Method and system for determining genome assembly sequence

Abstract

The disclosed embodiments provide a method and system for determining the genome assembly order. The method for determining the gene fragment assembly order includes: constructing an objective function with the goal of maximizing the overlap length of the ends after all gene fragments are sorted and assembled; constructing an occurrence number-position constraint function with the constraint that all gene fragments appear once in the spliced ​​sequence and that each sorted position of the spliced ​​sequence can only have one gene fragment; integrating the objective function and the occurrence number-position constraint function to obtain a global optimization function; using a quantum computer to optimize and solve the global optimization function to obtain a solution result, and determine the assembly order of each gene fragment. This solution determines the assembly order of each gene fragment by characterizing the genome assembly order with a function model equivalent to the Ising model, and using a quantum computer that can solve the Ising model to solve it, and using the computing speed advantage of the quantum computer to achieve a rapid solution to the gene fragment splicing problem.

Description

Technical Field

[0001] The present disclosure relates to the technical field of gene sequence assembly, and in particular to a method and system for determining genome assembly order. Background Art

[0002] Obtaining complete and accurate genome information of species is the basis for genetic research of species. At present, the main method for obtaining genome information of species is based on high-throughput sequencing. The high-throughput sequencing method first breaks the genome into a large number of gene fragments, adds connectors to the ends of the gene fragments, and obtains the base sequence of the gene fragments through base pair sequencing; then, the base sequence information of each gene fragment is compared to determine the overlapping information of the ends of the two fragments, and then the base sequences of each gene fragment are sorted based on the overlapping information to obtain complete genome information.

[0003] At present, the algorithm used to sort the base sequences of gene fragments based on overlapping information is a greedy algorithm. Since the number of gene fragments formed by the fragmentation of the genome of a general species is millions, the use of classical computers to determine the genome information of species based on the greedy algorithm is time-consuming and consumes computing resources, and cannot meet the needs of timely and rapid determination of genome sequences. Summary of the invention

[0004] In order to solve the problem that existing algorithms are time-consuming and consume computing resources in determining genome sequences based on classical computers, the embodiments of the present disclosure provide a new method and system for determining genome assembly order.

[0005] In a first aspect, the present disclosure provides a method for determining a genome assembly order, comprising:

[0006] Based on the overlap lengths of both ends of all gene fragments, an objective function is constructed with the goal of maximizing the overlap length of the ends after all gene fragments are sorted and assembled;

[0007] An occurrence number-position constraint function is constructed with the constraint that all gene fragments appear once in the spliced ​​sequence and that each sorted position of the spliced ​​sequence can only have one gene fragment;

[0008] Integrate the objective function and the occurrence count-position constraint function to obtain a global optimization function; the global optimization function is a quadratic unconstrained binary function or a function equivalent to the quadratic unconstrained binary function;

[0009] A quantum computer is used to optimize and solve the global optimization function to obtain a solution result, and the assembly order of each gene fragment is determined based on the solution result.

[0010] Optionally, the gene fragment is a fragment obtained by cutting a circular gene sequence and performing end repair;

[0011] The objective function is constructed based on the overlap lengths of both ends of all gene fragments, with the goal of minimizing the length of the spliced ​​sequence formed after all gene fragments are sorted and assembled, and includes:

[0012] Based on the overlapping lengths of both ends of all gene fragments, the objective function is constructed with the goal of maximizing the overlapping length of the ends and closing the ends after all gene fragments are sorted and assembled.

[0013] Optionally, the objective function is constructed based on the overlap lengths of both ends of all gene fragments, with the goal of maximizing the overlap lengths of the ends after all gene fragments are sorted and assembled and closing the ends, including:

[0014] use characterizing the objective function;

[0015] in is the number of gene fragments, For the The tail end and the The overlap length of the head end of each gene fragment, Characterization The gene fragment is placed in the splicing sequence Locations, For the The gene fragment is placed in the splicing sequence positions; Characterization The gene fragment is placed at the end of the splicing sequence. Characterization The gene fragments are placed at the initial position of the splicing sequence. , , and It is either 0 or 1.

[0016] Optionally, the occurrence count-position constraint function is constructed with the constraint that all gene fragments appear once in the spliced ​​sequence and each sorting position of the spliced ​​sequence can only have one gene fragment, including:

[0017] use Characterization The number of times a gene fragment appears in the spliced ​​sequence, and the Characterization The number of gene fragments placed in the sorting position is used to obtain the occurrence constraint function and the position constraint function respectively;

[0018] Summing the occurrence count constraint function and the position constraint function to obtain the occurrence count-position constraint function;

[0019] in: For the Does the gene fragment appear in The identifier of the sort position, It is either 0 or 1.

[0020] Optionally, both the objective function and the position constraint function are minimization functions;

[0021] The integrating the objective function and the occurrence number-position constraint function to obtain the global optimization function includes: performing weighted summation of the objective function and the occurrence number-position constraint function to obtain the global optimization function.

[0022] In a second aspect, the present disclosure provides a device for determining a genome assembly order, comprising:

[0023] A function construction unit is used to construct an objective function based on the overlap lengths of both ends of all gene fragments, with the goal of maximizing the overlap lengths of the ends after all gene fragments are sorted and assembled; and to construct an occurrence count-position constraint function with the constraint that all gene fragments appear once in the spliced ​​sequence and each sorted position of the spliced ​​sequence can only have one gene fragment;

[0024] A function integration unit, used to integrate the objective function and the occurrence number-position constraint function to obtain a global optimization function; the global optimization function is a quadratic unconstrained binary function or a function equivalent to the quadratic unconstrained binary function;

[0025] A quantum computing loading unit, used for optimizing and solving the global optimization function using a quantum computer to obtain a solution result;

[0026] The sorting order determination unit is used to determine the assembly order of each gene fragment based on the solution results.

[0027] Optionally, the gene fragment is a sequence obtained by cutting a circular gene sequence and performing end repair;

[0028] The function construction unit constructs the objective function based on the overlap lengths of both ends of all gene fragments, with the goal of minimizing the length of the spliced ​​sequence formed by splicing all gene fragments and forming a head-to-tail closed sequence.

[0029] Optionally, the gene fragment is a fragment obtained by cutting a circular gene sequence and performing end repair; the function construction unit constructs the objective function based on the overlapping length of both ends of all gene fragments, with the goal of maximizing the overlapping length of the ends and closing the ends after all gene fragments are sorted and assembled.

[0030] Optionally, the function construction unit adopts characterizing the objective function;

[0031] in is the number of gene fragments, For the The tail end and the The length of the end overlap of the head end of each gene fragment, Characterization The gene fragment is placed in the splicing sequence Locations, For the The gene fragment is placed in the splicing sequence positions; Characterization The gene fragment is placed at the end of the splicing sequence. Characterization The gene fragments are placed at the initial position of the splicing sequence. , , and It is either 0 or 1.

[0032] Optionally, the function construction unit adopts Characterization The number of times a gene fragment appears in the spliced ​​sequence, and the Characterization The number of gene fragments placed in the sorting position is used to obtain the occurrence constraint function and the position constraint function respectively;

[0033] Adding the occurrence count constraint function and the position constraint function to obtain the occurrence count-position constraint function;

[0034] in, For the Does the gene fragment appear in The identifier of the sort position, It is either 0 or 1.

[0035] In a third aspect, the present disclosure provides a system for determining the assembly order of gene fragments, including a first computing device and a second computing device based on quantum computing principles;

[0036] The first computing device includes a memory and a processor, the memory stores a computer program, and when the computer program is loaded by the processor, the processor executes the gene fragment assembly order as described above to obtain a global optimization function, and calls the second computing device to solve the global optimization function to obtain a solution result, and determines the sorting order of each gene fragment based on the solution result.

[0037] In a third aspect, an embodiment of the present disclosure provides a computing device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is loaded by the processor, the processor obtains a global optimization function according to the steps in the genome assembly order determination method as described above; and

[0038] Sending the global optimization function to a second computing device based on quantum computing principles, or sending a transformation function obtained by transforming the global optimization function to the second computing device;

[0039] Receive a solution result obtained by the second computing device solving the global optimization function or the transformation function, and determine the assembly order of each gene fragment based on the solution result.

[0040] By adopting the solution provided by the embodiment of the present disclosure, the gene fragment splicing problem is converted into a model equivalent to the Ising model, and a quantum computer that can solve the Ising model is used to solve the problem, the sorting order of each gene fragment is determined, and the computing speed advantage of the quantum computer is used to achieve a rapid solution to the gene fragment splicing problem. In addition, some quantum computing methods are based on the principles of physical machines, which can avoid falling into local optimality during the solution process, so that the final gene fragment sorting order is more in line with the ideal situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work, including:

[0043] Figure 1 is a flow chart of a method for determining a genome assembly order provided by an embodiment of the present disclosure;

[0044] Figure 2 is a schematic diagram of a device for determining the gene fragment assembly order provided by an embodiment of the present disclosure;

[0045] Figure 3 It is a schematic diagram of the structure of the gene fragment assembly order determination system provided in the embodiment of the present disclosure. DETAILED DESCRIPTION

[0046] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0047] The term "including" and its variations used in this document are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. In this document, relational terms such as "first" and "second" are merely 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.

[0048] In order to solve the problem of time-consuming gene fragment splicing based on a classical computer using a greedy algorithm, the present disclosure provides a method for determining the genome assembly order based on quantum computing. The genome assembly order determination method provided by the present disclosure is executed by a computing system.

[0049] Figure 1 is a flow chart of the method for determining the genome assembly order provided by the embodiment of the present disclosure. Figure 1 As shown, the method for determining the genome assembly order provided by the embodiment of the present disclosure includes S110-S140.

[0050] S110: Based on the overlap lengths of both ends of all gene fragments, an objective function is constructed with the goal of maximizing the overlap length of the ends after all gene fragments are sorted and assembled.

[0051] The end overlap length of the gene fragment is determined by comparing the ends of the base sequences of the gene fragments, and is the maximum length at which the end base sequences are the same.

[0052] For example, a genome sequence has three gene fragment base sequences, namely gene fragment 1" ATGCTCTAGTGCTAGTGA ", gene fragment 2" TGCTAGTGATCGATTGACTGAT " and gene fragment 3" TGACTGATGCACTGTAGGTGC "(in A, T, G, C Represent different types of bases respectively). According to the above order, the overlap length between the tail of gene segment 1 and the head of gene segment 2 is 9 (the corresponding base sequence is TGCTAGTGA), the overlap length between the tail of gene fragment 2 and the head of gene fragment 1 is 0 (no identical base pairs), the overlap length between the tail of gene fragment 1 and the head of gene fragment 3 is 3 (the corresponding base sequence is TGA), the overlap length between the head of gene fragment 3 and the tail of gene fragment 1 is 0, and the overlap length between the head of gene fragment 2 and the head of gene fragment 3 is 8 (the corresponding base sequence is TGACTGAT ), the overlap length between the head of gene segment 3 and the tail of gene segment 2 is 3 (the corresponding base sequence is TGC ).

[0053] It should be noted that even if the gene fragments are reversed and the head and tail are re-determined, it is only a reversal of the order, but the end overlap length of the gene fragments remains unchanged. It should also be noted that the example of using three gene fragments is only an explanation for the convenience of understanding this solution. In actual applications, the number of gene fragments may be millions, and the corresponding end overlap length will also be millions or even tens of millions.

[0054] In order to facilitate the subsequent description and analysis, mathematical symbols are used here to represent the overlap lengths of the two ends of the gene fragments. Assuming that the number of gene fragments is , No. The head of the gene fragment The end overlap length of a gene fragment is . Then the overlap length of all gene fragment ends can be Indicates that = 0, that is, the length of the end overlap of the head and tail of the same gene fragment will not be compared (in practical applications, considering that the base pair sequence in the gene fragment is very long, the probability of the same gene fragment appearing more than once in the genome sequence is the probability of the aforementioned base pair sequence combination appearing twice, which is basically an impossible event. The corresponding representation of the same gene fragment is that the head and tail will not be spliced ​​together, so it is directly Set to 0. In addition It will not be used in the following text, but is only used to show the completeness of the matrix).

[0055] After obtaining the overlap lengths of the two ends of all gene fragments, the computing device constructs an objective function with the goal of making the overlap length of the ends of all gene fragments after splicing the longest. With the longest overlap length of the ends of all gene fragments after splicing as the goal, the reverse thinking can be understood as making the splicing length formed after splicing all gene fragments the shortest. In other words, without considering the actual length of each gene fragment (that is, the actual number of base pairs contained), only the overlap length of the two ends of all gene fragments needs to be known to construct the objective function.

[0056] According to the analysis in the previous section, in order to make the overlap length of the ends of all gene fragments after splicing the longest, a maximization function that characterizes the maximum overlap length can be obtained. In the specific implementation of the present disclosure, the aforementioned maximization function is added with a negative value, that is, a minimization function determined based on the overlap between the ends of the gene fragments is obtained as the objective function (the reason will be analyzed later).

[0057] In the embodiment of the present disclosure, Characterization The position of each gene fragment in the spliced ​​sequence, and is 0 or 1, where A value of 0 indicates The gene fragment is not set in the first Locations, 1 indicates the The gene fragment is set in the first locations.

[0058] Accordingly, in some embodiments, the aforementioned objective function can be specifically According to the above meaning, and When both are 1, it indicates that The end of the gene fragment The head ends of the gene fragments are spliced ​​together, and the overlapping length of the head and tail of the two can be used as a part of the overlapping length of all gene fragments. The sum of the overlapping lengths of all gene fragments can be used as the objective function to characterize the longest overlapping length of the ends of the gene fragments after splicing.

[0059] The aforementioned S110 determines the objective function under the premise that the genome sequence is an open loop sequence. In practical applications, the genome sequences of some organisms or microorganisms are closed loops. Figuratively speaking, the genome sequence is connected end to end. In this case, considering the longest overlap length after genome sorting, it is necessary to consider not only the overlap length of each gene fragment in the middle with the preceding and following sequences, but also the overlap length of the gene fragments at the head and tail. In this case, the objective function can be specifically In the above objective function formula Characterization The gene fragment is at the end of the sorted sequence, The gene fragments are at the beginning of the sequencing sequence, and their overlap lengths with the corresponding ends are The sum of the end overlap lengths of all gene sequences can be obtained by multiplying and adding it to the end overlap lengths of other gene sequences.

[0060] S120: constructing an occurrence count-position constraint function with the constraint that all gene fragments appear once in the spliced ​​sequence and each sorting position of the spliced ​​sequence can only have one gene fragment.

[0061] As analyzed above, since the length of the base sequence in each gene fragment is very long, the probability of a gene fragment appearing more than once in the genome sequence is basically 0. In addition, since only one gene fragment can be placed in a sorting position, two gene fragments cannot be placed in the same sorting position. Based on the aforementioned premise, the embodiment of the present disclosure constrains that all gene fragments can only appear once in the spliced ​​sequence, and each sorting position of the spliced ​​sequence can only have one gene fragment, and constructs a constraint function. According to the aforementioned semantics, the constraint function is called the occurrence count-position constraint function.

[0062] In a specific implementation, the aforementioned S120 may include the following S121-S122.

[0063] S121: Construct an occurrence constraint function based on the constraint that all gene fragments appear once in the spliced ​​sequence.

[0064] Based on the goal of having all gene fragments appear only once in the spliced ​​sequence, it can be determined that the occurrence constraint function is a function with the goal of minimization. No. The number of times a gene fragment appears in the spliced ​​sequence is based on the The constraint expression that can be determined for each gene fragment is , the corresponding occurrence constraint function can be .

[0065] S122: Constructing a position constraint function based on the constraint that each sequencing position of the spliced ​​sequence can only have one gene fragment.

[0066] Since each sorting position can only have one gene fragment, it can be determined that the position constraint function is also a function with the minimum as the goal. Characterization The number of gene fragments placed at the sorting position is based on the The constraint expression that can determine the sorting position is , the corresponding position constraint function can be .

[0067] S123: summing the occurrence count constraint function and the position constraint function to obtain an occurrence count-position constraint function.

[0068] Because both the occurrence constraint function and the position constraint function are minimization constraint functions, the occurrence constraint function and the position constraint function are directly added together to obtain the occurrence-position constraint function. Because the aforementioned occurrence constraint function and position constraint function are two conditions with the same degree of importance, the weighted weights of the occurrence constraint function and the position constraint function are the same (here, the weights of the occurrence constraint function and the position constraint function are both 1).

[0069] According to the above analysis (specifically, the above specific function formula), the occurrence count-position constraint function is a quadratic unconstrained binary function. Of course, in other implementations, the occurrence count-position constraint function can also be represented by a formula equivalent to the quadratic unconstrained binary function (that is, the corresponding formula can be converted into a quadratic unconstrained binary function).

[0070] S130: Integrate the objective function and the occurrence number-position constraint function to obtain a global optimization function.

[0071] In a specific implementation, the computing device may perform a weighted summation of the objective function and the occurrence count-position constraint function, and use the summation result as the global optimization function. The weights used in the aforementioned weighted summation are predetermined weights, which are determined by experience and requirements. Based on the above analysis, the global optimization function can be a quadratic unconstrained binary function, or a function equivalent to the quadratic unconstrained binary function (that is, a function that can be converted into a quadratic unconstrained binary function).

[0072] Here, we analyze the function whose objective function is set as the minimization objective. In order to add the objective function and the occurrence count-position constraint function, it is necessary to ensure that the two have the same change trend as the gene fragment sorting optimization. The occurrence count-position constraint function is a function with minimization as the objective, and the corresponding objective function is also set as a function with minimization as the objective.

[0073] S140: Using a quantum computer to optimize and solve the global optimization function to obtain a solution result, and determining the assembly order of each gene fragment based on the solution result.

[0074] A quantum computer is a computer that can perform mathematical or logical operations based on the laws of quantum mechanics. The quantum computer used in the implementation of the present disclosure may be a computer constructed according to various annealing mechanisms with the goal of solving the Ising model. For example, a quantum computer may be a superconducting quantum annealing Ising machine, a coherent Ising machine (including an optical oscillation coherent Ising machine, an electrical oscillation coherent Ising machine, an optoelectronic oscillation coherent Ising machine), a CMOS analog Ising machine, a memristor array Ising machine, a spatial light modulation SLM Ising machine, etc., and the embodiments of the present disclosure are not particularly limited. In other words, as long as a specific quantum mechanics mechanism is adopted, a quantum computer that can solve the Ising model can be used to solve the aforementioned global optimization function.

[0075] Because the global optimization function is a quadratic unconstrained binary function or a function equivalent to it in form, and the quadratic unconstrained binary function (or quadratic unconstrained binary model) can be converted into the Ising model through a simple transformation, the quantum computer built based on the Ising model can also optimize and solve the global optimization function to obtain the solution result.

[0076] In specific implementation, the quantum approximate optimization algorithm can also be used to convert the aforementioned global optimization function into the Ising model, and the corresponding global optimization function can be maximized as Based on the above form, two rotation unitary matrices are determined: and ; represents the Hamiltonian, represents the real parameter associated with the Hamiltonian, represents the driving Hamiltonian, represents the real parameter associated with the driving Hamiltonian.

[0077] In some specific applications of the present disclosure, the quantum computer used is a hybrid quantum computer built based on degenerate optical parametric oscillation, which is essentially a coherent Ising machine. Specifically, the aforementioned hybrid quantum computer includes an optical part and an electrical part, wherein the optical part includes a laser, an amplifier, a periodically polarized lithium niobate crystal and an optical fiber loop. The laser is a pulsed laser, and an amplifier is configured at the output end. The laser emitted by the pulsed laser and amplified by the amplifier is processed by the lithium niobate crystal to obtain a frequency-doubled laser, and the equipped laser is used as a pump source to synchronously pump the PPLN crystal in an optical fiber loop to form a degenerate optical parametric oscillation, and hundreds of oscillation pulses can exist simultaneously in the optical fiber loop. The electrical part includes a phase detector, an analog-to-digital / digital-to-analog conversion part and a data processing part (FPGA or ASIC is mostly used in the specific implementation, and a conventional CPU or GPU can also be used in some applications). The laser output from the optical fiber loop and the baseband laser are measured by a phase detector to determine the phase measurement of the output light, and the measurement result is digitized and encoded data. The data processing part measures and feedback controls the optical pulse based on the encoded data.

[0078] As analyzed above, the aforementioned hybrid quantum computer uses laser pulses in optical fibers as quantum bits for calculations. In the process of degenerate optical parametric oscillation, the pump light is incident on the nonlinear optical crystal and split into two beams of light. The two beams have the same polarization direction and half the frequency of the pump light. They are in a compressed state and can be used as a quantum bit. By gradually increasing the power of the pump light and exceeding the oscillation threshold, the generated light becomes a coherent state, and the phase of the light is divided into two states (phase 0 state and π state). At this time, the phase can be set to ±1 of the spin, realizing the physical and chemical characterization of the Ising model, and can be used to solve the Ising model accordingly.

[0079] In the disclosed embodiment, after the global optimization function is converted into the Ising model, the solution result can be determined according to the following steps: a quantum circuit is built based on the Ising model according to the quantum approximate optimization algorithm, and the parameters in the circuit are initialized to achieve the initialization of the quantum state. Then the quantum circuit is run to obtain the quantum state, and the expected value of the global optimization function is calculated using the quantum state. For the same set of parameters , Repeat the measurement many times to obtain the distribution of quantum states. After obtaining the distribution of quantum states, the settlement result is determined based on the distribution probability.

[0080] Because the computing speed of quantum computers far exceeds that of classical computers based on gate circuits, after the global optimization function is constructed using the method of this solution, the quantum computer can also be used to solve the global optimization problem and determine the solution result. In the embodiment of the present disclosure, the solution result obtained by calculation is the aforementioned is 0 or 1, where 1 characterization The gene fragment is placed in the first The position of each gene fragment is directly determined.

[0081] The method for determining the gene fragment assembly order provided by the embodiment of the present disclosure is adopted. By converting the gene fragment splicing problem into an Ising model, a quantum computer that can solve the Ising model is used to solve the problem, the sorting order of each gene fragment is determined, and the computing speed advantage of the quantum computer is used to achieve a rapid solution to the gene fragment splicing problem. In addition, some quantum computing methods are based on the principles of physical machines, which can avoid falling into local optimality during the solution process, so that the final gene fragment assembly order is more in line with the ideal situation (in one case, it can be understood as a genome sequence feature that is more in line with reality).

[0082] In addition to providing the aforementioned method for determining the genome assembly order, the embodiments of the present disclosure also provide a device for determining the gene fragment assembly order. Figure 2 Schematic diagram of a device for determining the gene fragment assembly order provided by an embodiment of the present disclosure. Figure 2 As shown, the gene fragment assembly order determination device 200 provided in the embodiment of the present disclosure includes a function construction unit 201, a function integration unit 202, a quantum computing loading unit 203 and a sorting order determination unit 204.

[0083] The function construction unit 201 is used to construct an objective function based on the overlap lengths of both ends of all gene fragments, with the goal of maximizing the overlap length of the ends after all gene fragments are sorted and assembled; and to construct an occurrence count-position constraint function with the constraint that all gene fragments appear once in the spliced ​​sequence and each sorting position of the spliced ​​sequence can only have one gene fragment.

[0084] The function integration unit 202 is used to integrate the objective function and the occurrence number-position constraint function to obtain a global optimization function; the global optimization function is a quadratic unconstrained binary function or a function equivalent to the quadratic unconstrained binary function.

[0085] The quantum computing loading unit 203 is used to optimize and solve the global optimization function using a quantum computer to obtain a solution result.

[0086] The sorting order determination unit 204 is used to determine the assembly order of each gene fragment based on the solution result.

[0087] In some embodiments, the function construction unit constructs an objective function based on the overlap lengths of both ends of all gene fragments, with the goal of maximizing the overlap length of the ends and closing the ends after all gene fragments are sorted and assembled.

[0088] In some embodiments, the function construction unit 201 adopts represents the objective function; is the number of gene fragments, For the The tail end and the The overlap length of the head end of each gene fragment, Characterization The gene fragment is placed in the splicing sequence Locations, For the The gene fragment is placed in the splicing sequence positions; Characterization The gene fragment is placed at the end of the splicing sequence. Characterization The gene fragments are placed at the initial position of the splicing sequence. , , and It is either 0 or 1.

[0089] In some embodiments, the function construction unit 201 adopts Characterization The number of times a gene fragment appears in the spliced ​​sequence, and the Characterization The number of gene fragments placed in the sorting position is calculated, and the occurrence constraint function and the position constraint function are obtained respectively; the occurrence constraint function and the position constraint function are summed to obtain the occurrence-position constraint function; wherein: For the Does the gene fragment appear in The identifier of the sort position, It is either 0 or 1.

[0090] In some embodiments, the function integration unit 202 performs weighted summation of the objective function and the occurrence count-position constraint function to obtain a global optimization function.

[0091] The disclosed embodiments also provide a system for determining the gene fragment assembly order. Figure 3 Schematic diagram of the structure of the gene fragment assembly order determination system provided by the embodiment of the present disclosure. Figure 3The gene fragment assembly order determination system 300 includes a first computing device 301 and a second computing device 302. The first computing device 301 is a computer based on gate circuits, and the second computing device 302 is a second computing device 302 based on quantum computing principles (specifically capable of solving the Ising model). The first computing device 301 includes a memory 3011 and a processor 3012. The memory 3011 stores a computer program. When the computer program is loaded by the processor 3012, the processor 3012 executes the aforementioned genome assembly order determination method to obtain a global optimization function, and calls the second computing device 302 to solve the global optimization function to obtain a solution result, and determines the sorting order of each gene fragment based on the solution result.

[0092] The disclosed embodiment also provides a computing device (specifically, the aforementioned first computing device). The computing device includes a memory and a processor, the memory stores a computer program, and when the computer program is loaded by the processor, the processor obtains a global optimization function according to the steps in the genome assembly order determination method of the previous embodiment; and sends the global optimization function to a second computing device based on the quantum computing principle, or sends a transformation function obtained by transforming the global optimization function to the second computing device; receives the solution result obtained by the second computing device solving the global optimization function or the transformation function, and determines the assembly order of each gene fragment based on the solution result.

[0093] An embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for executing a genome assembly order determination method.

[0094] The embodiments of the present disclosure also provide a computer-readable medium storing a computer program. After the computer program is executed by a computing device, the computing device can execute the aforementioned method for determining the genome assembly order.

[0095] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining a genome assembly sequence, characterized in that: include: Constructing an objective function based on the overlap lengths of both ends of all gene fragments in the genome, wherein the objective function includes the sum of the overlap lengths of adjacent gene fragments in the genome assembled according to the splicing sequence, and the objective function aims to maximize the sum of the overlap lengths; A constraint function is constructed with the constraints that all gene fragments appear once in the spliced ​​sequence and that each sorting position of the spliced ​​sequence can only have one gene fragment; A global optimization function is obtained by weighted summing of the objective function and the constraint function, wherein the global optimization function is a quadratic unconstrained binary function or a function equivalent to the quadratic unconstrained binary function; A quantum computer is used to optimize and solve the global optimization function to obtain a solution result to determine the assembly order of each gene fragment.

2. The method according to claim 1, characterized in that The gene fragment is a fragment obtained by cutting the circular gene sequence and performing end repair; The objective function is constructed based on the overlap lengths of both ends of all gene fragments in the genome, including: The objective function is constructed based on the overlapping lengths of both ends of all gene fragments, with the goal of maximizing the sum of the overlapping lengths of the ends after all gene fragments are sorted and assembled and forming a closed genome.

3. The method according to claim 2, wherein the objective function is constructed based on the overlap lengths of both ends of all gene fragments, with the goal of maximizing the sum of the overlap lengths of the ends of all gene fragments after sorting and assembling and forming a closed genome, and comprising: use characterizing the objective function; in is the number of gene fragments, For the The tail end and the The overlap length of the head end of each gene fragment, Characterization The gene fragment is placed in the splicing sequence Locations, For the The gene fragment is placed in the splicing sequence positions; Characterization The gene fragment is placed at the end of the splicing sequence. Characterization The gene fragments are placed at the initial position of the splicing sequence. , , and It is either 0 or 1.

4. The method according to any one of claims 1 to 3, characterized in that: The constraint function is constructed based on the constraint that all gene fragments appear once in the spliced ​​sequence and each sorting position of the spliced ​​sequence can only have one gene fragment, including: use Characterization The gene fragments appear in the spliced ​​sequence with a number of constraints, and the Characterization The segment number constraints of the gene fragments placed in the sorting positions are used to obtain the occurrence function and position function ; The occurrence function and position function Perform summation to obtain the constraint function; in: For the Does the gene fragment appear in The identifier of the sort position, It is either 0 or 1.

5. The method according to any one of claims 1 to 3, characterized in that: The objective function and the constraint function are both minimization functions.

6. A device for determining genome assembly sequence, characterized in that: include: A function construction unit is used to construct an objective function based on the overlap lengths of both ends of all gene fragments in the genome, wherein the objective function includes the sum of the overlap lengths of adjacent gene fragments in the genome assembled according to the spliced ​​sequence, and the objective function takes the maximum sum of the overlap lengths as a goal; and a constraint function is constructed with the constraints that all gene fragments appear once in the spliced ​​sequence and each sorted position of the spliced ​​sequence can only have one gene fragment; A function integration unit, used for performing weighted summation of the objective function and the constraint function to obtain a global optimization function; the global optimization function is a quadratic unconstrained binary function or a function equivalent to the quadratic unconstrained binary function; A quantum computing loading unit, used for optimizing and solving the global optimization function using a quantum computer to obtain a solution result; The sorting order determination unit is used to determine the assembly order of each gene fragment based on the solution results.

7. The device according to claim 6, characterized in that The gene fragment is a fragment obtained by cutting the circular gene sequence and performing end repair; The function construction unit constructs the objective function based on the overlapping lengths of both ends of all gene fragments, with the goal of maximizing the sum of the overlapping lengths of the ends after all gene fragments are sorted and assembled and forming a closed genome.

8. The device according to claim 7, characterized in that The function building unit adopts characterizing the objective function; in is the number of gene fragments, For the The tail end and the The length of the end overlap of the head end of each gene fragment, Characterization The gene fragment is placed in the splicing sequence Locations, For the The gene fragment is placed in the splicing sequence positions; Characterization The gene fragment is placed at the end of the splicing sequence. Characterization The gene fragments are placed at the initial position of the splicing sequence. , , and It is either 0 or 1.

9. The device according to any one of claims 6 to 8, characterized in that: include: The function building unit adopts Characterization The gene fragments appear in the spliced ​​sequence with a number of constraints, and the Characterization The segment number constraints of the gene fragments placed in the sorting positions are used to obtain the occurrence function and position function ; The occurrence function and position function Add together to obtain the constraint function; in, For the Does the gene fragment appear in The identifier of the sort position, It is either 0 or 1.

10. A system for determining the assembly order of gene fragments, characterized in that: comprising a first computing device and a second computing device based on quantum computing principles; The first computing device includes a memory and a processor, the memory stores a computer program, and when the computer program is loaded by the processor, the processor executes the gene fragment assembly order determination method as described in any one of claims 1 to 5 to obtain a global optimization function, and calls the second computing device to solve the global optimization function to obtain a solution result, and determines the sorting order of each gene fragment based on the solution result.

11. A computing device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is loaded by the processor, the processor obtains a global optimization function according to the steps in the method for determining the genome assembly sequence according to any one of claims 1 to 5; and Sending the global optimization function to a second computing device based on quantum computing principles, or sending a transformation function obtained by transforming the global optimization function to the second computing device; Receive a solution result obtained by the second computing device solving the global optimization function or the transformation function, and determine the assembly order of each gene fragment based on the solution result.

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