A sequence analysis method, device and medium thereof

Abstract

The present application discloses a sequence analysis method, device and medium thereof, which relate to the field of biotechnology, and are used for sequence analysis. In view of the problems currently caused by the non-interoperability between software and hardware of different sequence analysis methods, a sequence analysis method is provided, which realizes sequence alignment independently and stores the obtained sequence alignment results so as to perform subsequent sequence analysis operations. Since the sequence alignment part is common to each sequence analysis method, the sequence alignment in different sequence analysis algorithms can be realized by the same software or hardware, without repeated design, and can also support the database of any sequence analysis algorithm, which is more conducive to practical implementation. In addition, sequence alignment is the part with the largest amount of calculation in each sequence analysis algorithm, and the efficiency of sequence analysis can be greatly improved without repeating sequence alignment, thereby improving the efficiency of biomolecule design.

Description

Technical Field

[0001] The present application relates to the field of biotechnology, and in particular to a sequence analysis method, device and medium thereof. Background Art

[0002] With the continuous development of biotechnology and genetic engineering, the development speed of known biological molecules has shown an explosive growth trend. The sequence information of these biological molecules determines their structure, function and evolutionary relationship, and has a very important reference value for the mutation, reconstruction and de novo design of biological molecules. Therefore, sequence analysis has become an important link in biosynthesis and molecular development. The purpose of sequence analysis is to make full use of existing bioinformatics data to help people develop new sequences to complete certain functions. Through sequence analysis, we can understand the biological, chemical and physical properties of related sequence data, providing a theoretical basis for subsequent development.

[0003] The commonly used sequence analysis methods at present are the Basic Local Alignment Search Tool (BLAST) and Multiple Sequence Alignment (MSA). BLAST obtains similarity metrics by analyzing the statistically important information of the local region of the sequence. It can be said that it calculates the local similarity of the sequence and then finds the sequence that is highly similar to the target sequence in terms of confirmed structure. MSA calculates the position-based alignment between multiple sequences and then infers the evolutionary or homology relationship between sequences.

[0004] Nowadays, both BLAST and MSA can be implemented by software and hardware. The software implementation method takes a long time to calculate and is inefficient. The hardware implementation has problems such as different sequence analysis devices are not universal, and the corresponding hardware device can only analyze the database that stores the corresponding sequence information. They all independently process and analyze the sequence information through corresponding designed software or hardware to obtain the analysis results. Therefore, in the application scenario where multiple sequence analysis methods are used at the same time, it is necessary to design corresponding software programs or hardware devices for each sequence analysis method, which is costly; they are not universal with each other and can only process the sequence information stored in the corresponding database; some operation steps that exist in different sequence analysis methods, such as sequence alignment, need to be repeated a lot, which is not conducive to improving the efficiency of sequence analysis.

[0005] Therefore, technicians in this field are in urgent need of a sequence analysis method to solve the above-mentioned problems caused by the incompatibility of software and hardware between different sequence analysis methods. Summary of the invention

[0006] The purpose of the present application is to provide a sequence analysis method, device and medium thereof, so as to solve the problems currently caused by the incompatibility between software and hardware of different sequence analysis methods.

[0007] In order to solve the above technical problems, the present application provides a sequence analysis method, comprising:

[0008] Receiving a query request sent by a client; wherein the query request includes a query string;

[0009] Acquire sequence information data from a database; wherein the sequence information data is known biological information data pre-stored in the database;

[0010] Perform sequence comparison on the query string and the sequence information data, and generate sequence comparison results;

[0011] The sequence alignment results are stored to facilitate subsequent sequence analysis based on query requests.

[0012] Preferably, performing sequence alignment on the query string and the sequence information data, and generating a sequence alignment result includes:

[0013] Decomposing the sequence information data into a number of substrings according to a preset substring length;

[0014] Determine a similarity matrix between the query string and each substring;

[0015] Substrings similar to the query string are determined based on the similarity matrix and used as sequence alignment results.

[0016] Preferably, the query character string and the sequence information data are aligned, and the sequence alignment result is generated by parallel computing.

[0017] Preferably, the query string and the sequence information data are subjected to sequence alignment, and the sequence alignment result is generated as follows:

[0018] The query string and sequence information data are aligned through VLSI or FPGA to generate a sequence alignment result.

[0019] Preferably, the query string and the sequence information data are subjected to sequence alignment, and the sequence alignment result is generated as follows:

[0020] The VLSI chips, which are linearly connected end to end, will be sequenced by the query string and the sequence information data, and generate sequence alignment results.

[0021] Preferably, the processing units in the VLSI chip are linearly connected end to end.

[0022] Preferably, obtaining sequence information data from a database comprises:

[0023] The sequence information data was obtained from the database via Ethernet.

[0024] In order to solve the above technical problems, the present application also provides a sequence analysis device, comprising:

[0025] A receiving module, used for receiving a query request sent by a client; wherein the query request includes a query string;

[0026] An acquisition module is used to acquire sequence information data from a database; wherein the sequence information data is known biological information data pre-stored in the database;

[0027] A comparison module is used to compare the query string with the sequence information data and generate a sequence comparison result;

[0028] The storage module is used to store the sequence comparison results to facilitate subsequent sequence analysis based on the query request.

[0029] In order to solve the above technical problems, the present application also provides a sequence analysis device, comprising:

[0030] Memory for storing computer programs;

[0031] A processor is used to implement the steps of the sequence analysis method as described above when executing a computer program.

[0032] In order to solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the sequence analysis method as described above are implemented.

[0033] The present application provides a sequence analysis method, which separates the steps of comparing the query string and the string data representing the known biological information data stored in the database in the sequence analysis algorithms such as BLAST and MSA to obtain the substring similar to the query string, and stores the sequence comparison results obtained after the sequence comparison, so that the subsequent different sequence analysis algorithms can perform subsequent corresponding operations on the sequence comparison results. Since the sequence comparison part is common to each sequence analysis method, the sequence comparison steps in the sequence analysis algorithms such as BLAST and MSA can be implemented by the same software or hardware device without repeated design. In addition, since the sequence comparison part is the part with the largest amount of calculation in each sequence analysis algorithm, in this method, only one comparison is required between each different substring and the query string, and the comparison result can be stored and called in subsequent different sequence analysis methods, without the need to re-compare in each different sequence analysis algorithm, which greatly improves the efficiency of sequence analysis. Finally, because sequence alignment is a common part of all sequence analysis algorithms, only this part of the steps can be implemented by a set of software or hardware. The corresponding software or hardware can also support the database of any sequence analysis algorithm, which is more conducive to practical implementation.

[0034] The sequence analysis device and computer-readable storage medium provided in the present application correspond to the above method and have the same effects as above. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A flow chart of a sequence analysis method provided by the present invention;

[0037] Figure 2 A structural diagram of a systolic array provided by the present invention;

[0038] Figure 3 A structural diagram of a comparison unit provided by the present invention;

[0039] Figure 4 A structural diagram of a sequence analysis device provided by the present invention;

[0040] Figure 5 A structural diagram of another sequence analysis device provided by the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] The core of this application is to provide a sequence analysis method, device and medium thereof.

[0043] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0044] The present application aims to solve the problem that when implementing a sequence analysis algorithm, a separate software program or hardware device must be designed for each sequence analysis algorithm. In addition, the sequence alignment part has a large computational load and is the same step in each sequence analysis algorithm. Therefore, when designing the software program or hardware device, this part is repeatedly designed. When analyzing the sequence, when the same sequence information is analyzed by different sequence analysis methods, the sequence alignment process must also be repeated, which is not conducive to improving the efficiency of sequence analysis. Therefore, if Figure 1 As shown, the present application provides a sequence analysis method, comprising:

[0045] S11: receiving a query request sent by a client.

[0046] The query request is a user's request for sequence analysis of a query string, including the query string. In the currently used sequence analysis process, the corresponding sequence analysis method is determined according to the query request sent by the client, the corresponding software program or hardware device is called, the known biological information data is obtained from the database, and the sequence analysis is performed to obtain the final sequence analysis result, which is returned to the client.

[0047] S12: Obtain sequence information data from a database.

[0048] The sequence information data is the known biological information data pre-stored in the database. Since the biological information data required for different sequence analyses are also different, for example, for the same known protein, the biological information data required by the BLAST algorithm and the MSA algorithm are not exactly the same, the databases between different sequence analysis methods cannot be universal.

[0049] However, for the same protein, the sequence fragments are the same and are stored in the database in the form of strings.

[0050] In addition, this embodiment does not limit the method of obtaining the sequence information data in the database. For example, when the method is implemented by a host computer, the database can be a hard disk or other storage medium of the host computer, and the sequence comparison can be performed by obtaining the sequence information data locally. However, it is also preferred that the sequence information data is obtained from the database via Ethernet. In this way, the database does not need to be set up locally, providing an implementation method for remote sequence analysis.

[0051] S13: Perform sequence comparison on the query character string and the sequence information data, and generate a sequence comparison result.

[0052] For the sequence alignment part, since this process is computationally intensive, it can be preferably implemented through parallel computing in practical applications. Specifically, a multi-processor computer with shared memory and a specially designed Very Large Scale Integration (VLSI) chip or Field Programmable Gate Array (FPGA) chip can be used to implement parallel computing of sequence alignment, thereby further improving the efficiency of sequence analysis.

[0053] Based on the above advantages, this embodiment provides a preferred solution: performing sequence comparison between the query character string and the sequence information data, and generating the sequence comparison result by parallel computing.

[0054] Based on the above embodiment, parallel computing for sequence alignment can be realized by parallel computers or professional chips such as VLSI chips and FPGA chips. According to actual data, the computing time and cost comparison of a 500-length residue sequence in a 40Mb database by a workstation (Sparc station), a supercomputer (Cray 2), a parallel processor (Conn.Mach) and a dedicated chip (VLSI) are shown in Table 1 below:

[0055] Table 1 Comparison of sequence analysis calculation time

[0056] computer Calculation time cost Price / performance ratio Sparc station 4.5h 15K 1370 Cray 2 12m 20M 82400 Conn.Mach 1m 7M 1700 VLSI 3.5s 50K 1

[0057] As can be seen from Table 1, the price / performance ratio of using a VLSI dedicated chip for sequence analysis is the best. Therefore, this embodiment also provides a preferred solution, in which the query string and the sequence information data are compared and the sequence comparison result is generated as follows:

[0058] The query string and sequence information data are aligned through VLSI or FPGA to generate a sequence alignment result.

[0059] S14: Storing the sequence comparison results to facilitate subsequent sequence analysis according to the query request.

[0060] As can be seen from the above, the present application divides the entire sequence analysis process into two parts, one part is the common sequence comparison part in the existing sequence analysis method, and the other part is the different parts in different sequence analysis methods. The realization of different parts can be implemented by software or hardware, and there is no need to repeatedly design the sequence comparison part, which further saves costs and is more conducive to practical application. In addition, since the sequence information data used for sequence comparison is universal, the sequence comparison part can be applied in any existing database without being restricted by the sequence analysis method.

[0061] A sequence analysis method provided by the present application is to independently implement the sequence comparison part common to different sequence analysis methods, and store the sequence analysis results for use in subsequent sequence analysis, so that when performing sequence analysis, there is no need to repeat the sequence comparison. Since the sequence comparison part is the part with the largest amount of calculation in each sequence analysis method, there is no need to repeat the sequence comparison, which can greatly improve the efficiency of sequence analysis. In addition, since the sequence comparison part is a common part in different sequence analysis methods, the bioinformatics data required by it is also stored in the database corresponding to different sequence analysis methods, and then, the separate implementation of sequence comparison can be applied in any database, which is more conducive to the realization of practical application.

[0062] Further, the specific implementation of sequence alignment is shown in the following preferred embodiment, where the query string and the sequence information data are aligned, and the sequence alignment result is generated, including:

[0063] The sequence information data is decomposed into several substrings according to the preset substring length.

[0064] Determine the similarity matrix between the query string and each substring.

[0065] Substrings similar to the query string are determined based on the similarity matrix and used as sequence alignment results.

[0066] That is, step S13 is decomposed into the above three steps to complete.

[0067] To further illustrate the preferred solution provided by this embodiment, the following is an illustration with reference to examples:

[0068] The query string is a sequence segment P of length lp, and a threshold K is set to represent the range of substring lengths; a sequence segment J stored in a database T is obtained, and its initial sequence position i=0; starting from the starting position i, the sequence segment J is divided into multiple character strings with a length range of [lp-K, lp+K], and a similarity matrix is ​​calculated with the query string P as substrings; then, the similarity between the current substring and the query string P can be judged according to the obtained similarity matrix, and the substrings that meet the similarity requirements are selected and stored in a storage medium R as sequence comparison results for subsequent sequence analysis.

[0069] Also based on the above-mentioned sequence comparison format, the comparison between each substring and the queried string is a simple calculation with the same rules, which can be run independently of each other and is very suitable for parallel processing. Therefore, the similarity calculation between each queried string and the sequence fragment J can be completed in one processing unit through a digital logic circuit. Furthermore, the entire sequence comparison process can be split into several processing units for implementation, which is very suitable for the modular design of electronic logic circuits. At the same time, the connection between adjacent substrings is relatively simple, which makes the communication and control between the modules that implement the similarity calculation of adjacent substrings more convenient. The similarity calculation between each sequence fragment J and the queried string P can be completed by a digital logic circuit. Figure 2 The systolic array shown is implemented, and the systolic array is also the processing unit mentioned above.

[0070] like Figure 2 As shown, 24 comparison units are exemplarily used as a pulsation sequence, but it should be noted that this embodiment does not limit the number of comparison units included in a pulsation sequence, and it can be determined according to needs in actual implementation.

[0071] Each comparison unit can be specifically Figure 3 As shown, including input t in 、a in and b in , and the output t out 、a out and b out , its logical relationship is:

[0072] t out ←t in ^(a in =b out )

[0073] a out ←a in

[0074] b out ←b in

[0075] Each systolic array is a processing unit. This embodiment also provides a preferred implementation scheme: the processing units are connected end to end in a linear manner.

[0076] Each processing unit is linearly connected end to end to form a computing pipeline, the end of the pipeline is a pipeline register, and control units and counters are arranged around the pipeline to form a similarity analysis chip (Similarity Analysis, SA chip). Specifically, this similarity analysis chip can be implemented by VLSI or FPGA.

[0077] This linear connection makes communication between processing units simpler and faster, reduces the delay caused by data communication in practical applications, and thus improves the efficiency of sequence analysis.

[0078] Similarly, this embodiment also provides another preferred solution, in which the similarity analysis chips are also connected head to tail linearly, thereby further improving the efficiency of sequence analysis.

[0079] If the similarity analysis chip is implemented by VLSI, a preferred solution is that step S13 is implemented by multiple VLSI chips that are linearly connected end to end, and parallel computing is used.

[0080] The preferred solution provided in this embodiment reduces the delay caused by data communication by adopting a head-to-tail linear connection between each processing unit and each similarity analysis chip, thereby further reducing the time required to complete sequence analysis and improving efficiency.

[0081] To further illustrate a sequence analysis method provided by the present application, the following is an explanation in combination with actual application scenarios:

[0082] A sequence analysis method provided in the present application can be implemented by a host computer, which can be a high-performance personal computer (PC), a workstation or a mobile terminal or other device with certain data processing capabilities.

[0083] Considering that the sequence analysis algorithm is more efficient when implemented by hardware devices, the host computer is also equipped with the above-mentioned similarity analysis chip. The similarity analysis chip is set on the PC board and completes the sequence comparison process together with the microcontroller and memory installed on the PC board.

[0084] Specifically, the sequence analysis PC board includes the following units:

[0085] The microcontroller, which can be a 32-bit or 64-bit processor, is responsible for responding to various interrupts, such as when the auxiliary memory of the sequence analysis PC board needs to be filled or emptied with relevant data, and when the results calculated by the SA module need to be sent to the host computer. In addition, the parameter values ​​in the shared database can be first notified to the microcontroller and then transmitted to the host computer by the microcontroller.

[0086] Erasable Programmable Read-Only Memory (EPROM): Used to diagnose other functional devices on the sequence analysis PC board and can be started by the host computer.

[0087] Static Random Access Memory (SRAM): used to store various data to complete sequence alignment.

[0088] SA module: specifically includes 16 SA chips connected end to end linearly, which are used to realize sequence alignment.

[0089] Shared database: which stores the address space of the sequence analysis PC board and is mapped into the shared memory in the host computer as a segment.

[0090] In addition, it should be explained that the SRAM and the SA module are connected via a First Input First Output (FIFO) channel, and when the SA module is working, the host computer is allowed to perform read / write operations on the data in the SRAM.

[0091] And a host computer can be connected to several sequence analysis PC boards through a bus structure (VME, PCI, etc.) to form a computer system for performing sequence analysis.

[0092] In the above embodiment, a sequence analysis method is described in detail, and the present application also provides a corresponding embodiment of a sequence analysis device. It should be noted that the present application describes the embodiments of the device part from two perspectives, one is based on the functional module perspective, and the other is based on the hardware perspective.

[0093] Based on the functional module perspective, such as Figure 4 As shown, this embodiment provides a sequence analysis device, including:

[0094] The receiving module 21 is used to receive a query request sent by a client; wherein the query request includes a query string.

[0095] The acquisition module 22 is used to acquire sequence information data from a database; wherein the sequence information data is known biological information data pre-stored in the database.

[0096] The comparison module 23 is used to compare the query string with the sequence information data and generate a sequence comparison result.

[0097] The storage module 24 is used to store the sequence comparison results to facilitate subsequent sequence analysis according to the query request.

[0098] Since the embodiments of the apparatus part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the apparatus part, which will not be repeated here.

[0099] A sequence analysis device provided by the present application obtains a query request sent by a client through a receiving module 21, and then obtains sequence information data through an acquisition module 22, so that the comparison module 23 performs sequence comparison, and the storage module 24 saves the comparison result for subsequent sequence analysis. Thus, the sequence comparison part common to each sequence method is independently implemented, so that there is no need to repeat the sequence comparison, thereby improving the efficiency of sequence analysis. And the sequence comparison is performed separately, so that the sequence comparison process can be implemented in any database, which is more conducive to the realization of practical applications.

[0100] Figure 5 A structural diagram of a sequence analysis device provided in another embodiment of the present application, such as Figure 5 As shown, a sequence analysis device includes: a memory 30 for storing a computer program;

[0101] The processor 31 is used to implement the steps of a sequence analysis method in the above embodiment when executing a computer program.

[0102] The sequence analysis device provided in this embodiment may include but is not limited to a smart phone, a tablet computer, a laptop computer, or a desktop computer.

[0103] Among them, the processor 31 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 31 can be implemented in at least one hardware form of a digital signal processor (DSP), an FPGA, and a programmable logic array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 31 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 31 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0104] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 30 is at least used to store the following computer program 301, wherein, after the computer program is loaded and executed by the processor 31, the relevant steps of a sequence analysis method disclosed in any of the aforementioned embodiments can be implemented. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 302 may include Windows, Unix, Linux, etc. Data 303 may include, but is not limited to, a sequence analysis method, etc.

[0105] In some embodiments, a sequence analysis device may further include a display screen 32 , an input / output interface 33 , a communication interface 34 , a power supply 35 , and a communication bus 36 .

[0106] Those skilled in the art will understand that Figure 5 The structure shown in the figure does not constitute a limitation of a sequence analysis device, and may include more or fewer components than those shown in the figure.

[0107] A sequence analysis device provided in an embodiment of the present application includes a memory and a processor. When the processor executes a program stored in the memory, it can implement the following method: a sequence analysis method.

[0108] The sequence analysis device provided by the present application executes a computer program stored in a memory through a processor, independently completes the sequence comparison part from different sequence analysis methods, and stores the sequence analysis results for subsequent use, without the need to repeat the sequence comparison, thus saving a large amount of repeated calculations, thereby greatly improving the efficiency of sequence analysis. In addition, since the sequence comparison is common to each sequence analysis method, the bioinformatics data required by the sequence comparison is also stored in the database corresponding to the different sequence analysis methods, so that the sequence comparison can be applied in any database, which is more conducive to the realization of practical applications.

[0109] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps recorded in the above method embodiment are implemented.

[0110] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0111] A computer-readable storage medium provided by the present application stores a computer program that can implement the above-mentioned sequence analysis method when executed, and the sequence comparison part is completed independently from different sequence analysis methods, without repeating the sequence comparison, thereby improving the efficiency of sequence analysis. And then improve the automation level of biological laboratory calculation design, cooperate with other parts of the entire biomolecule design, and realize the efficient design of the whole workflow. Greatly reduce the errors caused by complex and repetitive labor, greatly improve production efficiency and reliability. In addition, since the sequence comparison is common to each sequence analysis method, the bioinformatics data required by it is also stored in the database corresponding to different sequence analysis methods, and the software or hardware that realizes the sequence comparison alone can be applied in any database, which is more conducive to the realization of practical application.

[0112] A sequence analysis method, device and medium provided by the present application are described in detail above. Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts refer to the method part description. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, the present application can also be improved and modified, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

[0113] It should also be noted that, in this specification, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

Claims

1. A sequence analysis method, characterized in that: include: Receiving a query request sent by a client; wherein the query request includes a query string; Acquire sequence information data from a database; wherein the sequence information data is known biological information data pre-stored in the database; Performing sequence comparison between the query character string and the sequence information data, and generating a sequence comparison result; Storing the sequence comparison results to facilitate subsequent sequence analysis according to the query request; Wherein, performing sequence comparison between the query character string and the sequence information data and generating a sequence comparison result comprises: Decomposing the sequence information data into a plurality of substrings according to a preset substring length; Determine a similarity matrix between the queried string and each of the substrings; Determine the substring similar to the queried character string according to the similarity matrix and use it as the sequence comparison result; The step of performing sequence comparison between the query character string and the sequence information data and generating a sequence comparison result is implemented by a plurality of VLSI chips connected end to end linearly; Wherein, each of the VLSI chips adopts a parallel computing mode, and each of the VLSI chips comprises: a computing pipeline composed of a plurality of processing units connected end to end linearly, a pipeline register arranged at the end of the computing pipeline, and a control unit and a counter arranged around the computing pipeline; Among them, the processing units adopt a parallel computing method, and each processing unit is used to independently realize the similarity calculation between the queried character string and a sequence fragment; the sequence fragment is included in the sequence information data; the processing unit includes a plurality of comparison units, and each comparison unit is connected to the adjacent comparison unit.

2. The sequence analysis method according to claim 1, characterized in that Obtaining sequence information data from the database includes: The sequence information data is obtained from the database via Ethernet.

3. A sequence analysis device, characterized in that: include: A receiving module, configured to receive a query request sent by a client; wherein the query request includes a query string; An acquisition module, used to acquire sequence information data from a database; wherein the sequence information data is known biological information data pre-stored in the database; A comparison module, used for performing a sequence comparison between the query string and the sequence information data, and generating a sequence comparison result; A storage module, used to store the sequence comparison results, so as to facilitate subsequent sequence analysis according to the query request; Wherein, performing sequence comparison between the query character string and the sequence information data and generating a sequence comparison result comprises: Decomposing the sequence information data into a plurality of substrings according to a preset substring length; Determine a similarity matrix between the queried string and each of the substrings; Determine the substring similar to the queried character string according to the similarity matrix and use it as the sequence comparison result; The step of performing sequence comparison between the query character string and the sequence information data and generating a sequence comparison result is implemented by a plurality of VLSI chips connected end to end linearly; Wherein, each of the VLSI chips adopts a parallel computing mode, and each of the VLSI chips comprises: a computing pipeline composed of a plurality of processing units connected end to end linearly, a pipeline register arranged at the end of the computing pipeline, and a control unit and a counter arranged around the computing pipeline; Wherein, the processing units adopt a parallel computing mode, and each processing unit is used to independently realize the similarity calculation between the query character string and a sequence segment; the sequence segment is included in the sequence information data; The processing unit includes a plurality of comparison units, wherein the comparison units are used to implement similarity calculation between a queried character string and a substring; adjacent comparison units are used to implement similarity calculation between adjacent substrings.

4. A sequence analysis device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the sequence analysis method according to claim 1 or 2 when executing the computer program.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the sequence analysis method according to claim 1 or 2 are implemented.

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