Haploid modeling method based on generic genome genetic information

Abstract

The invention discloses a haploid modeling method based on generic genome genetic information, relates to the technical field of genetic engineering, and aims to ensure existence of'essential genes' in human genomes as much as possible through a core sequence mechanism and a sliding window mechanism in order to solve the problem of low haploid modeling efficiency in the prior art. And the generic genome is divided into a plurality of core windows, so that a single window becomes parallel after being a relatively independent individual, modeling supporting high parallelization is realized, and the modeling efficiency is improved on the basis of ensuring real modeling.

Description

Technical Field

[0001] The present invention relates to the field of gene engineering technology, and in particular to a haploid modeling method based on pan-genome genetic information. Background Art

[0002] Haploidy is of great significance in human genome research, mainly in simplifying genome analysis, improving data accuracy and promoting the progress of genetic research. Since the haploid genome contains only one set of chromosomes, it can avoid the interference of allele differences in diploidy, help to accurately assemble the genome sequence, and reveal the combination of alleles on chromosomes, that is, haplotypes, so as to better analyze the relationship between genetic variation and complex diseases. In addition, the haploid background simplifies gene expression regulation and epigenetic research, which is conducive to the analysis of gene function, especially in gene editing and cell engineering, the gene manipulation of haploid cells is more efficient. At the same time, through the study of Y chromosomes and mitochondrial DNA with haploid genomes, the genetic transmission pattern of humans can be reconstructed and the history of human evolution and migration can be revealed. Therefore, the haploid genome plays an irreplaceable role in precision medicine, genetics, genomics and human evolution research.

[0003] The modeling of haploid genomes can effectively increase the amount of haploid data, help build high-precision genetic maps, and can be used to train and evaluate the performance of machine learning algorithms in biological data mining. However, the existing haploid modeling methods have a low efficiency due to the large amount of pan-genome data, complex internal connections, and many circles and loops. Summary of the invention

[0004] The purpose of the present invention is to provide a haploid modeling method based on pan-genome genetic information to address the problem of low efficiency of existing haploid modeling.

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] A haploid modeling method based on pan-genomic genetic information comprises the following steps:

[0007] Step 1: Obtain pan-genome data and divide the sequence segments in the pan-genome data into core sequence segments and non-core sequence segments;

[0008] Step 2: Divide the core window based on the divided core sequence segments and non-core sequence segments;

[0009] Step 3: Use the linear reference haploid as an index, and use the core sequence segment and core window as a skeleton to perform haploid modeling in parallel with the core window.

[0010] Furthermore, the specific steps of step one are:

[0011] The pan-genome data is obtained, and the frequency of occurrence of sequences in the pan-genome data is counted. The sequence segments that all haploids in the pan-genome data have are core sequence segments, and the rest are non-core sequence segments.

[0012] Furthermore, the specific steps of step one are:

[0013] Step 11: Obtain pan-genome data, and obtain a reverse complementary sequence segment of a sequence segment in the pan-genome data;

[0014] Step 1 and 2: Add the reverse complementary sequences of all sequence segments in the pan-genome to the pan-genome in the same way as their original forward connections;

[0015] Step 13: Perform frequency statistics on the sequence segments in the pan-genome. If the number of occurrences of a sequence segment is the same as the total number of haploids in the pan-genome, the sequence segment is considered the core sequence segment of the pan-genome, and the rest are non-core sequence segments.

[0016] Furthermore, in the steps 1 and 3, frequency statistics of sequence segments in the pan-genome are performed through hash mapping.

[0017] Furthermore, the frequency statistics of sequence segments in the pan-genome using hash mapping are expressed as:

[0018]

[0019] Among them, H(t i ) is the haploid identification set, H is the hash map storage, t i is the number of the ith sequence segment, s j is the jth haploid number, N is the total number of samples, and T is the core sequence segment set.

[0020] Furthermore, the specific steps of step 2 are:

[0021] Step 21: Treat each sequence segment as an independent individual, use the linear reference haploid GRCh38 as an index, and divide the sequence window into every 5,000 sequence segments of the linear reference haploid GRCh38;

[0022] Step 22: For the same sequence window, respectively obtain the core sequence segments that are closest to the two endpoints of the sequence window, and the window formed by the two core sequence segments is the core window.

[0023] Further, the sequence window is expressed as:

[0024]

[0025] Among them, G i is the i-th sequence window, E i is the end point of the i-th sequence window, x i is the i-th sequence segment in the reference genome, L is the total number of haploid sequence segments constituting the linear reference, W is the number of set sequence segments, t j is the jth core sequence segment, p j is the jth core sequence segment, C i is the i-th core window, and k is the k-th window.

[0026] Furthermore, the specific steps of haploid modeling in step three are:

[0027] Step 31: Create a process pool, and evenly distribute the core windows according to the preset number of processes by using the window index and process number mapping method, and transfer each allocated core window to the corresponding single child process, and then map the core window from the page table to the physical memory through the unique virtual address space of the process;

[0028] Step 32: When there are multiple parallel sequence segments at the end of the sequence segment, a strategy based on the Mersenne Twister algorithm to generate high-quality pseudo-random numbers is adopted. One of the multiple pan-sequence segments is selected as the haploid component according to the pseudo-random number, and the depth-first search strategy is used to continue to randomly search for the subsequent sequence segments in the depth until the end sequence segment of the linear reference sequence is searched. It is expressed as:

[0029]

[0030] Where M is the Mersenne rotation function, r is the generated random number, For sequence segment r i The set of parallel sequence segments, t end To be the core sequence segment for the termination of depth-first search, t next is the received sequence segment, t ir is the sequence segment selected according to the random number, t current is the current sequence segment, length() indicates the length of the set, B() indicates searching for the set of all directly connected subsequence segments of the current sequence, and DFS() indicates that the depth-first strategy is always adopted for the current sequence segment during the search process;

[0031] In the process of haploid modeling, a path storage stack and a sequence segment access storage stack are set. Each time a sequence segment is searched downstream and its position in the haploid topological structure is determined, the path information is pushed into the path storage stack, and the sequence segment is pushed into the sequence segment access storage stack. After that, a redundancy determination is performed on each sequence segment obtained by the search, that is, whether the sequence segment exists in the sequence segment access storage stack is determined. If so, the sequence segment pushed into the stack is popped out, and the sequence segment is marked as an unselectable sequence in the pan-genome, and a random selection is performed here again. If not, the sequence segment is pushed into the sequence segment access storage stack, and the sequence segment is added to the path, and the path information and the path storage stack are updated, and then the search is continued;

[0032] The number of sequence segments of the linear reference haploid GRCh38 between the core sequence segments was calculated and used as the calibration distance between the two core sequence segments. If the calibration distance exceeded the set sequence window spacing, the legal distance in the modeling process was set to infinity. Otherwise, the legal distance was set to the square of the calibration distance. The legal distance is expressed as:

[0033]

[0034] Among them, p i is the sequence segment searched during modeling, L ij is the legal distance;

[0035] If the calibrated distance between two core sequence segments exceeds the legal spacing during modeling, the paths and sequence segments that have been pushed into the stack between the two core sequence segments are deleted through the path storage stack and the sequence segment access storage stack, and the beginning between the two core sequence segments is traced back to search again until the modeling of all core windows is completed to obtain the topological structure of each process. After that, the topological structures of the parallel processes are mapped and spliced ​​to obtain a complete haploid topological representation. Finally, the complete haploid topological representation is concretized as a base sequence to form the expected haploid and complete the modeling.

[0036] The topology of each process is represented as:

[0037]

[0038] Among them, S p S is a stack for storing explored paths. v A stack for storing accessed sequence segments, T k is the haploid partial topology of the kth window, n is the total number of windows, T final For the final complete haploid topology, For p i to p j All the paths pushed into the stack between pi and pj, and x is all the sequence segments pushed into the stack between pi and pj.

[0039] Furthermore, the pan-genome data is obtained based on Minigraph-Cactus.

[0040] Furthermore, the set number of sequence segments W=5000.

[0041] The beneficial effects of the present invention are:

[0042] This application aims to ensure the existence of "essential genes" in the human genome as much as possible through the core sequence mechanism and the sliding window mechanism. This application divides the pan-genome into multiple core windows to make a single window a relatively independent individual and then parallelize it, thereby supporting highly parallel modeling and improving modeling efficiency while ensuring authentic modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is the overall flow chart of this application. DETAILED DESCRIPTION

[0044] It should be particularly noted that, in the absence of conflict, the various embodiments disclosed in this application can be combined with each other.

[0045] Specific implementation method 1: This implementation method is a haploid modeling method based on pan-genome genetic information. The method of this application mainly includes the following three steps:

[0046] 1. Sequence division and window division. According to the probability of sequence occurrence and the traceable samples, the sequences in the pan-genome are divided into core sequence segments and non-core sequence segments. The sequence windows are divided according to the number of sequence segments, and then the core windows are divided according to the position of the core sequence.

[0047] 2. Use linear reference as index and parallel modeling of windows. Divide the pan-genome into segments and parallelize them according to the divided core windows to achieve efficient and orderly modeling.

[0048] 3. Determine the redundancy and distance of sequence segments to limit the frequency of sequence occurrence and abnormal distances between sequence segments during the modeling process, and maintain the haploid length in a relatively stable state.

[0049] Based on Minigraph-Cactus, we obtain the pan-genome and count the probability of sequence occurrence in the pan-genome. The sequences that all samples in the pan-genome have are the necessary (core) sequences.

[0050] Sequence partitioning and window partitioning

[0051] The reverse complementary sequences of all sequence segments in the pan-genome are added to the pan-genome according to their original forward connection mode. Then, for all haploid samples constituting the pan-genome, the sequence segment frequency statistics based on hash mapping are adopted (the frequency statistics of sequence segments in the pan-genome are performed using hash mapping). H is the hash mapping storage, ti is the i-th sequence segment number, sj is the j-th sample number, N is the total number of samples, T is the core sequence segment set, H(ti) is the sample identification set, and T is the screened core sequence segment set.

[0052]

[0053] If the number of occurrences of a sequence segment is the same as the total number of sample haploids, the sequence segment is recorded as the core sequence segment of the pan-genome, and the rest are marked as non-core sequence segments, so as to ensure that the core sequence segment will not be lost in subsequent simulations and maintain the relative stability of the genome haploid skeleton. After determining the core sequence, in order to fully segment the pan-genome, each sequence segment is first regarded as an independent individual, with the linear reference haploid GRCh38 (the most important sample, the pan-genome is constructed around it) as the index, and every 5,000 sequence segments of the linear reference haploid GRCh38 as a window, and the sequence window is divided as follows, where G i is the i-th sequence window, E i is the end point of the i-th sequence window, x i represents the i-th sequence segment in the reference genome, L is the total number of haploid sequence segments constituting the linear reference, W = 5000, t j represents the jth core sequence segment, p j are its coordinates. i is the ith core window.

[0054]

[0055] After the division is completed, each sequence window has a different number of core sequence segments. The core sequences closest to different endpoints of the same window are selected as the endpoints of the core window, so as to divide the core window. Under the premise of ensuring that the window sizes are relatively balanced, the independence of the windows is achieved.

[0056] Parallel simulation of linear referencing indexes

[0057] The linear reference haploid GRCh38 covers all the core windows of the pan-genome, so regular simulation can be performed according to the arrangement order of the core sequence segments in the linear reference. Therefore, a process pool is created, and the numerous core windows are evenly distributed according to the number of processes pre-specified by the user, using the method of mapping window index and process number, and the segmented window sets are accurately transferred to the corresponding single sub-processes. Through the unique virtual address space of the process, the page table is mapped to the physical memory, thereby realizing the memory isolation mechanism and allocating independent memory space to each sub-process. Avoiding possible memory interference and data conflicts between different sub-processes, thereby maximizing the independence and stability of each sub-process operation, and making the entire parallel modeling process efficient.

[0058] The underlying modeling rules adopt a random strategy. When encountering a sequence segment with multiple parallel sequences at the end, a strategy based on the Mersenne rotation algorithm to generate high-quality pseudo-random numbers is adopted to select one of the multiple pan-sequence segments as the haploid component, and the depth-first search strategy continues to search downward randomly until the end sequence segment of the linear reference sequence is searched. M is the Mersenne rotation function, r is the generated random number, is the sequence segment t i The set of parallel sequence segments, t end The core sequence segment to terminate the depth-first search

[0059]

[0060] Sequence segment redundancy determination and distance determination

[0061] In the process of random selection modeling, a path storage stack and a sequence segment access storage stack are set to record the segmented haploid topology structure and the sequence segments visited in the current modeling. Each time a sequence segment is searched downstream and its position in the haploid topology structure is determined, the path information is pushed into the path stack, and the received sequence segment is pushed into the access storage stack. Redundancy judgment is performed on each sequence segment obtained by the search. If the sequence segment exists in the access storage stack, the sequence segment pushed into the stack is popped out and marked as an unselectable sequence in the pan-genome, and the random selection of the pan-sequence segment here is performed again. Otherwise, the sequence segment is pushed into the sequence segment access storage stack, and the sequence segment is added to the path, the path information is updated, the path storage stack is updated, and the search continues according to the above random strategy.

[0062] The number of sequence segments of the linear reference haplotype GRCh38 between the core sequence segments is calculated as the calibration distance between the two core sequence segments. If the calibration distance exceeds the sequence window spacing, the legal distance in the modeling process is set to infinity; if the calibration distance does not exceed the window spacing, the legal distance is set to the square of the calibration distance. iis the sequence segment searched during modeling, L ij Legal distance

[0063]

[0064] If the distance between two core paths exceeds the legal spacing during modeling, the paths and sequence segments that have been pushed into the stack in this interval are deleted through the path storage stack and the sequence segment access storage stack, and the search is repeated to the beginning of this interval until all windows are modeled and the topological structure is obtained. The topology of the parallel processes is then mapped and spliced. According to the boundary conditions and connection rules between the topological structures constructed by different processes, it is ensured that each part can be seamlessly connected to form a complete haploid topological representation. Finally, the sequence segment topology is visualized as a base sequence to form the expected haploid, and the modeling is completed. Sp is the stack for storing explored paths, Sv is the stack for storing visited sequence segments, and T is the topological structure of the core sequence segments.

[0065]

[0066] The modeling method of the present application first extracts and analyzes the constituent samples of the pan-genome, performs sample backtracking and sequence occurrence probability statistics on all sequences in the pan-genome, and determines the necessity of sequence existence; a parallel, end-to-end haploid modeling is performed using the linear reference haploid as an index and the divided core sequences and core windows as a skeleton.

[0067] It should be noted that the specific implementation is only an explanation and description of the technical solution of the present invention, and cannot be used to limit the scope of protection of the rights. Any partial changes made according to the claims and description of the present invention should still fall within the scope of protection of the present invention.

Claims

1. A haploid modeling method based on pan-genomic genetic information, characterized in that The following steps are involved: Step 1: Obtain pan-genome data and divide the sequence segments in the pan-genome data into core sequence segments and non-core sequence segments; Step 2: Divide the core window based on the divided core sequence segments and non-core sequence segments; Step 3: Use the linear reference haploid as an index, and use the core sequence segment and core window as a skeleton to perform haploid modeling in parallel with the core window.

2. A haploid modeling method based on pan-genomic genetic information according to claim 1, characterized in that The specific steps of step one are: The pan-genome data is obtained, and the frequency of occurrence of sequences in the pan-genome data is counted. The sequence segments that all haploids in the pan-genome data have are core sequence segments, and the rest are non-core sequence segments.

3. A haploid modeling method based on pan-genomic genetic information according to claim 2, characterized in that The specific steps of step one are: Step 11: Obtain pan-genome data, and obtain a reverse complementary sequence segment of a sequence segment in the pan-genome data; Step 1 and 2: Add the reverse complementary sequences of all sequence segments in the pan-genome to the pan-genome in the same way as their original forward connections; Step 13: Perform frequency statistics on the sequence segments in the pan-genome. If the number of occurrences of a sequence segment is the same as the total number of haploids in the pan-genome, the sequence segment is considered the core sequence segment of the pan-genome, and the rest are non-core sequence segments.

4. A haploid modeling method based on pan-genomic genetic information according to claim 3, characterized in that In the step 1-3, the frequency statistics of the sequence segments in the pan-genome are performed through hash mapping.

5. A haploid modeling method based on pan-genomic genetic information according to claim 4, characterized in that The frequency statistics of sequence segments in the pan-genome using hash mapping are expressed as: Among them, H(t i ) is the haploid identification set, H is the hash map storage, t i is the number of the ith sequence segment, s j is the jth haploid number, N is the total number of samples, and T is the core sequence segment set.

6. A haploid modeling method based on pan-genomic genetic information according to claim 5, characterized in that The specific steps of step 2 are: Step 21: Treat each sequence segment as an independent individual, use the linear reference haploid GRCh38 as an index, and divide the sequence window into every 5,000 sequence segments of the linear reference haploid GRCh38; Step 22: For the same sequence window, respectively obtain the core sequence segments that are closest to the two endpoints of the sequence window, and the window formed by the two core sequence segments is the core window.

7. A haploid modeling method based on pan-genomic genetic information according to claim 6, characterized in that The sequence window is represented as: Among them, G i is the i-th sequence window, E i is the end point of the i-th sequence window, x i is the i-th sequence segment in the reference genome, L is the total number of haploid sequence segments constituting the linear reference, W is the number of set sequence segments, t j is the jth core sequence segment, p j is the jth core sequence segment, C i is the i-th core window, and k is the k-th window.

8. A haploid modeling method based on pan-genomic genetic information according to claim 7, characterized in that The specific steps of haploid modeling in step 3 are: Step 31: Create a process pool, and evenly distribute the core windows according to the preset number of processes by using the window index and process number mapping method, and transfer each allocated core window to the corresponding single child process, and then map the core window from the page table to the physical memory through the unique virtual address space of the process; Step 32: When there are multiple parallel sequence segments at the end of the sequence segment, a strategy based on the Mersenne Twister algorithm to generate high-quality pseudo-random numbers is adopted. One of the multiple pan-sequence segments is selected as the haploid component according to the pseudo-random number, and the depth-first search strategy is used to continue to randomly search for the subsequent sequence segments in the depth until the end sequence segment of the linear reference sequence is searched. It is expressed as: Where M is the Mersenne rotation function, r is the generated random number, is the sequence segment t i The set of parallel sequence segments, t end To be the core sequence segment for the termination of depth-first search, t next is the received sequence segment, t ir is the sequence segment selected according to the random number, t current is the current sequence segment, length() indicates the length of the set, B() indicates searching for the set of all directly connected subsequence segments of the current sequence, and DFS() indicates that the depth-first strategy is always adopted for the current sequence segment during the search process; In the process of haploid modeling, a path storage stack and a sequence segment access storage stack are set. Each time a sequence segment is searched downstream and its position in the haploid topological structure is determined, the path information is pushed into the path storage stack, and the sequence segment is pushed into the sequence segment access storage stack. After that, a redundancy determination is performed on each sequence segment obtained by the search, that is, whether the sequence segment exists in the sequence segment access storage stack is determined. If so, the sequence segment pushed into the stack is popped out, and the sequence segment is marked as an unselectable sequence in the pan-genome, and a random selection is performed here again. If not, the sequence segment is pushed into the sequence segment access storage stack, and the sequence segment is added to the path, and the path information and the path storage stack are updated, and then the search is continued; The number of sequence segments of the linear reference haploid GRCh38 between the core sequence segments was calculated and used as the calibration distance between the two core sequence segments. If the calibration distance exceeded the set sequence window spacing, the legal distance in the modeling process was set to infinity. Otherwise, the legal distance was set to the square of the calibration distance. The legal distance is expressed as: Among them, p i is the sequence segment searched during modeling, L ij is the legal distance; If the calibrated distance between two core sequence segments exceeds the legal spacing during modeling, the paths and sequence segments that have been pushed into the stack between the two core sequence segments are deleted through the path storage stack and the sequence segment access storage stack, and the beginning between the two core sequence segments is traced back to search again until the modeling of all core windows is completed to obtain the topological structure of each process. After that, the topological structures of the parallel processes are mapped and spliced ​​to obtain a complete haploid topological representation. Finally, the complete haploid topological representation is concretized as a base sequence to form the expected haploid and complete the modeling. The topology of each process is represented as: Among them, S p S is a stack for storing explored paths. v A stack for storing accessed sequence segments, T k is the haploid partial topology of the kth window, n is the total number of windows, T final For the final complete haploid topology, From pi to p j All the paths pushed into the stack between pi and pj, and x is all the sequence segments pushed into the stack between pi and pj.

9. A haploid modeling method based on pan-genomic genetic information according to claim 1, characterized in that The pan-genome data are obtained based on Minigraph-Cactus.

10. A haploid modeling method based on pan-genomic genetic information according to claim 7, characterized in that The number of the set sequence segments W=5000.

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