year relative survival rate of malignant tumors in my country is about 40.5%. Therefore, the study of malignant tumors has become a hot topic in medical research, and the fundamental task of basic research on malignant tumors is to clarify the molecular regulatory mechanism of tumor occurrence and development.

[0003] Current knowledge suggests that humans have a total of about 20,000 genes. Although not all genes are expressed or function in all cells, about 10,000 genes are expressed in most cells, and thousands of genes are differentially expressed in disease states. Gene regulation mainly occurs at three levels, namely ① regulation, transcriptional control, and translational control at the DNA level; ② microorganisms can change their metabolism to adapt to environmental changes through gene regulation, and this type of gene regulation is generally short-term and reversible; ③ gene regulation in multicellular organisms is the basis of cell differentiation, morphogenesis, and individual development, and this type of regulation is generally long-term and often irreversible. So many complex regulatory relationships constitute a more complex molecular regulatory network, and the occurrence and development of diseases such as cancer is caused by the loss of control of the homeostasis of the molecular regulatory network.

[0004] Even if the molecular regulatory network is constructed using existing methods, interactive analysis of the regulatory network is a severe challenge due to the complexity of the entire network. Currently, the commonly used software or method for analyzing regulatory networks is to use Cytoscpae software to input, configure, arrange, and calculate regulatory network files offline, which is time-consuming and labor-intensive. For extremely complex networks, the requirements for personal computers are also increased accordingly, which is not conducive to lightweight office work. Offline regulatory network analysis has high restrictions on gene types, which is not conducive to customized searches for sub-networks of genes of interest and their related biological information. Summary of the invention

[0005] The purpose of the present invention is to provide a method and a visualization platform for constructing a molecular regulatory network, to construct a visualization platform, to interactively analyze and layout the regulatory network, to make the analysis more real-time and convenient, and to make the network more concise and beautiful.

[0006] In order to achieve the above object, the basic scheme of the present invention is: a method for constructing a web-based interactive analysis and visualization platform for molecular regulatory networks, comprising the following steps:

[0007] Integrate and standardize the access to the regulatory network data, obtain the node set file and edge set file and import them into the server-side database;

[0008] Extract the parameters of the gene subnetwork on the server side and generate the data required for web-side response and visualization;

[0009] Based on the extracted data, use computer language to develop web interface and event response program;

[0010] Use programming languages ​​to develop web-side and server-side communication interfaces for transmitting parameters;

[0011] Complete the construction of the visualization platform.

[0012] The working principle and beneficial effects of this basic solution are as follows: This solution uses a web-based online database to provide online interactive analysis and visualization of molecular regulatory networks. Server-side software is developed to integrate, text the molecular regulatory network, extract the subnetworks required for web visualization, generate standardized format files, and communicate with the web side. On the web side, based on user operations, interactive analysis and layout of regulatory networks are performed, making the analysis more real-time and convenient, and the network more concise and beautiful.

[0013] Furthermore, the regulatory network has a plurality of preset layout options and can arbitrarily extract sub-networks of interest, and can automatically calculate the node positions according to a plurality of layout algorithms, including random, grid, concentric, breadthfirst or cose.

[0014] These layouts are classic and inherited layout methods, which are integrated to facilitate network analysis.

[0015] Further, the method to extract data on the server side is as follows:

[0016] Read the parameters of the sub-network to be extracted. The parameters of the sub-network include the id of the gene of interest and the name of the sub-network;

[0017] According to the parameters of the sub-network, read the data of the sub-network from the database;

[0018] The sub-network data is stored in JSON file format and TXT file format for building network diagrams on the web.

[0019] Simple operation and easy to use.

[0020] Further, the method of developing the web interface and event response program is as follows:

[0021] Use HTML hypertext markup language to define the structural framework of the web page, and then use CSS style sheet language to render and beautify the style appearance of HTML components;

[0022] Use javascript to realize page positioning and jumping, network layout switching and network-related data downloading, automatic search box prompts and sub-network searches;

[0023] The internal process of sub-network search includes parameter collection and storage in variables, receiving parameter variables by PHP language, checking whether each parameter meets the requirements, filling missing parameters with default values ​​and generating a cleaned parameter list, which is transmitted to the program using Perl language on the server in JSON format;

[0024] Requirements include: ① Gene names use the unified name of HGNC gene symbol; ② The subnetwork type belongs to C1-C7 or the 33 tumor types in the Human Cancer Genome Atlas TCGA; ③ Missing parameters are filled with the default value Null.

[0025] In this way, interactive analysis and layout of regulatory networks can be performed on the web, making it easier to retrieve regulatory networks of genes of interest and their detailed information.

[0026] Further, the method of developing the web-side and server-side communication interface using PHP language is as follows:

[0027] The generated JSON parameter file is transmitted to the server and the perl program is called. The execution of the program is completed by the php code. The server-side apache service interprets the returned php code, completes the events defined by the php code, and transmits it back to the browser for parsing and visualization.

[0028] The connection operation is simple and easy to use.

[0029] Furthermore, the computer language adopts HTML language, CSS language or JavaScript language.

[0030] You can choose any one according to your needs for flexible operation.

[0031] Furthermore, the structural framework of the web page includes a web page logo and title, a navigation bar, a control network diagram container, a network diagram toolbox container, a network diagram annotation container, a network diagram detailed description container, and detailed parameter containers of nodes, edges, and control axes in the network, documents, and contact information.

[0032] Define the structural framework of the web page in detail for subsequent use.

[0033] The present invention also provides an interactive analysis and visualization platform for building a web-based molecular regulatory network, including a server side, a web side and a processor. The processor executes the method of the present invention, controls the server side to connect with the web side, and completes the construction of the visualization platform.

[0034] The visualization platform can greatly facilitate researchers to analyze and visualize molecular regulatory networks, and can retrieve regulatory networks of genes of interest and their detailed information in a low-threshold, fast, convenient and customized manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the structure of the method for constructing a web-based interactive analysis and visualization platform for molecular regulatory networks of the present invention;

[0036] Figure 2 It is a schematic diagram of the structure of network data integration and standardized access and integration into MySQL database in a preferred solution of an embodiment of the present invention;

[0037] Figure 3 It is a structural schematic diagram of a control network layout mode of a preferred solution of an embodiment of the present invention. DETAILED DESCRIPTION

[0038] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0039] In the description of the present invention, it is necessary to understand that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0040] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0041] The present invention provides a method for constructing a molecular regulatory network, a method for identifying core regulatory factors in a regulatory network, and a method for establishing a web-based interactive analysis and visualization platform for molecular regulatory networks. The specific technical solutions are as follows:

[0042] In view of the defects and shortcomings of the prior art, the present invention discloses a method for constructing a high-confidence molecular regulatory network, (1) using text mining technology to mine the direct regulatory effects of each pair of nodes, and then using the expression level and the correlation of the pair of nodes under a specific pathological or cellular state to measure whether the regulatory relationship exists in the current network. (2) Under the premise that the regulatory mode of all nodes is known, (1) is performed to predict their regulatory mechanism. The construction method of this scheme includes the following steps:

[0043] Determine the environment of the molecular regulatory network to be constructed. The environment can be selected according to the interests of the implementer and is not conditionally limited, because the regulatory network only exists in a certain state and exists to regulate a certain phenotype. Preferably, the environment includes a determined tumor type or subtype.

[0044] Determine the molecular types of all nodes to be included in the regulatory network to be constructed. The molecular types are determined based on biological knowledge. For unclear molecular types of a gene, they can also be determined by consulting materials, such as biological literature, genecards database, etc. Preferably, the molecular types include transcription factors, receptors, ligands, miRNA (microRNA) and lncRNA (long non-coding RNA). Determining the molecular types will help determine whether there is a corresponding direct regulatory relationship between two molecules during text mining or database mining. For example, transcription factors regulate the transcription efficiency of target genes by binding to the target gene promoter before transcription.

[0045] Based on text mining literature and database mining technology, the regulatory relationships containing target molecule types that have been experimentally verified are integrated to obtain a regulatory relationship list, and the relationship list that matches the target molecule type in the list is extracted. The regulatory relationship list includes the pairwise regulatory relationships experimentally verified by literature reports, as well as the pairwise regulatory relationships that have been clearly recorded in the database and have been confirmed to exist.

[0046] Specifically, it can be but not limited to using a generalized linear model, a Boolean model or a Bayesian model to determine whether the mutual regulatory effect of all nodes (two-to-two relationships) of the molecular type to be analyzed exists. According to the extracted relationship list and the inferred results, a high-confidence molecular regulatory network for the corresponding environment is constructed. It can be implemented using a variety of software, various programming languages ​​or platforms, such as R language, cytoscape software, etc., for visual network construction. For example, using R language, with seven tumor immune subtypes as the environment, a subtype-enhanced immune regulatory network is constructed. The first column represents the upstream node (gene name) of the regulatory relationship, the second column represents the downstream node (gene name) of the regulatory relationship, the third column represents the two nodes in which immune cells are co-expressed, the fourth column represents the regulatory relationship in which tumor immune subtype, the fifth column represents the quantification of the intensity of the regulatory relationship (based on a generalized linear model), and the sixth column represents the identification of the regulation and direction of the upstream to downstream nodes.

[0047] In a preferred embodiment of the present invention, the text mining literature and database mining technology include the following steps:

[0048] By mining the key areas of literature and databases, we can extract the regulatory relationships that exist therein;

[0049] Based on artificial intelligence, existing literature is used for model training to obtain a literature integration mathematical model. The model can adopt a convolutional neural network model CNN, a recurrent neural network model RNN, etc.

[0050] Apply the literature integration mathematical model to the new literature set to obtain the desired verified gene regulatory relationship. For example, gene A plays a role in promoting tumors by regulating gene B. The regulatory relationship can be extracted by mining the abstract of the paper (understanding the semantics), and the relationship "A regulates B" can be obtained and recorded. However, when the number of documents is very large, manual reading and sorting of documents will consume a lot of manpower and material resources, or even be impossible to complete. Therefore, it is very convenient to perform semantic understanding of biomedical texts based on artificial intelligence. By applying the pre-trained model to the new literature set, the desired verified gene regulatory relationship can be obtained.

[0051] In a preferred embodiment of the present invention, the method for determining whether the mutual regulation of all nodes of the molecular type to be analyzed exists is as follows:

[0052] Calculate the Spearman correlation of the two nodes. If the correlation coefficient |rho|≥0.3 and P<0.05, proceed to the next step. Use a generalized linear regression model (such as a logistic regression model) to calculate the relationship between the two nodes and the phenotype (such as tumor vs. normal). If P<0.05, it is considered that the two nodes have a regulatory relationship and thus affect the phenotype.

[0053] In a preferred embodiment of the present invention, a method for constructing a high-confidence molecular regulatory network corresponding to an environment based on the extracted relationship list and the inferred results is:

[0054] Based on the node criteria and edge criteria, the cell score estimates are classified and divided into tertiles, namely low, medium, and high. If more than 66% of the samples in the cluster are classified as "medium" or "high", the node will be retained to a node set; if the consistency score of two connected nodes is greater than the P25 of the consistency score distribution, the edge is retained to obtain an edge set. The regulatory network is composed of its node set and edge set.

[0055] The solution also provides a computer medium, in which a program for executing the method of the present invention is stored. The computer medium is easy to use, and can be used to construct a molecular regulatory network on a variety of devices, thereby expanding the scope of use.

[0056] The core regulatory factor identification algorithm of the existing technology mainly characterizes the node centrality in network analysis by quantifying degree centrality (Degree Centrality, which is used to mathematically quantify the importance of a node in the network to the entire network). Degree centrality is the most direct measurement indicator. The larger the node degree of a node means, the higher the degree centrality of the node, and the more important the node is in the network. In graph theory and networks, degree refers to the number of connections between a point in the network (graph) and other points. For directed networks, the degree is divided into in-degree and out-degree. The in-degree refers to the number of edges pointing to the node, and the out-degree refers to the number of edges starting from the node and pointing to other nodes. For undirected networks, the degree is the number of edges connecting the node, regardless of the direction of the connection.

[0057] Existing algorithms believe that each gene in a molecular regulatory network has the same contribution to the signal network. In fact, even if different genes jointly regulate the same function, their weights are often different. Genes themselves also have the ability to regulate themselves (self-disturbance, defined as mutations in their own gene DNA, which causes the gene itself to be unable to function normally or greatly promote), such as genome mutations. Considering the role of upstream signals in general, it is impossible to effectively decompose the role of these signals (genes are subject to multiple regulation, which may come from transcription factors, miRNA, various enzymes, and genomic DNA mutations. The effects of these regulatory signals on target genes may be superimposed, that is, they are jointly promoted, so consider decomposing these regulatory effects). Thus, the identification results of core regulatory factors are not accurate enough. The present invention discloses a method for accurately identifying core regulatory factors in a regulatory network based on multi-omics data, using the correlation between the expression levels of molecules and molecules in specific pathological or cellular states to measure the weight of the influence of different molecules on the same downstream molecule. And by comprehensively detecting the genomic mutations, copy number variations and epigenomic changes of the nodes, the presence and size of node self-disturbance are measured. The control effect on a node is decomposed into self-disturbance and external disturbance (upstream signal), and the control effect of each upstream signal on the node is weighted. The method of this embodiment includes the following steps:

[0058] Obtain molecular regulatory networks, construct molecular regulatory networks, or obtain established regulatory networks from literature;

[0059] Based on genomic variation or non-specific modification regulation, the self-disturbance of regulatory network nodes is obtained;

[0060] Based on the specific modification and regulation relationship, the external perturbation encoding of the regulatory network nodes is obtained;

[0061] Calculate the perturbation score according to the self-perturbation and external perturbation encoding;

[0062] Set a threshold, compare the disturbance score with the threshold, and determine whether the regulatory network node is a core regulatory factor based on the comparison result.

[0063] In a preferred embodiment of the present invention, the method for obtaining a molecular regulatory network based on the node standard and the edge standard is as follows:

[0064] The cell score estimates are classified and divided into tertiles, low, medium, and high. If more than 66% of the samples in the cluster are classified as "medium" or "high", the node will be reserved for a node set. The same is true for the expression of ligands and receptors. Both ligands and receptors are proteins and belong to gene-type nodes. This score is their respective expression levels. The cell score estimate is based on an algorithm CIBERSORT, which is used to estimate the proportion of various immune cell subsets. It is a numerical estimate of the nodes in the network to quantify its level in the body. If the node is a gene, it is the expression level; if the node is a cell, it is the number or proportion of cells.

[0065] If the consistency score of two connected nodes is greater than P25 of the consistency score distribution, the edge is retained to obtain an edge set. The regulatory network consists of its node set and edge set. P25 is the 25th percentile, a type of percentile. n consistency scores form a statistical distribution. The distribution is sorted from small to large, and the value at the 25th percentile is P25.

[0066] The consistency score is the ratio of the horizontal common changes between two nodes. The higher the ratio of common changes, the greater the intensity of their mutual regulation, that is, the magnitude of the impact of upstream regulation (external disturbance) on it, which is called the weight above. The consistency score is calculated as follows:

[0067]

[0068] Similarly, tumor-enhancing interaction regulatory networks involving TF (transcription factors), miRNA, and lncRNA were identified based on node criteria (>66% of clustered samples were classified as "medium" or "high") and edge criteria (distribution of consistency scores >P25). Considering that miRNA-target and lncRNA-miRNA are expected to have a negative correlation based on the ceRNA theory, the ceRNA theory means endogenous competitive inhibitory RNA, because lncRNA can inhibit miRNA, and miRNA can inhibit its own target genes (including transcription factors), then lncRNA can indirectly promote the target genes of miRNA by inhibiting miRNA. Since the lncRNA, miRNA, and target genes here are all endogenous, that is, they are not input in vitro, but produced by their own cells, it is called endogenous competitive inhibition. In addition, the inhibitory effects are all reflected as negative correlations. The consistency score is calculated as follows:

[0069]

[0070] Among them, n low，low is the number of samples with node 1 low and node 2 low; n low，highis the number of samples where node 1 is low and node 2 is high; n high，high is the number of samples with node 1 high and node 2 high; n high，low is the number of samples with high values ​​in node 1 and low values ​​in node 2. Each node (such as a gene) has a value (indicating the expression level) in each sample, and m nodes × n samples form an m × n matrix. The low or high here indicates whether the node is low (less than P33, i.e., the 33rd percentile) or high (greater than P67, i.e., the 67th percentile) in the distribution of the values ​​of all samples.

[0071] In a preferred embodiment of the present invention, the method for obtaining the external disturbance code of the control network node is as follows:

[0072] Suppose the kth disturbance event of node i is encoded as:

[0073]

[0074] In a preferred embodiment of the present invention, the method for determining whether a regulatory network node is a core regulatory factor is as follows:

[0075] For a weighted graph G(V,E) with nodes V(G)>1 and edges E(G)>0, the sample space Ω is obtained by random sampling Kp times. Let the weighted perturbation score of node i be PS ran (i), i∈Ω, given a node j∈V(G), if the weighted score PS obs (i) Satisfy:

[0076]

[0077] Then, node j is the core regulatory factor in the weighted graph G(V,E) and has a very high order. Each node must calculate a P value, indicating whether the size of the perturbation score of the node is a statistically random event (if it is not a random event, it means that it is unusually large, and unusually large means statistically significant). When calculating the P value of any node, the perturbation score of the node is calculated and recorded as PS obs Among all other nodes, any node is selected to calculate its disturbance score and recorded as PS ran , with replacement sampling 100,000 times, forming a PS ran The distribution of PS obs In PS ran The percentile of the distribution of is the value of P. Generally, P<0.05 is used as the threshold. If P<0.05, it is considered that the perturbation score of this node is large enough.

[0078] Compared with the normal sample, the expression change of gene i in a specific state is quantified as:

[0079]

[0080] Among them, p i is the p-value of the differential expression hypothesis test (Welch t-test), and φ^(-1) is the inverse Gaussian cumulative distribution function; therefore, the perturbation score is defined as:

[0081]

[0082] Among them, CS is the abbreviation of Concordance Score. j is the consistency score between gene i and upstream regulatory gene j in an immune cluster, i.e., the external perturbation weight; OR is odds ratio, which is a value often calculated in statistics to represent the risk coefficient (the size of the impact). i，a Is with CS j The odds ratio (Fisher's test) of the a-th self-disturbance event of gene i in the same group, that is, the self-disturbance weight; m represents the number of genes.

[0083] The present invention also provides a system for accurately identifying core regulatory factors in a regulatory network based on the method of the present invention, comprising a molecular regulatory network construction unit, a self-disturbance acquisition unit, an external disturbance acquisition unit and a processing unit.

[0084] The molecular regulatory network construction unit is used to obtain the molecular regulatory network. The molecular regulatory network construction unit is connected to the input ends of the self-disturbance acquisition unit and the external disturbance acquisition unit respectively. The self-disturbance acquisition unit is used to obtain the self-disturbance of the regulatory network node. The external disturbance acquisition unit is used to obtain the external disturbance code of the regulatory network node. The processing unit is connected to the output ends of the self-disturbance acquisition unit and the external disturbance acquisition unit respectively. The processing unit is used to calculate the disturbance score, and compare the disturbance score with the threshold value to determine whether the regulatory network node is a core regulatory factor.

[0085] This scheme uses the regulatory network that exists in specific cells or pathological states. Self-disturbance is based on genomic variation or non-specific (whole genome, such as DNA methylation) modification regulation, and external perturbation is based on specific (only for one or a class of targets) modification regulation relationship. Concordance score 1 is used to define consistency for downstream promotion interaction nodes, and conversely, Concordance score 2 is used to define consistency for downstream inhibition interaction nodes. Only high-risk self-disturbance factors are taken into account in the algorithm, that is, log10 (odds ratio) ≥ 0.5. This algorithm can be developed using R language, has platform compatibility, and is relatively easy to implement.

[0086] Regulatory network analysis based on Cytoscape software is widely used in the prior art. Regulatory network refers to the network formed by the interaction between genes in a cell or a genome, and specifically refers to the interaction between genes caused by gene regulation. Regulation means that the function of a gene controls whether another gene plays a role or changes the size of the role in some way, such as transcriptional regulation.

[0087] Currently, the commonly used software or method for analyzing regulatory networks is to use Cytoscpae software, which is time-consuming and labor-intensive to input, configure, arrange, and calculate regulatory network files offline. For extremely complex networks, the requirements for personal computers are also increased accordingly, which is not conducive to lightweight office work. Offline regulatory network analysis not only has a high threshold, but is also not conducive to customized searches for sub-networks of genes of interest and their related biological information.

[0088] like Figure 1 As shown, the present invention discloses a method for constructing a web-based interactive analysis and visualization platform for molecular regulatory networks, which uses a web-based online database to provide online interactive analysis and visualization of molecular regulatory networks, and collects and provides very detailed biological information (sequence, cell localization, tissue expression level, protein quantification, etc.) for each node (such as gene) for easy reference. The method includes the following steps:

[0089] The regulatory relationship of the network depends on specific cells or pathological conditions. For example, there is mutual regulation between genes AB in myocardial cells, but not in liver cells; or there is such regulation in liver cancer, but not in normal liver cells. At the same time, it also depends on the entire network being searched.

[0090] Offline integration and standardized access to regulatory network data, the specific regulatory network is to use the method of the present invention to construct a molecular regulatory network or obtain a regulatory network that has been constructed from literature, obtain node set files and edge set files and import them into the MySQL (relational database management system) database on the server side. A regulatory network is described by its node set and edge set (regulatory relationship between two nodes), corresponding to nodes.txt and edges.txt files, respectively, which are imported into the MySQL database, such as Figure 2 shown.

[0091] Preferably, the regulatory network has multiple layout options and can arbitrarily extract the sub-network of interest, and automatically calculate its node positions according to multiple layout algorithms, including random, grid, concentric, breadthfirst or cose. Each layout has its own established layout method, and the established procedure determines the style of each node in each layout mode, such as Figure 3 As shown in the figure, for example, the grid layout presents the network as a matrix. When calculating the position of each node on the screen, it is only necessary to obtain the number of nodes, the diameter of the node, the degree of the node, the distance between nodes, the screen size and other parameters to determine it.

[0092] Software is developed to extract the parameters of the gene subnetwork (including the subnetwork of the gene of interest) on the server side, and to generate the data required for the web-side response and visualization, which can be uniformly communicated in json format. Based on the extracted data, a web-side interface and event response program are developed using a computer language. Preferably, the computer language uses html, css or javascript.

[0093] Use programming languages ​​to develop web-side and server-side communication interfaces for transmitting parameters, etc. Programming language courses use PHP (PHP: Hypertext Preprocessor, a scripting language executed on the server side, especially suitable for Web development and can be embedded in HTML) language, or ASP, NET language, etc. The generated JSON parameter file is transferred to the server side and the perl program is called. The execution of the program is completed by PHP code. The server-side Apache (transliterated as Apache, is the world's number one Web server software. It can run on almost all widely used computer platforms. Due to its cross-platform and security, it is widely used and is one of the most popular Web server software) service will interpret the returned PHP code, complete the events defined by the PHP code (i.e. extract the sub-network and generate the result file), and transmit it back to the browser for parsing and visualization. Complete the construction of the visualization platform, which uses the cytoscape.js extension to make the network visualization module compatible with multiple platforms. As long as the offline cytoscape software can be used for visualization, the web-side application will be more convenient.

[0094] In a preferred embodiment of the present invention, based on software using the perl programming language, the method for extracting data on the server side is as follows:

[0095] Read the parameters of the subnetwork to be extracted, including the ID of the gene of interest and the name of the subnetwork. According to the parameters of the subnetwork, read the subnetwork data from the MySQL database. Store the subnetwork data in JSON file format and TXT format file for building the network diagram on the web.

[0096] In a preferred embodiment of the present invention, the method for developing a web interface and an event response program is as follows:

[0097] The structure of the web page is defined using the HTML hypertext markup language, and then the CSS style sheet language is used to render and beautify the style and appearance of the HTML components. The structure of the web page includes the web page logo and title, navigation bar, control network diagram container, network diagram toolbox container, network diagram annotation container, network diagram detailed description container, and detailed parameter containers of nodes, edges, and control axes in the network, documents, and contact information.

[0098] Use javascript to achieve page positioning and jumping, network layout switching and network-related data downloading, search box automatic prompts and sub-network search. Taking sub-network search as an example, its internal process includes parameter collection and storage in variables, receiving parameter variables by PHP language, checking whether each parameter meets the requirements, filling missing parameters with default values ​​and generating a cleaned parameter list, which is transmitted to the server program using Perl language in JSON format;

[0099] Requirements include: ① Gene names use the unified name of HGNC gene symbol, refer to https: / / www.genenames.org / ; ② The subnetwork type belongs to C1-C7 or the 33 tumor types in the Human Cancer Genome Atlas TCGA, refer to https: / / www.cancer.gov / about-nci / organization / ccg / research / structural-genomics / tcga / studied-cancers; ③ Missing parameters are filled with the default value Null.

[0100] The present invention also provides an interactive analysis and visualization platform for building a web-based molecular regulatory network, including a server side, a web side and a processor. The processor executes the method described in the present invention, controls the server side to connect with the web side, and completes the construction of a visualization platform. The core of the platform is developed using HTML, CSS, JavaScript, PHP, and Perl languages. The server-side software is developed for integration, texting the molecular regulatory network, extracting the subnetwork required for web visualization, generating standardized format files, and communicating with the web side. On the web side, based on user operations, interactive analysis and layout of the regulatory network are performed, making the analysis more real-time and convenient, and making the network more concise and beautiful. It can greatly facilitate researchers to analyze and visualize the molecular regulatory network, and retrieve the regulatory network of interest genes and their detailed information in a low-threshold, fast, convenient, and customized manner.

[0101] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0102] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for constructing a web-based interactive analysis and visualization platform for molecular regulatory networks. It is characterized in that The steps include: Integrate and standardize the access to the regulatory network data, obtain the node set file and edge set file and import them into the server-side database; Extract the parameters of the gene subnetwork on the server side and generate the data required for web-side response and visualization; Based on the extracted data, use computer language to develop web interface and event response program; Develop web-side and server-side communication interfaces for transmitting parameters; Complete the construction of the visualization platform; The regulatory network has a variety of preset layout options and can arbitrarily extract sub-networks of interest, and its node positions can be automatically calculated according to a variety of layout algorithms, including random, grid, concentric, breadthfirst or cose; The method to extract data on the server side is as follows: Read the parameters of the sub-network to be extracted. The parameters of the sub-network include the id of the gene of interest and the name of the sub-network; According to the parameters of the sub-network, read the data of the sub-network from the database; Store the sub-network data in JSON and TXT file formats for building network diagrams on the web. The method for developing web interface and event response program is as follows: Use HTML hypertext markup language to define the structural framework of the web page, and then use CSS style sheet language to render and beautify the style appearance of HTML components; Use javascript to realize page positioning and jumping, network layout switching and network-related data downloading, automatic search box prompts and sub-network searches; The internal process of the sub-network search includes collecting parameters and storing them in variables, receiving the parameter variables by the PHP language, checking whether each parameter meets the requirements, and generating a cleaned parameter list, which is transmitted in JSON format to the program using the Perl language on the server; Requirements include: ① Gene names should use the unified name of HGNC gene symbol; ② The subnetwork type belongs to C1-C7 or the 33 tumor types in the Human Cancer Genome Atlas TCGA; ③ Missing parameters are filled with the default value Null.

2. The method for constructing a web-based interactive analysis and visualization platform for molecular regulatory networks according to claim 1, It is characterized in that The method for developing the web-side and server-side communication interface is as follows: The generated JSON parameter file is transmitted to the server and the perl program is called. The execution of the program is completed by the php code. The server-side apache service interprets the returned php code, completes the events defined by the php code, and transmits it back to the browser for parsing and visualization.

3. The method for constructing a web-based interactive analysis and visualization platform for molecular regulatory networks according to claim 1, It is characterized in that The computer language adopts HTML language, CSS language or JavaScript language.

4. The method for constructing a web-based interactive analysis and visualization platform for molecular regulatory networks according to claim 1, It is characterized in that The structural framework of the web page includes a web page logo and title, a navigation bar, a control network diagram container, a network diagram toolbox container, a network diagram annotation container, a network diagram detailed description container, and detailed parameter containers of nodes, edges, and control axes in the network, documents, and contact information.

5. A web-based interactive analysis and visualization platform for molecular regulatory networks. It is characterized in that It includes a server side, a web side and a processor, and the processor executes the method described in one of claims 1-4, controls the server side to connect with the web side, and completes the construction of the visualization platform.

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