A method, system and storage medium for gene bank data retrieval

Abstract

The present invention relates to the field of bioinformatics technology, and in particular to a method, system and storage medium for gene bank data retrieval. The present invention comprises the following steps: obtaining multi-source genome data, performing quality control on the multi-source genome data based on the FAIR principle, and using a distributed storage system for efficient storage and indexing, thereby obtaining a distributed genome database; performing cross-species homology analysis on the distributed genome database, and constructing a multi-level evolutionary tree model, thereby obtaining evolutionary relationship map data; mapping the gene structure and function between different species according to the evolutionary relationship map data, thereby obtaining cross-species gene mapping data; receiving retrieval query data input by a user; and identifying the query type of the retrieval query data to obtain optimized query data. The present invention realizes the comparison and prediction of gene functions between different species through a multi-level evolutionary tree model and cross-species gene mapping.

Description

Technical Field

[0001] The present invention relates to the technical field of bioinformatics, and in particular to a method, system and storage medium for gene bank data retrieval. Background Art

[0002] Gene bank data refers to various bioinformatics data related to genes stored in the gene bank, including gene sequence, gene function, gene annotation, expression data, protein sequence, and related metadata information. As an important resource in bioinformatics, the gene bank provides basic data support for studying gene structure, function, evolutionary relationship, etc. The DNA or RNA sequence in the genome is usually composed of A (adenine), T (thymine), G (guanine), and C (cytosine). Nucleic acid sequence is the most core data in the gene bank, usually stored as a complete genome sequence or a fragment of a gene (such as exon and intron sequences). Genes encode specific nucleic acid sequences of proteins, and these coding regions in the genome generate functional proteins through transcription and translation processes. Gene bank data retrieval refers to the process of searching, matching, and analyzing gene data in the gene bank using specific algorithms or tools according to the user's query requirements. However, current methods for retrieving gene bank data often have the following problems: traditional methods based on sequence similarity are not accurate enough in identifying distantly related homologous genes and are prone to miss important evolutionary relationships; as the amount of data increases, traditional single-machine processing methods are stretched in computing power and cannot meet the needs of large-scale data analysis. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method, system and storage medium for gene bank data retrieval to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for gene bank data retrieval comprises the following steps:

[0005] Step S1: Acquire multi-source genome data, perform quality control on the multi-source genome data based on the FAIR principle, and use a distributed storage system for efficient storage and indexing, thereby obtaining a distributed genome database;

[0006] Step S2: Perform cross-species homology analysis on the distributed genome database and construct a multi-level evolutionary tree model to obtain evolutionary relationship map data; map the gene structure and function between different species based on the evolutionary relationship map data to obtain cross-species gene mapping data;

[0007] Step S3: receiving search query data input by the user; identifying the search query data by query type to obtain optimized query data; performing preliminary screening of the optimized query data based on sequence length according to cross-species gene mapping data to obtain a candidate sequence set;

[0008] Step S4: Perform distant homologous sequence detection on the candidate sequence set to obtain cross-species gene homology data; perform multi-dimensional analysis based on homologous sequences based on the cross-species gene homology data to obtain retrieval result data, wherein the multi-dimensional analysis includes gene function annotation comparison, colinearity analysis and expression spectrum analysis.

[0009] The quality control based on the FAIR principle of the present invention ensures the high quality and reliability of the data and reduces the impact of errors and redundant data; unified naming rules and format standards improve the comparability and interoperability of data, which is convenient for subsequent analysis and sharing; distributed storage systems and indexing mechanisms improve data access speed and storage efficiency, and support the management of large-scale genomic data; adding detailed metadata information enhances the comprehensibility and availability of data, and facilitates researchers to conduct multi-dimensional data analysis. Cross-species homology analysis reveals the evolutionary relationship of genes between different species, helping to identify conserved genes and unique genes; multi-level evolutionary tree models provide detailed evolutionary relationship maps, which help to understand the evolutionary paths of genes in different species; cross-species gene mapping data makes it possible to compare gene structures and functions between different species, promoting the transfer research of functional genes and the prediction of new gene functions; evolutionary relationship maps and gene mapping data provide a solid foundation for multi-species comparative genomics research and promote comprehensive research in the field of biology. By structuring and optimizing query data, the accuracy and relevance of retrieval are improved, and unnecessary data processing time is reduced; the query range is expanded by using gene mapping data, so that users can retrieve more relevant homologous genes, enhancing the comprehensiveness of the search; the preliminary screening method based on sequence length and k-mer comparison significantly improves the search speed, which is suitable for rapid retrieval of large-scale genomic data; the automated query optimization and screening process enables users to obtain the required gene information more conveniently, improving the ease of use and user satisfaction of the system. By constructing a homology probability model and evolutionary distance correction, the accuracy of distant homologous sequence detection is improved, ensuring the reliability of the search results; combining gene function annotation, collinearity and expression spectrum analysis, the function and relationship of genes are evaluated from multiple angles, providing comprehensive gene information; multi-dimensional analysis and comprehensive filtering mechanisms ensure the high relevance and high quality of the final search results, meeting the research needs of users; the functional annotation and collinearity data provided help to understand the biological function and evolutionary background of genes, and promote gene function research and disease association research. Through the above steps, the gene bank data retrieval method can not only efficiently manage and store massive amounts of genomic data, but also provide users with accurate and comprehensive gene retrieval services, greatly promoting the research and application of genomics and related fields.

[0010] The present invention also provides a gene bank data retrieval system for executing the above-mentioned gene bank data retrieval method, the gene bank data retrieval system comprising:

[0011] The data acquisition module is used to acquire multi-source genome data, perform quality control on the multi-source genome data based on the FAIR principle, and use a distributed storage system for efficient storage and indexing, thereby obtaining a distributed genome database;

[0012] Homology analysis module, used to perform cross-species homology analysis on distributed genome databases and construct multi-level evolutionary tree models to obtain evolutionary relationship map data; based on the evolutionary relationship map data, the gene structure and function between different species are mapped to obtain cross-species gene mapping data;

[0013] The retrieval query optimization module is used to receive the retrieval query data input by the user; identify the query type of the retrieval query data to obtain the optimized query data; perform preliminary screening of the optimized query data based on the sequence length according to the cross-species gene mapping data to obtain a candidate sequence set;

[0014] The homologous sequence detection module is used to detect distant homologous sequences on the candidate sequence set to obtain cross-species gene homology data; multi-dimensional analysis based on homologous sequences is performed on the cross-species gene homology data to obtain retrieval result data, where the multi-dimensional analysis includes gene function annotation comparison, colinearity analysis and expression spectrum analysis.

[0015] The present invention significantly enhances the efficiency of acquisition, analysis and application of multi-source genome data. The data acquisition module first ensures the integrity and quality of multi-source genome data. By following the FAIR principle (discoverability, accessibility, interoperability and reusability), it not only improves the credibility of the data, but also ensures its effective use in scientific research. In addition, the use of a distributed storage system for efficient storage and indexing not only optimizes the data management process, but also lays the foundation for subsequent rapid retrieval and analysis. The homology analysis module provides in-depth evolutionary relationship map data by performing cross-species homology analysis and multi-level evolutionary tree model construction on the distributed genome database, helping researchers to reveal the similarities and differences between gene structures and functions among different species. This process lays a solid foundation for subsequent gene mapping, promotes cross-species comparison of gene functions, and enhances the understanding of gene evolution mechanisms. The retrieval query optimization module further enhances the user interaction experience. By receiving the retrieval query data input by the user and performing type identification, the query process is optimized, so that users can more conveniently obtain the required information. At the same time, the preliminary screening based on sequence length ensures the relevance and accuracy of the candidate sequence set, providing a high-quality data source for subsequent homologous sequence detection. The homologous sequence detection module not only identifies cross-species gene homology data through the detection and multi-dimensional analysis of distant homologous sequences, but also comprehensively evaluates the role and impact of genes in different organisms through gene function annotation comparison, colinearity analysis and expression spectrum analysis. This series of analysis results provides researchers with rich information and helps to deepen biological research, especially in understanding the relationship between gene function and evolution, which has important scientific value and practical significance. In general, the synergistic effect of these modules makes the processing and analysis of genomic data more systematic and efficient, and promotes research progress in the field of genomics.

[0016] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program is executed to implement any one of the above-mentioned methods for gene bank data retrieval. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0018] Figure 1 It is a schematic diagram of the steps of the method for retrieving gene bank data of the present invention;

[0019] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0020] Figure 3 for Figure 1Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0021] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0023] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0024] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for retrieving gene bank data, the method comprising the following steps:

[0025] Step S1: Acquire multi-source genome data, perform quality control on the multi-source genome data based on the FAIR principle, and use a distributed storage system for efficient storage and indexing, thereby obtaining a distributed genome database;

[0026] Step S2: Perform cross-species homology analysis on the distributed genome database and construct a multi-level evolutionary tree model to obtain evolutionary relationship map data; map the gene structure and function between different species based on the evolutionary relationship map data to obtain cross-species gene mapping data;

[0027] Step S3: receiving search query data input by the user; identifying the search query data by query type to obtain optimized query data; performing preliminary screening of the optimized query data based on sequence length according to cross-species gene mapping data to obtain a candidate sequence set;

[0028] Step S4: Perform distant homologous sequence detection on the candidate sequence set to obtain cross-species gene homology data; perform multi-dimensional analysis based on homologous sequences based on the cross-species gene homology data to obtain retrieval result data, wherein the multi-dimensional analysis includes gene function annotation comparison, colinearity analysis and expression spectrum analysis.

[0029] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a method for retrieving data from a gene bank according to the present invention. In this example, the method for retrieving data from a gene bank includes the following steps:

[0030] Step S1: Acquire multi-source genome data, perform quality control on the multi-source genome data based on the FAIR principle, and use a distributed storage system for efficient storage and indexing, thereby obtaining a distributed genome database;

[0031] The embodiment of the present invention obtains multi-source genome data, including nucleic acid sequence data, protein sequence data, and gene annotation and expression data. The data is quality assessed and standardized through a quality control process based on the FAIR (Findability, Accessibility, Interoperability, Reusability) principle. This process involves the application of automated tools to detect and mark low-quality, repeated, and contaminated sequences. Then, through a distributed storage system, using a distributed storage architecture such as Hadoop or Ceph, data is efficiently stored. Lossless compression technology (such as CRAM format) is used in the data storage process, and metadata including species information, gene function description, experimental source, etc. are added to each genome data item. Finally, the stored data is indexed and index management is performed through Elasticsearch or HBase to form a searchable distributed genome database.

[0032] Step S2: Perform cross-species homology analysis on the distributed genome database and construct a multi-level evolutionary tree model to obtain evolutionary relationship map data; map the gene structure and function between different species based on the evolutionary relationship map data to obtain cross-species gene mapping data;

[0033] The embodiment of the present invention extracts nucleic acid sequences and protein sequences from a distributed genome database. Then, sequence alignment tools such as BLAST or ClustalW are used for global and local alignment, and high-quality sequence alignment data is obtained by standardizing the length and removing the preprocessing of low-complexity regions. On this basis, the Smith-Waterman algorithm is applied for fine alignment, and homologous gene families are identified according to the sequence similarity matrix. Further classification of homologous genes is performed by a gene tree construction algorithm (such as a gene tree construction method based on Bayesian inference), including identification of orthologous and paralogous genes. Subsequently, a multi-level evolutionary tree model is constructed based on the maximum likelihood method to generate evolutionary relationship map data. Finally, the map is used to map the gene structure and function between different species to generate cross-species gene mapping data.

[0034] Step S3: receiving search query data input by the user; identifying the search query data by query type to obtain optimized query data; performing preliminary screening of the optimized query data based on sequence length according to cross-species gene mapping data to obtain a candidate sequence set;

[0035] The embodiment of the present invention receives the retrieval query data input by the user, and performs semantic and structural processing on the data. First, the input query data is segmented, part-of-speech tagged, and named entity recognized through natural language processing technology (NLP) to generate structured query data. Then, based on the predefined query template, the query type is pattern matched to identify the intent of the query and generate optimized query data. According to the cross-species gene mapping data, a screening algorithm based on sequence length is applied to perform preliminary screening of the query to filter out sequences that do not meet the length requirements. The screening criteria for sequence length can be set according to the application scenario. For example, in medical applications, gene sequences less than 500 bases may need to be filtered out. After screening, a candidate sequence set is obtained for subsequent analysis.

[0036] Step S4: Perform distant homologous sequence detection on the candidate sequence set to obtain cross-species gene homology data; perform multi-dimensional analysis based on homologous sequences based on the cross-species gene homology data to obtain retrieval result data, wherein the multi-dimensional analysis includes gene function annotation comparison, colinearity analysis and expression spectrum analysis.

[0037] The embodiment of the present invention performs distant homologous sequence detection on the candidate sequence set screened out. First, the characteristic information of the sequence is extracted, including sequence length, GC content, protein domain, etc., and a distant homology probability model is constructed. The model can be constructed based on Hidden Markov Model (HMM) or machine learning algorithm to evaluate the probability of distant homology for each candidate sequence. According to the set similarity threshold, cross-species homologous sequences that meet the conditions are screened out. Subsequently, these homology data are used to perform multi-dimensional sequence analysis, including gene function annotation comparison (such as comparison of GO terms or KEGG pathways), colinearity analysis (through tools such as MCScanX) and expression spectrum analysis (using RNA-seq data to perform correlation analysis of gene expression). Finally, the final retrieval result data is obtained through these multi-dimensional analyses, and sorted and output according to similarity and functional correlation.

[0038] The quality control based on the FAIR principle of the present invention ensures the high quality and reliability of the data and reduces the impact of errors and redundant data; unified naming rules and format standards improve the comparability and interoperability of data, which is convenient for subsequent analysis and sharing; distributed storage systems and indexing mechanisms improve data access speed and storage efficiency, and support the management of large-scale genomic data; adding detailed metadata information enhances the comprehensibility and availability of data, and facilitates researchers to conduct multi-dimensional data analysis. Cross-species homology analysis reveals the evolutionary relationship of genes between different species, helping to identify conserved genes and unique genes; multi-level evolutionary tree models provide detailed evolutionary relationship maps, which help to understand the evolutionary paths of genes in different species; cross-species gene mapping data makes it possible to compare gene structures and functions between different species, promoting the transfer research of functional genes and the prediction of new gene functions; evolutionary relationship maps and gene mapping data provide a solid foundation for multi-species comparative genomics research and promote comprehensive research in the field of biology. By structuring and optimizing query data, the accuracy and relevance of retrieval are improved, and unnecessary data processing time is reduced; the query range is expanded by using gene mapping data, so that users can retrieve more relevant homologous genes, enhancing the comprehensiveness of the search; the preliminary screening method based on sequence length and k-mer comparison significantly improves the search speed, which is suitable for rapid retrieval of large-scale genomic data; the automated query optimization and screening process enables users to obtain the required gene information more conveniently, improving the ease of use and user satisfaction of the system. By constructing a homology probability model and evolutionary distance correction, the accuracy of distant homologous sequence detection is improved, ensuring the reliability of the search results; combining gene function annotation, collinearity and expression spectrum analysis, the function and relationship of genes are evaluated from multiple angles, providing comprehensive gene information; multi-dimensional analysis and comprehensive filtering mechanisms ensure the high relevance and high quality of the final search results, meeting the research needs of users; the functional annotation and collinearity data provided help to understand the biological function and evolutionary background of genes, and promote gene function research and disease association research. Through the above steps, the gene bank data retrieval method can not only efficiently manage and store massive amounts of genomic data, but also provide users with accurate and comprehensive gene retrieval services, greatly promoting the research and application of genomics and related fields.

[0039] Preferably, step S1 comprises the following steps:

[0040] Step S11: Acquire multi-source genome data, including nucleic acid sequence data, protein sequence data, and gene annotation and expression data;

[0041] Step S12: applying a unified naming rule and format standard to the multi-source genome data, thereby obtaining standard multi-source genome data;

[0042] Step S13: Evaluate the standard multi-source genome data based on the FAIR principle, and use automated tools to detect and mark low-quality sequences, repeated sequences, and contaminated sequences, thereby obtaining quality-marked genome data;

[0043] Step S14: screening the standard multi-source genome data according to the quality-marked genome data, thereby obtaining high-quality genome data;

[0044] Step S15: adding metadata information to the high-quality genome data and performing lossless compression based on the CRAM format to obtain compressed genome data, wherein the metadata information includes species information, gene function description, and experimental source;

[0045] Step S16: Use a distributed storage system to efficiently store and process the compressed genome data, and create an index, thereby obtaining a distributed genome database.

[0046] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in the embodiment of the present invention, step S1 includes the following steps:

[0047] Step S11: Acquire multi-source genome data, including nucleic acid sequence data, protein sequence data, and gene annotation and expression data;

[0048] The embodiments of the present invention obtain multi-source genomic data from different public databases (such as NCBI, Ensembl, etc.) or proprietary databases, and the data types include nucleic acid sequence data, protein sequence data, gene annotation and expression data. These data can be species-specific or cross-species genomic information. Nucleic acid sequence data are usually stored in FASTA or FASTQ format, while protein sequence data are commonly stored in PEFF format. Gene annotation data includes gene location, gene function, alternative splicing form, etc., and expression data is usually RNA-seq sequencing data. Depending on the research needs, additional epigenetic information or single-cell sequencing data may be introduced to ensure that the acquired genomic data is widely representative.

[0049] Step S12: applying a unified naming rule and format standard to the multi-source genome data, thereby obtaining standard multi-source genome data;

[0050] The embodiments of the present invention apply unified naming rules and format standards to genomic data obtained from multiple sources. First, the data format of nucleic acid and protein sequences will be converted to standard FASTA or GFF3 format to ensure data compatibility. In terms of unified naming rules, genes are renamed using standardized name systems such as Gene Ontology (GO) or NCBI Gene IDs to solve the problem of inconsistent naming between different databases. This step also includes removing redundant sequences and inconsistent annotations to ensure the uniqueness of each genome entry. Batch format conversion and application of naming rules are performed through automated scripts (such as the Biopython library in Python) to improve processing efficiency.

[0051] Step S13: Evaluate the standard multi-source genome data based on the FAIR principle, and use automated tools to detect and mark low-quality sequences, repeated sequences, and contaminated sequences, thereby obtaining quality-marked genome data;

[0052] The embodiment of the present invention evaluates the standardized multi-source genome data based on the FAIR principle (discoverability, accessibility, interoperability, and reusability). First, an automated tool (such as FastQC) is used to evaluate the quality of the nucleic acid sequence, and low-quality bases, excessive N bases, and abnormal GC content in the sequence are detected. Next, the CD-HIT tool is used to detect and mark repetitive sequences and identify redundant regions in the genome. In addition, tools such as ContamMix or Kraken are used to detect sources of contamination in the sequence, especially exogenous DNA or contaminants introduced in the experiment. After detection, all low-quality, repeated or contaminated sequences will be marked, and quality-marked genome data will be generated for subsequent screening.

[0053] Step S14: screening the standard multi-source genome data according to the quality-marked genome data, thereby obtaining high-quality genome data;

[0054] The embodiment of the present invention performs data screening based on quality-marked genomic data. First, remove sequences marked as low quality or contaminated, which usually affect the accuracy of the analysis results. For repeated sequences, non-redundant data sets can be retained by selecting representative sequences (such as the longest or most complete sequences). This screening step can be completed using custom scripts combined with bioinformatics tools (such as SeqKit). The screening criteria can be flexibly adjusted according to the application scenario. For example, in cancer genomics research, the focus may be on retaining genomic sequences in highly variable areas. Ultimately, a strictly screened high-quality genomic data set is obtained.

[0055] Step S15: adding metadata information to the high-quality genome data and performing lossless compression based on the CRAM format to obtain compressed genome data, wherein the metadata information includes species information, gene function description, and experimental source;

[0056] The embodiment of the present invention adds metadata information based on high-quality genomic data. Metadata includes species origin, gene function description, experimental source (such as sequencing platform, sequencing depth, etc.). This information is retrieved from external databases (such as KEGG, UniProt, etc.) and automatically matched to the corresponding genome entries. Then, the high-quality genomic data is losslessly compressed using the CRAM format. The CRAM format has a higher compression ratio than the traditional BAM format and is suitable for the storage of large-scale genomic data. During the compression process, the genomic data is converted and compressed using the Samtools or HTSlib tools to ensure the integrity and readability of the data during storage.

[0057] Step S16: Use a distributed storage system to efficiently store and process the compressed genome data, and create an index, thereby obtaining a distributed genome database.

[0058] The embodiment of the present invention uses a distributed storage system (such as Hadoop HDFS, Ceph, etc.) to store compressed genomic data. First, the compressed genomic data is uploaded to the distributed storage system, and the redundant backup function of the distributed file system is used to ensure the security and high availability of the data. Then, the data is indexed based on a distributed database (such as HBase or Elasticsearch), and the key fields of the index include gene name, species name, gene function, etc. By creating a distributed index, users can efficiently retrieve and access genomic data, realize fast query and analysis, and finally obtain an operational distributed genomic database.

[0059] The present invention obtains various types of genomic data (nucleic acid, protein, annotation, expression data) to ensure the richness and diversity of the database and support multi-dimensional gene analysis; providing a comprehensive data source helps researchers conduct comprehensive research from gene sequence to functional expression, and promotes the development of genomics and functional genomics; the acquisition of multi-source data lays a solid foundation for subsequent data integration, standardization and analysis, and improves data utilization efficiency. Unified naming rules and format standards ensure data consistency, avoid data confusion and parsing errors caused by irregular naming; standardized data formats promote interoperability between different data sources, facilitate data integration and sharing; unified formats simplify subsequent data processing and analysis steps, and improve the efficiency and reliability of the workflow. Through the evaluation and automated detection of the FAIR principle, the searchability, accessibility, interoperability and reusability of the data are ensured, and the overall quality of the data is improved; the use of automated tools significantly improves the efficiency of the detection and labeling process, and reduces the time and labor cost of manual intervention; automated detection helps to quickly identify and eliminate low-quality, repeated and contaminated sequences, and ensure the accuracy and reliability of subsequent analysis; quality labels enable data users to clearly understand the quality status of the data, and improve the credibility and use value of the data. The screening process effectively eliminates low-quality and redundant data, optimizes database content, and improves the efficiency of data processing and analysis; high-quality data ensures the accuracy and reliability of subsequent genetic analysis and reduces errors caused by data quality issues; streamlined high-quality data reduces the consumption of storage and computing resources and improves the overall performance and scalability of the system. Adding detailed metadata information makes the data more interpretable and usable, making it easier for researchers to understand and use the data; metadata provides rich background information and supports more complex and in-depth analysis, such as gene function research, species comparison, etc.; lossless compression (such as CRAM format) reduces storage space while ensuring data integrity and saving storage costs; compressed data is easier to transmit and access, which improves the efficiency of data sharing and distribution. The distributed storage system can be flexibly expanded to adapt to the ever-increasing amount of genomic data and ensure the sustainability of the system; distributed storage provides data redundancy and fault-tolerant mechanisms to ensure high availability and security of data and reduce the risk of data loss and service interruption; the establishment of an efficient indexing mechanism improves data retrieval speed, supports rapid access and query of large-scale data, and meets high-performance computing needs; the distributed storage system can effectively utilize multi-node resources, optimize the allocation of storage and computing resources, and improve the overall performance and efficiency of the system; the distributed architecture can process access requests from multiple users at the same time, ensuring the stability and response speed of the system under high concurrency.Through the above steps, the entire data acquisition, standardization, quality control, compression and storage process forms an efficient, reliable and scalable genome database foundation, which greatly improves the management and utilization efficiency of genome data and provides a solid foundation for subsequent homology analysis, evolutionary relationship construction and efficient retrieval.

[0060] Preferably, step S2 comprises the following steps:

[0061] Step S21: extracting nucleic acid sequence and protein sequence information from a distributed genome database, and performing sequence alignment preprocessing to obtain preprocessed sequence data, wherein the sequence alignment preprocessing specifically includes sequence length standardization and removal of low-complexity regions;

[0062] Step S22: performing a global alignment based on multiple sequences on the preprocessed sequence data, and performing a local refined alignment using the Smith-Waterman algorithm, thereby obtaining a sequence similarity matrix;

[0063] Step S23: Identify homologous gene families based on the sequence similarity matrix, and distinguish homologous genes and paralogous genes by gene tree reconstruction method, so as to obtain homologous gene classification data;

[0064] Step S24: constructing a multi-level evolution model based on the maximum likelihood method for homologous gene classification data according to preset species taxonomy data, thereby obtaining evolutionary relationship map data;

[0065] Step S25: Mapping the gene structures and functions between different species according to the evolutionary relationship map data, thereby obtaining cross-species gene mapping data.

[0066] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 Detailed step flow diagram of step S2 in the embodiment of the present invention, step S2 includes the following steps:

[0067] Step S21: extracting nucleic acid sequence and protein sequence information from a distributed genome database, and performing sequence alignment preprocessing to obtain preprocessed sequence data, wherein the sequence alignment preprocessing specifically includes sequence length standardization and removal of low-complexity regions;

[0068] In the embodiment of the present invention, first, nucleic acid sequence and protein sequence information are extracted from a distributed genome database, and the extracted sequences are usually stored in FASTA format. In order to improve the accuracy and efficiency of subsequent alignments, these sequences need to be preprocessed. Sequence alignment preprocessing includes two steps: first, the sequence length is standardized, and the sequence is padded to a uniform length using tools (such as MAFFT or Clustal Omega) for multiple sequence alignment; second, the Dust or SEG algorithm is used to remove low-complexity regions, which are prone to introduce noise and reduce the accuracy of the alignment. After preprocessing, the generated sequence data ensures a uniform length and a high sequence complexity, laying the foundation for subsequent alignment analysis.

[0069] Step S22: performing a global alignment based on multiple sequences on the preprocessed sequence data, and performing a local refined alignment using the Smith-Waterman algorithm, thereby obtaining a sequence similarity matrix;

[0070] The embodiment of the present invention performs a global multiple sequence alignment on the pre-processed sequence data, uses tools such as MAFFT or ClustalOmega to perform a preliminary global alignment of all sequences, and generates a preliminary alignment result. Next, for some key areas, a local alignment is performed using the Smith-Waterman algorithm. The Smith-Waterman algorithm is a dynamic programming algorithm specifically used for local sequence alignment, which can identify shorter but significant sequence similarities. Through this refined local alignment, the high resolution of the alignment results is ensured, especially those local homology regions that are easily overlooked in the global alignment. The final output is a matrix reflecting the similarity between sequences, i.e., a sequence similarity matrix.

[0071] Step S23: Identify homologous gene families based on the sequence similarity matrix, and distinguish homologous genes and paralogous genes by gene tree reconstruction method, so as to obtain homologous gene classification data;

[0072] The embodiment of the present invention uses tools such as OrthoFinder or PhyloTreePruner to identify homologous gene families based on sequence similarity matrices. Homologous gene families are gene groups with common ancestors that are identified by analyzing gene sequence similarity. According to the values ​​in the similarity matrix, gene families are first divided. Then, gene tree reconstruction methods (such as maximum likelihood method or Bayesian method) are used to distinguish orthologous genes from paralogous genes. Orthologous genes usually originate from species differentiation, while paralogous genes originate from gene duplication events. Through this process, detailed homologous gene classification data is generated to provide a basis for further research on evolutionary relationships.

[0073] Step S24: constructing a multi-level evolution model based on the maximum likelihood method for homologous gene classification data according to preset species taxonomy data, thereby obtaining evolutionary relationship map data;

[0074] The embodiment of the present invention is based on the classification data of homologous genes and uses species taxonomy data (such as NCBI Taxonomy or KEGG Orthology) to perform further evolutionary analysis on these genes. First, a multi-level evolutionary model is constructed based on the Maximum Likelihood Method (MLM) using tools such as IQ-TREE or RAxML. The maximum likelihood method finds the most likely evolutionary tree by evaluating the probabilities of different evolutionary trees. During the construction process, the model parameters can be adjusted according to different evolutionary hypotheses, such as variable substitution rates or different mutation models. The evolutionary relationship map data finally generated shows the evolutionary history of genes between species, providing a framework for comparative analysis of cross-species genes.

[0075] Step S25: Mapping the gene structures and functions between different species according to the evolutionary relationship map data, thereby obtaining cross-species gene mapping data.

[0076] The embodiment of the present invention further analyzes the gene structure and function between different species based on the evolutionary relationship map data. Gene function annotation tools (such as BLAST or InterProScan) are used to infer and map the functions of genes and identify the functional conserved regions across species. At the same time, by comparing gene structures, the conserved exon and intron structures in different species are identified. Based on this information, cross-species gene mapping data is constructed to ensure the comparability of gene functions and structures between different species. The cross-species gene mapping data finally output can be used for species evolution analysis, functional gene annotation, and cross-species gene function research.

[0077] The present invention extracts and pre-processes nucleic acid and protein sequences, removes low-complexity regions and standardizes lengths, thereby ensuring the accuracy and consistency of subsequent analysis. Sequence alignment preprocessing improves the quality of data and reduces noise. Combining global multiple sequence alignment and local refined alignment of the Smith-Waterman algorithm, the accuracy of sequence similarity analysis is further improved, a sequence similarity matrix is ​​generated, and a solid foundation is provided for homologous gene identification. Identification and differentiation of homologous gene families are carried out through the similarity matrix, homologous genes and paralogous genes are effectively distinguished, and the accuracy and reliability of gene classification are improved. A multi-level evolutionary model is constructed using the maximum likelihood method to generate an evolutionary relationship map, which provides a detailed and multidimensional perspective for the evolutionary relationship between different species. Based on the evolutionary map, the mapping of cross-species gene structure and function is carried out, and the cross-species analysis of gene function is realized, which helps to reveal the structural and functional variations in gene evolution and promote the research progress of evolutionary biology and functional genomics. This series of steps ensures the complete process of data from extraction, processing, alignment to evolutionary analysis, and provides an efficient and accurate analysis method for cross-species gene research.

[0078] Preferably, step S23 includes the following steps:

[0079] Step S231: preliminarily grouping the preprocessed sequence data according to the sequence similarity matrix, and determining homologous gene families by a preset similarity threshold, thereby obtaining preliminary homologous gene family data;

[0080] The embodiment of the present invention performs preliminary grouping of the preprocessed sequence data according to the sequence similarity matrix. The specific operation is to use the numerical value of sequence similarity to classify sequences with similarity greater than a preset threshold (such as 70% or 80%) into the same group, thereby determining a preliminary homologous gene family. The similarity threshold can be adjusted according to research needs and the genetic diversity of different species. Through this grouping method, genes with similar functions or common evolutionary origins can be effectively classified into a preliminary homologous gene family, and preliminary homologous gene family data can be generated for subsequent analysis.

[0081] Step S232: performing multiple sequence alignment on each gene family in the preliminary homologous gene family data, and constructing a gene tree based on the Bayesian method, thereby obtaining gene family evolutionary tree data;

[0082] The embodiment of the present invention uses a multiple sequence alignment tool (such as MUSCLE or MAFFT) to perform multiple sequence alignment on the members of each gene family for preliminary homologous gene family data to identify the conserved regions and variable regions between sequences. Based on the multiple sequence alignment, the Bayesian Inference Method is used to construct an evolutionary tree of the gene family. The Bayesian method is based on a probability model and takes into account the uncertainty in evolution. Commonly used tools include MrBayes or BEAST. This method estimates the probability distribution of the evolutionary tree through multiple iterations, and finally obtains a gene family evolutionary tree data with statistical support for further homologous gene analysis.

[0083] Step S233: identifying gene duplication events based on minimal duplication events according to the gene family evolutionary tree data, and determining gene divergence points by comparing with a preset species tree, thereby obtaining gene duplication event data;

[0084] The embodiment of the present invention uses the Minimal Duplication Events principle to identify gene duplication events based on gene family evolutionary tree data. The identification of minimal duplication events depends on the topological structure of the gene tree. By comparing with the preset species tree, the nodes (gene divergence points) where genes are duplicated or lost are identified. When the gene tree is inconsistent with the species tree, it indicates that there may be a gene duplication event. Through this process, gene duplication event data including gene divergence points and duplication events are generated, providing a basis for subsequent classification of homologous genes.

[0085] Step S234: classifying members in the gene family according to the gene duplication event data, identifying members produced by gene duplication before species differentiation as paralogous genes, and identifying genes after species differentiation as orthologous genes, thereby obtaining preliminary homologous gene classification data;

[0086] The embodiment of the present invention classifies genes in a gene family according to gene duplication event data. First, the relationship between gene family members is determined using gene divergence points: if a gene duplication event occurs before species differentiation, the gene members generated by the event are identified as paralogous genes; if the gene duplication event occurs after species differentiation, they are identified as orthologous genes. Through this classification method, preliminary homologous gene classification data is obtained, which provides a basis for subsequent verification and optimization of homologous gene classification.

[0087] Step S235: performing gene arrangement order and genome context comparison between different species on the preliminary homologous gene classification data, thereby performing homology relationship determination verification, thereby obtaining optimized homologous gene classification data;

[0088] Based on the preliminary classification of homologous genes, the embodiments of the present invention compare the gene arrangement order (colinearity of gene order) and genomic context (neighboring regions of genes) between different species. This process is usually manually checked using a genome browser (such as JBrowse or IGV), and can also be batch analyzed through automated tools to verify the homology of genes. By comparing the conservative gene arrangement order and context in different species, the preliminary homologous gene classification is verified or adjusted to generate verified optimized homologous gene classification data.

[0089] Step S236: calculating the ratio of the non-synonymous substitution rate to the synonymous substitution rate of each pair of homologous genes based on the optimized homologous gene classification data, thereby obtaining homologous gene evolution feature data;

[0090] The embodiment of the present invention calculates the ratio of the non-synonymous substitution rate (Ka) to the synonymous substitution rate (Ks) (Ka / Ks ratio) of each pair of homologous genes based on the optimized homologous gene classification data. This ratio is often used to evaluate the evolutionary pressure of homologous genes. If the ratio is close to 1, it means that the gene has undergone neutral selection; if the ratio is greater than 1, it indicates that the gene is under positive selection pressure; and if the ratio is less than 1, it indicates that the gene is under negative selection pressure. The Ka / Ks ratio can be calculated using tools such as BioPerl or KaKs_Calculator to obtain the evolutionary characteristic data of homologous genes, providing an evolutionary basis for the final classification of homologous genes.

[0091] Step S237: Optimize the credibility of the classification result according to the homologous gene evolution feature data and the optimized homologous gene classification data, so as to obtain the homologous gene classification data.

[0092] The embodiment of the present invention further optimizes the credibility of the classification results based on the homologous gene evolutionary feature data. The specific operation is to confirm the rationality of the classification results by making a comprehensive comparison with the known gene function annotations, evolutionary paths and species taxonomy data. On this basis, the credibility of the classification results is optimized, and the misclassifications that may be caused by sequence variation or calculation errors are eliminated, and finally accurate homologous gene classification data is obtained, providing a reliable basis for cross-species gene research.

[0093] The whole process of the present invention has gradually improved the identification and classification of homologous genes, and provided a more accurate basis for gene evolution research. First, the sequence data after pretreatment is preliminarily grouped by sequence similarity matrix, and the homologous gene family is determined according to the preset similarity threshold value. This operation ensures the accuracy of the preliminary division of the homologous gene family, and can effectively screen out gene sequences with significant similarity to obtain preliminary homologous gene family data. Then, by performing multiple sequence alignment on each gene family in the preliminary homologous gene family, and adopting the Bayesian method to construct a gene tree, this gene tree construction method can provide more robust inference under an uncertain evolutionary background, and the generated gene family evolutionary tree data has laid a foundation for subsequent gene duplication event identification. Gene duplication events are identified based on minimal duplication events, and the gene divergence point is accurately located by comparing the gene family evolutionary tree with the preset species tree. This step can help identify the historical events of species differentiation and gene duplication, further clarify the origin and evolutionary path of gene duplication, and obtain gene duplication event data. The gene family members were classified by gene duplication event data, and the members produced by gene duplication before species differentiation were identified as paralogous genes, while the genes after species differentiation were identified as orthologous genes. This classification operation helps to clarify the functional evolution process of homologous genes and obtain preliminary homologous gene classification data. The accuracy of the preliminary homologous gene classification was further verified by comparing the gene arrangement order and genomic context between different species. The comparison of the relative position of genes and the genomic background environment ensured the accurate determination of homologous relationships, and finally generated optimized homologous gene classification data. Next, by calculating the ratio of the non-synonymous substitution rate (Ka) to the synonymous substitution rate (Ks) of each pair of homologous genes, this ratio can reflect the natural selection pressure in the process of gene evolution, and then reveal the evolutionary characteristics of homologous genes. This step provides important evolutionary information for studying gene function changes and species adaptability. Based on the evolutionary characteristic data of homologous genes and the optimized homologous gene classification data, the credibility of the classification results of homologous genes is optimized. This step ensures that the final homologous gene classification data is further improved in accuracy and reliability. Overall, these steps gradually improved the accuracy and credibility of homologous gene identification through multi-level analysis and optimization. The resulting homologous gene classification data has a high evolutionary interpretation capability, laying a solid foundation for subsequent functional research and evolutionary analysis. This process not only improves the efficiency of homologous gene research, but also enhances the application value of analysis results in cross-species gene research.

[0094] Preferably, step S25 comprises the following steps:

[0095] Step S251: performing comparative analysis on gene structures among different species according to the evolutionary relationship map data, thereby obtaining gene structure comparison data, wherein the comparative analysis of gene structure includes exon-intron structure comparison, promoter region analysis, and regulatory element identification;

[0096] The embodiment of the present invention selects homologous genes between different species according to the evolutionary relationship map data, and performs a detailed comparative analysis on their gene structures. The comparison of gene structure includes the comparison of exon-intron structure, and the specific steps are to use tools such as GENSCAN or Augustus to annotate the coding region and non-coding region of the genomic sequence of homologous genes, and identify and compare the exon and intron composition of each gene. For promoter region analysis, tools such as PromoterScan are used to predict the promoter sequence of the gene, identify regulatory elements, particularly conservative cis-regulatory elements (such as transcription factor binding sites). Through this analysis method, the conservation and variability of homologous genes between different species can be revealed, and gene structure comparison data can be generated to provide a basis for subsequent functional studies.

[0097] Step S252: using gene ontology and pathway databases to annotate and compare gene functions among different species based on evolutionary relationship map data, and identifying gene sets with conservative and differentiated functions through enrichment analysis, thereby obtaining gene function comparison data;

[0098] The embodiment of the present invention uses Gene Ontology (GO) and pathway databases (such as KEGG, Reactome) to perform functional annotations on homologous genes based on the analysis results. First, the functional categories of homologous genes are determined based on the evolutionary relationship map data, and the GO database is used to annotate the molecular functions, cellular components and biological processes of each gene. Then, in combination with the pathway database, the biological pathways in which homologous genes participate among species are identified. Next, a functional enrichment analysis (such as GO enrichment or KEGG enrichment analysis) is performed to identify functionally conserved gene sets (consistent functions in multiple species) and functionally differentiated gene sets (different functions in different species). In this step, tools such as DAVID and Metascape can be used for enrichment analysis to obtain gene function comparison data and reveal the functional evolution laws of cross-species genes.

[0099] Step S253: construct a cross-species gene mapping network based on the gene structure comparison data and the gene function comparison data, thereby obtaining cross-species gene mapping data.

[0100] The embodiment of the present invention constructs a cross-species gene mapping network based on the obtained gene structure comparison data and gene function comparison data. The specific implementation steps are: first, based on the conservation and variability of the gene structure, determine the correspondence between homologous genes and mark these relationships in the network. Then, using the gene function comparison data, perform functional mapping on each pair of homologous genes to construct a cross-species gene function network. In this process, graph theory tools (such as Cytoscape) are used to construct genes and their functional associations into a network diagram, in which nodes represent genes and edges represent gene correspondences and functional associations between different species. Through this network, the structural and functional evolution patterns of genes between different species can be clearly displayed, and finally cross-species gene mapping data are generated for further evolutionary biology research.

[0101] The present invention establishes a mapping relationship between gene structure and function across species through systematic comparative analysis, and improves the accuracy and comprehensiveness of cross-species gene research. First, based on the evolutionary relationship map data, the gene structure between different species is compared in terms of exon-intron structure, promoter region and regulatory elements, which can deeply understand the structural changes of genes during evolution. This analysis provides important information on the differences in gene structure between different species, helps identify evolutionary conserved regions and specific regulatory elements, and helps to reveal the potential mechanism of gene expression regulation. Based on the gene ontology (GO) and pathway database, the gene functions between species are annotated and compared, and the gene sets with conservative and differentiated functions are identified in combination with enrichment analysis. This step can not only reveal the similarities and differences in biological functions of genes in different species, but also provide strong evidence for studying species-specific functions. The obtained gene function comparison data lays the foundation for cross-species function research. A cross-species gene mapping network is constructed based on gene structure comparison data and gene function comparison data. This network organically links the gene structure and function of different species to form a comprehensive gene mapping system. Through this mapping, the functional evolution trajectory of genes in different species can be revealed, helping researchers to better understand how genes are conserved and differentiated as species evolve. This not only enhances the systematic nature of cross-species gene research, but also provides a high-value reference for subsequent functional research, evolutionary analysis, and prediction of new gene functions. The effect of this series of steps is to effectively integrate gene structure and functional information, providing more accurate and extensive basic data and analysis frameworks for biological evolution and gene function research.

[0102] Preferably, step S251 includes the following steps:

[0103] Step S2511: extracting target gene sequences and their upstream and downstream regions of different species from the distributed genome database according to the evolutionary relationship map data, and performing sequence alignment to obtain multi-species gene sequence alignment data;

[0104] The embodiment of the present invention extracts the target gene sequence of different species from the distributed genome database according to the evolutionary relationship map data, including the upstream and downstream regions of the gene. Use sequence alignment tools such as MAFFT or ClustalW to carry out multiple sequence alignment processing on these gene sequences to ensure that the corresponding positions of genes and their regulatory regions in different species are accurately aligned. Set a conservative threshold in the alignment process to ensure high-quality alignment results. By this method, obtain multi-species gene sequence alignment data, ensure that subsequent gene structure and function are more uniformly referenced.

[0105] Step S2512: using a gene prediction algorithm to predict exon-intron boundaries for the multi-species gene sequence alignment data, and verifying and correcting them according to the existing gene structure annotations, thereby obtaining exon-intron structure data;

[0106] The embodiment of the present invention uses a gene prediction algorithm (such as GeneMark or FGENESH) to process the alignment data of multi-species gene sequences and predict the exon-intron boundaries of genes of each species. Subsequently, the prediction results are compared and verified with the gene structure annotations in the existing database, and the wrong or inaccurate prediction results are corrected to ensure the accuracy and consistency of the prediction. In this way, exon-intron structure data is generated, laying the foundation for accurate cross-species structural comparison.

[0107] Step S2513: performing cross-species comparison on the exon-intron structure data, calculating the number of exons, intron length, and splice site conservation, and identifying regions with significant structural differences, thereby obtaining exon-intron structure comparison data;

[0108] The embodiment of the present invention conducts an in-depth analysis of exon-intron structure data, and compares the number of exons, intron length, and conservation of splice sites between different species. By calculating the differences in these structural features and using significance test tools (such as ANOVA analysis) to identify regions with significant structural differences, the conservation or variation characteristics of gene structures between different species are clarified. Finally, exon-intron structure comparison data is generated to reveal key difference regions in gene evolution.

[0109] Step S2514: identifying the promoter sequence and transcription start site of the upstream region in the multi-species gene sequence alignment data, thereby obtaining promoter region data;

[0110] The embodiment of the present invention uses a promoter prediction tool (such as Promoter 2.0 or TSSPlant) to analyze the upstream region in the multi-species gene sequence alignment data to identify the promoter sequence and transcription start site. The analysis is intended to determine the promoter position and length across species, ensure that the comparison of promoter regions across species is based on accurate identification, and generate promoter region data.

[0111] Step S2515: performing sequence conservation analysis and transcription factor binding site prediction on the promoter region data, and comparing the length, GC content, and key regulatory elements of the promoter regions between different species, thereby obtaining promoter region comparison data;

[0112] The embodiment of the present invention uses conservation analysis tools (such as MEME or MAST) to perform sequence conservation analysis on promoter region data and predict transcription factor binding sites between different species. By comparing the length, GC content and key regulatory elements of the promoter region, the conservative or highly different regulatory regions are identified and promoter region comparison data is generated. This analysis helps to reveal the cross-species conservation and species specificity of the promoter region in regulating gene expression.

[0113] Step S2516: Identify enhancers, silencers, and insulators on the multi-species gene sequence alignment data, and annotate regulatory elements to obtain regulatory element data;

[0114] The embodiment of the present invention uses a regulatory element identification tool (such as EnhancerFinder or CisFinder) to analyze the multi-species gene sequence alignment data to identify potential enhancer, silencer and insulator regulatory elements. According to the existing regulatory element database annotation (such as JASPAR or TRANSFAC), the identified regulatory elements are functionally annotated. In this way, regulatory element data is generated to ensure that cross-species comparison and analysis of regulatory elements have an accurate basis.

[0115] Step S2517: performing cross-species comparison on the regulatory element data, and performing species-specific and common regulatory element identification statistics based on the position, number, type, and conservation of the regulatory elements, thereby obtaining regulatory element comparison data;

[0116] The embodiment of the present invention compares the regulatory element data across species, focusing on comparing the location, quantity, type and conservation of regulatory elements in different species. Species-specific and common regulatory elements are identified through statistical analysis, ensuring that the conservation and variation patterns of regulatory elements between different species are clearly captured, and generating regulatory element comparison data.

[0117] Step S2518: construct a multi-dimensional gene structure feature vector based on the exon-intron structure comparison data, the promoter region comparison data, and the regulatory element comparison data, and perform dimensionality reduction processing to obtain gene structure comparison data.

[0118] The embodiment of the present invention constructs a multi-dimensional gene structure feature vector based on exon-intron structure comparison data, promoter region comparison data and regulatory element comparison data. These feature vectors are processed using dimensionality reduction methods such as principal component analysis (PCA) to extract the key features that best characterize the differences in gene structure, and finally generate gene structure comparison data. This data is used to systematically analyze the evolutionary laws of gene structure across species.

[0119] The present invention provides a deep and accurate understanding of cross-species gene structure difference analysis through multi-level gene structure comparison. First, the target genes and their upstream and downstream regions of different species are extracted from the distributed genome database, and the accurate comparison of gene sequences is obtained by sequence alignment to ensure that the subsequent analysis is based on a consistent comparison framework. The exon-intron boundaries are identified using a gene prediction algorithm, and the exon-intron structure data obtained are corrected in combination with existing annotations to make the obtained exon-intron structure data more accurate. By comparing the number of exons, intron length and splicing site conservation across species, it helps to identify regions with significant structural differences, providing key structural evidence for the study of gene evolution and functional changes. Through the analysis of the upstream promoter region, the key promoter sequence and transcription start site are identified, and the conservation and transcription factor binding site prediction are performed. This lays the foundation for understanding the differences in gene expression regulation between species. The analysis is further extended to the identification and comparison of regulatory elements such as enhancers, silencers and insulators, and the specific and shared regulatory elements of different species are statistically analyzed, providing detailed data for the complexity and diversity of gene regulation. By integrating the data of exon-intron structure comparison, promoter region comparison and regulatory element comparison into a multi-dimensional gene structure feature vector and performing dimensionality reduction processing, the analysis of complex data can be simplified. This process significantly improves the efficiency and accuracy of cross-species gene structure comparison, and helps to more fully understand the structural changes of genes during species evolution and their potential functional impacts. Overall, these steps provide a systematic and reliable framework for comprehensive analysis of gene structure and cross-species gene function research, making gene comparison at the evolutionary and functional levels more intuitive and efficient.

[0120] Preferably, step S3 comprises the following steps:

[0121] Step S31: receiving search query data input by the user;

[0122] The system of the embodiment of the present invention receives the search query data input by the user, which may include gene names, sequence fragments or other gene-related text information. By providing an input interface, the user can submit a free-form query request. In order to ensure that the data can be correctly processed in subsequent steps, the system will automatically pre-process the input data, such as removing irrelevant characters, to ensure the integrity and accuracy of the input data as the basis for subsequent queries.

[0123] Step S32: performing word segmentation, part-of-speech tagging, and named entity recognition on the search query data, thereby obtaining structured query data;

[0124] The embodiment of the present invention performs natural language processing operations on the search query data, including word segmentation, part-of-speech tagging, and named entity recognition. Using tools such as NLTK or spaCy, the query data is decomposed into a structured form to extract gene names, sequence information, and other key information. This step ensures that the input free text is structured into a computer-understandable format, thereby generating structured query data, which is convenient for subsequent pattern matching and query optimization.

[0125] Step S33: performing pattern matching on the structured query data based on a predefined query type template library, and identifying the query intent and type to obtain optimized query data;

[0126] The system of the embodiment of the present invention performs pattern matching on structured query data based on a predefined query type template library. The query type template library contains definitions of different query types (such as gene name retrieval, sequence similarity retrieval, etc.). The system automatically identifies the intent of the query (such as finding gene homologous sequences or querying gene functions) and determines the corresponding query type through rules or machine learning algorithms, and finally generates optimized query data. The optimized query data is clearer, which improves the efficiency and accuracy of the query.

[0127] Step S34: performing cross-species expansion on the optimized query data based on the cross-species gene mapping data, expanding the query scope to homologous genes, thereby obtaining cross-species query statement data;

[0128] The system of the embodiment of the present invention uses cross-species gene mapping data to expand the optimized query data to homologous genes of multiple species. The specific operation includes searching for homologous genes for the gene or sequence in the query, and expanding the query scope from a single species to multiple species containing homologous genes, and generating cross-species query statement data. Through this step, the user's query scope is significantly expanded to cover relevant gene information of multiple species.

[0129] Step S35: extracting sequence length information based on the cross-species query statement data, and setting a length tolerance range, thereby obtaining sequence length screening standard data;

[0130] The system of the embodiment of the present invention extracts the length information of each gene or sequence based on the cross-species query statement data, and sets a length tolerance range according to user requirements or system preset standards to generate sequence length screening standard data. This standard usually includes an allowable length variation range, such as a tolerance of ±10%, so as to filter out sequences that do not meet the conditions in subsequent screening.

[0131] Step S36: performing preliminary sequence length screening on the cross-species gene mapping data according to the sequence length screening standard data, thereby obtaining a length matching sequence set;

[0132] The embodiment of the present invention performs a preliminary screening of all homologous genes or sequences in the cross-species gene mapping data according to the sequence length screening standard data. The system compares the sequence lengths, filters out the sequences that are not within the set tolerance range, ensures that the remaining sequences are consistent with the length requirements of the query request, and generates a length matching sequence set. This step ensures that the sequences of the screening results meet the basic conditions of the query requirements.

[0133] Step S37: performing a rapid sequence similarity assessment based on k-mer alignment according to the length matching sequence set, thereby obtaining preliminary screening similarity data;

[0134] The embodiment of the present invention uses k-mer alignment technology to quickly evaluate the similarity of length-matched sequence sets. K-mer is a method of segmenting a sequence into k consecutive sub-segments. This technology can quickly evaluate the similarity between sequences by comparing k-mer frequencies. The system calculates the k-mer similarity score of each sequence and generates preliminary screening similarity data to provide a basis for subsequent similarity filtering.

[0135] Step S38: filtering the sequences with higher similarity from the preliminary screening similarity data according to a preset similarity threshold, thereby obtaining a candidate sequence set.

[0136] The embodiment of the present invention filters the preliminary screening similarity data according to the similarity threshold preset by the system (e.g., similarity higher than 90%). The system only retains sequences with high similarity to ensure a high match with the query sequence, thereby generating a candidate sequence set. Through this similarity screening, the system greatly reduces the number of sequences that need further analysis and improves the accuracy and relevance of the query results.

[0137] The present invention realizes efficient response and accurate result output to user search queries through a series of data processing and analysis technologies. First, the search query data input by the user is received to lay the foundation for the whole process. Next, the search query data is segmented, part-of-speech tagged and named entity recognized, and the original query data is converted into structured query data, making it easier to process later. This process ensures the accuracy and consistency of the query data and provides a reliable basis for subsequent pattern matching. Pattern matching is performed using a predefined query type template library to identify query intent and type, thereby generating optimized query data. This step significantly improves the intelligent level of the query and enables the system to understand the real needs of the user. Subsequently, the query data is optimized based on the cross-species gene mapping data expansion, and the query range is extended to homologous genes, which not only improves the coverage of the query, but also enhances the relevance of the results. The sequence length information is further extracted and the length tolerance range is set, which provides a clear standard for the subsequent screening. According to these standards, the cross-species gene mapping data is preliminarily screened to ensure that the selected sequence meets the user's needs and obtain a length matching sequence set. This process effectively reduces irrelevant data and improves retrieval efficiency. Next, the k-mer alignment method is used to quickly evaluate the sequence similarity of the length-matched sequence set, and the sequences with higher similarity are quickly identified to obtain the preliminary screening similarity data. Finally, the preliminary screening similarity data is filtered according to the preset similarity threshold to ensure that the final candidate sequence set has high similarity and relevance, thereby providing users with more accurate search results. Overall, this series of steps not only improves the search efficiency, but also optimizes the relevance and accuracy of the results, ultimately enhancing the user's query experience.

[0138] Preferably, step S4 comprises the following steps:

[0139] Step S41: extracting sequence features based on sequence length, GC content and protein domain from the candidate sequence set, thereby obtaining sequence feature data;

[0140] Step S42: constructing a probability model of distant homologous sequences based on the sequence feature data, thereby obtaining a homology probability model;

[0141] Step S43: using a homology probability model to detect distant homologous sequences in the candidate sequence set, and screening by setting a threshold, thereby obtaining preliminary cross-species gene homology data;

[0142] Step S44: performing correction on the preliminary cross-species gene homology data based on evolutionary distance, thereby obtaining cross-species gene homology data;

[0143] Step S45: performing gene function annotation comparison analysis based on cross-species gene homology data to obtain function annotation comparison data, wherein the gene function annotation comparison includes comparison of GO terms, KEGG pathways, and protein domains;

[0144] Step S46: performing a collinearity analysis at the genome level based on the cross-species gene homology data, thereby obtaining collinearity analysis data;

[0145] Step S47: performing expression profile analysis based on tissue-specific expression and expression level correlation on cross-species gene homology data based on the distributed genome database, thereby obtaining expression profile analysis result data;

[0146] Step S48: filtering and sorting the candidate sequence set according to the functional annotation comparison data, the colinearity analysis data and the expression profile analysis result data, so as to obtain the search result data.

[0147] The embodiment of the present invention extracts features from a candidate sequence set, including sequence length, GC content, and protein domain information. First, the system automatically calculates the length and GC content of each sequence based on the candidate gene sequence, and uses an existing protein database (such as Pfam) to identify and annotate the protein domain of the sequence. This process is completed with the help of sequence feature extraction tools such as Biopython to generate complete sequence feature data, which will serve as the basis for the subsequent homology probability model construction. Based on the sequence feature data system, a probability model of distant homologous sequences is constructed by a statistical learning method. The model takes into account key features such as sequence length, GC content, and protein domain, and uses algorithms such as hidden Markov models (HMM) or Bayesian networks for modeling to calculate the probability that each candidate sequence is a distant homologous sequence. The model parameters are adjusted by cross-validation, and a homology probability model for subsequent detection is finally obtained. The homology probability model system is used to detect distant homologous sequences for each sequence in the candidate sequence set. During the detection process, by calculating the matching score of each sequence with the reference homology model, the system sets a predetermined threshold (such as 0.8) and screens out sequences above the threshold, and finally obtains preliminary cross-species gene homology data. The screening in this step ensures that the preliminary results have high homology by setting a higher threshold. According to the preliminary cross-species gene homology data, the system corrects the evolutionary distance between sequences. The specific operation includes re-correcting the evolutionary relationship of candidate sequences based on the existing species evolutionary tree data, applying computational tools such as MEGA or PAML, using the maximum likelihood method or Bayesian inference method, correcting the evolutionary differences of distant homologous sequences, and finally generating accurate cross-species gene homology data. This correction process improves the accuracy of homology prediction. The system performs gene function annotation comparison analysis based on cross-species gene homology data. In the specific operation, the system first extracts the corresponding annotation information from the GO term (Gene Ontology), KEGG pathway and protein domain database, and compares and analyzes it with the functional data of candidate genes. The conserved and differential functional modules are identified through enrichment analysis methods (such as Fisher's exact test), and finally functional annotation comparison data is generated. This step provides a deeper understanding of gene function. Based on cross-species gene homology data, the system analyzes the collinearity of the genome. Collinearity analysis identifies conserved regions in the genome by comparing the order of genes in the genome in multiple species. This analysis uses tools such as MCScanX to calculate the arrangement and conservation between genes and generate collinearity analysis data. Collinearity relationships help explain the conserved genomic structural features of genes between species during evolution. The system performs expression profile analysis on cross-species gene homology data based on distributed genome databases. The system extracts gene expression data from different tissues and uses expression profile databases such as GTEx or ENSEMBL to calculate the tissue-specific expression levels of genes and the correlation of expression levels under different conditions.The similarity of expression is measured by the Pearson correlation coefficient or the Spearman correlation coefficient to generate expression profile analysis result data. This analysis helps to reveal the functional role of genes in different species or tissues. The system filters and sorts the candidate sequence set based on the functional annotation comparison data, collinearity analysis data, and expression profile analysis result data. First, the system screens out sequences that are closely related to the function of the target gene based on the functional annotation and collinearity results, and then further filters out sequences with significant expression differences or low expression levels based on the expression profile analysis results. Finally, the system sorts the screened sequences and generates optimized retrieval result data to ensure that the final output results are biologically meaningful and meet the needs of user queries.

[0148] The present invention significantly improves the bioinformatics value and practicality of the candidate sequence set through a series of fine analysis and processing. First, the candidate sequence set is subjected to feature extraction based on sequence length, GC content and protein domain to generate sequence feature data, which provides key biological information for subsequent analysis and helps to identify the basic properties and potential functions of the sequence. Next, a probability model of distant homologous sequences is constructed based on the sequence feature data, which can effectively capture the similarity between sequences and provide a scientific basis for subsequent homology detection. The candidate sequence set is detected for distant homologous sequences using the probability model, and sequences with high homology are screened out by setting a threshold, thereby obtaining preliminary cross-species gene homology data. This step ensures the rigor and reliability of the analysis. Subsequently, the preliminary homology data is corrected for evolutionary distance, which enhances the accuracy and biological significance of the data and makes the inference of homology more credible. Next, functional annotation comparison analysis is performed based on cross-species gene homology data, including comparison of GO terms, KEGG pathways and protein domains. This process not only reveals the biological function of genes, but also helps researchers understand the conservation and differences of genes between different species. Through the colinearity analysis at the genome level, the conservative arrangement of homologous genes in the genome is identified, which is crucial for understanding the evolutionary process and functional changes of genes. Based on the distributed genome database, the expression spectrum analysis of cross-species gene homology data is further carried out, and the correlation between tissue-specific expression and expression level is combined to obtain the expression spectrum analysis result data. This step can provide additional evidence for the biological function of the candidate sequence, especially the expression pattern under specific biological conditions. Finally, according to the functional annotation comparison data, colinearity analysis data and expression spectrum analysis result data, the candidate sequence set is filtered and sorted to ensure that the final retrieval result data has high biological relevance and practicality. This series of steps not only improves the depth and breadth of data analysis, but also provides a more valuable basis for biological research, enhancing the credibility and application potential of research results.

[0149] The present invention also provides a gene bank data retrieval system for executing the above-mentioned gene bank data retrieval method, the gene bank data retrieval system comprising:

[0150] The data acquisition module is used to acquire multi-source genome data, perform quality control on the multi-source genome data based on the FAIR principle, and use a distributed storage system for efficient storage and indexing, thereby obtaining a distributed genome database;

[0151] Homology analysis module, used to perform cross-species homology analysis on distributed genome databases and construct multi-level evolutionary tree models to obtain evolutionary relationship map data; based on the evolutionary relationship map data, the gene structure and function between different species are mapped to obtain cross-species gene mapping data;

[0152] The retrieval query optimization module is used to receive the retrieval query data input by the user; identify the query type of the retrieval query data to obtain the optimized query data; perform preliminary screening of the optimized query data based on the sequence length according to the cross-species gene mapping data to obtain a candidate sequence set;

[0153] The homologous sequence detection module is used to detect distant homologous sequences on the candidate sequence set to obtain cross-species gene homology data; multi-dimensional analysis based on homologous sequences is performed on the cross-species gene homology data to obtain retrieval result data, where the multi-dimensional analysis includes gene function annotation comparison, colinearity analysis and expression spectrum analysis.

[0154] The present invention significantly enhances the efficiency of acquisition, analysis and application of multi-source genome data. The data acquisition module first ensures the integrity and quality of multi-source genome data. By following the FAIR principle (discoverability, accessibility, interoperability and reusability), it not only improves the credibility of the data, but also ensures its effective use in scientific research. In addition, the use of a distributed storage system for efficient storage and indexing not only optimizes the data management process, but also lays the foundation for subsequent rapid retrieval and analysis. The homology analysis module provides in-depth evolutionary relationship map data by performing cross-species homology analysis and multi-level evolutionary tree model construction on the distributed genome database, helping researchers to reveal the similarities and differences between gene structures and functions among different species. This process lays a solid foundation for subsequent gene mapping, promotes cross-species comparison of gene functions, and enhances the understanding of gene evolution mechanisms. The retrieval query optimization module further enhances the user interaction experience. By receiving the retrieval query data input by the user and performing type identification, the query process is optimized, so that users can more conveniently obtain the required information. At the same time, the preliminary screening based on sequence length ensures the relevance and accuracy of the candidate sequence set, providing a high-quality data source for subsequent homologous sequence detection. The homologous sequence detection module not only identifies cross-species gene homology data through the detection and multi-dimensional analysis of distant homologous sequences, but also comprehensively evaluates the role and impact of genes in different organisms through gene function annotation comparison, colinearity analysis and expression spectrum analysis. This series of analysis results provides researchers with rich information and helps to deepen biological research, especially in understanding the relationship between gene function and evolution, which has important scientific value and practical significance. In general, the synergistic effect of these modules makes the processing and analysis of genomic data more systematic and efficient, and promotes research progress in the field of genomics.

[0155] The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program is executed to implement any one of the above-mentioned methods for gene bank data retrieval.

[0156] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0157] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for retrieving gene bank data, characterized in that: The following steps are involved: Step S1: Acquire multi-source genome data, perform quality control on the multi-source genome data based on the FAIR principle, and use a distributed storage system for efficient storage and indexing, thereby obtaining a distributed genome database. Step S1 includes: Step S11: Acquire multi-source genome data, including nucleic acid sequence data, protein sequence data, and gene annotation and expression data; Step S12: applying a unified naming rule and format standard to the multi-source genome data, thereby obtaining standard multi-source genome data; Step S13: Evaluate the standard multi-source genome data based on the FAIR principle, and use automated tools to detect and mark low-quality sequences, repeated sequences, and contaminated sequences, thereby obtaining quality-marked genome data; Step S14: screening the standard multi-source genome data according to the quality-marked genome data, thereby obtaining high-quality genome data; Step S15: adding metadata information to the high-quality genome data and performing lossless compression based on the CRAM format to obtain compressed genome data, wherein the metadata information includes species information, gene function description, and experimental source; Step S16: using a distributed storage system to efficiently store and process the compressed genome data, and to create an index, thereby obtaining a distributed genome database; Step S2: Perform cross-species homology analysis on the distributed genome database and construct a multi-level evolutionary tree model to obtain evolutionary relationship map data; map the gene structure and function between different species according to the evolutionary relationship map data to obtain cross-species gene mapping data. Step S2 includes: Step S21: extracting nucleic acid sequence and protein sequence information from a distributed genome database, and performing sequence alignment preprocessing to obtain preprocessed sequence data, wherein the sequence alignment preprocessing specifically includes sequence length standardization and removal of low-complexity regions; Step S22: performing a global alignment based on multiple sequences on the preprocessed sequence data, and performing a local refined alignment using the Smith-Waterman algorithm, thereby obtaining a sequence similarity matrix; Step S23: Identify homologous gene families based on the sequence similarity matrix, and distinguish homologous genes and paralogous genes by gene tree reconstruction method, so as to obtain homologous gene classification data; Step S24: constructing a multi-level evolution model based on the maximum likelihood method for homologous gene classification data according to preset species taxonomy data, thereby obtaining evolutionary relationship map data; Step S25: mapping the gene structures and functions between different species according to the evolutionary relationship map data, thereby obtaining cross-species gene mapping data; Step S3: receiving search query data input by the user; identifying the search query data by query type to obtain optimized query data; performing preliminary screening of the optimized query data based on sequence length according to cross-species gene mapping data to obtain a candidate sequence set; Step S4: Perform distant homologous sequence detection on the candidate sequence set to obtain cross-species gene homology data; perform multi-dimensional analysis based on homologous sequences based on the cross-species gene homology data to obtain retrieval result data, wherein the multi-dimensional analysis includes gene function annotation comparison, colinearity analysis and expression spectrum analysis.

2. The method for gene bank data retrieval according to claim 1, characterized in that: Step S23 includes the following steps: Step S231: preliminarily grouping the preprocessed sequence data according to the sequence similarity matrix, and determining homologous gene families by a preset similarity threshold, thereby obtaining preliminary homologous gene family data; Step S232: performing multiple sequence alignment on each gene family in the preliminary homologous gene family data, and constructing a gene tree based on the Bayesian method, thereby obtaining gene family evolutionary tree data; Step S233: identifying gene duplication events based on minimal duplication events according to the gene family evolutionary tree data, and determining gene divergence points by comparing with a preset species tree, thereby obtaining gene duplication event data; Step S234: classifying members in the gene family according to the gene duplication event data, identifying members produced by gene duplication before species differentiation as paralogous genes, and identifying genes after species differentiation as orthologous genes, thereby obtaining preliminary homologous gene classification data; Step S235: performing gene arrangement order and genome context comparison between different species on the preliminary homologous gene classification data, thereby performing homology relationship determination verification, thereby obtaining optimized homologous gene classification data; Step S236: calculating the ratio of the non-synonymous substitution rate to the synonymous substitution rate of each pair of homologous genes based on the optimized homologous gene classification data, thereby obtaining homologous gene evolution feature data; Step S237: Optimize the credibility of the classification result according to the homologous gene evolution feature data and the optimized homologous gene classification data, so as to obtain the homologous gene classification data.

3. The method for gene bank data retrieval according to claim 2, characterized in that: Step S25 includes the following steps: Step S251: performing comparative analysis on gene structures among different species according to the evolutionary relationship map data, thereby obtaining gene structure comparison data, wherein the comparative analysis of gene structure includes exon-intron structure comparison, promoter region analysis, and regulatory element identification; Step S252: using gene ontology and pathway databases to annotate and compare gene functions among different species based on evolutionary relationship map data, and identifying gene sets with conservative and differentiated functions through enrichment analysis, thereby obtaining gene function comparison data; Step S253: construct a cross-species gene mapping network based on the gene structure comparison data and the gene function comparison data, thereby obtaining cross-species gene mapping data.

4. The method for gene bank data retrieval according to claim 3, characterized in that: Step S251 includes the following steps: Step S2511: extracting target gene sequences and their upstream and downstream regions of different species from the distributed genome database according to the evolutionary relationship map data, and performing sequence alignment to obtain multi-species gene sequence alignment data; Step S2512: using a gene prediction algorithm to predict exon-intron boundaries for the multi-species gene sequence alignment data, and verifying and correcting them according to the existing gene structure annotations, thereby obtaining exon-intron structure data; Step S2513: performing cross-species comparison on the exon-intron structure data, calculating the number of exons, intron length, and splice site conservation, and identifying regions with significant structural differences, thereby obtaining exon-intron structure comparison data; Step S2514: identifying the promoter sequence and transcription start site of the upstream region in the multi-species gene sequence alignment data, thereby obtaining promoter region data; Step S2515: performing sequence conservation analysis and transcription factor binding site prediction on the promoter region data, and comparing the length, GC content, and key regulatory elements of the promoter regions between different species, thereby obtaining promoter region comparison data; Step S2516: Identify enhancers, silencers, and insulators on the multi-species gene sequence alignment data, and annotate regulatory elements to obtain regulatory element data; Step S2517: performing cross-species comparison on the regulatory element data, and performing species-specific and common regulatory element identification statistics based on the position, number, type, and conservation of the regulatory elements, thereby obtaining regulatory element comparison data; Step S2518: construct a multi-dimensional gene structure feature vector based on the exon-intron structure comparison data, the promoter region comparison data, and the regulatory element comparison data, and perform dimensionality reduction processing to obtain gene structure comparison data.

5. The method for gene bank data retrieval according to claim 4, characterized in that: Step S3 includes the following steps: Step S31: receiving search query data input by the user; Step S32: performing word segmentation, part-of-speech tagging, and named entity recognition on the search query data, thereby obtaining structured query data; Step S33: performing pattern matching on the structured query data based on a predefined query type template library, and identifying the query intent and type to obtain optimized query data; Step S34: performing cross-species expansion on the optimized query data based on the cross-species gene mapping data, expanding the query scope to homologous genes, thereby obtaining cross-species query statement data; Step S35: extracting sequence length information based on the cross-species query statement data, and setting a length tolerance range, thereby obtaining sequence length screening standard data; Step S36: performing preliminary sequence length screening on the cross-species gene mapping data according to the sequence length screening standard data, thereby obtaining a length matching sequence set; Step S37: performing a rapid sequence similarity assessment based on k-mer alignment according to the length matching sequence set, thereby obtaining preliminary screening similarity data; Step S38: filtering the sequences with higher similarity from the preliminary screening similarity data according to a preset similarity threshold, thereby obtaining a candidate sequence set.

6. The method for gene bank data retrieval according to claim 5, characterized in that: Step S4 includes the following steps: Step S41: extracting sequence features based on sequence length, GC content and protein domain from the candidate sequence set, thereby obtaining sequence feature data; Step S42: constructing a probability model of distant homologous sequences based on the sequence feature data, thereby obtaining a homology probability model; Step S43: using a homology probability model to detect distant homologous sequences in the candidate sequence set, and screening by setting a threshold, thereby obtaining preliminary cross-species gene homology data; Step S44: performing correction on the preliminary cross-species gene homology data based on evolutionary distance, thereby obtaining cross-species gene homology data; Step S45: performing gene function annotation comparison analysis based on cross-species gene homology data to obtain function annotation comparison data, wherein the gene function annotation comparison includes comparison of GO terms, KEGG pathways, and protein domains; Step S46: performing a collinearity analysis at the genome level based on the cross-species gene homology data, thereby obtaining collinearity analysis data; Step S47: performing expression profile analysis based on tissue-specific expression and expression level correlation on cross-species gene homology data based on the distributed genome database, thereby obtaining expression profile analysis result data; Step S48: filtering and sorting the candidate sequence set according to the functional annotation comparison data, the colinearity analysis data and the expression profile analysis result data, so as to obtain the search result data.

7. A gene bank data retrieval system, characterized in that: Used to execute the gene bank data retrieval method according to claim 1, the gene bank data retrieval system comprises: The data acquisition module is used to acquire multi-source genome data, perform quality control on the multi-source genome data based on the FAIR principle, and use a distributed storage system for efficient storage and indexing, thereby obtaining a distributed genome database; Homology analysis module, used to perform cross-species homology analysis on distributed genome databases and construct multi-level evolutionary tree models to obtain evolutionary relationship map data; based on the evolutionary relationship map data, the gene structure and function between different species are mapped to obtain cross-species gene mapping data; The retrieval query optimization module is used to receive the retrieval query data input by the user; identify the query type of the retrieval query data to obtain the optimized query data; perform preliminary screening of the optimized query data based on the sequence length according to the cross-species gene mapping data to obtain a candidate sequence set; The homologous sequence detection module is used to detect distant homologous sequences on the candidate sequence set to obtain cross-species gene homology data; multi-dimensional analysis based on homologous sequences is performed on the cross-species gene homology data to obtain retrieval result data, where the multi-dimensional analysis includes gene function annotation comparison, colinearity analysis and expression spectrum analysis.

8. A computer-readable storage medium storing a computer program, characterized in that: The computer program is executed to implement the method for gene bank data retrieval according to any one of claims 1 to 6.

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