Method and device for sequencing and automatic typing of thalassemia based on a third-generation sequencing platform

Abstract

The present application relates to the technical field of bioinformatics, in particular to a method and device for sequencing and automatic typing of thalassemia based on a third-generation sequencing platform, which comprises the following steps: aligning sequencing data of a sample to be tested to a human reference genome, inferring candidate structural variation breakpoints of the sample to be tested based on the alignment result by using a prior probability model and a local breakpoint database, determining the inferred type based on the candidate structural variation breakpoints and the sequencing data, and determining the thalassemia type of the sample to be tested based on the inferred type and local assembly of micro variations; or obtaining reference genome sequences of each type, aligning the sequencing data of the sample to be tested to the reference genome sequences of each type, and determining the thalassemia type of the sample to be tested according to the alignment score. The method not only ensures the sensitivity and accuracy of complex genotype detection, but also takes into account the detection efficiency of clinical large-scale screening, effectively solving the problem that traditional technology cannot balance comprehensiveness and timeliness.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics technology, specifically to an automated genotyping method and apparatus for thalassemia based on a third-generation sequencing platform. Background Technology

[0002] Thalassemia is an autosomal recessive genetic disorder caused by defects in globin genes, leading to impaired synthesis of globin peptide chains. It is prevalent in southern my country. Traditional molecular genetic diagnosis of thalassemia involves multiple globin genes, including HBA1, HBA2, HBB, HBD, HBG1, HBG2, and HBQ1. Among these, the HBA2 and HBA1 genes in the α-globin gene cluster are the most complex. The HBA2 and HBA1 gene sequences are highly homologous (homology >96%) and often exhibit large deletions (such as -- SEA -α 3.7 -α 4.2 Complex structural variations such as repetition, etc.

[0003] Sanger sequencing and next-generation sequencing (NGS) technologies, due to their short read lengths, cannot reliably distinguish variations within the HBA2 and HBA1 homologous regions, and have limited ability to identify complex structural variations, easily leading to missed detections or false positives. Third-generation sequencing platforms (such as PacBio SMRT sequencing and Oxford Nanopore sequencing), with their long read lengths, can completely traverse the HBA2 / HBA1 homologous regions and common structural variation breakpoints, offering significant advantages in this application scenario.

[0004] However, current thalassemia data analysis based on third-generation sequencing still heavily relies on manual interpretation combined with other methodological methods for validation. The analysis process is cumbersome, time-consuming, and labor-intensive, and the lack of standardized criteria prevents automated and rapid genotyping analysis, thus limiting its widespread application in large-scale clinical screening and diagnosis. Therefore, there is an urgent need to develop a method and strategy that can fully utilize the long read length advantages of third-generation sequencing to achieve automated, rapid, and accurate thalassemia genotyping analysis. Summary of the Invention

[0005] This invention provides an automated thalassemia sequencing and typing analysis method and device based on a third-generation sequencing platform, to solve the problems that third-generation sequencing thalassemia analysis relies on manual labor, is inefficient, and lacks standards, thus failing to meet the needs of rapid automated typing in clinical practice.

[0006] In a first aspect, the present invention provides an automated genotyping analysis method for thalassemia based on a third-generation sequencing platform, comprising the following steps: acquiring sequencing data of the sample to be tested; aligning the sequencing data to a human reference genome; inferring candidate structural variant breakpoints of the sample to be tested using a prior probability model and a local breakpoint database based on the alignment results; analyzing the sequencing data based on the candidate structural variant breakpoints to determine the inferred genotype; and determining the thalassemia genotype of the sample to be tested based on the inferred genotype combined with minor variations in local assembly; or, acquiring reference genome sequences for each genotype; aligning the sequencing data of the sample to be tested to reference genome sequences for each genotype; and determining the thalassemia genotype of the sample to be tested based on the alignment score.

[0007] This invention achieves automated thalassemia genotyping through two parallel approaches. The first approach utilizes prior probability models, local breakpoint databases, and local assembly techniques to achieve full genotypic coverage and accurate analysis of structural and minor variations. The second approach aligns sequencing data to reference genomes for each genotype and calculates alignment scores, enabling rapid identification and efficient screening of known genotypes. This strategy, combining "deep analysis" and "rapid alignment," ensures both the sensitivity and accuracy of complex genotyping detection while maintaining the efficiency of large-scale clinical screening, effectively solving the problem of traditional technologies struggling to balance comprehensiveness and timeliness.

[0008] In some optional implementations, sequencing data is aligned to a human reference genome. Based on the alignment results, candidate structural variant breakpoints of the test sample are inferred using a prior probability model and a local breakpoint database. The inferred type is determined by parsing the sequencing data based on the candidate structural variant breakpoints. The thalassemia type of the test sample is determined based on the inferred type combined with locally assembled minor variants. This includes: aligning the sequencing data of the test sample with the human reference genome to obtain alignment results; inferring candidate structural variant breakpoints of the test sample based on a prior probability model and breakpoint information in a local breakpoint database, and constructing a breakpoint file based on the candidate structural variant breakpoints; splitting the alignment results based on the breakpoint file to obtain the inferred type; performing local assembly based on the alignment results to obtain minor variants; and determining the thalassemia type of the test sample based on the inferred type and minor variants.

[0009] This implementation method accurately infers structural variation breakpoints through a priori probability model and a local breakpoint database, effectively solving the problem of analyzing complex variations in the HBA1 / HBA2 homologous region and providing data support for cis-trans structural inference. Combined with local assembly technology, it efficiently obtains minute variations, ultimately achieving automated and accurate determination of the genotype of the entire thalassemia lineage, significantly improving the diagnostic efficacy of difficult samples and compound heterozygous mutations.

[0010] In some optional implementations, inferring candidate structural variation breakpoints for the test sample based on a prior probability model and breakpoint information in a local breakpoint database, and constructing a breakpoint file based on the candidate structural variation breakpoints, includes: acquiring known structural variation breakpoint information and prior distribution features recorded in the local breakpoint database; scanning the sequencing data of the test sample using a prior probability model to extract variation signal features and candidate structural variation breakpoints from the sequencing data; using the known structural variation breakpoint information and prior distribution features as prior probabilities, and the variation signal features as observation data, calculating the posterior probability of the candidate structural variation breakpoints based on a probabilistic statistical algorithm; selecting one or more target locations whose posterior probabilities satisfy a preset threshold, determining the target locations as target structural variation breakpoints for the test sample, and generating a breakpoint file based on the target structural variation breakpoints.

[0011] This implementation introduces local high-frequency breakpoint information and prior distribution features into a statistical probability model, uses variant signal features as observation data, and dynamically calculates the posterior probability of a specific breakpoint in the sample to be tested. This statistical probability-based screening mechanism can effectively filter sequencing noise and random errors, significantly improve the confidence and accuracy of structural variant breakpoint identification, and thus construct a more accurate breakpoint file.

[0012] In some optional implementations, determining the thalassemia type of the test sample based on the inferred type and minor variations includes: determining a first cis-trans relationship between minor variations in the test sample; determining a second cis-trans relationship between inferred types in the test sample; determining a third cis-trans relationship between minor variations and inferred types in the test sample; and comprehensively determining the thalassemia type of the test sample based on the first cis-trans relationship, the second cis-trans relationship, and the third cis-trans relationship.

[0013] This implementation method achieves precise analysis of the haplotype structure of chromosomes in the test sample by systematically analyzing the cis-trans relationships between minor variations, between inferred types, and between minor variations and inferred types. This enables accurate differentiation of variation combinations on different alleles, avoids misjudgment of genetic variations, and significantly improves the accuracy and reliability of determining complex genotypes of thalassemia.

[0014] In some optional implementations, obtaining reference genome sequences for each thalassemia type, aligning the sequencing data of the sample to be tested to the reference genome sequences for each thalassemia type, and determining the thalassemia type of the sample to be tested based on the alignment score includes: retrieving reference genome sequences for each known thalassemia type from a local thalassemia type reference sequence database; aligning the sequencing data of the sample to be tested to the reference genome sequences for each thalassemia type to obtain the typology alignment results; calculating the maximum consistency alignment score of the typology alignment results according to a preset base matching score rule, and normalizing the maximum consistency alignment score based on the sequence length of the sequencing data of the sample to be tested to determine the thalassemia type of the sample to be tested.

[0015] This implementation method constructs a local type reference sequence database, directly aligns the sequencing data of the sample to be tested to the reference genome of each type, and makes a comprehensive score judgment based on the maximum consistency alignment score, base matching and sequence length. This whole sequence alignment strategy can intuitively reflect the overall similarity between the sample and each known type, effectively avoid the cumulative error in the process of complex variant inference, significantly improve the identification efficiency and classification accuracy of common and rare thalassemia types, and provide reliable technical support for rapid and accurate clinical diagnosis.

[0016] In some optional implementations, after obtaining reference genome sequences for each type, aligning the sequencing data of the sample to be tested to the reference genome sequences for each type, and determining the thalassemia type of the sample to be tested based on the alignment score, the method further includes: constructing an alignment matrix based on the genotyping alignment results, wherein the alignment matrix is ​​used to record the alignment of the sequencing reads of the sample to be tested with the reference genome sequences for each type; using a greedy algorithm based on a preset continuity condition to traverse the alignment matrix, obtaining the coordinate distribution and relative positional relationship of the sequencing reads on the reference genome, inferring the boundary position of structural variations, so as to define the alignment region where structural variations occur; and mapping the alignment region back to the human reference genome to obtain the location information of the structural variations.

[0017] This implementation method constructs an alignment matrix and uses a greedy mode to parse the coordinate relationship of read segments. It can infer the boundary position of structural variations from the genotyping alignment results and accurately map the alignment region back to the human reference genome. This allows for the acquisition of precise physical location information of structural variations on the basis of conventional genotyping, effectively making up for the shortcomings of simply relying on sequence alignment to accurately define the breakpoints of complex structural variations. It significantly improves the resolution and diagnostic depth of thalassemia gene detection.

[0018] Secondly, the present invention also provides an automated thalassemia sequencing and genotyping analysis device based on a third-generation sequencing platform. The device includes an acquisition module and a first processing module, or the device includes an acquisition module and a second processing module. The acquisition module acquires the sequencing data of the sample to be tested. The first processing module aligns the sequencing data to a human reference genome, and based on the alignment results, uses a prior probability model and a local breakpoint database to infer the inferred thalassemia type. Based on the inferred thalassemia type combined with minor variations in local assembly, the thalassemia type of the sample to be tested is determined. The second processing module acquires reference genome sequences for each type, aligns the sequencing data of the sample to be tested to the reference genome sequences for each type, and determines the thalassemia type of the sample to be tested based on the alignment score.

[0019] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the above-described automatic genotyping analysis method for thalassemia based on a third-generation sequencing platform according to the first aspect or any corresponding embodiment thereof.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the automated genotyping analysis method for thalassemia based on a third-generation sequencing platform according to the first aspect or any corresponding embodiment described above.

[0021] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the automated genotyping analysis method for thalassemia based on a third-generation sequencing platform according to the first aspect above or any corresponding embodiment thereof. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a first flowchart of an automated genotyping analysis method for thalassemia based on a third-generation sequencing platform according to an embodiment of the present invention; Figure 3 This is a second flowchart of an automated genotyping analysis method for thalassemia based on a third-generation sequencing platform according to an embodiment of the present invention; Figure 4 This is a third flowchart of an automated genotyping analysis method for thalassemia based on a third-generation sequencing platform according to an embodiment of the present invention. Figure 5 This is a flowchart illustrating an example of an automated phenotypic analysis method for thalassemia based on a third-generation sequencing platform according to an embodiment of the present invention. Figure 6 This is a diagram illustrating the soft-cut mode and splitting comparison of the segment reading at the missing breakpoint; Figure 7 It is β IVS II-654(C>T) Mutations and β CD 71 / 72(+A) A schematic diagram of the cis-trans determination results of mutations; Figure 8 This is a schematic diagram of an HTML analysis report interface that includes comparison reads and variation information. Figure 9 This is a schematic diagram of the IGV alignment result viewing interface used in the traditional manual interpretation process in Comparative Example 1; Figure 10 This is a schematic diagram showing the comparison of the typing accuracy of the method of the present invention with that of Comparative Example 1 in 20 special clinical samples; Figure 11 This is a bar chart showing the comparison of the typing accuracy of the method of the present invention with that of Comparative Example 1 in 20 special clinical samples; Figure 12 This is a first structural block diagram of an automated phenotypic analysis device for thalassemia sequencing based on a third-generation sequencing platform according to an embodiment of the present invention; Figure 13 This is a second structural block diagram of an automated phenotypic analysis device for thalassemia sequencing based on a third-generation sequencing platform according to an embodiment of the present invention; Figure 14 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] As an optional application scenario of this invention, such as Figure 1 As shown, application 101 is installed in terminal device 110, and user 130 can interact with application 101 through terminal device 110 and / or access device of terminal device 110.

[0028] For example, application 101 can be any application that provides question-and-answer related services. For instance, application 101 could be a question-and-answer interactive application, such as a text-to-text application, an image-to-text application, etc. Figure 1 In the application scenario shown, if application 101 is active, the terminal device 110 can display the interface 102 of application 101. The interface 102 may include various pages that application 101 can provide, such as interactive pages, settings pages, query pages, etc.

[0029] In some embodiments, terminal device 110 is communicatively connected to server 120 to provide services to application 101. Terminal device 110 may be a mobile terminal, fixed terminal, or portable terminal, etc., including but not limited to mobile phones, desktop computers, laptop computers, multimedia tablets, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, terminal device 110 may also support any type of interface, and server 120 may be various types of computing systems or servers capable of providing computing power, including but not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0030] According to an embodiment of the present invention, an embodiment of an automated genotyping analysis method for thalassemia based on a third-generation sequencing platform is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] This embodiment provides an automated genotyping analysis method for thalassemia based on a third-generation sequencing platform, which can be used on the aforementioned mobile terminals, such as mobile phones and tablets. Figure 2This is a first flowchart of an automated genotyping analysis method for thalassemia based on a third-generation sequencing platform according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain sequencing data of the sample to be tested.

[0032] Specifically, sequencing data is a collection of raw information obtained by single-molecule sequencing of nucleic acids in the sample to be tested using a third-generation sequencing platform. It can generate ultra-long reads with lengths of tens of thousands or even millions of bases. This data not only completely preserves the nucleotide sequence of the sample, but also directly captures epigenetic information such as base modifications. Moreover, it can effectively cross complex regions and repetitive sequences of the genome without PCR amplification, providing a high-fidelity, long-read data foundation for subsequent accurate analysis of large-fragment structural variations and complex genotypes.

[0033] Step S202: Align the sequencing data to the human reference genome. Based on the alignment results, use a prior probability model and a local breakpoint database to infer candidate structural variant breakpoints of the sample to be tested. Based on the candidate structural variant breakpoints, analyze the sequencing data to determine the inferred type. Based on the inferred type and the small variations in local assembly, determine the thalassemia type of the sample to be tested.

[0034] This approach, by combining a prior probability model with a local breakpoint database, effectively filters sequencing noise and significantly improves the confidence and accuracy of identifying breakpoints for structural variations such as large fragment deletions. Simultaneously, by incorporating local assembly techniques to analyze minute variations, it achieves full genotypic coverage of point mutations and structural variations, thereby accurately determining complex thalassemia genotypes and effectively avoiding the problems of missed diagnoses and misdiagnoses common with traditional techniques.

[0035] It should be noted that step S202 can be replaced by the following steps: obtaining reference genome sequences for each type, aligning the sequencing data of the sample to be tested to the reference genome sequences for each type, and determining the thalassemia type of the sample based on the alignment score. This direct whole-sequence alignment strategy can intuitively reflect the overall similarity between the sample and each known type, simplifying the analysis process of complex variations, significantly improving the identification speed and classification efficiency of thalassemia types, and providing efficient and reliable technical support for rapid screening and accurate diagnosis of large-scale clinical samples.

[0036] The automated genotyping method for thalassemia based on a third-generation sequencing platform provided in this embodiment achieves automated thalassemia genotyping through two parallel approaches. The first approach utilizes prior probability models, local breakpoint databases, and local assembly techniques to achieve full genotypic coverage and accurate analysis of structural and minor variations. The second approach aligns sequencing data to reference genomes for each genotype and calculates alignment scores, enabling rapid confirmation and efficient screening of known genotypes. This strategy, combining "deep analysis" and "rapid alignment," ensures both the sensitivity and accuracy of complex genotype detection while maintaining the efficiency of large-scale clinical screening, effectively solving the problem of traditional technologies struggling to balance comprehensiveness and timeliness.

[0037] This embodiment provides an automated genotyping analysis method for thalassemia based on a third-generation sequencing platform, which can be used on the aforementioned mobile terminals, such as mobile phones and tablets. Figure 3 This is a second flowchart of an automated phenotypic analysis method for thalassemia based on a third-generation sequencing platform according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Obtain sequencing data of the sample to be tested.

[0038] Step S302: Align the sequencing data of the sample to be tested with the human reference genome to obtain the alignment results.

[0039] In one optional implementation, before comparing the sequencing data of the sample to be tested with a human reference genome, the method further includes: data evaluation and filtering of the sequencing data. Specifically, data evaluation includes statistical analysis of sequencing read lengths and quality values; filtering includes removing low-quality reads and adapter sequences. For example, the sequencing data of the sample to be tested is compared with the human reference genome using minimap2 or a similar long read alignment tool.

[0040] Step S303: Based on the prior probability model and the breakpoint information in the local breakpoint database, infer the candidate structural variation breakpoints of the sample to be tested, and construct a breakpoint file based on the candidate structural variation breakpoints.

[0041] In one alternative implementation, based on a prior probability model and local breakpoint database records, possible breakpoints of the sample to be tested are inferred, and a breakpoint file is constructed, including steps Sa1 to Sa4.

[0042] Step Sa1: Obtain known structural variation breakpoint information and prior distribution characteristics recorded in the local breakpoint database.

[0043] The local breakpoint database contains common known types of thalassemia (including but not limited to -- SEA-α 3.7 -α 4.2 -- THAI -- FIL 、、ααα anti3.7 , ααα anti4.2 、HKαα、antiHKαα、β SEA-HPFH β Chinese (Aγδβ)0 β Hb Gγ-β Ulsan Information on breakpoints, gene annotations, and clinical significance of structural variations (etc.).

[0044] Step Sa2: Use a prior probability model to scan the sequencing data of the sample to be tested, and extract the variant signal features and candidate structural variant breakpoints in the sequencing data.

[0045] For example, the prior probability model can be a Bayesian prior model. The prior inputs of the Bayesian prior model are based on known structural variations associated with thalassemia.

[0046] Step Sa3: Using the known structural variation breakpoint information and prior distribution characteristics as prior probabilities, and the variation signal characteristics as observation data, calculate the posterior probability of the candidate structural variation breakpoint based on a probabilistic statistical algorithm.

[0047] Step Sa4: Select one or more target locations whose posterior probabilities meet the preset threshold, determine the target locations as the target structural variation breakpoints of the sample to be tested, and generate breakpoint files based on the target structural variation breakpoints.

[0048] Among them, the breakpoint files generated based on inference effectively solve the problem of difficulty in genetically displaying the results of third-generation sequencing of thalassemia, especially for non-deletion structural variations.

[0049] Step S304: Based on the comparison of breakpoint files, the results are split to obtain the inferred type.

[0050] The splitting process involves virtually splitting long read segments that cross breakpoints and then performing rigorous region comparisons on the split segments. The intermediate files generated during the splitting process can be used for cis- and trans-structural inference. Simultaneously, a local variant annotation database is used to automatically annotate the inference results, replacing the manual literature review process and significantly improving analysis efficiency.

[0051] Step S305: Perform local assembly based on the alignment results to obtain minor variations.

[0052] Specifically, local assembly based on alignment results is performed on third-generation sequencing reads using GATK, Clair3, or similar tools to accurately acquire small variations such as single nucleotide variants (SNVs) and small insertions / deletions (Indels).

[0053] Currently, in third-generation sequencing analysis, small variations such as single nucleotide variants and small insertions / deletions are mostly obtained through stacking combined with full-length alignment, which is computationally intensive and time-consuming. This embodiment employs a local assembly technique based on third-generation sequencing reads, and uses a local breakpoint database to accurately verify and screen the assembly results. This significantly reduces computational resource consumption and runtime while ensuring the accuracy of small variation detection.

[0054] Step S306: Determine the thalassemia type of the sample to be tested based on the inferred type and minor variations.

[0055] In one alternative implementation, determining the thalassemia type of the sample to be tested based on the inferred type and minor variations includes steps S3061 to S3064.

[0056] Step S3061: Determine the first cis-trans relationship between small variations in the sample to be tested.

[0057] Step S3062: Determine the second cis-inverse relationship between the inferred types in the test sample.

[0058] Step S3063: Determine the third cis-trans relationship between minor variations and inferred types in the test sample.

[0059] Step S3064: Determine the thalassemia type of the sample to be tested based on the first cis-trans relationship, the second cis-trans relationship, and the third cis-trans relationship.

[0060] The calculation of cis-trans structure relationship is achieved by analyzing the co-occurrence of multiple variant sites on the same sequencing read or the phase information between different reads, thereby determining whether the variant is located on the same chromosome.

[0061] Compared to traditional methods that are limited to cis-trans relationship determination between minor variants, this embodiment further extends the scope of cis-trans relationship analysis to include relationships between structural variants and between minor and structural variants. This full-spectrum cis-trans relationship determination has crucial clinical significance in the accurate diagnosis of thalassemia. For example, γ-globin deletion type (Aγδβ)0 and point mutation CD17M, when present alone, only manifest as mild thalassemia, but when they coexist in a double heterozygous (i.e., trans) form, they can lead to severe thalassemia. This embodiment, by accurately analyzing the phase relationships of such complex variants, can effectively avoid missed diagnoses and misjudgments in clinical practice.

[0062] Furthermore, after determining the thalassemia type of the sample based on the inferred type and minor variations, the process also includes generating a data analysis report and an IGV visualization report. Specifically, the data analysis report includes sample information, detected structural and minor variations, comprehensive results of cis-trans relationship determination, and database annotations; the IGV report is a visual snapshot containing comparison details, breakpoint locations, and the inferred type, for quick manual review.

[0063] The visualization snapshot is an independent static HTML file, independent of network and IGV Desktop software for displaying regional BAM files. The IGV visualization report includes inferred genotype display and known Benign / Likely_Benign hints, which can assist reviewers in quick viewing. The analysis report includes quality control information, annotation information on minor and structural variations, and final conclusions, which can greatly simplify the review process and improve review efficiency. The thalassemia sequencing automatic genotyping analysis method based on the third-generation sequencing platform provided in this embodiment accurately infers structural variation breakpoints through a priori probability model and a local breakpoint database, effectively solving the problem of complex variation analysis in the HBA1 / HBA2 homologous region and providing data support for cis-trans structure inference. Combined with local assembly technology, it efficiently obtains minor variations, ultimately achieving automated and accurate determination of the genotype of the entire thalassemia pedigree, significantly improving the diagnostic efficacy of difficult samples and compound heterozygous mutations.

[0064] This embodiment provides an automated genotyping analysis method for thalassemia based on a third-generation sequencing platform, which can be used on the aforementioned mobile terminals, such as mobile phones and tablets. Figure 4 This is a third flowchart of the automated genotyping analysis method for thalassemia based on a third-generation sequencing platform according to an embodiment of the present invention, as shown below. Figure 4 As shown, the process includes the following steps: Step S401: Obtain sequencing data of the sample to be tested.

[0065] Step S402: Retrieve the reference genome sequences of known thalassemia types from the local type reference sequence database.

[0066] The local type reference sequence database is a pre-built dedicated database that records the standard reference genome sequences of all known thalassemia subtypes, as well as the characteristic nucleotide sequences, specific genetic loci, and structural variation characteristics of each genotype. It can provide a standardized and accurate comparison benchmark for the typing and type identification of sequencing data of the samples to be tested.

[0067] Step S403: Align the sequencing data of the sample to be tested to the reference genome sequence of each type to obtain the typing alignment results.

[0068] Step S404: Calculate the maximum consistency score of the typing comparison results according to the preset base matching score rules, and normalize the maximum consistency score based on the sequencing data sequence length of the sample to be tested to determine the thalassemia type of the sample to be tested.

[0069] The maximum consistency alignment algorithm can be used to calculate the genotyping score, with a pre-defined uniform base scoring rule: 1 point for a perfect match between a single base and the corresponding genotype reference sequence, -0.2 points for a missing base, -0.1 points for a mismatch or unsuccessful alignment, and an increment of -0.1 points for consecutive mismatches or unsuccessful alignments. For each sequence of sequencing data to be tested, it is compared with the reference sequence for each pre-defined thalassemia subtype, generating a specific total alignment score for each subtype. After normalizing and correcting the score based on the full length of the sequencing data, the thalassemia genotype of the sample is quickly determined by the score matching degree.

[0070] Step S405: Construct an alignment matrix based on the genotyping alignment results.

[0071] The alignment matrix is ​​used to record the alignment of the sequencing reads of the sample under test with the reference genome sequences of each type.

[0072] Step S406: Use a greedy algorithm based on preset continuity conditions to traverse the alignment matrix, obtain the coordinate distribution and relative position of the sequencing reads on the reference genome, infer the boundary position of structural variations, and define the alignment region where structural variations occur.

[0073] Step S407: Map the alignment region back to the human reference genome to obtain the location information of the structural variation.

[0074] The thalassemia sequencing-automated genotyping analysis method based on a third-generation sequencing platform provided in this embodiment constructs a local genotype reference sequence database, directly aligns the sequencing data of the sample to be tested to the reference genome of each genotype, and makes a comprehensive score judgment based on the maximum consistency alignment score, base matching status, and sequence length. This whole sequence alignment strategy can intuitively reflect the overall similarity between the sample and each known genotype, effectively avoids the cumulative error in the process of complex variant inference, and significantly improves the identification efficiency and genotyping accuracy of common and rare thalassemia genotypes, providing reliable technical support for rapid and accurate clinical diagnosis.

[0075] To more clearly illustrate the automated genotyping analysis method for thalassemia based on a third-generation sequencing platform in this embodiment, a specific example is given, such as... Figure 5 As shown, this example includes the following steps: I. Data Preparation and Preprocessing We acquired the human reference genome hg38 (GRCh38) and the sequencing data of a thalassemia clinical sample sequenced using the G-Seq500 platform. We used NnoFilt and fastcat software to assess the quality of the raw data, statistically analyzing read length distribution and quality values, and filtering out low-quality reads (Q value < 15) and adapter sequences. We then used minimap2 (parameter: -ax map-pb) to align high-quality reads to the hg38 reference genome, obtaining the alignment result file (BAM format).

[0076] II. Breakpoint Inference Based on Bayesian Priors Load the local thalassemia breakpoint database, which includes common structural variants of α- and β-thalassemia—including but not limited to— SEA (chr16:165397-184700),-α 3.7 (chr16:173384-177187),-α 4.2 (chr16:169818-174075),-- THAI (chr16:149863-183310),-- FIL (chr16:151641-182316), β SEA-HPFH (chr11:5229059-5236362), β Chinese (Aγδβ)0 (chr11:5248821-5256487), β Hb Gγ-β Ulsan The precise breakpoint coordinates and ±20bp fluctuation range of (chr11:5254670-5256487) are used as the Bayesian prior. Using the breakpoint locations in the database as the primary basis, the prior probability is determined by rapid decay at both ends. Soft-clipped reads and read pairs with abnormally long inserted segments are scanned in the alignment results. For each read supporting a breakpoint, the posterior probability of its possible breakpoint is calculated using the Bayesian formula P(breakpoint|data) ∝ P(data|breakpoint) × P(breakpoint). All supporting read information is integrated, and the breakpoint with the highest posterior probability is written to the breakpoint file. Figure 6 It showed ααα anti4.2 Illustration of soft cut mode and split comparison at missing breakpoints.

[0077] III. Segment Segmentation and Type Inference Based on the breakpoint coordinates recorded in the breakpoint file, long read segments crossing breakpoints in the comparison results are virtually split, and the split sub-read segments are rigorously compared separately. The proportion of the split comparison read segments to the normal comparison read segments of the same length is statistically analyzed; when this proportion reaches a preset ratio, it is identified as this type. anti4.2 When the proportion of the segment split at the breakpoint to the normal comparative segment of the same length is greater than 90%, it is considered a possible ααα.anti4.2 Similarly, to infer the type of the other chromosome on chromosome 16, when the result is excluding ααα... anti4.2 Other than structural variants (including non-structural variants), it is determined to be a heterozygous variant (or a compound heterozygous variant); if the result is also ααα anti4.2 When this occurs, it is determined to be a homozygous deletion. Based on this, the thalassemia type of the sample is determined (e.g., ααα). anti4.2 / αα represents a 4.2-triad heterozygous duplication). The inferred genotype is automatically annotated by retrieving common names, HGVS names, pathogenicity, and other information from the local annotation database. The same procedure is applied to chromosome 11.

[0078] IV. Detection of minute variations Based on the alignment results, GATK and Clair3 were used to accurately identify single nucleotide variants (SNVs) and small insertions / deletions (Indels), and the detected variants were compared and annotated with a local thalassemia variant database. Ignoring benign / suspected benign sites, this example simultaneously detected the IVS II-654 (C>T; HBB:c.316-197C>T) point mutation and the CD 71 / 72 (+A; HBB:c.217dup) point mutation.

[0079] V. Forward and Reverse Calculation Based on the results of step four, this example also detected β. IVS II-654(C>T) Mutations and β CD 71 / 72(+A) Mutation. Parse the alignment files and obtain the coverage β values ​​respectively. IVS II-654(C>T) Mutations and β CD 71 / 72(+A) For long passages, calculate the Jaccard similarity and determine if it is a reverse expression. For example... Figure 7 As shown, the IGV results indicate that carrying β IVS II-654(C>T) The mutated read sequences do not carry β. CD 71 / 72(+A) Mutation, while carrying β CD 71 / 72(+A) The mutated reads do not carry β. IVS II-654(C>T) Mutation, i.e., β IVS II-654(C>T) Mutations and β CD 71 / 72(+A) The mutation is located in the trans configuration. Combined with step three, the analysis result is ααα. anti4.2 / αα;β IVS II-654(C>T) / β CD 71 / 72(+A) Clinically, it can lead to moderate to severe anemia.

[0080] VI. Report Generation Integrating all the above stratified analysis results, an automatically generated formatted text report is produced, including: sample information, detected minor variations (location, reference base, mutant base, zygotic type, common name, HGVS name, pathogenicity, etc.), detected structural variations (location, zygotic type, common name, HGVS name, pathogenicity, etc.), and a PDF report containing cis / trans relationship determination (distinguishing between cis / trans); simultaneously, an automatically generated HTML report is produced containing alignment reads, sample type, and information on known benign / suspected benign sites, such as... Figure 8 As shown, the alignment of the supported read segments, breakpoint locations, and variant bases are clearly displayed.

[0081] Comparative Example 1 This comparative study employed the same α- and β-thalassemia sample analysis using a traditional third-generation sequencing manual interpretation SOP workflow. Data analysts manually reviewed the alignment results using IGV software (e.g., ...). Figure 9 As shown in the figure, the location of missing breakpoints was determined by visually identifying soft-splitting reads and areas of abnormal coverage, combined with literature databases and experience for type determination; minor variations were identified using the same method; cis-trans relationships were determined by manually tracking read phase information. The entire time taken from importing raw data to completing type identification and report writing, as well as the final type identification results, were recorded. The accuracy of the type identification was compared with the gold standard using multiplex ligation probe amplification (MLPA) technology and Sanger sequencing validation results.

[0082] Analysis of experimental results: (1) From the perspective of classification accuracy See Figure 10 We collected 20 clinically specific samples of thalassemia validated by the gold standard (covering -- SEA Heterozygous deletion, -α3.7 heterozygous / homozygous deletion, -α4.2 heterozygous deletion, ααα anti4.2 Heterozygous repeat, ααα anti3.7 (This includes heterozygous duplication, HKαα heterozygous and compound heterozygous types, and point mutations, etc.). The method in this example correctly genotyped all 20 samples, achieving 100% accuracy. In contrast, the manual interpretation method in Example 1 resulted in two antiHKαα heterozygous deletion samples being classified as indeterminate due to high homology in the breakpoint region and atypical soft-splitting read characteristics. One compound heterozygous subsample's cis-trans relationship was not identified. Furthermore, it was discovered that the actual breakpoint of the first segment of the measured HKαα sample was chr16:174074, not chr16:174075 recorded in the database. This demonstrates that the method of this invention is superior to traditional manual interpretation in identifying complex variants and determining cis-trans relationships.

[0083] (2) From the perspective of analysis time consumption See Figure 11The automated analysis strategy in this example takes an average of approximately 2.5 minutes per sample (including report generation), with the time remaining relatively constant across samples. In contrast, the manual interpretation strategy in Comparative Example 1 takes an average of approximately 14 minutes per sample, with longer processing times (up to 25 minutes) for complex samples. This invention significantly shortens the analysis cycle and improves analysis efficiency by more than five times.

[0084] This embodiment also provides an automated genotyping analysis device for thalassemia sequencing based on a third-generation sequencing platform. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0085] This embodiment provides an automated phenotypic analysis device for thalassemia sequencing based on a third-generation sequencing platform, such as... Figure 12 As shown, it includes an acquisition module 1201 and a first processing module 1202.

[0086] The acquisition module 1201 is used to acquire the sequencing data of the sample to be tested.

[0087] The first processing module 1202 is used to align sequencing data to a human reference genome, infer candidate structural variant breakpoints of the test sample based on the alignment results using a prior probability model and a local breakpoint database, analyze the sequencing data based on the candidate structural variant breakpoints to determine the inferred type, and determine the thalassemia type of the test sample based on the inferred type combined with local assembly of minor variants.

[0088] In some optional implementations, the first processing module 1202 is specifically used for: comparing the sequencing data of the sample to be tested with the human reference genome to obtain the comparison results; inferring candidate structural variant breakpoints of the sample to be tested based on the prior probability model and breakpoint information in the local breakpoint database, and constructing a breakpoint file based on the candidate structural variant breakpoints; performing split processing on the comparison results based on the breakpoint file to obtain the inferred type; performing local assembly based on the comparison results to obtain minor variants; and determining the thalassemia type of the sample to be tested based on the inferred type and minor variants.

[0089] In some optional implementations, the first processing module 1202 is specifically used to: acquire known structural variation breakpoint information and prior distribution features recorded in the local breakpoint database; scan the sequencing data of the sample to be tested using a prior probability model to extract variation signal features and candidate structural variation breakpoints from the sequencing data; use the known structural variation breakpoint information and prior distribution features as prior probabilities, use the variation signal features as observation data, and calculate the posterior probability of the candidate structural variation breakpoints based on a probabilistic statistical algorithm; select one or more target locations whose posterior probabilities satisfy a preset threshold, determine the target locations as target structural variation breakpoints of the sample to be tested, and generate a breakpoint file based on the target structural variation breakpoints.

[0090] In some optional implementations, the first processing module 1202 is specifically used to: determine a first cis-trans relationship between minor variations in the test sample; determine a second cis-trans relationship between inferred types in the test sample; determine a third cis-trans relationship between minor variations and inferred types in the test sample; and comprehensively determine the thalassemia type of the test sample based on the first cis-trans relationship, the second cis-trans relationship, and the third cis-trans relationship.

[0091] This embodiment provides an automated phenotypic analysis device for thalassemia sequencing based on a third-generation sequencing platform, such as... Figure 13 As shown, it includes an acquisition module 1201 and a first processing module 1302.

[0092] The acquisition module 1201 is used to acquire the sequencing data of the sample to be tested.

[0093] The second processing module 1302 is used to obtain reference genome sequences for each type, align the sequencing data of the sample to be tested to the reference genome sequences for each type, and determine the thalassemia type of the sample to be tested based on the alignment score.

[0094] In some optional implementations, the second processing module 1302 is specifically used to: retrieve reference genome sequences of known thalassemia types from a local type reference sequence database; align the sequencing data of the sample to be tested to the reference genome sequences of each type to obtain the genotyping alignment results; calculate the maximum consistency alignment score of the genotyping alignment results according to a preset base matching score rule, and normalize the maximum consistency alignment score based on the sequence length of the sequencing data of the sample to be tested to determine the thalassemia type of the sample to be tested.

[0095] In some optional implementations, after obtaining the reference genome sequences for each type, aligning the sequencing data of the sample to be tested to the reference genome sequences for each type, and determining the thalassemia type of the sample to be tested based on the alignment score, the second processing module 1302 is specifically used to: construct an alignment matrix based on the genotyping alignment results, wherein the alignment matrix is ​​used to record the alignment of the sequencing reads of the sample to be tested with the reference genome sequences for each type; traverse the alignment matrix using a greedy algorithm based on a preset continuity condition, obtain the coordinate distribution and relative positional relationship of the sequencing reads on the reference genome, infer the boundary position of structural variations, so as to define the alignment region where structural variations occur; and map the alignment region back to the human reference genome to obtain the location information of the structural variations.

[0096] The automated thalassemia sequencing and genotyping device based on a third-generation sequencing platform provided in this invention can execute the automated thalassemia sequencing and genotyping method based on a third-generation sequencing platform provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0097] Figure 14 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0098] The following is a detailed reference. Figure 14 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 1401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1402 or a program loaded from memory 1408 into random access memory (RAM) 1403. The RAM 1403 also stores various programs and data required for the operation of the electronic device. The processor 1401, ROM 1402, and RAM 1403 are interconnected via a bus 1404. An input / output (I / O) interface 1405 is also connected to the bus 1404.

[0099] Typically, the following devices can be connected to I / O interface 1405: input devices 1406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 1407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 1408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1409. Communication device 1409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 14Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0100] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 1409, or installed from a memory 1408, or installed from a ROM 1402. When the computer program is executed by the processor 1401, it performs the functions defined in the automated genotyping analysis method for thalassemia based on a third-generation sequencing platform according to embodiments of the present invention.

[0101] Figure 14 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0102] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the automated thalassemia sequencing and genotyping method based on a third-generation sequencing platform shown in the above embodiments is implemented.

[0103] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0104] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An automated genotyping method for thalassemia based on a third-generation sequencing platform, characterized in that, The method includes: Obtain sequencing data from the sample to be tested; The sequencing data is aligned to the human reference genome. Based on the alignment results, a prior probability model and a local breakpoint database are used to infer candidate structural variant breakpoints of the test sample. The sequencing data is then analyzed based on the candidate structural variant breakpoints to determine the inferred type. The thalassemia type of the test sample is determined based on the inferred type combined with minor variations in local assembly. Alternatively, obtain reference genome sequences for each type, align the sequencing data of the sample to be tested to the reference genome sequences for each type, and determine the thalassemia type of the sample to be tested based on the alignment score.

2. The method according to claim 1, characterized in that, The sequencing data is aligned to the human reference genome. Based on the alignment results, a prior probability model and a local breakpoint database are used to infer candidate structural variation breakpoints of the sample to be tested. Based on the candidate structural variation breakpoints, the sequencing data is analyzed to determine the inferred type. Determining the thalassemia type of the test sample based on the inferred type combined with minor variations in local assembly includes: The sequencing data of the sample to be tested are compared with the human reference genome to obtain the comparison results; Based on the prior probability model and the breakpoint information in the local breakpoint database, candidate structural variation breakpoints of the sample to be tested are inferred, and a breakpoint file is constructed based on the candidate structural variation breakpoints. Based on the breakpoint file, the comparison result is split and processed to obtain the inferred type; Based on the comparison results, local assembly is performed to obtain minor variations; Based on the inferred type and the minor variation, the thalassemia type of the sample to be tested is determined.

3. The method according to claim 2, characterized in that, The step of inferring candidate structural variation breakpoints for the sample under test based on a prior probability model and breakpoint information in a local breakpoint database, and constructing a breakpoint file based on the candidate structural variation breakpoints, includes: Obtain known structural variation breakpoint information and prior distribution characteristics recorded in the local breakpoint database; The prior probability model is used to scan the sequencing data of the sample to be tested, and the variant signal features and candidate structural variant breakpoints in the sequencing data are extracted. The known structural variation breakpoint information and prior distribution features are used as prior probabilities, and the variation signal features are used as observation data. The posterior probability of the candidate structural variation breakpoint is calculated based on a probabilistic statistical algorithm. One or more target locations with posterior probabilities satisfying a preset threshold are selected, and the target locations are determined as the target structural variation breakpoints of the sample to be tested. Breakpoint files are generated based on the target structural variation breakpoints.

4. The method according to claim 2, characterized in that, The step of determining the thalassemia type of the sample to be tested based on the inferred type and the minor variation includes: Determine the first cis-trans relationship between the minute variations in the test sample; Determine the second cis-inverse relationship between the inferred types in the test samples; Determine the third cis-trans relationship between the minute variation in the test sample and the inferred type. The thalassemia type of the sample to be tested is determined by comprehensively considering the first cis-trans relationship, the second cis-trans relationship, and the third cis-trans relationship.

5. The method according to claim 1, characterized in that, The steps of obtaining reference genome sequences for each type, aligning the sequencing data of the sample to be tested to the reference genome sequences for each type, and determining the thalassemia type of the sample to be tested based on the alignment score include: Retrieve reference genome sequences for known thalassemia types from the local type reference sequence database; The sequencing data of the sample to be tested are compared with the reference genome sequences of each type to obtain the typing comparison results; According to the preset base matching score rules, the maximum consistency score of the typing comparison results is calculated, and the maximum consistency score is normalized based on the sequencing data sequence length of the sample to be tested to determine the thalassemia type of the sample to be tested.

6. The method according to claim 1, characterized in that, After obtaining reference genome sequences for each type, aligning the sequencing data of the sample to be tested to the reference genome sequences for each type, and determining the thalassemia type of the sample to be tested based on the alignment score, the process further includes: An alignment matrix is ​​constructed based on the genotyping alignment results, wherein the alignment matrix is ​​used to record the alignment of the sequencing reads of the sample to be tested with the reference genome sequences of each genotype; A greedy algorithm based on a preset continuity condition is used to traverse the alignment matrix to obtain the coordinate distribution and relative position of the sequencing reads on the reference genome, and to infer the boundary position of structural variations in order to define the alignment region where structural variations occur. The alignment region is mapped back to the human reference genome to obtain the location information of the structural variation.

7. An automated phenotypic analysis device for thalassemia sequencing based on a third-generation sequencing platform, characterized in that, include: The acquisition module is used to acquire the sequencing data of the sample to be tested; The first processing module is used to align the sequencing data to the human reference genome, infer the inferred type based on the alignment results using a prior probability model and a local breakpoint database, and determine the thalassemia type of the sample to be tested based on the inferred type and small variations in local assembly. Alternatively, the second processing module is used to obtain reference genome sequences for each type, align the sequencing data of the sample to be tested to the reference genome sequences for each type, and determine the thalassemia type of the sample to be tested based on the alignment score.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the automated genotyping analysis method for thalassemia based on a third-generation sequencing platform as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the thalassemia sequencing autogenization analysis method based on a third-generation sequencing platform as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The method includes computer instructions for causing a computer to execute the automated genotyping analysis method for thalassemia based on a third-generation sequencing platform, as described in any one of claims 1 to 6.

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