A genomic position-indexed cross-database retrieval method for biological information databases

Abstract

The application discloses a genome position indexed biological information database cross-database retrieval method, which comprises the following steps: creating a cross-database biological information retrieval table; traversing entries of a plurality of existing biological information databases to obtain first gene data; uniformly expressing the first gene data according to genome positions to obtain uniformly expressed indexes; associating the uniformly expressed indexes with the first gene data to obtain second gene data; storing the second gene data into the cross-database biological information retrieval table; performing associated retrieval in the cross-database biological information retrieval table according to a to-be-retrieved genome position to obtain a first retrieval result; performing interval operation on the first retrieval result to obtain a second retrieval result; and retrieving corresponding existing biological information databases according to the second retrieval result to obtain biological information data associated with the to-be-retrieved genome position.

Description

Technical Field

[0001] This invention relates to the fields of computational science and biomedicine, and particularly to the field of cross-database retrieval of multi-source bioinformatics databases. Background Technology

[0002] Cross-database retrieval, also known as "integrated retrieval," "cross-platform retrieval," "unified retrieval," "federated retrieval," and "parallel retrieval," refers to a system where users can concurrently search multiple databases through a unified search interface. The search results from each database are then sorted, merged, and submitted to the user in a single submission. This achieves "virtual resource integration" in situations where physical resources are dispersed. Ideally, a cross-database retrieval system should provide users with a simple, fast, and comprehensive navigation and search system, meeting the needs of users at different levels. It should utilize a single search entry point and a unified search method to concurrently search multiple distributed heterogeneous data sources, integrate the search results, and present them to the user in a unified format.

[0003] Currently, common cross-database retrieval solutions include: 1. Meta-Search Engine: A meta-search engine is a tool that retrieves information by simultaneously querying multiple databases or search engines. It can integrate search results from multiple search engines, eliminate duplicate results, and provide more comprehensive and useful search results. 2. OpenSearch Protocol: The OpenSearch Protocol is a standardized protocol based on XML and Atom protocols, enabling search engines or applications to perform cross-database retrieval in a universal way. Search engines using the OpenSearch Protocol allow users to obtain a wider range of search results by searching for relevant content on different sites. 3. Search API: Many databases provide search APIs, which allow access to and searching of data within the database. Search APIs can be used for cross-database retrieval because they provide a standardized interface for interacting with different databases. For example, PubMed provides a RESTful API that can be used to search and retrieve medical literature from the PubMed database. 4. Federated Search Platform: A federated search platform is a cross-database retrieval tool for a specific domain that can simultaneously search multiple databases and resources, providing a one-stop retrieval service. These platforms typically have customized search engines and search algorithms that can better meet the information needs of specific fields.

[0004] Bioinformatics databases and retrieval systems in life sciences face unique integration challenges. Life sciences have entered a multi-omics era, and acquiring omics data such as genomes, transcriptomes, proteomes, metagenomics, and epigenomics for various species consumes significant resources and costs. Mining this data using various types of bioinformatics analysis tools generates a vast amount of knowledge, which needs to be queried, utilized, or processed and integrated again. Therefore, the storage, annotation, and retrieval of omics data are particularly important. Although major bioinformatics databases and retrieval systems storing omics data are currently open and shared, most databases are largely independent, leading to data fragmentation and difficulties in integration during analysis. Therefore, designing a reasonable cross-database retrieval scheme is a prerequisite for effectively utilizing data and transforming the advantages of massive data resources into knowledge.

[0005] However, unlike other fields, omics data and annotations are mostly in symbolic formats such as sequences and genes, resulting in inconsistent representations and massive data volumes. A single storage unit cannot extract clear and concise keywords. Therefore, indexing entries from multiple database sources by adding standardized keywords is not suitable for omics data. Cross-database retrieval of related information is inaccurate and retrieval performance is poor. There is a need to define indexing and query methods more suitable for omics data to support multi-database retrieval in bioinformatics databases and retrieval systems. Summary of the Invention

[0006] In their research on cross-database retrieval of bioinformatics multi-source databases, the inventors discovered that existing methods of extracting keywords to build indexes are not suitable for biological omics data. Cross-database retrieval of related information recommendations is not accurate enough, and retrieval performance is poor. This problem is mainly due to the symbolic representation of omics data and annotation knowledge in bioinformatics databases, which makes keyword indexing difficult, query relevance not high enough, and retrieval performance poor.

[0007] Through analysis of the characteristics of biological omics data, the inventors discovered that using genomic location can establish a unified index for most omics data and annotation knowledge. Based on this idea, this invention discloses a cross-database retrieval method for bioinformatics databases using genomic location as the index, comprising the following steps:

[0008] Create a cross-database bioinformatics retrieval table;

[0009] The first gene data is obtained by traversing the entries of multiple existing bioinformatics databases.

[0010] The first gene data is uniformly represented according to its genomic location to obtain a unified representation index;

[0011] The unified representation index is associated with the first gene data to obtain the second gene data;

[0012] The second gene data is stored in the cross-library bioinformatics retrieval table;

[0013] Based on the location of the genome to be searched, an associated search is performed in the cross-library bioinformatics retrieval table to obtain the first search result;

[0014] Perform interval operations on the first search result to obtain the second search result;

[0015] Based on the second search result, the corresponding existing bioinformatics database is searched to obtain bioinformatics data associated with the genome location to be searched.

[0016] In one embodiment of the present invention, the step of uniformly representing the first gene data according to genomic location is performed based on a fragmented index calculation method, including:

[0017] Add an interface to the existing bioinformatics database to obtain genomic location index values.

[0018] In one embodiment of the present invention, the execution steps of the interface for obtaining genomic location index values ​​include:

[0019] Step S31: Construct the index values ​​into an index tree, which is a tree-like data structure;

[0020] Step S32: Set the storage space size of each node of the index tree, the depth of the index tree, and the number of index tree branches to obtain the starting index value of each level;

[0021] Step S33: Traverse the first gene data;

[0022] Step S34: Based on the genomic location of the traversed entry, find the location of the index tree node that accommodates the genomic location, and obtain the storage level and storage offset;

[0023] Step S35: Obtain the index value of the genome location in the entry based on the number of storage layers and the storage offset.

[0024] In one embodiment of the present invention, the second gene data includes:

[0025] Index value of genomic location;

[0026] Database identifier;

[0027] Genome location;

[0028] Item number.

[0029] In one embodiment of the present invention, the index tree is stored in binary format.

[0030] In one embodiment of the present invention, the index value is constructed as a tree data structure from the root node down.

[0031] In one embodiment of the present invention, the step of setting the storage space size of each node of the index tree further includes:

[0032] Step S321: Set the storage space size of the index tree leaf nodes;

[0033] Step S322: Multiply the storage space size of the leaf node by the number of branches of the index tree to obtain the storage space size of the parent node corresponding to the leaf node;

[0034] Step S323: Traverse the index tree to obtain the storage space size of each node.

[0035] In one embodiment of the present invention, the step of finding the index tree node location that accommodates the genomic location further includes:

[0036] The search process is conducted in the direction of searching the index tree from bottom to top and from left to right.

[0037] In one embodiment of the present invention, the step of finding the index tree node location that accommodates the genomic location further includes:

[0038] Step S341: Calculate the start and end values ​​of the genome location with the storage space size of the leaf node to obtain the start offset and end offset;

[0039] Step S342: Determine the starting point offset and the ending point offset;

[0040] Step S343: If the start offset and the end offset are located at the same node, return the current level of the index tree and the start offset;

[0041] Step S344: If they are not in the same node, search up one level and execute step S342.

[0042] In one embodiment of the present invention, the step of obtaining the index value of the genome position in the entry based on the number of storage layers and the storage offset further includes:

[0043] The starting index value of the storage layer is obtained based on the number of storage layers;

[0044] Add the starting index of the storage layer and the storage offset and return it.

[0045] In one embodiment of the present invention, the step of performing an association search in the cross-library bioinformatics retrieval table based on the location of the genome to be searched further includes:

[0046] Step S61: Calculate the start and end values ​​of the genome location to be searched with the storage space size of the leaf node to obtain the start offset and end offset;

[0047] Step S62: Obtain the starting index value of the current layer based on the starting index value of each layer and the number of layers in the index tree;

[0048] Step S63: Add the current layer start index value to the start offset and end offset respectively to obtain the associated index start and associated index end;

[0049] Step S64: Traverse the index tree according to its level;

[0050] Step S65: Return the start and end information of the associated indexes for all layers.

[0051] In one embodiment of the present invention, the step of performing interval operation on the first search result further includes:

[0052] Iterate through the items in the first search result and find all items whose start and end ranges of the associated index overlap.

[0053] Based on the starting and ending points of the associated indexes in the overlapping projects, the entry information in the cross-database bioinformatics retrieval table is obtained.

[0054] In one embodiment of the present invention, the calculation steps further include displacement calculation.

[0055] In one embodiment of the present invention, the step of retrieving data from the corresponding existing bioinformatics database based on the second retrieval result further includes:

[0056] Add a query interface for external use to the existing bioinformatics database. The parameter of the interface is the entry number.

[0057] This invention also discloses a cross-database retrieval device for bioinformatics databases indexed by genomic location, comprising:

[0058] At least one cross-library bioinformatics retrieval table;

[0059] The unified representation module is used to retrieve entries from existing bioinformatics databases, uniformly represent the retrieved information based on genomic location, and store the uniform representation information in the cross-database bioinformatics retrieval table.

[0060] The association retrieval module is used to perform association retrieval and interval operations on the cross-database bioinformatics retrieval table based on the location of the genome to be retrieved, and then retrieve the bioinformatics data associated with the location of the genome to be retrieved based on the results of the interval operations in the existing bioinformatics database.

[0061] This invention also discloses a cross-database retrieval system for bioinformatics databases indexed by genomic location, comprising computer equipment and / or mobile terminals, a server, and a database, wherein,

[0062] The server also includes the aforementioned devices;

[0063] Computer equipment and / or mobile terminals are connected to the server;

[0064] Cross-library biological data retrieval is performed through interaction between software and servers running on computer devices and / or mobile terminals.

[0065] The present invention also discloses a storage medium storing a computer program, which, when executed by a processor, implements any of the methods described above. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating the steps of a cross-database retrieval method for bioinformatics databases indexed by genomic location, according to an embodiment of the present invention.

[0067] Figure 2 This is a flowchart illustrating the execution steps of the interface for obtaining genomic location index values ​​in one embodiment of the present invention.

[0068] Figure 3 This is a method for constructing an index tree in one embodiment of the present invention.

[0069] Figure 4 This is a flowchart illustrating the steps of setting the storage space size of each node in the index tree according to an embodiment of the present invention.

[0070] Figure 5 This is a flowchart illustrating the steps of finding the index tree node location that accommodates the genome location in one embodiment of the present invention.

[0071] Figure 6 This is a schematic diagram of the storage content of a cross-database bioinformatics retrieval table in one embodiment of the present invention.

[0072] Figure 7 This is a flowchart illustrating the steps of performing an association search in the cross-library bioinformatics retrieval table based on the location of the genome to be searched, according to one embodiment of the present invention.

[0073] Figure 8 This is a block diagram of a cross-database retrieval device for bioinformatics databases indexed by genomic location, according to an embodiment of the present invention.

[0074] Figure 9 This is a block diagram of a cross-database retrieval system for bioinformatics databases indexed by genomic location, according to one embodiment of the present invention.

[0075] In the attached figures, the following labels are used:

[0076] 1: A cross-database retrieval device for bioinformatics databases indexed by genomic location

[0077] 2: Unified Expression Module

[0078] 3: Related Search Module

[0079] 4: Cross-library bioinformatics retrieval table

[0080] 10: A cross-database retrieval system for bioinformatics databases indexed by genomic location

[0081] 11: Server

[0082] 12: Database

[0083] 13: Computer equipment

[0084] 14: Mobile terminal Detailed Implementation

[0085] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that references to "an embodiment," "embodiment," "example embodiment," etc., in the specification refer to the described embodiment including specific features, structures, or characteristics, but not necessarily including these specific features, structures, or characteristics. Furthermore, such expressions do not refer to the same embodiment. Moreover, when describing specific features, structures, or characteristics in conjunction with embodiments, whether or not explicitly described, it is indicated that incorporating such features, structures, or characteristics into other embodiments is within the knowledge scope of those skilled in the art.

[0086] The specification and subsequent claims use certain terms to refer to specific modules, components, or parts. Those skilled in the art will understand that users or manufacturers may use different names or terms to refer to the same module, component, or part. This specification and subsequent claims do not distinguish modules, components, or parts by differences in name, but rather by differences in function. The terms "comprising" and "including" used throughout the specification and subsequent claims are open-ended and should be interpreted as "including but not limited to." Furthermore, the term "connection" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connections via other means.

[0087] Furthermore, in the following description and claims, numerous terms will be referenced, which should be defined as having the following meanings. The singular forms “a” and “the” include plural referents, unless the context clearly specifies otherwise. “Optional” or “optionally” indicates that an event or situation subsequently described may or may not occur, and the description includes both the scenario where the event occurs and the scenario where the event does not occur.

[0088] Please refer to Figures 1 to 6 , Figure 1 This is a flowchart illustrating the steps of a cross-database retrieval method for bioinformatics databases indexed by genomic location, according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the execution steps of the interface for obtaining genomic location index values ​​in one embodiment of the present invention. Figure 3 This is a method for constructing an index tree in one embodiment of the present invention. Figure 4 This is a flowchart illustrating the steps of setting the storage space size of each node in the index tree according to an embodiment of the present invention. Figure 5 This is a flowchart illustrating the steps of finding the index tree node location that accommodates the genome location in one embodiment of the present invention. Figure 6 This is a schematic diagram of the storage content of a cross-database bioinformatics retrieval table in one embodiment of the present invention.

[0089] This invention discloses a method for cross-database retrieval of bioinformatics databases indexed by genomic location, comprising the following steps:

[0090] Step S1: Create cross-library bioinformatics retrieval table 4;

[0091] Step S2: Traverse the entries of multiple existing bioinformatics databases to obtain the first gene data;

[0092] Step S3: The first gene data is uniformly represented according to its genomic location to obtain a uniform representation index;

[0093] Step S4: Associate the unified representation index with the first gene data to obtain the second gene data;

[0094] Step S5: Store the second gene data in the cross-library bioinformatics retrieval table 4;

[0095] Step S6: Perform an association search in the cross-library bioinformatics retrieval table 4 based on the location of the genome to be searched to obtain the first search result;

[0096] Step S7: Perform interval operations on the first search result to obtain the second search result;

[0097] Step S8: Based on the second search result, search the corresponding existing bioinformatics database to obtain bioinformatics data associated with the genome location to be searched.

[0098] The aforementioned bioinformatics databases could be, for example, {DataBase1, DataBase2, ..., DataBaseN}, comprising a total of N bioinformatics databases.

[0099] In one embodiment of the present invention, the step of uniformly representing the first gene data according to genomic location is performed based on a fragmented index calculation method, including:

[0100] Add an interface to the existing bioinformatics database for obtaining genome location index values;

[0101] In one embodiment of the present invention, the execution steps of the interface for obtaining genomic location index values ​​include:

[0102] Step S31: Construct the index values ​​into an index tree, which is a tree-like data structure;

[0103] Step S32: Set the storage space size of each node of the index tree, the depth of the index tree, and the number of index tree branches to obtain the starting index value of each level;

[0104] Step S33: Traverse the first gene data;

[0105] Step S34: Based on the genomic location of the traversed entry, find the location of the index tree node that accommodates the genomic location, and obtain the storage level and storage offset;

[0106] Step S35: Obtain the index value of the genome location in the entry based on the number of storage layers and the storage offset.

[0107] In one embodiment of the present invention, the second gene data includes:

[0108] Index value of genomic location;

[0109] Database identifier;

[0110] Genome location;

[0111] Item number.

[0112] In one embodiment of the present invention, the index tree is stored in binary format.

[0113] In one embodiment of the present invention, the index value is constructed as a tree data structure from the root node down.

[0114] In one embodiment of the present invention, the step of setting the storage space size of each node of the index tree further includes:

[0115] Step S321: Set the storage space size of the index tree leaf nodes;

[0116] Step S322: Multiply the storage space size of the leaf node by the number of branches of the index tree to obtain the storage space size of the parent node corresponding to the leaf node;

[0117] Step S323: Traverse the index tree to obtain the storage space size of each node.

[0118] In one embodiment of the present invention, the step of finding the index tree node location that accommodates the genomic location further includes:

[0119] The search process is conducted in the direction of searching the index tree from bottom to top and from left to right.

[0120] In one embodiment of the present invention, the step of finding the index tree node location that accommodates the genomic location further includes:

[0121] Step S341: Calculate the start and end values ​​of the genome location with the storage space size of the leaf node to obtain the start offset and end offset;

[0122] Step S342: Determine the starting point offset and the ending point offset;

[0123] Step S343: If the start offset and the end offset are located at the same node, return the current level of the index tree and the start offset;

[0124] Step S344: If they are not in the same node, search up one level and execute step S342.

[0125] In one embodiment of the present invention, the step of obtaining the index value of the genome position of the entry based on the number of storage layers and the storage offset further includes:

[0126] The starting index value of the storage layer is obtained based on the number of storage layers;

[0127] Add the starting index of the storage layer and the storage offset and return it.

[0128] Specifically, for example, an interface listKeys() can be added to a bioinformatics database.

[0129] The listKeys interface calculates the index value for each entry in the database by using its genomic location information (start, end) and calling binFromRangeStandard below.

[0130]

[0131]

[0132] like Figure 3 As shown, the index tree is constructed as a tree-like data structure, assigned from the root node down. For example, the tree has 2 branches, a depth of 3, and each leaf node has a storage space size of 4kb, or 12 bits. binOffsets is an array constructed based on the number of tree levels and the number of nodes at each level. binOffsets is constructed as [1+4+16,4+1,1], which is [21,5,1].

[0133] For example, if the index tree has 8 branches and a depth of 4, with each leaf node having a storage space of 128kb (17 bits), then `binOffsets` is constructed as [1+8+64+512,64+8+1,8+1,1], which is [585,73,9,1]. Each value in the array represents the starting index of the corresponding level in the index tree. `binFirstShift` represents the number of bits corresponding to the storage space of the leaf node, i.e., 128kb corresponds to 17 bits. `binNextShift` corresponds to the number of bits in the number of branches in the index tree; for an 8-branch tree, `binNextShift` is 3.

[0134] The working principle of binFromRangeStandard is as follows: For the input genome position (start, end), first, the start, end, and leaf node storage space sizes are right-shifted to obtain the starting offsets startBin and endBin. Then, starting from the bottom layer of the tree, it searches from left to right to find a node that can accommodate the genome position value. The way to find this node is to check whether startBin and endBin are equal. If they are equal, then the node that can accommodate the value is at the bottom layer of the tree. Based on the layer number i and the starting index value binOffsets[i] of the current layer, binOffsets[i] is added to startBin to obtain the index value that can accommodate the genome position value. If startBin and endBin are not equal, it searches to the next layer. At this time, startBin and endBin are right-shifted by binNextShift positions. If startBin and endBin are equal, the node that can accommodate the value is found at the current layer, and the index value binOffsets[i] + startBin is returned. If startBin and endBin are still not equal, it continues to search to the next layer until the root node is found.

[0135] Then, the index values ​​of genomic locations calculated using binFromRangeStandard are associated with the corresponding information in the source bioinformatics database and stored in cross-database bioinformatics retrieval table 4. After traversing all bioinformatics databases, a unified representation of the integration of multiple bioinformatics databases, cross-database bioinformatics retrieval table 4, is obtained, such as... Figure 6 As shown.

[0136] It should be noted that the number of branches, the depth of the tree, and the storage space size of each leaf node in the above index tree are all exemplary examples for better understanding of the present invention. The method of finding the node storing the genome location can also be from right to left, and there can be other ways to construct the index tree. The present invention is not limited to these.

[0137] Next, based on the location of the genome to be searched, a related search is performed in Table 4 of the cross-library bioinformatics retrieval system, which has been uniformly described.

[0138] Please participate Figure 7 , Figure 7 This is a flowchart illustrating the steps of performing an association search in the cross-library bioinformatics retrieval table 4 based on the location of the genome to be searched, according to one embodiment of the present invention.

[0139] The steps of related retrieval further include:

[0140] Step S61: Calculate the start and end values ​​of the genome location to be searched with the storage space size of the leaf node to obtain the start offset and end offset;

[0141] Step S62: Obtain the starting index value of the current layer based on the starting index value of each layer and the number of layers in the index tree;

[0142] Step S63: Add the current layer start index value to the start offset and end offset respectively to obtain the associated index start and associated index end;

[0143] Step S64: Traverse the index tree according to its level;

[0144] Step S65: Return the start and end information of the associated indexes for all layers.

[0145] In one embodiment of the present invention, the step of performing interval operation on the first search result further includes:

[0146] Iterate through the items in the first search result and find all items whose start and end ranges of the associated index overlap.

[0147] Based on the starting point and ending point of the association index in the overlapping projects, the entry information in the cross-database bioinformatics retrieval table 4 is obtained.

[0148] In one embodiment of the present invention, the calculation steps further include displacement calculation.

[0149] In one embodiment of the present invention, the step of retrieving data from the corresponding existing bioinformatics database based on the second retrieval result further includes:

[0150] Add a query interface for external use to the existing bioinformatics database. The parameter of the interface is the entry number.

[0151] The relevant implementation code for the above-mentioned related search is as follows:

[0152]

[0153] By using the above method, data entries related to the genome location to be searched are retrieved at each level of the index tree. Interval operations are then performed to find data entries with overlapping genome locations. Based on the entry number in the source bioinformatics database where the data entry is located, the annotations and information of the entry can be retrieved in the source bioinformatics database. This allows for the retrieval of related information in different bioinformatics databases using a single location information.

[0154] To access detailed information in the original database, a query for the corresponding bioinformatics database is required based on the entry sequence number (ID). Therefore, an API `search_by_ID()` is added to each database to implement the ID search functionality.

[0155] Please see Figure 8 , Figure 8 This is a block diagram of a cross-database retrieval device for bioinformatics databases indexed by genomic location, according to an embodiment of the present invention.

[0156] This invention also discloses a cross-database retrieval device 1 for bioinformatics databases indexed by genomic location, comprising:

[0157] At least one cross-library bioinformatics retrieval table 4;

[0158] The unified representation module 2 is used to retrieve entries from existing bioinformatics databases, uniformly represent the retrieved information according to genomic location, and store the uniform representation information in the cross-database bioinformatics retrieval table 4.

[0159] The association retrieval module 3 is used to perform association retrieval and interval operations in the cross-database bioinformatics retrieval table 4 based on the location of the genome to be retrieved, and to retrieve the bioinformatics data associated with the location of the genome to be retrieved based on the results of the interval operations in the existing bioinformatics database.

[0160] Please see Figure 9 , Figure 9 This is a block diagram of a cross-database retrieval system for bioinformatics databases indexed by genomic location, according to one embodiment of the present invention.

[0161] This invention also discloses a cross-database retrieval system 10 for bioinformatics databases indexed by genomic location, comprising:

[0162] Computer device 13 and / or mobile terminal 14, server 11 and database 12, wherein,

[0163] Server 11 also includes the aforementioned cross-database retrieval device 1 for bioinformatics databases indexed by genomic location;

[0164] Computer device 13 and / or mobile terminal 14 are connected to server 11;

[0165] Cross-database biological data retrieval is performed through interaction between software running on computer device 13 and / or mobile terminal 14 and server 11.

[0166] Mobile terminal 14 can be a mobile phone and / or tablet computer, etc., and computer device 13 can be a personal computer PC, workstation, etc.

[0167] The present invention also discloses a storage medium storing a computer program, which, when executed by a processor, implements any of the methods described above.

[0168] The aforementioned processor-executable computer program may be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0169] In summary, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can devise various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the protection scope of the patent application of the present invention.

Claims

1. A method for cross-database retrieval of bioinformatics databases indexed by genomic location. Its characteristics are Therefore, the following steps are included: Create a cross-database bioinformatics retrieval table; The first gene data is obtained by traversing the entries of multiple existing bioinformatics databases. The first gene data is uniformly represented according to its genomic location to obtain a unified representation index; The unified representation index is associated with the first gene data to obtain the second gene data; The second gene data is stored in the cross-library bioinformatics retrieval table; Based on the location of the genome to be searched, an associated search is performed in the cross-library bioinformatics retrieval table to obtain the first search result; Perform interval operations on the first search result to obtain the second search result; Based on the second search result, the corresponding existing bioinformatics database is searched to obtain bioinformatics data associated with the genome location to be searched.

2. The method as described in claim 1, characterized in that, The step of uniformly representing the first gene data according to genomic location is performed based on a fragmented index calculation method, including: Add an interface to the existing bioinformatics database to obtain genomic location index values.

3. The method as described in claim 2, characterized in that, The execution steps of the interface for obtaining genome location index values ​​include: Step S31: Construct the index values ​​into an index tree, which is a tree-like data structure; Step S32: Set the storage space size of each node of the index tree, the depth of the index tree, and the number of index tree branches to obtain the starting index value of each level; Step S33: Traverse the first gene data; Step S34: Based on the genomic location of the traversed entry, find the location of the index tree node that accommodates the genomic location, and obtain the storage level and storage offset; Step S35: Obtain the index value of the genome location in the entry based on the storage layer number and storage offset.

4. The method as described in claim 2, characterized in that, The second gene data includes: Index value of genomic location; Database identifier; Genome location; Item number.

5. The method as described in claim 3, characterized in that, The index tree is stored in binary format.

6. The method as described in claim 5, characterized in that, The index value is constructed as a tree data structure, built from the root node down.

7. The method as described in claim 6, characterized in that, The step of setting the storage space size for each node of the index tree further includes: Step S321: Set the storage space size of the index tree leaf nodes; Step S322: Multiply the storage space size of the leaf node by the number of branches of the index tree to obtain the storage space size of the parent node corresponding to the leaf node; Step S323: Traverse the index tree to obtain the storage space size of each node.

8. The method as described in claim 7, characterized in that, The step of finding the index tree node location that accommodates the genomic location further includes: The search process is conducted in the direction of searching the index tree from bottom to top and from left to right.

9. The method as described in claim 8, characterized in that, The step of finding the index tree node location that accommodates the genomic location further includes: Step S341: Calculate the start and end values ​​of the genome location with the storage space size of the leaf node to obtain the start offset and end offset; Step S342: Determine the starting point offset and the ending point offset; Step S343: If the start offset and the end offset are located at the same node, return the current level of the index tree and the start offset; Step S344: If they are not in the same node, search up one level and execute step S342.

10. The method as described in claim 3, characterized in that, The step of obtaining the index value of the genomic location in the entry based on the storage layer number and storage offset further includes: The starting index value of the storage layer is obtained based on the number of storage layers; Add the starting index of the storage layer and the storage offset and return it.

11. The method as described in claim 7, characterized in that, The step of performing an association search in the cross-library bioinformatics retrieval table based on the location of the genome to be searched further includes: Step S61: Calculate the start and end values ​​of the genome location to be searched with the storage space size of the leaf node to obtain the start offset and end offset; Step S62: Obtain the starting index value of the current layer based on the starting index value of each layer and the number of layers in the index tree; Step S63: Add the current layer start index value to the start offset and end offset respectively to obtain the associated index start and associated index end; Step S64: Traverse the index tree according to its level; Step S65: Return the start and end information of the associated indexes for all layers.

12. The method as described in claim 11, characterized in that, The step of performing interval operations on the first search result further includes: Iterate through the items in the first search result and find all items whose start and end ranges of the associated index overlap. Based on the starting and ending points of the associated indexes in the overlapping projects, the entry information in the cross-database bioinformatics retrieval table is obtained.

13. The method as described in claim 12, characterized in that, The step of retrieving the corresponding existing bioinformatics database based on the second search result further includes: Add a query interface for external use to the existing bioinformatics database. The parameter of the interface is the entry number.

14. The method as described in claim 9 or 11, characterized in that, The calculation steps further include displacement calculations.

15. A cross-database retrieval device for bioinformatics databases indexed by genomic location, used to achieve... The method according to any one of claims 1 to 14, characterized in that it comprises: At least one cross-library bioinformatics retrieval table; The unified representation module is used to retrieve entries from existing bioinformatics databases, uniformly represent the retrieved information based on genomic location, and store the uniform representation information in the cross-database bioinformatics retrieval table. The association retrieval module is used to perform association retrieval and interval operations on the cross-database bioinformatics retrieval table based on the location of the genome to be retrieved, and then retrieve the bioinformatics data associated with the location of the genome to be retrieved based on the results of the interval operations in the existing bioinformatics database.

16. A cross-database retrieval system for bioinformatics databases indexed by genomic location, characterized in that, include: Computer equipment and / or mobile terminals, servers and databases, among which, The server further includes the apparatus of claim 15; Computer equipment and / or mobile terminals are connected to the server; Cross-library biological data retrieval is performed through interaction between software and servers running on computer devices and / or mobile terminals.

17. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 14.

Images