Virus data processing method based on high-throughput sequencing data

Abstract

The invention discloses a virus data processing method based on high-throughput sequencing data, which comprises the following steps that: a server can analyze original sequencing data through a neural network model so as to determine each virus pedigree to which each virus contained in a target sample belongs by utilizing information contained in the original sequencing data; therefore, virus data processing based on the high-throughput sequencing data can be carried out on the target sample based on the virus pedigree to which each virus belongs in the target sample output by the neural network model, so that the efficiency and accuracy of virus data processing based on the high-throughput sequencing data are improved.

Description

Technical Field

[0001] The present specification relates to the field of bioinformatics analysis technology, and in particular to a virus data processing method based on high-throughput sequencing data. Background Art

[0002] Currently, when viruses replicate in the host, genetic mutations may occur due to errors in the replication process and pressure from the host's immune system, forming a group of related but slightly different virus strains, the so-called quasispecies. The composition and evolutionary dynamics of viral quasispecies in this host are closely related to clinical manifestations such as the severity of the disease, the ability of the virus to evade the host's immune response, and the development of antiviral drug resistance. Therefore, analyzing viral quasispecies to study their characteristics is crucial for a deeper understanding of viral pathogenic mechanisms and optimizing treatment strategies.

[0003] Generally speaking, although a large amount of raw sequencing data can be obtained at one time through high-throughput sequencing technology when processing virus data based on high-throughput sequencing data, it is relatively difficult to extract and parse meaningful quasi-species features from massive data, which leads to low efficiency of virus data processing based on high-throughput sequencing data.

[0004] Therefore, how to improve the efficiency of virus data processing based on high-throughput sequencing data is an urgent problem to be solved. Summary of the invention

[0005] This specification provides a virus data processing method based on high-throughput sequencing data to partially solve the above-mentioned problems existing in the prior art.

[0006] This manual adopts the following technical solutions: This specification provides a method for processing virus data based on high-throughput sequencing data, including: Reading raw sequencing data of the target sample, wherein the raw sequencing data is used to characterize the nucleic acid sequence of the target sample; Inputting the raw sequencing data into a preset recognition model to determine the virus lineages to which the viruses contained in the target sample belong through the recognition model; and Determine each variant site contained in the original sequencing data and the variant frequency of each variant site, and determine the linkage relationship between the variant sites, wherein the linkage relationship is used to reflect the inheritance pattern of whether some of the variant sites in each variant site are transmitted to the offspring as a whole; For each viral lineage, a mutation site that matches a preset specific mutation site corresponding to the viral lineage is determined from the mutation sites as a mutation site belonging to the viral lineage; The genetic relationship between each variant site is determined based on the viral lineage to which each variant site belongs, the mutation frequency of each variant site, and the linkage relationship between the variant sites, so as to determine the haplotype sequences of each virus contained in the target sample based on the genetic relationship, and the task is performed based on the haplotype sequences of each virus. The haplotype sequences of the virus are used to reflect the combination of at least some variant sites that have a close genetic relationship among the variant sites.

[0007] Optionally, determining each variant site contained in the original sequencing data specifically includes: Preprocessing the raw sequencing data to obtain preprocessed raw sequencing data, wherein the preprocessing includes: at least one of quality assessment, shearing, and deduplication; Determine each variant site contained in the raw sequencing data after the preprocessing.

[0008] Optionally, the original sequencing data is composed of segmented data, wherein, for each segmented data, the segmented data is used to characterize a short sequence fragment in the nucleic acid sequence of the target sample; Determining each variant site contained in the original sequencing data specifically includes: For each segmented data, the segmented data is compared with a preset viral reference genome to determine a number corresponding to the segmented data, wherein the number is used to characterize the position of the segmented data on the viral reference genome; According to the number corresponding to each segmented data, the segmented data are sorted and reorganized into segmented data sequences as each virus sequence, and the difference points between the each virus sequence and the virus reference genome are detected as each mutation site contained in the original sequencing data.

[0009] Optionally, determining the linkage relationship between the variant sites specifically includes: For each variant site set, the co-occurrence frequency between the variant sites in the variant site set is determined according to the distribution of the variant sites in each segmented data, and the linkage relationship between the variant sites in the variant site set is determined according to the co-occurrence frequency, wherein the variant site set contains at least two variant sites, and the co-occurrence frequency is used to reflect the frequency of each variant site in the variant site combination appearing in the same segmented data.

[0010] Optionally, for each viral lineage, determining a variation site that matches a preset specific variation site corresponding to the viral lineage from the variation sites as a variation site belonging to the viral lineage specifically includes: Obtain the expected frequency of each viral lineage; For each viral lineage, a mutation site that matches a preset specific mutation site corresponding to the viral lineage is determined from the mutation sites as a candidate mutation site belonging to the viral lineage; Determining the reference variation frequency of the candidate variation site according to the expected frequency of each viral lineage; If it is determined that the difference between the reference mutation frequency of the candidate mutation site and the mutation frequency of the candidate mutation site is less than a preset difference threshold, the candidate mutation site is determined to be a mutation site belonging to the viral lineage.

[0011] Optionally, the raw sequencing data is input into a preset recognition model to determine the virus lineages to which the viruses contained in the target sample belong through the recognition model, specifically including: Inputting the raw sequencing data into a preset recognition model to determine the virus lineages to which the viruses contained in the target sample belong, and the credibility of each virus lineage through the recognition model; Determine the genetic relationship between each mutation site based on the virus lineage to which each mutation site belongs, the mutation frequency of each mutation site, and the linkage relationship between each mutation site, specifically including: According to the credibility of each virus lineage, the target virus lineage is screened out from each virus lineage; The genetic relationship between the variant sites is determined based on the target virus lineage, the variation frequency of each variant site belonging to the target virus lineage, and the linkage relationship between the variant sites.

[0012] Optionally, performing tasks according to the haplotype sequences of each virus specifically includes: For each viral haplotype sequence, determining the frequency value of the viral haplotype sequence in the original sequencing data as the abundance value of the viral haplotype sequence; According to the abundance value of each viral haplotype sequence, a dominant haplotype sequence is screened out from the viral haplotype sequences, and the task is executed according to the dominant haplotype sequence.

[0013] This specification provides a virus data processing device based on high-throughput sequencing data, including: An acquisition module is used to read the original sequencing data of the target sample, wherein the original sequencing data is used to characterize the nucleic acid sequence of the target sample; An identification module, used to input the raw sequencing data into a preset identification model, so as to determine the virus lineages to which the viruses contained in the target sample belong through the identification model; A determination module, used to determine each variant site contained in the original sequencing data and the variant frequency of each variant site, and to determine the linkage relationship between the variant sites, wherein the linkage relationship is used to reflect the inheritance mode of whether some variant sites in each variant site are transmitted to the offspring as a whole; A matching module, for determining, for each viral lineage, from the various mutation sites, a mutation site that matches a preset specific mutation site corresponding to the viral lineage as a mutation site belonging to the viral lineage; An execution module is used to determine the genetic relationship between each variant site based on the viral lineage to which each variant site belongs, the mutation frequency of each variant site, and the linkage relationship between the variant sites, so as to determine each viral haplotype sequence contained in the target sample based on the genetic relationship, and perform tasks based on each viral haplotype sequence, wherein the viral haplotype sequence is used to reflect the combination of at least some variant sites that have a close genetic relationship among the variant sites.

[0014] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects: In the virus data processing method based on high-throughput sequencing data provided in the present specification, the original sequencing data of the target sample is first read, wherein the original sequencing data is used to characterize the nucleic acid sequence of the target sample, and then the original sequencing data is input into a preset recognition model to determine the virus lineages to which the viruses contained in the target sample belong through the recognition model, and to determine the mutation sites and the mutation frequency of each mutation site contained in the original sequencing data, and to determine the linkage relationship between the mutation sites, wherein the linkage relationship is used to reflect the relative positions of the mutation sites in the original sequencing data and whether some of the mutation sites in each mutation site are used as a whole. The genetic pattern that is passed on to the offspring as a whole is determined. For each viral lineage, the variant sites that match the preset specific variant sites corresponding to the viral lineage are determined from each variant site as the variant sites belonging to the viral lineage. The genetic relationship between each variant site is determined according to the viral lineage to which each variant site belongs, the mutation frequency of each variant site, and the linkage relationship between each variant site. The haplotype sequence of each virus contained in the target sample is determined based on the genetic relationship, and the task is executed according to each viral haplotype sequence. The viral haplotype sequence is used to reflect the combination of at least some variant sites that have a close genetic relationship among the variant sites.

[0015] It can be seen from the above method that the original sequencing data can be analyzed through the neural network model to use the information contained in the original sequencing data to determine the viral lineages to which each virus contained in the target sample belongs. Then, based on the viral lineages to which each virus contained in the target sample belongs output by the neural network model, the target sample can be processed for viral data based on high-throughput sequencing data to improve the efficiency and accuracy of viral data processing based on high-throughput sequencing data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings: Figure 1 A schematic diagram of a process for processing virus data based on high-throughput sequencing data provided in this specification; Figure 2 A schematic diagram of a target sample provided in this specification; Figure 3 A schematic diagram of a virus data processing device based on high-throughput sequencing data provided in this specification; Figure 4 A method corresponding to the Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0018] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0019] During the replication process of the virus in the host, due to replication errors and immune pressure, genetic mutations may occur, resulting in a series of related mutants. These mutants constitute a viral quasispecies. These mutants belonging to the same viral quasispecies are genetically similar to the original virus strain, but may differ in certain characteristics, such as replication speed, transmission ability, or sensitivity to drugs. The formation and evolution of viral quasispecies are of great significance for understanding the clinical manifestations of viral infections such as infection severity, immune escape, and drug resistance.

[0020] At present, virus data processing based on high-throughput sequencing data mainly relies on sequencing a small number of isolated strains, which makes it difficult to fully reveal the full picture of quasispecies within the host. Although high-throughput sequencing technology can obtain a large number of virus sequences at one time, the process from raw sequencing data obtained through high-throughput sequencing technology to quasispecies variation analysis is relatively difficult, and it is difficult to accurately identify the variation pattern within the host in massive data. In addition, the efficiency of processing massive data is low, which makes it difficult to meet the needs of large-scale population research.

[0021] Figure 1 A schematic diagram of a method for processing viral data based on high-throughput sequencing data provided in this specification, comprising the following steps: S101: Reading original sequencing data of a target sample, wherein the original sequencing data is used to characterize the nucleic acid sequence of the target sample.

[0022] In this specification, the business platform can use samples that need to undergo virus data processing based on high-throughput sequencing data as target samples, so that it can read the original sequencing data obtained after the target sample is sequenced in advance by a high-throughput sequencing platform, and then perform virus data processing based on high-throughput sequencing data based on the read original sequencing data of the target sample.

[0023] The target samples mentioned above may be samples containing viral genetic information, such as body fluids (such as blood, saliva, urine, cerebrospinal fluid, etc.), tissue samples, cell cultures of organisms (such as humans, animals or plants) infected with RNA viruses. Another example is virus particles extracted from the environment.

[0024] The above-mentioned original sequencing data may be sequence information of deoxyribonucleic acid DNA fragments or RNA fragments (when the virus contained in the above-mentioned target sample is an RNA virus, it is usually necessary to reverse transcribe the RNA into DNA first) obtained after high-throughput sequencing of the above-mentioned target sample. The DNA fragments or RNA fragments here may be obtained by splitting the nucleic acid sequence of the virus in the target sample, wherein the sequence information of each DNA fragment or RNA fragment is a segmented data constituting the original sequencing data, and the sequence information of each DNA fragment or RNA fragment may be presented in the form of bases (A, T, C, G), and each sequence fragment has a corresponding unique identifier (this unique identifier is automatically generated during the sequencing process), and each of the above-mentioned DNA fragments or RNA fragments is a short sequence fragment.

[0025] It should be noted that after obtaining the original sequencing data of the target samples by performing high-throughput sequencing on the above-mentioned target samples, the original sequencing data can be saved in a database, so that when the business platform needs to perform virus data processing based on high-throughput sequencing data on the target samples, the original sequencing data of the target samples can be read for analysis.

[0026] In this specification, the execution entity used to implement the virus data processing method based on high-throughput sequencing data may refer to a designated device such as a server set up on a business platform, or may refer to a terminal device such as a desktop computer or a laptop computer. For the sake of ease of description, the following only takes the server as the execution entity as an example to illustrate the virus data processing method based on high-throughput sequencing data provided in this specification.

[0027] S102: Inputting the original sequencing data into a preset recognition model to determine the virus lineages to which the viruses contained in the target sample belong through the recognition model.

[0028] In this specification, after reading the raw sequencing data of the target sample, the server can input the raw sequencing data into a preset recognition model to identify the raw sequencing data through the recognition model to determine the viral lineages to which each virus contained in the target sample belongs.

[0029] Among them, the above-mentioned recognition model can be set according to actual needs. Preferably, the above-mentioned recognition model can be a bidirectional encoder representation model BERT.

[0030] The above-mentioned recognition model needs to be trained before it can be deployed to the server for the recognition of the original sequencing data. The method for training the recognition model here can be, for each virus lineage, selecting a virus species belonging to the virus lineage as a sample virus species, segmenting the nucleic acid sequence data of the sample virus species, obtaining each sample segmentation data, and inputting each sample segmentation data into the initial recognition model so that the initial recognition model recognizes each sample segmentation data, and obtains the recognition result of the virus lineage to which each sample segmentation data belongs output by the initial recognition model. Then, according to the deviation between the recognition result output by the initial recognition model and the recognition result of the virus lineage to which each sample segmentation data actually belongs, the pre-training loss can be determined (the pre-training loss here is positively correlated with the above-mentioned deviation), and then the initial recognition model can be trained with minimizing the pre-training loss as the optimization goal to obtain the pre-trained recognition model.

[0031] Furthermore, the server can obtain sample sequencing data and input the sample sequencing data into a pre-trained recognition model to determine the virus lineages to which the viruses contained in the training samples corresponding to the sample sequencing data belong through the pre-trained recognition model, and then the deviation between the labels of the virus lineages to which the viruses contained in the training samples corresponding to the sample sequencing data belong and the training samples corresponding to the sample sequencing data can be determined according to the pre-trained recognition model to determine the recognition loss, and then the model parameters of the pre-trained recognition model can be fine-tuned with minimizing the recognition loss as the optimization goal to obtain a trained recognition model.

[0032] The labels of the training samples are used to characterize the virus lineages to which the viruses actually contained in the training samples belong.

[0033] The above-mentioned recognition loss is positively correlated with the deviation between the virus lineages to which the viruses contained in the training samples corresponding to the sample sequencing data are assigned and the labels of the training samples corresponding to the sample sequencing data determined by the recognition model.

[0034] It should be noted that the above-mentioned viral lineages can refer to different evolutionary branches formed by genetic variation of viruses in the host or in the population. Each viral lineage represents a group of viral species with similar genetic characteristics. The viral lineage reflects the changes of viruses over time, especially the adaptive changes in the face of selection pressures (such as host immune response, drug treatment, etc.).

[0035] In addition, before inputting the raw sequencing data into the recognition model, the server can also preprocess the raw sequencing data to obtain preprocessed raw sequencing data, and then input the preprocessed raw sequencing data into the recognition model to determine the viral lineages to which each virus contained in the target sample belongs through the recognition model.

[0036] The above-mentioned preprocessing includes at least one of quality assessment, clipping, and deduplication.

[0037] S103: Determine each variant site contained in the original sequencing data and the variant frequency of each variant site, and determine the linkage relationship between the variant sites, wherein the linkage relationship is used to reflect the relative position of each variant site in the original sequencing data and whether some of the variant sites in each variant site are passed on to the offspring as a whole.

[0038] Furthermore, the server may determine each variant site contained in the original sequencing data and the variant frequency of each variant site, and determine the linkage relationship between each variant site.

[0039] In actual application scenarios, during the high-pass sequencing of the target sample, the nucleic acid sequence of the target sample needs to be cut to obtain each segmented data. In this process, factors such as sequencing errors and random cutting of short sequence fragments may affect the generation and position information of short sequence fragments, thereby affecting each segmented data. Therefore, after obtaining the original sequencing data, and before determining the mutation sites contained in the original sequencing data, the mutation frequency of each mutation site and the linkage relationship between the mutation sites, each segmented data needs to be compared to determine the position of each segmented data on the viral reference genome, so that the relative position of each segmented data can be determined to restore the complete nucleic acid sequence.

[0040] Based on this, the server can compare each segmented data contained in the original sequencing data after preprocessing with the preset viral reference genome to determine the number corresponding to the segmented data, and then sort and reorganize the segmented data into segmented data sequences as each viral sequence according to the number corresponding to each segmented data, and detect the differences between each viral sequence and the viral reference genome as each mutation site contained in the original sequencing data.

[0041] The above number is used to represent the position of the segmented data on the viral reference genome.

[0042] Furthermore, the server can determine the frequency of each variant site in the original sequencing data through a preset detection tool as the variant frequency of the variant site.

[0043] The detection tools mentioned above may be tools such as Samtools, VarScan, etc. for monitoring variations in genome sequences.

[0044] Furthermore, the server may determine the linkage relationship between the variant sites according to the variant frequency of each variant site and the position of each variant site.

[0045] Among them, the above-mentioned linkage relationship is used to reflect the inheritance pattern of whether some of the variant sites in each variant site are passed on to the offspring as a whole.

[0046] For example, if two variant sites are located very close to each other, the probability of recombination (i.e., exchange of chromosome segments) between them is low. Therefore, the variants at these two sites are more likely to be copied and transmitted together, so there is a strong linkage relationship between the two variant sites.

[0047] For another example: if one variant site often appears at the same time as another variant site, it may be due to selection pressure or other biological mechanisms that make the two variant sites tend to appear together (for example, when variant site a and variant site b appear at the same time, it may confer higher drug resistance to the virus, making the combination of variant site a and variant site b more common in the virus population), so there is a strong linkage relationship between the two variant sites.

[0048] Among them, the method for the server to determine the linkage relationship between each variant site based on the variation frequency of each variant site and the position of each variant site can be that the server can determine, for each variant site set, the co-occurrence frequency between each variant site in the variant site set according to the distribution of each variant site in each segmented data, and determine the linkage relationship between each variant site in the variant site set according to the co-occurrence frequency between each variant site in the variant site set.

[0049] Wherein, for each variant site set, the variant site set contains at least two variant sites.

[0050] The above co-occurrence frequency is used to reflect the frequency at which each variant site in the variant site combination appears in the same segment data.

[0051] The method for the above-mentioned server to determine the linkage relationship between each variant site in the variant site set based on the co-occurrence frequency between each variant site in the variant site set can be that the server determines the expected co-occurrence frequency of each variant site in the variant site set based on the variation frequency of each variant site in the variant site set, and then determines whether there is a strong linkage relationship between each variant site in the variant site set based on whether the ratio between the expected co-occurrence frequency of each variant site in the variant site set and the co-occurrence frequency between each variant site in the variant site set exceeds a preset ratio threshold.

[0052] For ease of understanding, the method for the server to determine the linkage relationship between each mutation site based on the mutation frequency of each mutation site and the position of each mutation site is described in detail below in conjunction with an embodiment.

[0053] For example: if the total number of segmented data contained in the original sequencing data is 10,000, the number of segmented data containing variation site A is 3,000, the number of segmented data containing variation site B is 2,000, the number of segmented data containing variation site C is 4,000, the number of segmented data containing variation site D is 1,000, the number of segmented data containing both variation site A and variation site B is 1,500, and the number of segmented data containing both variation site A and variation site B is 400.

[0054] At this time, the mutation frequency of variant site A is 30%, the mutation frequency of variant site B is 20%, the frequency of variant site C is 40%, and the frequency of variant site D is 10%. The co-occurrence frequency of variant site A and variant site B is 15%, and the co-occurrence frequency of variant site C and variant site D is 4%. Based on the mutation frequency of variant site A and the mutation frequency of variant site B, it can be inferred that the expected co-occurrence frequency between variant site A and variant site B is 30%*20%, that is, 6%, while the actual co-occurrence frequency of variant site A and variant site B is 15%. It can be determined that the co-occurrence frequency between variant site A and variant site B is significantly different from the co-occurrence frequency of random distribution, indicating that there may be some association between them (that is, a strong linkage relationship).

[0055] Similarly, the expected co-occurrence frequency between variant site C and variant site D is 40%*10%, or 4%, while the actual co-occurrence frequency of variant site A and variant site B together is 4%. From this, it can be determined that the co-occurrence frequency between variant site A and variant site B is the same as the co-occurrence frequency of random distribution, indicating that there may be a weak association between them (i.e., a weak linkage relationship).

[0056] S104: For each viral lineage, a mutation site that matches a preset specific mutation site corresponding to the viral lineage is determined from the mutation sites as a mutation site belonging to the viral lineage.

[0057] In this specification, the server can determine, for each viral lineage, from among the various mutation sites, a mutation site that matches a preset specific mutation site corresponding to the viral lineage as a mutation site belonging to the viral lineage.

[0058] Among them, for each viral lineage, the specific mutation site of the viral lineage may refer to the mutation site that frequently appears in the viral lineage but rarely or does not appear in other lineages. These specific mutations can be used to distinguish different viral lineages.

[0059] In the above content, for each mutation site, if the server determines that there is the above-mentioned linkage relationship between the mutation site and a specific mutation site corresponding to a viral lineage, it can be considered that the mutation site matches the specific mutation site of this viral lineage.

[0060] Furthermore, the server can also predetermine the expected frequency of each viral lineage, so that for each viral lineage, the server can determine from each mutation site a mutation site that matches a preset specific mutation site corresponding to the viral lineage as a candidate mutation site belonging to the viral lineage, and then determine the reference mutation frequency of the candidate mutation site belonging to the viral lineage based on the expected frequency of each viral lineage. If the difference between the reference mutation frequency of the candidate mutation site and the mutation frequency of the candidate mutation site is determined to be less than a preset difference threshold, the candidate mutation site can be determined to be a mutation site belonging to the viral lineage.

[0061] Among them, the expected frequency of each of the above-mentioned viral lineages can be pre-set, and of course, can also be predicted based on the original sequencing data of the target sample through a preset prediction model.

[0062] It should be noted that since the variant sites belonging to a viral lineage are often inherited as a whole, the variant frequencies of the variant sites belonging to a viral lineage should be similar. That is, when the expected frequency of a viral lineage is 30%, the reference variant frequency of each variant site belonging to this viral lineage should be 30%. Therefore, the server can determine the reference variant frequency of the candidate variant sites belonging to the viral lineage based on the expected frequency of each viral lineage.

[0063] In actual application scenarios, some mutation sites belong to more than two viral lineages at the same time. In this case, the reference mutation frequency of the mutation site can be the sum of the expected frequencies of the viral lineages to which the mutation site belongs.

[0064] For example: If a variant site a belongs to both viral lineage A and viral lineage B, and the expected frequency of viral lineage A is 25% and the expected frequency of viral lineage B is 30%, then the reference variant frequency of the variant site should be 55%.

[0065] Since the reference lineage frequency of each virus lineage represents the proportion of all virus particles in the sample in different lineages, the sum of the frequencies of each virus lineage to which each virus contained in a sample belongs should be approximately equal to 100%, as shown in the following example: Figure 2 shown.

[0066] Figure 2 A schematic diagram of a target sample provided in this specification.

[0067] Combination Figure 2It can be seen that when a sample consists of viruses from two viral lineages (i.e., viral lineage A and viral lineage B), when the reference lineage frequency of viral lineage A is 75%, the reference lineage frequency of viral lineage B should be about 25%, that is, the sum of the reference lineage frequency of viral lineage A and the reference lineage frequency of viral lineage B should be approximately 100%.

[0068] Based on this, the server can also determine the frequency of each virus lineage according to the mutation sites belonging to each virus lineage, and then determine whether the sum of the frequencies of each virus lineage is approximately equal to 100%. If so, for each virus lineage, the candidate mutation sites belonging to the virus lineage can be used as mutation sites belonging to the virus lineage.

[0069] S105: Determine the genetic relationship between each variant site based on the viral lineage to which each variant site belongs, the mutation frequency of each variant site, and the linkage relationship between the variant sites, so as to determine each viral haplotype sequence contained in the target sample based on the genetic relationship, and perform the task based on each viral haplotype sequence, wherein the viral haplotype sequence is used to reflect the combination of at least some variant sites that have a close genetic relationship among the variant sites.

[0070] In this specification, the server can determine the genetic relationship between each variant site based on the viral lineage to which each variant site belongs, the mutation frequency of each variant site, and the linkage relationship between the variant sites, so as to determine the haplotype sequences of each virus contained in the target sample based on the genetic relationship between the variant sites.

[0071] Specifically, the server can screen out mutation sites having a mutation frequency higher than a preset mutation frequency threshold from the mutation sites according to the mutation frequency of each mutation site, as high-frequency mutation sites, and then divide each high-frequency mutation site into different high-frequency mutation site combinations according to the linkage relationship between the mutation sites and the viral lineage to which each high-frequency mutation site belongs.

[0072] Among them, for each high-frequency variation site combination, it can be considered that there is a close genetic relationship between the high-frequency variation sites contained in the high-frequency variation site combination, and then the haplotype sequences of each virus contained in the target sample can be determined based on the genetic relationship between the high-frequency variation sites.

[0073] In addition, the server can also screen out the target virus lineage from each virus lineage based on the credibility of each virus lineage, and then determine the genetic relationship between each mutation site based on the target virus lineage, the mutation frequency of each mutation site belonging to the target virus lineage, and the linkage relationship between each mutation site, so as to determine the haplotype sequences of each virus contained in the target sample based on the genetic relationship.

[0074] Among them, the credibility of each of the above-mentioned virus lineages can be obtained through the recognition model.

[0075] Furthermore, the server can determine the frequency value of each viral haplotype sequence in the original sequencing data as the abundance value of the viral haplotype sequence, and then can screen out the dominant haplotype sequence from each viral haplotype sequence according to the abundance value of each viral haplotype sequence, and perform tasks according to the dominant haplotype sequence.

[0076] It should be noted that the above tasks to be performed can be determined according to actual needs. For example, the server can display the determined dominant haplotype sequence to the user so that the user can develop a vaccine based on the dominant haplotype sequence contained in the target sample.

[0077] For another example, the server can determine the relationship between the dominant haplotype contained in the target sample and drug metabolism, and thus recommend a list of drugs that can be used to the user based on the relationship between the dominant haplotype and drug metabolism.

[0078] From the above content, it can be seen that the server can analyze the original sequencing data through the neural network model, and use the information contained in the original sequencing data to determine the viral lineages to which each virus contained in the target sample belongs. Then, based on the viral lineages to which each virus contained in the target sample belongs output by the neural network model, the server can perform virus data processing based on high-throughput sequencing data on the target sample to improve the efficiency and accuracy of virus data processing based on high-throughput sequencing data.

[0079] The above are one or more implementations of the virus data processing method based on high-throughput sequencing data of this specification. Based on the same idea, this specification also provides a corresponding virus data processing device based on high-throughput sequencing data, such as Figure 3 shown.

[0080] Figure 3 A schematic diagram of a virus data processing device based on high-throughput sequencing data provided in this specification includes: An acquisition module 301 is used to read the original sequencing data of the target sample, where the original sequencing data is used to characterize the nucleic acid sequence of the target sample; The identification module 302 is used to input the raw sequencing data into a preset identification model to determine the virus lineages to which the viruses contained in the target sample belong through the identification model; The determination module 303 is used to determine each variant site contained in the original sequencing data and the variant frequency of each variant site, and determine the linkage relationship between the variant sites, wherein the linkage relationship is used to reflect the inheritance mode of whether some variant sites in each variant site are transmitted to the offspring as a whole; A matching module 304 is used to determine, for each viral lineage, from the various mutation sites, a mutation site that matches a preset specific mutation site corresponding to the viral lineage as a mutation site belonging to the viral lineage; The execution module 305 is used to determine the genetic relationship between each variant site based on the viral lineage to which each variant site belongs, the mutation frequency of each variant site and the linkage relationship between the variant sites, so as to determine each viral haplotype sequence contained in the target sample based on the genetic relationship, and perform tasks based on each viral haplotype sequence. The viral haplotype sequence is used to reflect the combination of at least some variant sites that have a close genetic relationship among the variant sites.

[0081] Optionally, the determination module 303 is specifically used to preprocess the original sequencing data to obtain preprocessed original sequencing data, wherein the preprocessing includes: at least one of quality assessment, shearing, and deduplication; and determine each variant site contained in the preprocessed original sequencing data.

[0082] Optionally, the original sequencing data is composed of segmented data, wherein, for each segmented data, the segmented data is used to characterize a short sequence fragment in the nucleic acid sequence of the target sample; The determination module 303 is specifically used to compare each segmented data with a preset viral reference genome to determine a number corresponding to the segmented data, where the number is used to characterize the position of the segmented data on the viral reference genome; sort and reorganize the segmented data into segmented data sequences as viral sequences according to the number corresponding to each segmented data, and detect the difference points between the viral sequences and the viral reference genome as the variant sites contained in the original sequencing data.

[0083] Optionally, the determination module 303 is specifically used to determine, for each variant site set, the co-occurrence frequency between each variant site in the variant site set according to the distribution of each variant site in each segmented data, and determine the linkage relationship between each variant site in the variant site set according to the co-occurrence frequency, wherein the variant site set contains at least two variant sites, and the co-occurrence frequency is used to reflect the frequency of each variant site in the variant site combination appearing in the same segmented data.

[0084] Optionally, the matching module 304 is specifically used to obtain the expected frequency of each viral lineage; for each viral lineage, determine from the various mutation sites a mutation site that matches a preset specific mutation site corresponding to the viral lineage as a candidate mutation site belonging to the viral lineage; according to the expected frequency of each viral lineage, determine a reference mutation frequency of the candidate mutation site; if it is determined that the difference between the reference mutation frequency of the candidate mutation site and the mutation frequency of the candidate mutation site is less than a preset difference threshold, then determine that the candidate mutation site is a mutation site belonging to the viral lineage.

[0085] Optionally, the identification module 302 is specifically used to input the original sequencing data into a preset identification model, so as to determine the virus lineages to which the viruses contained in the target sample belong, and the credibility of each virus lineage through the identification model; The determination module 303 is specifically used to screen out a target virus lineage from each virus lineage according to the credibility of each virus lineage; and determine the genetic relationship between each variant site according to the target virus lineage, the mutation frequency of each variant site belonging to the target virus lineage, and the linkage relationship between the variant sites.

[0086] Optionally, the execution module 305 is specifically used to determine, for each viral haplotype sequence, a frequency value of the viral haplotype sequence appearing in the original sequencing data as an abundance value of the viral haplotype sequence; based on the abundance value of each viral haplotype sequence, screen out a dominant haplotype sequence from the viral haplotype sequences, and perform tasks based on the dominant haplotype sequence.

[0087] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A virus data processing method based on high-throughput sequencing data is provided.

[0088] This manual also provides Figure 4 The one shown corresponds to Figure 1 A schematic diagram of the electronic device. Figure 4 As mentioned above, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1The virus data processing method based on high-throughput sequencing data. Of course, in addition to the software implementation, this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0089] For a technical improvement, it can be clearly distinguished whether it is a hardware improvement (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or a software improvement (improvement of the method flow). However, with the development of technology, many improvements in the method flow today can be regarded as direct improvements in the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device PLD (such as a field programmable gate array FPGA) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language HDL. There is not only one kind of HDL, but many kinds, such as ABEL, AHDL, Confluence, CUPL, HDCal, JHDL, Lava, Lola, MyHDL, PALASM, RHDL, etc., and the most commonly used ones are VHDL and Verilog. Those skilled in the art should also be aware that it is only necessary to program the method flow slightly in logic using the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0090] The controller can be implemented in any suitable manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit ASIC, a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0091] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0092] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0093] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0098] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0099] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media such as modulated data signals and carrier waves.

[0100] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0101] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0103] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0104] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A virus data processing method based on high-throughput sequencing data, characterized in that: include: Reading raw sequencing data of the target sample, wherein the raw sequencing data is used to characterize the nucleic acid sequence of the target sample; Inputting the raw sequencing data into a preset recognition model to determine the virus lineages to which the viruses contained in the target sample belong through the recognition model; as well as Determine each variant site contained in the original sequencing data and the variant frequency of each variant site, and determine the linkage relationship between the variant sites, wherein the linkage relationship is used to reflect the inheritance pattern of whether some of the variant sites in each variant site are transmitted to the offspring as a whole; For each viral lineage, a mutation site that matches a preset specific mutation site corresponding to the viral lineage is determined from the mutation sites as a mutation site belonging to the viral lineage; The genetic relationship between each variant site is determined based on the viral lineage to which each variant site belongs, the mutation frequency of each variant site, and the linkage relationship between the variant sites, so as to determine the haplotype sequences of each virus contained in the target sample based on the genetic relationship, and the task is performed based on the haplotype sequences of each virus. The haplotype sequences of the virus are used to reflect the combination of at least some variant sites that have a close genetic relationship among the variant sites.

2. The method according to claim 1, characterized in that Determining each variant site contained in the original sequencing data specifically includes: Preprocessing the raw sequencing data to obtain preprocessed raw sequencing data, wherein the preprocessing includes: at least one of quality assessment, shearing, and deduplication; Determine each variant site contained in the raw sequencing data after the preprocessing.

3. The method according to claim 1, characterized in that The original sequencing data is composed of segmented data, wherein for each segmented data, the segmented data is used to characterize a short sequence fragment in the nucleic acid sequence of the target sample; Determining each variant site contained in the original sequencing data specifically includes: For each segmented data, the segmented data is compared with a preset viral reference genome to determine a number corresponding to the segmented data, wherein the number is used to characterize the position of the segmented data on the viral reference genome; According to the number corresponding to each segmented data, the segmented data are sorted and reorganized into segmented data sequences as each virus sequence, and the difference points between the each virus sequence and the virus reference genome are detected as each mutation site contained in the original sequencing data.

4. The method according to claim 3, characterized in that Determining the linkage relationship between the variant sites specifically includes: For each variant site set, the co-occurrence frequency between the variant sites in the variant site set is determined according to the distribution of the variant sites in each segmented data, and the linkage relationship between the variant sites in the variant site set is determined according to the co-occurrence frequency, wherein the variant site set contains at least two variant sites, and the co-occurrence frequency is used to reflect the frequency of each variant site in the variant site combination appearing in the same segmented data.

5. The method according to claim 1, characterized in that For each virus lineage, a mutation site that matches a preset specific mutation site corresponding to the virus lineage is determined from the mutation sites as the mutation site belonging to the virus lineage, specifically including: Obtain the expected frequency of each viral lineage; For each viral lineage, a mutation site that matches a preset specific mutation site corresponding to the viral lineage is determined from the mutation sites as a candidate mutation site belonging to the viral lineage; Determining the reference variation frequency of the candidate variation site according to the expected frequency of each viral lineage; If it is determined that the difference between the reference mutation frequency of the candidate mutation site and the mutation frequency of the candidate mutation site is less than a preset difference threshold, the candidate mutation site is determined to be a mutation site belonging to the viral lineage.

6. The method according to claim 1, characterized in that The raw sequencing data is input into a preset recognition model to determine the virus lineages to which the viruses contained in the target sample belong through the recognition model, specifically including: Inputting the raw sequencing data into a preset recognition model to determine the virus lineages to which the viruses contained in the target sample belong, and the credibility of each virus lineage through the recognition model; Determine the genetic relationship between each mutation site based on the virus lineage to which each mutation site belongs, the mutation frequency of each mutation site, and the linkage relationship between each mutation site, specifically including: According to the credibility of each virus lineage, the target virus lineage is screened out from each virus lineage; The genetic relationship between the variant sites is determined based on the target virus lineage, the variation frequency of each variant site belonging to the target virus lineage, and the linkage relationship between the variant sites.

7. The method according to claim 1, characterized in that The tasks are executed according to the haplotype sequences of each virus, specifically including: For each viral haplotype sequence, determining the frequency value of the viral haplotype sequence in the original sequencing data as the abundance value of the viral haplotype sequence; According to the abundance value of each viral haplotype sequence, a dominant haplotype sequence is screened out from the viral haplotype sequences, and the task is executed according to the dominant haplotype sequence.

8. A virus data processing device based on high-throughput sequencing data, characterized in that: include: An acquisition module is used to read the original sequencing data of the target sample, wherein the original sequencing data is used to characterize the nucleic acid sequence of the target sample; An identification module, used to input the raw sequencing data into a preset identification model, so as to determine the virus lineages to which the viruses contained in the target sample belong through the identification model; A determination module, used to determine each variant site contained in the original sequencing data and the variant frequency of each variant site, and determine the linkage relationship between the variant sites, wherein the linkage relationship is used to reflect the inheritance mode of whether some variant sites in each variant site are transmitted to the offspring as a whole; A matching module, for determining, for each viral lineage, from the various mutation sites, a mutation site that matches a preset specific mutation site corresponding to the viral lineage as a mutation site belonging to the viral lineage; An execution module is used to determine the genetic relationship between each variant site based on the viral lineage to which each variant site belongs, the mutation frequency of each variant site, and the linkage relationship between the variant sites, so as to determine each viral haplotype sequence contained in the target sample based on the genetic relationship, and perform tasks based on each viral haplotype sequence, wherein the viral haplotype sequence is used to reflect the combination of at least some variant sites that have a close genetic relationship among the variant sites.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 7 is implemented.

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