Method, device and equipment for detecting thalassemia gene deletion, medium and program product

Abstract

The invention relates to a thalassemia gene deletion detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring contrast sample data and to-be-detected sample data of a to-be-detected thalassemia missing sample; constructing a baseline data set according to the contrast sample data; performing depth analysis on the to-be-detected sample data to obtain depth ratio data; performing ratio matrix calculation according to the baseline data set and the depth ratio data to obtain a sample ratio matrix; and performing thalassemia gene deletion judgment on the sample ratio matrix according to a preset deletion judgment condition to obtain a judgment result. By adopting the method, the gene deletion conditions of various types of thalassemia can be efficiently and accurately detected.

Description

Technical Field

[0001] The present application relates to the technical field of gene mutation detection, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for detecting thalassemia gene deletion. Background Art

[0002] With the development of gene mutation detection technology, there are endless detection technologies for thalassemia genes, but it is impossible to efficiently and accurately detect various types of thalassemia genes. In traditional technology, Gap-PCR combined with high-throughput sequencing technology is used for thalassemia detection, and its experimental operation is cumbersome and time-consuming. The use of Sanger sequencing technology to detect thalassemia has low throughput and high cost. In addition, the use of multiplex ligation-dependent probe amplification technology is not suitable for the detection of new mutations. Gap-PCR technology can only detect known deletion types, not mutant types. PCR-RDB technology (PCR-reverse dot hybridization technology) is mainly used for the detection of known mutations or deletion types, and cannot detect new mutations. Summary of the invention

[0003] Based on this, it is necessary to provide a thalassemia gene deletion detection method, device, computer equipment, computer-readable storage medium and computer program product that can efficiently and accurately detect various types of thalassemia gene deletions in response to the above-mentioned technical problems.

[0004] In a first aspect, the present application provides a method for detecting thalassemia gene deletion, comprising:

[0005] Obtaining control sample data and test sample data of the thalassemia-deficient sample to be tested;

[0006] constructing a baseline data set based on the control sample data;

[0007] Performing depth analysis on the sample data to be tested to obtain depth ratio data;

[0008] Performing ratio matrix calculation based on the baseline data set and the depth ratio data to obtain a sample ratio matrix;

[0009] The sample ratio matrix is ​​subjected to thalassemia gene deletion determination according to preset deletion determination conditions to obtain a determination result.

[0010] In one embodiment, constructing a baseline data set based on the control sample data includes:

[0011] Performing sequence alignment based on the pre-constructed reference genome and the control sample data to obtain aligned sample data;

[0012] Performing deviation correction on the amplicon depth data of the comparison sample data to obtain corrected sample data;

[0013] Calculate the amplicon depth ratio of the corrected sample data according to the first preset model to obtain depth ratio data;

[0014] The depth ratio data is subjected to double-weighted calculation according to a second preset model to obtain the baseline data set.

[0015] In one embodiment, the method further comprises:

[0016] Access public database data and first-generation sequencing results;

[0017] Designing primers according to the public database data and first-generation sequencing results to obtain target primers;

[0018] Performing amplicon sequence fusion according to the target primers to obtain a target amplicon fusion sequence;

[0019] The target amplicon fusion sequence is combined with preset human reference genome data to obtain the reference genome.

[0020] In one embodiment, the preset deletion judgment condition includes: a first preset threshold value and a second preset threshold value; the judgment result includes: a first type judgment result and a second type judgment result; the thalassemia gene deletion judgment is performed on the sample ratio matrix according to the preset deletion judgment condition to obtain the judgment result, including:

[0021] Performing thalassemia gene deletion determination on the first type of data of the sample ratio matrix according to the first preset threshold value to obtain the first type determination result;

[0022] The thalassemia gene deletion is determined based on the second preset threshold and the second type of data of the sample ratio matrix to obtain the second type of determination result.

[0023] In one embodiment, performing thalassemia gene deletion determination according to the second preset threshold and the second type of data of the sample ratio matrix to obtain the second type of determination result includes:

[0024] Performing polynomial fitting on the second type of data based on a preset filter to obtain smoothed data;

[0025] Performing double-weighted calculation on the smoothed data to obtain smoothed weighted data;

[0026] The smoothed weighted data is subjected to thalassemia gene deletion determination according to the second preset threshold value to obtain the second type determination result.

[0027] In one embodiment, obtaining control sample data and test sample data of the thalassemia-deficient sample to be tested includes:

[0028] Obtaining high-throughput sequencing data of the thalassemia deletion sample to be tested, and performing data quality control on the high-throughput sequencing data to obtain quality control sequencing data;

[0029] The quality control sequencing data is subjected to sample screening to obtain the control sample data and the test sample data.

[0030] In a second aspect, the present application also provides a thalassemia gene deletion detection device, comprising:

[0031] An acquisition module, used to obtain control sample data and test sample data of the thalassemia-deficient sample to be tested;

[0032] A baseline construction module, used to construct a baseline data set according to the control sample data;

[0033] A depth analysis module, used for performing a depth analysis on the sample data to be tested to obtain depth ratio data;

[0034] A matrix calculation module, used for performing ratio matrix calculation according to the baseline data set and the depth ratio data to obtain a sample ratio matrix;

[0035] The determination module is used to perform thalassemia gene deletion determination on the sample ratio matrix according to preset deletion determination conditions to obtain a determination result.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Obtaining control sample data and test sample data of the thalassemia-deficient sample to be tested;

[0038] constructing a baseline data set based on the control sample data;

[0039] Performing depth analysis on the sample data to be tested to obtain depth ratio data;

[0040] Performing ratio matrix calculation based on the baseline data set and the depth ratio data to obtain a sample ratio matrix;

[0041] The sample ratio matrix is ​​subjected to thalassemia gene deletion determination according to preset deletion determination conditions to obtain a determination result.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtaining control sample data and test sample data of the thalassemia-deficient sample to be tested;

[0044] constructing a baseline data set based on the control sample data;

[0045] Performing depth analysis on the sample data to be tested to obtain depth ratio data;

[0046] Performing ratio matrix calculation based on the baseline data set and the depth ratio data to obtain a sample ratio matrix;

[0047] The sample ratio matrix is ​​subjected to thalassemia gene deletion determination according to preset deletion determination conditions to obtain a determination result.

[0048] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0049] Obtaining control sample data and test sample data of the thalassemia-deficient sample to be tested;

[0050] constructing a baseline data set based on the control sample data;

[0051] Performing depth analysis on the sample data to be tested to obtain depth ratio data;

[0052] Performing ratio matrix calculation based on the baseline data set and the depth ratio data to obtain a sample ratio matrix;

[0053] The sample ratio matrix is ​​subjected to thalassemia gene deletion determination according to preset deletion determination conditions to obtain a determination result.

[0054] The above-mentioned thalassemia gene deletion detection method, device, computer equipment, computer-readable storage medium and computer program product obtain the control sample data and the test sample data of the thalassemia deletion sample to be tested; construct a baseline data set according to the control sample data; perform a deep analysis on the test sample data to obtain depth ratio data; perform ratio matrix calculation based on the baseline data set and the depth ratio data to obtain a sample ratio matrix; perform thalassemia gene deletion judgment on the sample ratio matrix according to the preset deletion judgment condition to obtain a judgment result. Therefore, a baseline data set is constructed according to the control sample data, and a deep analysis is performed on the test sample data to obtain depth ratio data, and a ratio matrix is ​​calculated based on the baseline data set and the depth ratio data, so as to efficiently and accurately calculate the sample ratio matrix, and perform thalassemia gene deletion judgment on the sample ratio matrix according to the preset deletion judgment condition, so as to efficiently and accurately judge the judgment result containing various types of thalassemia gene deletion conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 This is a diagram of the application environment of a method for detecting thalassemia gene deletion in an embodiment;

[0057] Figure 2 A schematic diagram of a process for detecting thalassemia gene deletion in one embodiment;

[0058] Figure 3 A schematic diagram of a process for detecting thalassemia gene deletion in one embodiment;

[0059] Figure 4 A schematic diagram of a process for detecting thalassemia gene deletion in another embodiment;

[0060] Figure 5 is a structural block diagram of a thalassemia gene deletion detection device in one embodiment;

[0061] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0063] The thalassemia gene deletion detection method provided in the embodiments of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the control sample data and the test sample data of the thalassemia missing sample to be tested; constructs a baseline data set according to the control sample data; performs a deep analysis on the test sample data to obtain the depth ratio data; calculates the ratio matrix according to the baseline data set and the depth ratio data to obtain the sample ratio matrix; and performs a thalassemia gene missing determination on the sample ratio matrix according to the preset missing determination condition to obtain the determination result. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The server 104 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0064] In an exemplary embodiment, Figure 2 As shown, a method for detecting thalassemia gene deletion is provided, and the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 202 to 210. Among them:

[0065] Step 202, obtaining control sample data and test sample data of the thalassemia-deficient sample to be tested.

[0066] Among them, the thalassemia deficiency samples to be tested can use sample data containing α-thalassemia deficiency. The α-thalassemia deficiency can be negative or positive. A negative α-thalassemia deficiency indicates that the sample does not have α-thalassemia deficiency, and all thalassemia deficiency samples to be tested have been verified by qPCR.

[0067] In some embodiments, obtaining control sample data and test sample data of the thalassemia deletion sample to be tested includes: obtaining high-throughput sequencing data of the thalassemia deletion sample to be tested, and performing data quality control on the high-throughput sequencing data to obtain quality control sequencing data; performing sample screening on the quality control sequencing data to obtain control sample data and test sample data.

[0068] In some embodiments, according to the operating requirements of the self-developed amplicon library construction and sequencing kit, a library is constructed for the DNA sample of the thalassemia deletion sample to be tested, thereby obtaining high-throughput sequencing data of the thalassemia deletion sample to be tested, and the high-throughput sequencing data is subjected to data quality control according to the basic quality control requirements of the sequencing kit, thereby obtaining quality control sequencing data.

[0069] In some embodiments, the quality control sequencing data are screened according to the α-thalassemia deficiency status of the thalassemia deficiency samples to be tested, the quality control sequencing data indicating negative α-thalassemia deficiency status are used as control sample data, and the quality control sequencing data are directly used as the sample data to be tested. The screening method of the control sample data and the sample data to be tested can also be grouped and processed in other ways to improve the subsequent processing efficiency of the control sample data and the sample to be tested, but is not limited to this.

[0070] Step 204: construct a baseline data set based on the control sample data.

[0071] Among them, the missing status of α-thalassemia in the control sample data represents negative sample data.

[0072] In some embodiments, a baseline data set is constructed based on control sample data, including: performing sequence alignment based on a pre-constructed reference genome and control sample data to obtain aligned sample data; performing bias correction on amplicon depth data of the control sample data to obtain corrected sample data; performing amplicon depth ratio calculation on the corrected sample data according to a first preset model to obtain depth ratio data; and performing double-weighted calculation on the depth ratio data according to a second preset model to obtain a baseline data set.

[0073] The sequence alignment can be performed by the bwa (Burrows-Wheeler Aligner) alignment method, and the depth of the amplicon corresponding to the sample data can be compared by using cnvkit statistics, wherein cnvkit is a CNV detection tool that can be used for precise genome analysis, and the deviation correction can be performed by using the GC deviation correction method. The first preset model is a mathematical model formula for homogenizing the data, and the second preset model can be a biweight_location algorithm model.

[0074] In some embodiments, first, a sequence alignment is performed based on a pre-constructed reference genome and control sample data to obtain aligned sample data, i.e., a bam (Binary compressed SAM Format) alignment file. According to the relative position of the amplicon genome of the control sample data, a bioinformatics method is used to calculate the amplicon depth data of each control sample data based on the bam file, and the amplicon depth data is bias-corrected using the GC content to obtain the corrected sample data of the amplicon.

[0075] In some embodiments, the depth ratio of the amplicon is calculated for the corrected sample data according to the first preset model to obtain depth ratio data. First, the median depth-median of the amplicon region of the corrected sample data is calculated, and the first preset model is used to normalize the coverage depth of the amplicon in the sample to reduce the influence of the sequencing data volume on the depth of the amplicon region, obtain the normalized data of the amplicon, and use the normalized data as the depth ratio data, wherein the first preset model is shown in formula (1):

[0076] NDi = Di / depth-median * 2000 (Formula 1);

[0077] Among them, the normalized data of the amplicon is represented by ND, D represents the amplicon, and i represents the number of each amplicon. It should be noted that multiplying by 2000 is to standardize the normalized data to 2000, and the amplicon depth data refers to the coverage depth of the amplicon area.

[0078] In some embodiments, the depth ratio data is doubly weighted by using a doubly weighted biweight_location algorithm model. It should be noted that the doubly weighted biweight_location algorithm model used uses the median absolute deviation (MAD) to estimate the median of the data set. Compared with the traditional arithmetic mean and median, it is insensitive to outliers. Therefore, in data sets with outliers, it can provide a more reliable central trend estimate, so that the baseline data set BDi can be established more accurately based on the calculation results.

[0079] Step 206: perform depth analysis on the sample data to be tested to obtain depth ratio data.

[0080] Among them, the depth analysis refers to performing bwa sequence alignment and GC deviation correction on the sample data to be tested in the manner of constructing a baseline data set in step 204, and then achieving depth normalization through the first preset model to obtain depth ratio data of the sample data to be tested.

[0081] In some embodiments, after bwa alignment and GC correction are performed on the sample data to be tested, depth ratio data of the sample data to be tested is obtained by depth normalization.

[0082] Step 208 , calculate a ratio matrix based on the baseline data set and the depth ratio data to obtain a sample ratio matrix.

[0083] The ratio matrix calculation is to use the constructed baseline data set to calculate the depth ratio of each amplicon region of the depth ratio data, abbreviated as DR, and use DR as the sample ratio matrix. The ratio matrix calculation is shown in the following formula 2:

[0084] DRi=NDi / BDi (Formula 2);

[0085] The baseline depth ratio data of the corresponding amplicon for constructing the baseline data set is defined as Baseline Depth, abbreviated as BD, i is the number of the corresponding amplicon, and ND represents the depth ratio data of the sample data to be tested.

[0086] In some embodiments, depth ratio data of the corresponding amplicons of the baseline data set is determined according to the baseline data set, and a ratio matrix is ​​calculated for the baseline depth ratio data and the depth ratio data according to Formula 2 to obtain a sample ratio matrix DRi of the corresponding amplicons.

[0087] Step 210, performing thalassemia gene deletion determination on the sample ratio matrix according to preset deletion determination conditions to obtain a determination result.

[0088] Among them, the thalassemia gene deletion determination requires a more accurate determination of different types of thalassemia gene deletions. The types of thalassemia gene deletions may be SEA, THAI, α3.7, α4.2 and C-region. Among them, SEA and THAI belong to the first type of thalassemia gene deletion, and α3.7, α4.2 and C-region belong to the second type of thalassemia gene deletion.

[0089] In some embodiments, the preset deletion judgment conditions include: a first preset threshold and a second preset threshold; the judgment results include: a first type of judgment result and a second type of judgment result; the sample ratio matrix is ​​subjected to thalassemia gene deletion judgment according to the preset deletion judgment conditions to obtain a judgment result, including: performing thalassemia gene deletion judgment on the first type of data of the sample ratio matrix according to the first preset threshold to obtain a first type of judgment result; performing thalassemia gene deletion judgment according to the second preset threshold and the second type of data of the sample ratio matrix to obtain a second type of judgment result.

[0090] Among them, the first preset threshold is the threshold data set for the first largest type of thalassemia gene deletion, the second preset threshold is the threshold data set for the second largest type of thalassemia gene deletion, the first type of data is the sample ratio matrix for SEA and THAI types, the second type of data is the sample ratio matrix for α3.7, α4.2 and C-region types, the first type of judgment result is the judgment result for the first largest type of thalassemia gene deletion, and the second type of judgment result is the judgment result for the second largest type of thalassemia gene deletion.

[0091] In some embodiments, the first type of data of the sample ratio matrix is ​​judged for thalassemia gene deletion according to a first preset threshold to obtain a first type of judgment result, that is, when the first type of data of the sample ratio matrix is ​​not less than the first preset threshold, the first type of judgment result can be confirmed as SEA and THAI deletion.

[0092] It should be noted that for the first type of thalassemia gene deletion, both SEA and THAI types of thalassemia gene deletions use a fusion comparison algorithm to determine the thalassemia gene deletion. Therefore, it is only necessary to calculate the ND value according to formula 1. After a large number of pre-statistical analysis of SEA, THAI negative and positive samples, it can be obtained that the ND value only needs to be not less than 200 to confirm the SEA and THAI deletion. Therefore, for the first type of thalassemia gene deletion, the first preset threshold is set to 200.

[0093] In some embodiments, thalassemia gene absence is determined based on the second type of data according to the second preset threshold and the sample ratio matrix to obtain a second type of determination result, including: performing polynomial fitting on the second type of data based on a preset filter to obtain smoothed data; performing double weighted calculation on the smoothed data to obtain smoothed weighted data; and determining thalassemia gene absence on the smoothed weighted data according to the second preset threshold to obtain a second type of determination result.

[0094] Among them, the preset filter uses a Savitzky-Golay filter, which is a digital filter that smoothes data by performing polynomial fitting within a data window. This method can effectively remove high-frequency noise while retaining signal characteristics.

[0095] In some embodiments, first, for the second type of data, i.e., the DR value data for the second largest type of thalassemia gene deletion, the Savitzky-Golay filter is first used to smooth the DR values ​​of α3.7, α4.2 and all amplicons in the C-region in the DR value data to reduce noise, and then the double-weighted position algorithm is used again to perform double-weighted calculations to obtain weighted smoothed weighted data of α3.7, α4.2 and C-region. The double-weighted position algorithm is the biweight_location algorithm, which uses the Python astropy package.

[0096] It should be further explained that, according to the statistics of a large number of negative and positive results, when the second preset threshold value of the smoothed weighted data is not less than 0.7, it is negative; when it is less than 0.7 and not less than 0.1, it is a heterozygous deletion; when it is less than 0.1, it is a pure and missing. That is, according to the division of the second preset threshold value, the smoothed weighted data can be used to determine the thalassemia gene deletion and obtain the second type of determination result.

[0097] Among them, the smoothed weighted data is the Depth ratio value, denoted as depth-ratio.

[0098] In the above-mentioned thalassemia gene deletion detection method, the control sample data and the sample data to be tested of the thalassemia deletion sample to be tested are obtained; a baseline data set is constructed based on the control sample data; a depth analysis is performed on the sample data to be tested to obtain depth ratio data; a ratio matrix is ​​calculated based on the baseline data set and the depth ratio data to obtain a sample ratio matrix; and a thalassemia gene deletion determination is performed on the sample ratio matrix according to a preset deletion determination condition to obtain a determination result. Therefore, a baseline data set is constructed based on the control sample data, and a depth analysis is performed on the sample data to be tested to obtain depth ratio data, and a ratio matrix is ​​calculated based on the baseline data set and the depth ratio data, so as to efficiently and accurately calculate the sample ratio matrix, and the thalassemia gene deletion determination is performed on the sample ratio matrix according to the preset deletion determination condition, so as to efficiently and accurately determine the determination result containing various types of thalassemia gene deletion conditions.

[0099] In an exemplary embodiment, Figure 3 As shown, the thalassemia gene deletion detection method further includes: a reference genome pre-construction step, and the reference genome pre-construction step includes steps 302 to 308. Among them:

[0100] Step 302, obtaining public database data and first-generation sequencing results.

[0101] Among them, the public database data can adopt two public databases hbvar and ithanet, and the first-generation sequencing results adopt the Sanger sequencing results.

[0102] In some embodiments, public database data and first-generation sequencing results can be obtained from a sequencing gene database, but are not limited thereto.

[0103] Step 304, design primers based on the public database data and the first generation sequencing results to obtain target primers.

[0104] Among them, primer design is the process of designing amplicons according to primer design requirements.

[0105] In some embodiments, for the first major type of SEA and THAI, the first primer design requirement is adopted, and the GAP-PCR method is directly used to design amplicons as primers. According to the hbvar, ithanet and sanger sequencing results, two pairs of short fragment specific primer pairs are designed, and these two pairs of short fragment specific primer pairs are used as target primers (as SEA primers and THAI primers, respectively). Therefore, there is no need for secondary interruption and no need for separate amplification, which simplifies the experimental process. There is no need to use a tiling method for tiling the missing region of nearly 30Kb, thereby reducing the number of amplicons in the homologous region, which is beneficial to the stability of the experiment and can efficiently amplify the fusion sequence of SEA and THAI.

[0106] In some embodiments, for the second largest type of α3.7 and α4.2, the second primer design requirements are adopted, a tiling design of the deletion region is adopted, and additional common region amplicons (C-region) of large fragment deletions such as SEA, THAI, MED, FIL, BRIT, and SA are designed. The common region amplicons are used as target primers and will be defined as the first preset common region.

[0107] It should be noted that the common region of α3.7, α4.2 and other large fragment deletions is named C-region. The second primer design requirements are: α3.7 and α4.2 use amplicon sequence fusion amplicon, and the designed amplicon contains mismatched bases based on the homologous region. After the design is completed, blast is used again to confirm that the designed amplicon contains at least one mismatch compared with the homologous region to ensure the accuracy of data comparison.

[0108] It should be further explained that C-region designed three efficient and stable amplicons. These three amplicons were obtained by screening by calculating the CV value of the sequencing depth. The screening CV threshold was no more than 15%. The CV values ​​of the three selected amplicons were 12, 64%, 10.85%, and 10.85%, respectively.

[0109] In some embodiments, it is also necessary to design a control amplicon for data normalization. The design principle is to select a series of genomic regions with good conservation and stability, design primers, ensure the stability and uniformity of amplification, and make the CV threshold of the amplicon no greater than 20%.

[0110] Step 306: perform amplicon sequence fusion according to the target primers to obtain a target amplicon fusion sequence.

[0111] In some embodiments, amplicon sequences are fused according to target primers to obtain target amplicon fusion sequences of corresponding types for use in constructing an accurate reference genome.

[0112] Step 308: merge the target amplicon fusion sequence with the preset human reference genome data to obtain a reference genome.

[0113] The preset human reference genome data refers to the genome data of a normal person.

[0114] In some embodiments, the target amplicon fusion sequence newly created by fusion of the target primers through the amplicon sequence is merged with the human reference genome, and an index file is re-established to obtain high-quality sequencing data for BWA comparison of the sequencing data.

[0115] In this embodiment, by obtaining public database data and first-generation sequencing results, and designing primers according to the public database data and the first-generation sequencing results, target primers are obtained, amplicon sequences are fused according to the target primers to obtain target amplicon fusion sequences, and the target amplicon fusion sequences are merged with preset human reference genome data to obtain a reference genome. This allows for designing corresponding target primers for different types of primers, does not require secondary interruption and does not require separate amplification, simplifies the experimental process, does not require tiling of deletion regions close to 30 Kb, reduces the number of amplicons in homologous regions, is beneficial to the stability of the experiment, and constructs an accurate reference genome for sequencing data comparison.

[0116] In order to understand the solution of this application more clearly, Figure 4 The details are as follows:

[0117] First, the offline data is used to ensure that the thalassemia-deficient samples to be tested are obtained and qualified for quality inspection. The control sample data of the thalassemia-deficient samples to be tested and the test sample data are uniformly subjected to the deep analysis process of bwa ​​sequencing, GC correction and depth homogenization. Then, the depth ratio data of the test sample data is obtained, and a baseline data set is constructed based on the control sample data. The ratio matrix is ​​calculated based on the baseline data set and the depth ratio data, that is, the corresponding Figure 4The depth-ratio calculation and depth fusion process in the above method are performed to obtain the sample ratio matrix. Finally, the sample ratio matrix is ​​subjected to thalassemia missingness determination according to the preset missingness determination conditions to obtain the determination result.

[0118] In some embodiments, the sample ratio matrix is ​​subjected to thalassemia deficiency determination according to preset deficiency determination conditions, and the determination results are shown in the following Table 1:

[0119] Table 1 Determination results and threshold determination of α-thalassemia deletion

[0120]

[0121] The depth ratio values ​​corresponding to α3.7, α4.2 and C-region calculated by the biweight_location algorithm are recorded as depth-ratio-3.7, depth-ratio-4.7 and depth-ratio-C-region respectively. The ND values ​​of SEA and THAI are recorded as ND-SEA and ND-THAI.

[0122] It should be noted that in Table 1, * represents all values, and Unknown is an unknown missing type, which is not within the scope of this study and can be assumed to be negative.

[0123] In an exemplary embodiment, in order to better reflect the practical application effect of this solution, the determination results of 635 samples of thalassemia deletion to be tested were compared with the gold standard. All the determination results were completely consistent with the gold standard, which means that the sensitivity, specificity and accuracy of this solution all reached 100%, proving that this solution has extremely high feasibility. The determination results of 50 samples randomly selected are shown in Table 2 below:

[0124] Table 2: Detailed display of α-thalassemia deletion type results

[0125]

[0126] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0127] Based on the same inventive concept, the embodiment of the present application also provides a thalassemia gene deletion detection device for implementing the thalassemia gene deletion detection method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more thalassemia gene deletion detection device embodiments provided below can refer to the limitations of the thalassemia gene deletion detection method above, and will not be repeated here.

[0128] In an exemplary embodiment, Figure 5 As shown, a thalassemia gene deletion detection device is provided, including: an acquisition module 501, a baseline construction module 502, a depth analysis module 503, a matrix calculation module 504 and a determination module 505, wherein:

[0129] An acquisition module 501 is used to acquire control sample data and test sample data of the thalassemia-deficient sample to be tested;

[0130] A baseline construction module 502 is used to construct a baseline data set based on the control sample data;

[0131] A depth analysis module 503 is used to perform depth analysis on the sample data to be tested to obtain depth ratio data;

[0132] A matrix calculation module 504 is used to perform ratio matrix calculation based on the baseline data set and the depth ratio data to obtain a sample ratio matrix;

[0133] The determination module 505 is used to perform thalassemia gene deletion determination on the sample ratio matrix according to a preset deletion determination condition to obtain a determination result.

[0134] In some embodiments, the baseline construction module 502 is also used to perform sequence alignment based on a pre-constructed reference genome and control sample data to obtain aligned sample data; perform bias correction on amplicon depth data of the aligned sample data to obtain corrected sample data; perform amplicon depth ratio calculation on the corrected sample data according to a first preset model to obtain depth ratio data; perform double-weighted calculation on the depth ratio data according to a second preset model to obtain a baseline data set.

[0135] In some embodiments, the device also includes: a reference genome construction module for obtaining public database data and first-generation sequencing results; designing primers based on the public database data and first-generation sequencing results to obtain target primers; fusing amplicon sequences based on the target primers to obtain target amplicon fusion sequences; merging the target amplicon fusion sequence with preset human reference genome data to obtain a reference genome.

[0136] In some embodiments, the preset deletion judgment conditions include: a first preset threshold and a second preset threshold; the judgment results include: a first type of judgment result and a second type of judgment result; the judgment module 505 is also used to perform thalassemia gene deletion judgment on the first type of data of the sample ratio matrix according to the first preset threshold to obtain a first type of judgment result; and to perform thalassemia gene deletion judgment on the second type of data of the sample ratio matrix according to the second preset threshold to obtain a second type of judgment result.

[0137] In some embodiments, the determination module 505 is also used to perform polynomial fitting on the second type of data based on a preset filter to obtain smoothed data; perform double-weighted calculation on the smoothed data to obtain smoothed weighted data; and perform thalassemia gene deletion determination on the smoothed weighted data according to a second preset threshold to obtain a second type of determination result.

[0138] In some embodiments, the acquisition module 501 is also used to obtain control sample data and test sample data of the thalassemia deletion sample to be tested, including: obtaining high-throughput sequencing data of the thalassemia deletion sample to be tested, and performing data quality control on the high-throughput sequencing data to obtain quality control sequencing data; performing sample screening on the quality control sequencing data to obtain control sample data and test sample data.

[0139] In the above-mentioned thalassemia gene deletion detection device, the control sample data and the sample data to be tested of the thalassemia deletion sample to be tested are obtained; a baseline data set is constructed according to the control sample data; a depth analysis is performed on the sample data to be tested to obtain depth ratio data; a ratio matrix is ​​calculated according to the baseline data set and the depth ratio data to obtain a sample ratio matrix; and a thalassemia gene deletion determination is performed on the sample ratio matrix according to the preset deletion determination conditions to obtain a determination result. Therefore, a baseline data set is constructed according to the control sample data, and a depth analysis is performed on the sample data to be tested to obtain depth ratio data, and a ratio matrix is ​​calculated according to the baseline data set and the depth ratio data, so that the sample ratio matrix is ​​calculated efficiently and accurately, and the thalassemia gene deletion determination is performed on the sample ratio matrix according to the preset deletion determination conditions, so that the determination result containing various types of thalassemia gene deletion conditions can be efficiently and accurately determined.

[0140] Each module in the above-mentioned thalassemia gene deletion detection device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0141] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store thalassemia deletion samples to be tested. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for detecting thalassemia gene deletion is implemented.

[0142] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0143] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above-mentioned thalassemia gene deletion detection method when executing the computer program.

[0144] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned thalassemia gene deletion detection method are implemented.

[0145] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned thalassemia gene deletion detection method.

[0146] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0147] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0148] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0149] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for detecting thalassemia gene deletion, characterized in that: The method comprises: Obtaining control sample data and test sample data of the thalassemia-deficient sample to be tested; constructing a baseline data set based on the control sample data; Performing depth analysis on the sample data to be tested to obtain depth ratio data; Performing ratio matrix calculation based on the baseline data set and the depth ratio data to obtain a sample ratio matrix; The sample ratio matrix is ​​subjected to thalassemia gene deletion determination according to preset deletion determination conditions to obtain a determination result.

2. The method according to claim 1, characterized in that The step of constructing a baseline data set according to the control sample data includes: Performing sequence alignment based on the pre-constructed reference genome and the control sample data to obtain aligned sample data; Performing deviation correction on the amplicon depth data of the comparison sample data to obtain corrected sample data; Calculate the amplicon depth ratio of the corrected sample data according to the first preset model to obtain depth ratio data; The depth ratio data is subjected to double-weighted calculation according to a second preset model to obtain the baseline data set.

3. The method according to claim 2, characterized in that The method further comprises: Access public database data and first-generation sequencing results; Designing primers according to the public database data and first-generation sequencing results to obtain target primers; Performing amplicon sequence fusion according to the target primers to obtain a target amplicon fusion sequence; The target amplicon fusion sequence is combined with preset human reference genome data to obtain the reference genome.

4. The method according to claim 1, characterized in that: The preset deletion determination condition includes: a first preset threshold value and a second preset threshold value; the determination result includes: a first type determination result and a second type determination result; the determination result obtained by performing thalassemia gene deletion determination on the sample ratio matrix according to the preset deletion determination condition includes: Performing thalassemia gene deletion determination on the first type of data of the sample ratio matrix according to the first preset threshold value to obtain the first type determination result; The thalassemia gene deletion is determined based on the second preset threshold and the second type of data of the sample ratio matrix to obtain the second type of determination result.

5. The method according to claim 4, characterized in that The step of performing thalassemia gene deletion determination according to the second preset threshold and the second type of data of the sample ratio matrix to obtain the second type of determination result includes: Performing polynomial fitting on the second type of data based on a preset filter to obtain smoothed data; Performing double-weighted calculation on the smoothed data to obtain smoothed weighted data; The smoothed weighted data is subjected to thalassemia gene deletion determination according to the second preset threshold value to obtain the second type determination result.

6. The method according to claim 1, characterized in that The step of obtaining control sample data and test sample data of the thalassemia-deficient sample to be tested comprises: Obtaining high-throughput sequencing data of the thalassemia deletion sample to be tested, and performing data quality control on the high-throughput sequencing data to obtain quality control sequencing data; The quality control sequencing data is subjected to sample screening to obtain the control sample data and the test sample data.

7. A thalassemia gene deletion detection device, characterized in that: The device comprises: An acquisition module, used to obtain control sample data and test sample data of the thalassemia-deficient sample to be tested; A baseline construction module, used to construct a baseline data set according to the control sample data; A depth analysis module, used for performing a depth analysis on the sample data to be tested to obtain depth ratio data; A matrix calculation module, used for performing ratio matrix calculation according to the baseline data set and the depth ratio data to obtain a sample ratio matrix; The determination module is used to perform thalassemia gene deletion determination on the sample ratio matrix according to preset deletion determination conditions to obtain a determination result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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