Genome sequencing method and device, electronic equipment and storage medium

Abstract

The invention discloses a genome sequencing method and device, electronic equipment and a storage medium, and relates to the technical field of information. A to-be-sequenced sample and a genome target interval with a preset length in the to-be-sequenced sample are obtained; performing genome sequencing on the to-be-sequenced sample based on a preset genome sequencing program to obtain a first genome sequence; sequencing the directionally amplified genome target interval based on a preset genome sequencing program to obtain a second genome sequence; constructing a target reference genome sequence data set based on the target population; and inferring the first genome sequence and the second genome sequence by referring to the target reference genome sequence data set to obtain a third genome sequence. Compared with the prior art, the method has the advantages that the third genome sequence is deduced by directionally amplifying the genome target interval and constructing the target reference genome sequence data set based on the target population, so that the result of the third genome sequence obtained by sequencing is more accurate, and the method can be better applied to scenes with high-depth data requirements.

Description

Technical Field

[0001] The present disclosure relates to the field of information technology, and in particular to a genome sequencing method and device, electronic equipment, and storage medium. Background Art

[0002] At present, according to the survey results of low-depth whole genome sequencing data analysis solutions, the accuracy of low-depth data obtained using low-depth whole genome sequencing still cannot fully meet the needs of some genetic interpretation products or scenarios that require higher sequencing depth of variant data, such as carrier screening, drug genome screening, etc. Summary of the invention

[0003] The present disclosure provides a genome sequencing method and device, electronic device and storage medium, which are mainly intended to solve the problem of low accuracy of low-depth data obtained by low-depth whole genome sequencing in related technologies.

[0004] According to a first aspect of the present disclosure, a method for genome sequencing is provided, comprising:

[0005] Obtaining a sample to be sequenced and a genomic target interval of a preset length in the sample to be sequenced;

[0006] Performing genome sequencing on the sample to be sequenced based on a preset genome sequencing program to obtain a first genome sequence; and

[0007] Sequencing the directional amplified genomic target interval based on a preset genomic sequencing program to obtain a second genomic sequence;

[0008] Construct a target reference genome sequence dataset based on the target population;

[0009] The first genome sequence and the second genome sequence are inferred with reference to the target reference genome sequence data set to obtain a third genome sequence.

[0010] Optionally, sequencing the directional amplified genomic target interval based on a preset genomic sequencing program to obtain a second genomic sequence comprises:

[0011] Directively amplifying the genomic target interval by a preset multiple to obtain the genomic target interval amplified by the preset multiple;

[0012] The target genome interval is sequenced based on a preset genome sequencing program to obtain a second genome sequence.

[0013] Optionally, constructing a target reference genome sequence dataset based on a target population includes:

[0014] Obtaining the first reference genome sequence dataset of target populations in different regions; and

[0015] Acquire a second reference genome sequence dataset of a target population in a target region;

[0016] The first reference genome sequence dataset is supplemented and optimized based on the second reference genome sequence dataset to obtain the target reference genome sequence dataset.

[0017] Optionally, inferring the first genome sequence and the second genome sequence with reference to the target reference genome sequence dataset to obtain a third genome sequence includes:

[0018] Obtaining a first genotype and a first genotype probability of a variant site corresponding to the target reference genome sequence dataset and the first genome sequence; and

[0019] Obtain a second genotype and a second genotype probability of a variant site corresponding to the target reference genome sequence dataset and the second genome sequence;

[0020] Inputting the first genotype, the second genotype, the first genotype probability, and the second genotype probability into a preset algorithm;

[0021] Based on the preset algorithm, genotype inference and genotype probability reassignment are performed on the variable sites in the first genome sequence and the second genome sequence, and haplotype status is determined to obtain the third genome sequence.

[0022] Optionally, before inferring the first genome sequence and the second genome sequence with reference to the target reference genome sequence dataset to obtain a third genome sequence, the method includes:

[0023] Compare the first genome sequence after quality control filtration with the target reference genome sequence data set to obtain first comparison result data; and

[0024] The second genome sequence after quality control filtering is compared with the target reference genome sequence data set to obtain second comparison result data.

[0025] Optionally, the method further includes:

[0026] performing variation analysis on the first genome sequence and the second genome sequence respectively to obtain variation analysis data;

[0027] A preset purpose analysis is performed based on the comparison result data and the variation analysis data to obtain genetic interpretation analysis data corresponding to the sample to be sequenced, and the comparison result data is the combined data of the first comparison result data and the second comparison result data.

[0028] According to a second aspect of the present disclosure, there is provided a genome sequencing apparatus, comprising:

[0029] An acquisition unit, used to acquire a sample to be sequenced and a genomic target interval of a preset length in the sample to be sequenced;

[0030] A sequencing unit is used to perform genome sequencing on the sample to be sequenced based on a preset genome sequencing program to obtain a first genome sequence; the sequencing unit is also used to sequence the genome target interval of the directed amplification based on a preset genome sequencing program to obtain a second genome sequence;

[0031] A construction unit, used to construct a target reference genome sequence dataset based on a target population;

[0032] A reference unit is used to infer the first genome sequence and the second genome sequence with reference to the target reference genome sequence data set to obtain a third genome sequence.

[0033] Optionally, the sequencing unit further comprises:

[0034] an amplification module, used to perform a directional amplification of the genomic target interval by a preset multiple to obtain the genomic target interval amplified by the preset multiple;

[0035] The sequencing module is used to sequence the target genome interval based on a preset genome sequencing program to obtain a second genome sequence.

[0036] Optionally, the construction unit includes:

[0037] An acquisition module is used to acquire a first reference genome sequence data set of a target population in a different region; and the acquisition module is also used to acquire a second reference genome sequence data set of a target population in a target region;

[0038] An optimization module is used to supplement and optimize the first reference genome sequence dataset based on the second reference genome sequence dataset to obtain the target reference genome sequence dataset.

[0039] Optionally, the reference unit includes:

[0040] An acquisition module, used to acquire a first genotype and a first genotype probability of a variant site corresponding to the target reference genome sequence data set and the first genome sequence; and the acquisition module is also used to acquire a second genotype and a second genotype probability of a variant site corresponding to the target reference genome sequence data set and the second genome sequence;

[0041] An input module, used for inputting the first genotype, the second genotype, the first genotype probability and the second genotype probability into a preset algorithm;

[0042] A determination module is used to perform genotype inference and genotype probability reassignment on the variant sites in the first genome sequence and the second genome sequence based on the preset algorithm, and complete the determination of the haplotype state to obtain the third genome sequence.

[0043] Optionally, the device further comprises:

[0044] A first comparison unit is used to compare the first genome sequence after quality control filtering with the target reference genome sequence data set to obtain first comparison result data; and

[0045] The second comparison unit is used to compare the second genome sequence after quality control filtering with the target reference genome sequence data set to obtain second comparison result data.

[0046] Optionally, the device further comprises:

[0047] A first analysis unit is used to perform variation analysis on the first genome sequence and the second genome sequence respectively to obtain variation analysis data;

[0048] The second analysis unit is used to perform a preset purpose analysis based on the comparison result data and the variation analysis data to obtain genetic interpretation analysis data corresponding to the sample to be sequenced, wherein the comparison result data is the combined data of the first comparison result data and the second comparison result data.

[0049] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0050] at least one processor; and

[0051] a memory communicatively connected to the at least one processor; wherein,

[0052] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.

[0053] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0054] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.

[0055] The method and device, electronic device and storage medium for genome sequencing provided by the present disclosure include: obtaining a sample to be sequenced and a genome target interval of a preset length in the sample to be sequenced; performing genome sequencing on the sample to be sequenced based on a preset genome sequencing program to obtain a first genome sequence; and sequencing the genome target interval amplified by a preset genome sequencing program to obtain a second genome sequence; constructing a target reference genome sequence data set based on a target population; and inferring the first genome sequence and the second genome sequence with reference to the target reference genome sequence data set to obtain a third genome sequence. Compared with the related art, the sample to be sequenced and the genome target interval in the sample to be sequenced are obtained, the sample to be sequenced is sequenced to obtain the first genome sequence, the genome target interval is sequenced after targeted amplification to obtain the second genome sequence, a target reference genome sequence data set is constructed based on the target population, and the first genome sequence and the second genome sequence are inferred with reference to the target reference genome sequence data set to obtain a third genome sequence; by targeted amplification of the genome target interval, and constructing a target reference genome sequence data set based on the target population to infer the third genome sequence, the third genome sequence result obtained by sequencing is more accurate and can be more applied to scenarios with high-depth data requirements.

[0056] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0058] Figure 1 A schematic diagram of a genome sequencing method provided by an embodiment of the present disclosure;

[0059] Figure 2 A schematic diagram of a method for constructing a target reference genome dataset provided in an embodiment of the present disclosure;

[0060] Figure 3 A schematic diagram of a structure of a target reference genome dataset provided in an embodiment of the present disclosure;

[0061] Figure 4 A schematic diagram of a flow chart of a genotype inference algorithm Glimpse provided in an embodiment of the present disclosure;

[0062] Figure 5 A schematic diagram of an automated analysis program workflow provided by an embodiment of the present disclosure;

[0063] Figure 6 A schematic diagram of the effect of a genome sequencing method provided by an embodiment of the present disclosure;

[0064] Figure 7 A schematic diagram of the structure of a genome sequencing device provided in an embodiment of the present disclosure;

[0065] Figure 8 A schematic diagram of the structure of another genome sequencing device provided in an embodiment of the present disclosure;

[0066] Fig. 9 A schematic block diagram of an exemplary electronic device 400 provided for an embodiment of the present disclosure. DETAILED DESCRIPTION

[0067] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0068] The genome sequencing method and apparatus, electronic device, and storage medium of the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0069] Figure 1 A schematic flow chart of a genome sequencing method provided in an embodiment of the present disclosure.

[0070] like Figure 1 As shown, the method comprises the following steps:

[0071] Step 101, obtaining a sample to be sequenced and a genomic target interval of a preset length in the sample to be sequenced.

[0072] As a refinement of the above step 101, the sample to be sequenced is a substance containing genetic material of the target organism, such as saliva, etc., the genomic target interval of the preset length is a genomic sequence of base pairs of the preset length, and the genomic target interval is obtained based on pre-designed primers. In this embodiment, the length of the genomic target interval generally does not exceed 300bp. It should be understood that the above description is only exemplary, and this embodiment does not limit the length of the genomic target interval.

[0073] Step 102, performing genome sequencing on the sample to be sequenced based on a preset genome sequencing program to obtain a first genome sequence; and sequencing the genome target interval that has been directionally amplified based on a preset genome sequencing program to obtain a second genome sequence.

[0074] As a refinement of the above step 102, the preset genome sequencing program can be high-depth whole genome sequencing or low-depth whole genome sequencing, the sample to be sequenced is sequenced based on the preset genome program to obtain a first genome sequence, and the genome target interval of the directed amplification is sequenced based on the preset genome program to obtain a second genome sequence. The directed amplification is to amplify the genome target interval by a preset multiple number.

[0075] Step 103: construct a target reference genome sequence dataset based on the target population.

[0076] As a refinement of the above step 103, in order to make the constructed target reference genome sequence dataset have more directional genetic structure characteristics, the target population is selected for construction, and the target reference genome dataset constructed based on the target population is more representative of the genetic structure characteristics of the target population.

[0077] Step 104: Infer the first genome sequence and the second genome sequence with reference to the target reference genome sequence dataset to obtain a third genome sequence.

[0078] As a refinement of the above step 104, it should be understood that the order of the first genome sequence and the second genome sequence obtained through step 102 is chaotic, and there are missing gene sites therein. The third genome sequence is obtained by comparing, filling and inferring the first genome sequence and the second genome sequence based on the target reference genome sequence data set.

[0079] The method for genome sequencing provided by the present disclosure mainly includes the following technical solutions: obtaining a sample to be sequenced and a genome target interval of a preset length in the sample to be sequenced; performing genome sequencing on the sample to be sequenced based on a preset genome sequencing program to obtain a first genome sequence; and sequencing the genome target interval that is directed amplified based on a preset genome sequencing program to obtain a second genome sequence; constructing a target reference genome sequence data set based on a target population; and inferring the first genome sequence and the second genome sequence with reference to the target reference genome sequence data set to obtain a third genome sequence. Compared with the related art, the method obtains a sample to be sequenced and obtains a genome target interval in the sample to be sequenced, sequences the sample to be sequenced to obtain the first genome sequence, and sequence the genome target interval after directed amplification to obtain the second genome sequence, constructs a target reference genome sequence data set based on a target population, and infers the first genome sequence and the second genome sequence with reference to the target reference genome sequence data set to obtain a third genome sequence; by directed amplifying the genome target interval, and constructing a target reference genome sequence data set based on the target population to infer the third genome sequence, the third genome sequence result obtained by sequencing is more accurate and can be more applied to scenarios with high-depth data requirements.

[0080] As a refinement of the above embodiment, when executing step 102 to sequence the genomic target interval that has been directionally amplified based on a preset genome sequencing program to obtain a second genome sequence, the following implementation methods can be used but are not limited to, for example: directionally amplifying the genomic target interval by a preset multiple to obtain the genomic target interval that has been quantitatively amplified by a preset multiple; sequencing the genomic target interval based on a preset genome sequencing program to obtain a second genome sequence.

[0081] As a refinement of the above embodiment, in order to solve the problem of insufficient sequencing depth of certain important genetic variation interpretation sites with a relatively small increase in economic cost, when performing targeted amplification of the genomic target interval at a preset multiple, the polymerase chain reaction technology can be used but not limited to. The preset multiple is not limited in this embodiment. After completing the targeted amplification of the genomic target interval, the amplified genomic target interval is sequenced based on a preset genome sequencing program to obtain the second genome sequence. The preset genome sequencing program has been explained in the above embodiment. For the sake of clarity and conciseness, it will not be repeated in this embodiment.

[0082] As a refinement of the above embodiment, the genomic target interval is obtained based on pre-designed primers, and the primers can be designed and optimized iteratively according to the actual genetic interpretation analysis range. Therefore, it can flexibly and effectively cover most of the variant interpretation sites with high requirements for sequencing depth, thereby meeting the needs of a series of genetic interpretation products or scenarios with high requirements for variant data sequencing depth.

[0083] Figure 2 A schematic diagram of a method for constructing a target reference genome dataset provided in an embodiment of the present disclosure, Figure 3 A schematic diagram of a target reference genome dataset constructed according to an embodiment of the present disclosure; Figure 2 and Figure 3 As shown:

[0084] Step 201, obtaining a first reference genome sequence data set of target populations in different regions; and obtaining a second reference genome sequence data set of target populations in a target region;

[0085] As a refinement of the above step 201, the genome sequences of multi-regional target populations are obtained, and the genome sequences of the multi-regional target populations are constructed as a first reference genome sequence data set; the genome sequences of the target populations in a specific region are obtained to construct a second reference genome sequence data set. It can be seen from the above description that the first reference genome data set contains a large number of populations and rich genetic polymorphisms, while the second reference genome sequence data set contains a small number of populations, but the genetic structure characteristics can better represent the specific population.

[0086] Step 202: supplement and optimize the first reference genome sequence dataset based on the second reference genome sequence dataset to obtain the target reference genome sequence dataset.

[0087] In order to obtain a target reference genome sequence dataset that is rich in gene polymorphism and whose genetic structure characteristics are more consistent with a specific population, the first reference genome sequence dataset is supplemented and optimized based on the second reference genome sequence dataset to obtain the target reference genome sequence dataset, which greatly improves the sensitivity of genotype inference.

[0088] As a refinement of the above embodiment, when performing step 104 to infer the first genome sequence and the second genome sequence with reference to the target reference genome sequence dataset to obtain a third genome sequence, the following implementation methods may be used but are not limited to, for example: obtaining a first genotype and a first genotype probability of the variant site corresponding to the target reference genome sequence dataset and the first genome sequence; and obtaining a second genotype and a second genotype probability of the variant site corresponding to the target reference genome sequence dataset and the second genome sequence; inputting the first genotype, the second genotype, the first genotype probability and the second genotype probability into a preset algorithm; performing genotype inference and genotype probability reassignment on the variant sites in the first genome sequence and the second genome sequence based on the preset algorithm, and completing the determination of the haplotype state to obtain the third genome sequence.

[0089] As a refinement of the above embodiment, the first genotype, the second genotype and the genotype all refer to the base types of a pair of alleles, the genotype probability, the first genotype probability and the second genotype probability all refer to the probability of the genotype appearing, the variation site is the site where insertion and deletion, structural variation, etc. occur, and the haplotype is a haploid genotype.

[0090] In order to more clearly illustrate the above embodiment, an exemplary description is provided here, including: based on all possible variant sites and types in the target reference genome sequence data set, extracting the genotype and genotype probability corresponding to the first genome sequence. And based on all possible variant sites and types in the target reference genome sequence data set, extracting the genotype and genotype probability corresponding to the second genome sequence, and then using a preset algorithm to infer / fill in the genotypes and reassign the genotype probabilities of the variant sites derived from the target reference genome data set, the first genome sequence, and the second genome sequence itself, and at the same time performing haplotype status determination, finally obtaining the third genome sequence. The preset algorithm may be a circular sampling algorithm, and this embodiment does not limit the selection of the preset algorithm.

[0091] As a refinement of the above embodiment, the preset algorithm uses a data conversion algorithm (positional Burrows-Wheeler transform, PBWT) when storing, querying and retrieving genotype / haplotype and genotype probability information, thereby simplifying and accelerating the entire process; and the preset algorithm is simplified and linearly related to the number of genotypes or haplotypes to be analyzed, thereby reducing the overall computational time complexity to a linear level rather than the cubic level of conventional genotype inference algorithms.

[0092] Figure 4A schematic diagram of a genotype inference algorithm Glimpse provided in an embodiment of the present disclosure, such as Figure 4 As shown:

[0093] Exemplarily, based on all possible variant sites and types in the reference data set (Reference Panel), the genotype (Genotype) and genotype probability (GenotypeLikelihoods) information corresponding to the low-depth whole genome sequencing sample data is extracted as input. Then, through the Gibbs sampling process and continuous iterations (Gibbs sampling procedure and iterations), the variant sites derived from the reference data set and the sample itself are subjected to genotype inference / filling (Imputation) and genotype probability reassignment, and haplotype state determination (Phasing) is performed at the same time. The above description is only an exemplary description and does not constitute a limitation of the present disclosure.

[0094] As a refinement of the above embodiment, before executing step 104 to infer the first genome sequence and the second genome sequence with reference to the target reference genome sequence dataset to obtain a third genome sequence, the method may also adopt but is not limited to the following implementation methods, for example: comparing the first genome sequence that has completed quality control filtering with the target reference genome sequence dataset to obtain first comparison result data; and comparing the second genome sequence that has completed quality control filtering with the target reference genome sequence dataset to obtain second comparison result data.

[0095] As a refinement of the above embodiment, in order to obtain high-quality first genome sequence and second genome sequence, quality control filtering is performed on them respectively, that is, invalid, repeated and other low-quality genome sequences are screened out, and then they are compared with the target reference genome sequence data set to obtain the first comparison result data and the second comparison result data. The comparison result data includes the base sequence of the relevant genome and the quality score of the sequencing. This embodiment does not limit the content contained in the comparison result data.

[0096] As a refinement of the above embodiment, the method also includes but is not limited to the following implementation methods, for example: performing variation analysis on the first genome sequence and the second genome sequence respectively to obtain variation analysis data; performing a preset purpose analysis based on the comparison result data and the variation analysis data to obtain genetic interpretation analysis data corresponding to the sample to be sequenced, and the comparison result data is the combined data of the first comparison result data and the second comparison result data.

[0097] The preset purpose analysis includes application scenarios such as carrier screening, drug genome screening, polygenic risk scoring, kinship analysis, ancestral component analysis, etc. The variation analysis data includes variation site addresses, variation gene sequences, etc. This embodiment does not limit the application scenarios included in the preset purpose analysis and the content included in the variation analysis data.

[0098] It can be seen from the above embodiments that the comparison result data and the variation analysis data can be applied to all scenarios included in the preset purpose analysis, and the corresponding genetic interpretation analysis data can be obtained after analysis. The genome sequencing method of the present invention effectively utilizes the genotype probability information of low-depth whole genome sequencing data, and integrates it into personalized genetic interpretation products such as ancestral analysis and kinship analysis. Compared with simply considering only genome information, this method improves the overall accuracy of the analysis results; effectively designs and utilizes the high-depth data advantages of directional amplification of genome target intervals, and meets the needs of personalized genetic interpretation products such as carrier screening and drug genome screening, which enables the low-depth whole genome sequencing data analysis method system to achieve the same genetic interpretation analysis effect as chip sequencing data or high-depth whole genome sequencing data; effectively consolidates the advantage of high genome coverage of low-depth whole genome sequencing data, compared with chip sequencing data, it can meet the needs of genetic interpretation analysis products or scenarios such as whole genome association analysis and polygenic risk scoring model analysis that require higher genome coverage without additional economic costs or other improvements.

[0099] Corresponding to the above-mentioned genome sequencing method, the present invention also proposes a computer program. To illustrate the working process of the computer program, please refer to Figure 5 , Figure 5 A schematic diagram of an automated analysis program workflow provided in an embodiment of the present disclosure is provided herein. Figure 5 Provide scenario-based instructions, such as:

[0100] Saliva sampling is performed on a biological sample. After DNA library construction, low-depth whole-genome sequencing with an average depth of 2X is usually performed, accompanied by the corresponding genome target interval directional amplification sequencing. After that, the original sequencing data Fastq is used as the input of the low-depth whole-genome sequencing data automation analysis program. After filtering, alignment, genotype inference based on the reference data set, and target interval directional amplification sequencing data variation analysis, the Bam format alignment result file and the complete VCF format variation information file after splicing and integration are obtained. After that, the Bam and VCF files are used as the input of the downstream personalized genetic variation interpretation and analysis process to perform a series of ancestry analysis, kinship analysis, carrier screening, drug genome screening, whole-genome association analysis, and polygenic risk scoring model analysis. Finally, the genetic interpretation analysis report corresponding to the biological sample is produced, and the corresponding interpretation result file data can be provided upon request.

[0101] Figure 6 A schematic diagram of the effect of a genome sequencing method provided in an embodiment of the present disclosure; Figure 6 As shown:

[0102] Compared with other existing technical solutions (gencove and GATK), the overall (SNP and InDel variations) achieves higher genotype inference accuracy and sensitivity, as well as lower computational running time complexity.

[0103] In summary, this embodiment can achieve the following effects:

[0104] 1. By targeted amplification of the target genome interval and constructing a target reference genome sequence data set based on the target population to infer the third genome sequence, the third genome sequence result obtained by sequencing is more accurate and more applicable to scenarios with high-depth data requirements.

[0105] 2. The optimization of genotype inference algorithms and reference dataset construction methods has led to improvements in accuracy, sensitivity and computing performance.

[0106] 3. Combine genomic target interval directed amplification sequencing technology to solve the problem of insufficient sequencing depth at important genetic variation interpretation sites.

[0107] 4. Build a downstream personalized genetic variation interpretation product system that adapts to the characteristics of low-depth whole genome sequencing data.

[0108] 5. Construction and implementation of the entire low-depth whole genome sequencing data analysis system.

[0109] Corresponding to the above-mentioned genome sequencing method, the present invention also provides a genome sequencing device. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.

[0110] Figure 7 A schematic diagram of a genome sequencing device provided in an embodiment of the present disclosure is shown in FIG. Figure 5 As shown, including:

[0111] An acquisition unit 31 is used to acquire a sample to be sequenced and a genome target interval of a preset length in the sample to be sequenced;

[0112] A sequencing unit 32 is used to perform genome sequencing on the sample to be sequenced based on a preset genome sequencing program to obtain a first genome sequence; the sequencing unit is also used to sequence the genome target interval of the directed amplification based on a preset genome sequencing program to obtain a second genome sequence;

[0113] A construction unit 33 is used to construct a target reference genome sequence data set based on the target population;

[0114] The reference unit 34 is used to infer the first genome sequence and the second genome sequence with reference to the target reference genome sequence data set to obtain a third genome sequence.

[0115] The present disclosure provides a device for genome sequencing, and the main technical scheme includes: obtaining a sample to be sequenced and a genome target interval of a preset length in the sample to be sequenced; performing genome sequencing on the sample to be sequenced based on a preset genome sequencing program to obtain a first genome sequence; and sequencing the genome target interval that is directed amplified based on a preset genome sequencing program to obtain a second genome sequence; constructing a target reference genome sequence data set based on a target population; and inferring the first genome sequence and the second genome sequence with reference to the target reference genome sequence data set to obtain a third genome sequence. Compared with the related art, the sample to be sequenced is obtained and the genome target interval in the sample to be sequenced is obtained, the sample to be sequenced is sequenced to obtain the first genome sequence, the genome target interval is directed amplified and sequenced to obtain the second genome sequence, a target reference genome sequence data set is constructed based on a target population, and the first genome sequence and the second genome sequence are inferred with reference to the target reference genome sequence data set to obtain a third genome sequence; by directed amplifying the genome target interval, and constructing a target reference genome sequence data set based on the target population to infer the third genome sequence, the third genome sequence result obtained by sequencing is more accurate and can be more applied to scenarios with high-depth data requirements.

[0116] Figure 8 A schematic diagram of the structure of another genome sequencing device provided in an embodiment of the present disclosure,

[0117] Furthermore, in a possible implementation of this embodiment, as Figure 8 As shown, the sequencing unit 32 includes:

[0118] An amplification module 321 is used to perform a directional amplification of the genome target interval by a preset multiple to obtain the genome target interval amplified by the preset multiple;

[0119] The sequencing module 322 is used to sequence the target genome interval based on a preset genome sequencing program to obtain a second genome sequence.

[0120] Furthermore, in a possible implementation of this embodiment, as Figure 8 As shown, the construction unit 33 includes:

[0121] An acquisition module 331 is used to acquire a first reference genome sequence data set of a target population in a different region; and the acquisition module is also used to acquire a second reference genome sequence data set of a target population in a target region;

[0122] The optimization module 332 is used to supplement and optimize the first reference genome sequence dataset based on the second reference genome sequence dataset to obtain the target reference genome sequence dataset.

[0123] Furthermore, in a possible implementation of this embodiment, as Figure 8 As shown, the reference unit 34 includes:

[0124] An acquisition module 341 is used to acquire a first genotype and a first genotype probability of a variant site corresponding to the target reference genome sequence dataset and the first genome sequence; and the acquisition module is also used to acquire a second genotype and a second genotype probability of a variant site corresponding to the target reference genome sequence dataset and the second genome sequence;

[0125] An input module 342, for inputting the first genotype, the second genotype, the first genotype probability and the second genotype probability into a preset algorithm;

[0126] The determination module 343 is used to perform genotype inference and genotype probability reassignment on the variant sites in the first genome sequence and the second genome sequence based on the preset algorithm, and complete the determination of the haplotype state to obtain the third genome sequence.

[0127] Furthermore, in a possible implementation of this embodiment, as Figure 8 As shown, the device also includes:

[0128] A first comparison unit 35 is used to compare the first genome sequence after quality control filtering with the target reference genome sequence data set to obtain first comparison result data; and

[0129] The second comparison unit 36 ​​is used to compare the second genome sequence after quality control filtering with the target reference genome sequence data set to obtain second comparison result data.

[0130] Furthermore, in a possible implementation of this embodiment, as Figure 8 As shown, the device also includes:

[0131] A first analysis unit 37, used to perform variation analysis on the first genome sequence and the second genome sequence respectively to obtain variation analysis data;

[0132] The second analysis unit 38 is used to perform a preset purpose analysis based on the comparison result data and the variation analysis data to obtain genetic interpretation analysis data corresponding to the sample to be sequenced, and the comparison result data is the combined data of the first comparison result data and the second comparison result data.

[0133] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is no longer limited in this embodiment.

[0134] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0135] Fig. 9 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0136] like Fig. 9As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 402 or a computer program loaded from a storage unit 408 to a RAM (Random Access Memory) 403. In RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An I / O (Input / Output) interface 405 is also connected to the bus 404.

[0137] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0138] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as a method for genome sequencing. For example, in some embodiments, the method for genome sequencing may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned genome sequencing method in any other appropriate manner (eg, by means of firmware).

[0139] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general-purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0140] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0141] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0142] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0143] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0144] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0145] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.

[0146] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0147] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for genome sequencing, characterized in that: include: Obtaining a sample to be sequenced and a genomic target interval of a preset length in the sample to be sequenced; Performing genome sequencing on the sample to be sequenced based on a preset genome sequencing program to obtain a first genome sequence; as well as Sequencing the directional amplified genomic target interval based on a preset genomic sequencing program to obtain a second genomic sequence; Construct a target reference genome sequence dataset based on the target population; The first genome sequence and the second genome sequence are inferred with reference to the target reference genome sequence data set to obtain a third genome sequence.

2. The method according to claim 1, characterized in that The step of sequencing the directional amplified genomic target interval based on a preset genomic sequencing program to obtain a second genomic sequence comprises: Directively amplifying the genomic target interval by a preset multiple to obtain the genomic target interval amplified by the preset multiple; The target genome interval is sequenced based on a preset genome sequencing program to obtain a second genome sequence.

3. The method according to claim 1, characterized in that The constructing of a target reference genome sequence dataset based on a target population comprises: Obtaining the first reference genome sequence dataset of target populations in different regions; and Acquire a second reference genome sequence dataset of a target population in a target region; The first reference genome sequence dataset is supplemented and optimized based on the second reference genome sequence dataset to obtain the target reference genome sequence dataset.

4. The method according to claim 1, characterized in that The inferring the first genome sequence and the second genome sequence with reference to the target reference genome sequence data set to obtain a third genome sequence comprises: Obtaining a first genotype and a first genotype probability of a variant site corresponding to the target reference genome sequence dataset and the first genome sequence; and Obtain a second genotype and a second genotype probability of a variant site corresponding to the target reference genome sequence dataset and the second genome sequence; Inputting the first genotype, the second genotype, the first genotype probability, and the second genotype probability into a preset algorithm; Based on the preset algorithm, genotype inference and genotype probability reassignment are performed on the variable sites in the first genome sequence and the second genome sequence, and haplotype status is determined to obtain the third genome sequence.

5. The method according to claim 4, characterized in that Before inferring the first genome sequence and the second genome sequence with reference to the target reference genome sequence dataset to obtain a third genome sequence, the method includes: Compare the first genome sequence after quality control filtration with the target reference genome sequence data set to obtain first comparison result data; and The second genome sequence after quality control filtering is compared with the target reference genome sequence data set to obtain second comparison result data.

6. The method according to claim 5, characterized in that The method further comprises: performing variation analysis on the first genome sequence and the second genome sequence respectively to obtain variation analysis data; A preset purpose analysis is performed based on the comparison result data and the variation analysis data to obtain genetic interpretation analysis data corresponding to the sample to be sequenced, and the comparison result data is the combined data of the first comparison result data and the second comparison result data.

7. A genome sequencing device, characterized in that: include: An acquisition unit, used to acquire a sample to be sequenced and a genomic target interval of a preset length in the sample to be sequenced; A sequencing unit is used to perform genome sequencing on the sample to be sequenced based on a preset genome sequencing program to obtain a first genome sequence; the sequencing unit is also used to sequence the genome target interval of the directed amplification based on a preset genome sequencing program to obtain a second genome sequence; A construction unit, used to construct a target reference genome sequence dataset based on a target population; A reference unit is used to infer the first genome sequence and the second genome sequence with reference to the target reference genome sequence data set to obtain a third genome sequence.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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