Method for detecting ultralow frequency mutation in high-throughput sequencing data

Abstract

The invention provides a method for detecting ultralow frequency mutation in high-throughput sequencing data, which belongs to the technical field of data processing, and specifically comprises the following steps: acquiring occurrence data of different pollution types of sample data under different historical reference amplification operation times, the method comprises the steps of determining initial sequencing depth of a target sample based on occurrence data of different pollution types, determining an ultralow-frequency mutation detection result of the target sample by taking the initial sequencing depth as a constraint condition, and determining mutation types and distribution data of different mutation points according to distribution data of the ultralow-frequency mutation detection result. Adjusting the initial sequencing depth of the target sample according to a preset compensation factor to obtain the sequencing depth when determining that the initial sequencing depth of the target sample does not meet the requirement by combining the historical occurrence probabilities of the mutation types of the different mutation points; the sequencing depth is determined not to be adjusted by using the change condition of the distribution data of the ultralow-frequency mutation detection result, so that the accuracy of the sequencing result is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method for detecting ultra-low frequency mutations in high-throughput sequencing data. Background Art

[0002] High-throughput sequencing technology, also known as "next-generation" sequencing technology, is different from traditional Sanger (dideoxy method) sequencing. It is a technology that can perform parallel sequencing on a large number of nucleic acid molecules at one time. Usually, one sequencing reaction can produce no less than 100Mb of sequencing data.

[0003] Specifically, in the invention patent application CN118197525A "A method for automatically generating an NGS test report", a method for automatically generating a test report based on high-throughput sequencing technology is provided, but at the same time there are the following technical problems: When performing high-throughput sequencing, the accuracy of ultra-low frequency mutation detection data is crucial. Therefore, how to combine the detection data of ultra-low frequency mutation data to dynamically adjust the sequencing depth cannot improve the accuracy and reliability of sequencing results while ensuring the difficulty of sequencing.

[0004] In response to the above technical problems, the present invention provides a method for detecting ultra-low frequency mutations in high-throughput sequencing data. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, a method for detecting ultra-low frequency mutations in high-throughput sequencing data is provided.

[0006] A method for detecting ultra-low frequency mutations in high-throughput sequencing data, specifically comprising: S1 determines control targets of different control factors in a PCR amplification operation based on the analysis results of the PCR amplification data of the target sample, and determines the historical reference amplification operation times of the PCR amplification operation based on the analysis results of the control targets and the data volume of the target sample; S2 obtains occurrence data of different contamination types of sample data under different historical reference amplification operation times, and determines the initial sequencing depth of the target sample based on the occurrence data of different contamination types; S3 takes the initial sequencing depth as a constraint condition, determines the ultra-low frequency mutation detection result of the target sample, determines the mutation type and distribution data of different mutation points according to the distribution data of the ultra-low frequency mutation detection result, and determines that when the initial sequencing depth of the target sample does not meet the requirement in combination with the historical occurrence probability of the mutation type of different mutation points, proceeds to the next step; S4 adjusts the initial sequencing depth of the target sample according to a preset compensation factor to obtain a sequencing depth, until it is determined that no adjustment of the sequencing depth is needed based on changes in the distribution data of the ultra-low frequency mutation detection results.

[0007] The beneficial effects of the present invention are: Whether the initial sequencing depth of the target sample meets the requirements is determined based on the mutation types and distribution data of different mutation points and the historical probability of occurrence of the mutation types, thereby realizing the determination of the data anomaly probability of the target sample from the perspective of the number of different mutation types and the historical probability of occurrence, and further laying the foundation for determining whether to adjust the initial sequencing depth based on the difference in data anomaly probabilities. This ensures the accuracy of the sequencing results while ensuring the sequencing processing efficiency.

[0008] By utilizing the changes in the distribution data of the ultra-low frequency mutation detection results to determine whether the sequencing depth needs to be adjusted, the technical problem of the accuracy of the sequencing results failing to meet the requirements due to the sequencing depth not meeting the requirements is avoided. The dynamic adjustment of the sequencing depth from the perspective of the changes in the sequencing results is achieved, which further ensures the accuracy of the sequencing results and solves the technical problem of the dynamic adjustment of the sequencing depth.

[0009] A further technical solution is that the control factors include temperature, purification time, incubation time, ethanol rinsing concentration, denaturation reaction time and cooling centrifugation time.

[0010] A further technical solution is that the data volume of the target sample is determined according to the length of the gene fragment of the target sample.

[0011] A further technical solution is that the method for determining the number of historical reference amplification operations of the PCR amplification operation is: Determine the deviation of the control target of the PCR amplification operation and different historical amplification operation times at different control factors based on the analysis result of the control target, and use the deviation to determine the average value of the deviation of the control target at different control factors as the control target deviation; Determining data deviations from different historical amplification operation times according to the data volume of the target sample; A historical reference amplification operation number in the historical amplification operation number is determined based on the control target deviation and the data deviation.

[0012] A further technical solution is to determine that no adjustment of sequencing depth is required, specifically including: Determine the change quantity of the mutation point of the target sample according to the change of the distribution data of the ultra-low frequency mutation detection result; The change in the number of mutation points of the target sample is used to determine whether the sequencing depth needs to be adjusted.

[0013] A further technical solution is that when the change amount of the mutation point of the target sample is not within a preset change amount range, it is determined that the sequencing depth needs to be adjusted.

[0014] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0017] Figure 1 It is a flow chart of a method for detecting ultra-low frequency mutations in high-throughput sequencing data; Figure 2 is a flow chart of a method for determining the number of historical reference amplification operations for PCR amplification operations; Figure 3 is a flow chart of a method for determining an initial sequencing depth of a target sample; Figure 4 is a flow chart of a method for determining that no adjustments to sequencing depth need to be made. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification. Example

[0019] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a method for detecting ultra-low frequency mutations in high-throughput sequencing data is provided, which specifically includes: S1 determines control targets of different control factors in a PCR amplification operation based on the analysis results of the PCR amplification data of the target sample, and determines the historical reference amplification operation times of the PCR amplification operation based on the analysis results of the control targets and the data volume of the target sample; S2 obtains occurrence data of different contamination types of sample data under different historical reference amplification operation times, and determines the initial sequencing depth of the target sample based on the occurrence data of different contamination types; S3 takes the initial sequencing depth as a constraint condition, determines the ultra-low frequency mutation detection result of the target sample, determines the mutation type and distribution data of different mutation points according to the distribution data of the ultra-low frequency mutation detection result, and determines that when the initial sequencing depth of the target sample does not meet the requirement in combination with the historical occurrence probability of the mutation type of different mutation points, proceeds to the next step; S4 adjusts the initial sequencing depth of the target sample according to a preset compensation factor to obtain a sequencing depth, until it is determined that no adjustment of the sequencing depth is needed based on changes in the distribution data of the ultra-low frequency mutation detection results.

[0020] Furthermore, the control factors include temperature, purification time, incubation time, ethanol rinsing concentration, denaturation reaction time and cooling centrifugation time.

[0021] Specifically, the data volume of the target sample is determined according to the length of the gene fragment of the target sample.

[0022] It should be noted that if Figure 2 As shown, the method for determining the number of historical reference amplification operations of the PCR amplification operation is: Determine the deviation of the control target of the PCR amplification operation and different historical amplification operation times at different control factors based on the analysis result of the control target, and use the deviation to determine the average value of the deviation of the control target at different control factors as the control target deviation; Determining data deviations from different historical amplification operation times according to the data volume of the target sample; A historical reference amplification operation number in the historical amplification operation number is determined based on the control target deviation and the data deviation.

[0023] Furthermore, the historical reference amplification operation number in the historical amplification operation number is the historical amplification operation number in which the control target deviation and the data deviation are both within a preset range.

[0024] Optionally, the method for determining the number of historical reference amplification operations of the PCR amplification operation is: Determine the deviation of the PCR amplification operation from the control targets of different control factors using the analysis result of the control target, and proceed to the next step when it is determined by using the deviation that there is no deviation control factor among the different control factors; Determining, according to the data volume of the target sample, a data deviation coefficient for different historical amplification operation times; A historical reference amplification operation number in the historical amplification operation number is determined based on the data deviation coefficient.

[0025] Furthermore, the deviation control factor is a control factor whose deviation amount is not within a preset deviation range.

[0026] Specifically, the method for determining the number of historical reference amplification operations of the PCR amplification operation is: S11 determines the data deviation coefficient with different historical amplification operation times according to the data volume of the target sample; S12: determining the deviation of the control targets of the PCR amplification operation and different historical amplification operation times at different control factors based on the analysis result of the control target, and determining the deviation coefficient of the control targets at different control factors based on the deviation; S13 determines a comprehensive deviation coefficient based on the deviation coefficients of the control targets of different control factors and the data deviation coefficient, and uses the comprehensive deviation coefficient to determine the number of historical reference amplification operations in the number of historical amplification operations.

[0027] It should be noted that when the comprehensive deviation coefficient is within the preset deviation interval, the historical number of amplification operations is determined to be the historical reference number of amplification operations.

[0028] Optionally, before entering step S12, it is also necessary to determine whether the data deviation coefficient of the historical number of amplification operations meets the requirements. When the data deviation coefficient is greater than the preset deviation coefficient, it is determined that the historical number of amplification operations does not belong to the historical reference number of amplification operations. When the data deviation coefficient is not greater than the preset deviation coefficient, proceed to step S12.

[0029] Optionally, the above step S12 includes steps S121-S125, specifically: S121 determines the deviation of the PCR amplification operation from different historical amplification operation times and the control targets of different control factors according to the analysis result of the control target. When the deviation of the control targets of different control factors are all not within the preset deviation range, it is determined that the historical amplification operation times do not belong to the historical reference amplification operation times. When there is a control factor whose deviation of the control target is within the preset deviation range, the process proceeds to step S122. S122 uses the deviation amount to determine the deviation coefficient of the control target at different control factors. When there is a control factor whose deviation coefficient does not meet the requirements, it is determined that the historical amplification operation number does not belong to the historical reference amplification operation number. When there is no control factor whose deviation coefficient does not meet the requirements, it goes to step S123; S123: When it is determined by using the deviation coefficients of different control factors that there is a control factor whose deviation coefficient is within the preset deviation coefficient interval, the process proceeds to step S124; when there is no control factor whose deviation coefficient is within the preset deviation coefficient interval, the process proceeds to step S13; S124: when the number of control factors whose deviation coefficients are within the preset deviation coefficient interval does not meet the requirement, it is determined that the historical amplification operation times do not belong to the historical reference amplification operation times; when the number of control factors whose deviation coefficients are within the preset deviation coefficient interval meets the requirement, the process proceeds to step S125; S125 determines the control factor deviation amount by using the deviation coefficients of different control factors. When the control factor deviation amount does not meet the requirements, it is determined that the historical amplification operation times do not belong to the historical reference amplification operation times. When the control factor deviation amount meets the requirements, it proceeds to step S13.

[0030] Furthermore, the contamination types include misidentification, base mismatch, and gene mutation.

[0031] It can be understood that the occurrence data includes the occurrence times of different pollution types and the number of fragments corresponding to the pollution types with different occurrence times.

[0032] Specifically, Figure 3 As shown, the method for determining the initial sequencing depth of the target sample is: Using the occurrence data of different contamination types, determine the number of fragments corresponding to different contamination types under different historical reference amplification operation times; Determining the number of contamination operations in different historical reference amplification operation numbers based on the number of fragments; The initial sequencing depth of the target sample is determined according to the number of contamination operations.

[0033] Furthermore, the number of contamination operations is the number of historical reference amplification operations in which the number of fragments is greater than the preset number of fragments.

[0034] It should also be noted that the initial sequencing depth of the target sample is determined according to the matching sequencing depth corresponding to the number of contamination operations.

[0035] It is understood that the method for determining the initial sequencing depth of the target sample is: Using the occurrence data of different contamination types, determine the number of fragments corresponding to different contamination types under different historical reference amplification operation times; An average number of fragments in different historical reference amplification operation times based on the number of fragments; The initial sequencing depth of the target sample is determined according to the average number of fragments.

[0036] Specifically, the method for determining the initial sequencing depth of the target sample is: S21 determines the number of fragments corresponding to different contamination types under different historical reference amplification operation times based on the occurrence data of different contamination types; Optionally, the above step S21 includes steps S211-S213, which are specifically: S211 determines the number of fragments corresponding to different pollution types under different historical reference amplification operation times based on the occurrence data of different pollution types. When there are different numbers of fragments corresponding to pollution types and the number of historical reference amplification operations that do not meet the requirements, the initial sequencing depth of the target sample is determined based on the set sequencing depth. When there are no different numbers of fragments corresponding to pollution types and the number of historical reference amplification operations that do not meet the requirements, proceed to step S212. S212 determines the total number of fragments corresponding to different contamination types based on the number of fragments corresponding to different contamination types under different historical reference amplification operation times. When there is a contamination type whose total number of fragments does not meet the requirement, the initial sequencing depth of the target sample is determined based on the set sequencing depth. When there is no contamination type whose total number of fragments does not meet the requirement, proceed to step S213; S213 determines the type anomaly coefficients of different contamination types according to the total number of fragments corresponding to different contamination types and the number of historical reference amplification operations corresponding to different contamination types. When there is a contamination type whose type anomaly word count does not meet the requirements, the initial sequencing depth of the target sample is determined based on the set sequencing depth. When there is no contamination type whose type anomaly word count does not meet the requirements, proceed to step S22.

[0037] S22 determines the contamination deviation coefficients of different historical reference amplification operation times according to the number of fragments corresponding to different contamination types; Optionally, the above step S22 includes steps S221-S223, specifically: S221 determines the pollution deviation coefficients of different historical reference amplification operation times according to the number of fragments corresponding to different pollution types. When there is a historical reference amplification operation time whose pollution deviation coefficient does not meet the requirement, the initial sequencing depth of the target sample is determined based on the set sequencing depth. When there is no historical reference amplification operation time whose pollution deviation coefficient does not meet the requirement, proceed to step S222; S222 divides the historical reference amplification operation times into different pollution deviation intervals according to the pollution deviation coefficients of different historical reference amplification operation times. When the historical reference amplification operation times within the preset pollution deviation interval do not meet the requirements, the initial sequencing depth of the target sample is determined based on the set sequencing depth. When the historical reference amplification operation times within the preset pollution deviation interval meet the requirements, proceed to step S223. S223 determines the interval deviation coefficients in different contamination deviation intervals based on the contamination deviation coefficients of the historical reference amplification operation times in different contamination deviation intervals. When the number of contamination deviation intervals within the preset interval coefficient range does not meet the requirements, the initial sequencing depth of the target sample is determined based on the set sequencing depth. When the number of contamination deviation intervals within the preset interval coefficient range does not meet the requirements, proceed to step S23.

[0038] S23 determines a comprehensive contamination deviation coefficient according to the contamination deviation coefficients of different historical reference amplification operation times, and uses the comprehensive contamination deviation coefficient to determine the initial sequencing depth of the target sample.

[0039] Furthermore, the historical occurrence probability of the mutation type of the mutation point is determined according to the historical occurrence times of the mutation type.

[0040] Specifically, determining that the initial sequencing depth of the target sample does not meet the requirement specifically includes: Determine the number of mutation points of different mutation types based on the distribution data of different mutation points; Determine the sequencing abnormality probability of different mutation types based on the number of mutation points of different mutation types and the historical occurrence probability; The comprehensive abnormality probability is determined according to the sequencing abnormality probability of different mutation types, and the comprehensive abnormality probability is used to determine whether the initial sequencing depth of the target sample meets the requirements.

[0041] Specifically, the sequencing abnormality probability of the mutation type is determined according to the ratio of the number of mutation points of the mutation type to the historical occurrence probability multiplied by a preset proportional factor.

[0042] It can be understood that the comprehensive abnormality probability is determined according to the maximum value of the sequencing abnormality probability of different mutation types.

[0043] It should be noted that when the comprehensive abnormality probability is greater than the preset probability threshold, it is determined that the initial sequencing depth of the target sample does not meet the requirements.

[0044] It is understandable that determining that the initial sequencing depth of the target sample does not meet the requirements specifically includes: Determining the number of mutation points of different mutation types based on the distribution data of different mutation points, and when there is a mutation type in which the number of mutation points is greater than a preset number threshold, determining that the initial sequencing depth of the target sample does not meet the requirement; When there is no mutation type with a number of mutation points greater than the preset threshold: When the total number of mutation points of different mutation types is within the preset number range: When it is determined based on the historical occurrence probabilities of different mutation types that there is no mutation type whose historical occurrence probability is less than a preset occurrence probability threshold, it is determined that the initial sequencing depth of the target sample meets the requirement; When determining that there is a mutation type whose historical probability of occurrence is less than a preset probability threshold based on the historical probability of occurrence of different mutation types: The mutation type whose historical occurrence probability is less than the preset occurrence probability threshold is used as the screening mutation type, and when the total number of mutation points of the screening mutation type meets the requirement: it is determined that the initial sequencing depth of the target sample meets the requirement; When the total number of mutation points of the selected mutation type does not meet the requirements or the total number of mutation points of different mutation types is not within the preset number range: Determining the sequencing abnormality probabilities of different mutation types based on the number of mutation points of different mutation types and the historical occurrence probabilities, and when there is a mutation type whose sequencing abnormality probability does not meet the requirements, determining that the initial sequencing depth of the target sample does not meet the requirements; When there is no mutation type whose sequencing abnormality probability does not meet the requirements: The comprehensive abnormality probability is determined according to the sequencing abnormality probability of different mutation types, and the comprehensive abnormality probability is used to determine whether the initial sequencing depth of the target sample meets the requirements.

[0045] Furthermore, the sequencing depth is determined according to the product of the preset compensation factor and the initial sequencing depth.

[0046] Specifically, Figure 4 As shown, it is determined that no adjustment of sequencing depth is required, including: Determine the change quantity of the mutation point of the target sample according to the change of the distribution data of the ultra-low frequency mutation detection result; The change in the number of mutation points of the target sample is used to determine whether the sequencing depth needs to be adjusted.

[0047] It should also be noted that when the change amount of the mutation point of the target sample is not within the preset change amount range, it is determined that the sequencing depth needs to be adjusted.

[0048] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0049] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for detecting ultra-low frequency mutations in high-throughput sequencing data, characterized in that: Specifically include: Determine control targets of different control factors in the PCR amplification operation based on the analysis results of the PCR amplification data of the target sample, and determine the historical reference amplification operation times of the PCR amplification operation based on the analysis results of the control targets and the data volume of the target sample; Acquire occurrence data of different contamination types of sample data under different historical reference amplification operation times, and determine the initial sequencing depth of the target sample based on the occurrence data of the different contamination types; Taking the initial sequencing depth as a constraint condition, determining the ultra-low frequency mutation detection result of the target sample, determining the mutation types and distribution data of different mutation points according to the distribution data of the ultra-low frequency mutation detection result, and determining that the initial sequencing depth of the target sample does not meet the requirement in combination with the historical occurrence probabilities of the mutation types of different mutation points, proceeding to the next step; The initial sequencing depth of the target sample is adjusted according to a preset compensation factor to obtain the sequencing depth, until it is determined that no adjustment of the sequencing depth is required based on changes in the distribution data of the ultra-low frequency mutation detection results.

2. The method for detecting ultra-low frequency mutations in high-throughput sequencing data according to claim 1, characterized in that: The control factors include temperature, purification time, incubation time, ethanol rinsing concentration, denaturation reaction time and cooling centrifugation time.

3. The method for detecting ultra-low frequency mutations in high-throughput sequencing data according to claim 1, characterized in that: The data volume of the target sample is determined according to the length of the gene fragment of the target sample.

4. The method for detecting ultra-low frequency mutations in high-throughput sequencing data according to claim 1, characterized in that: The method for determining the number of historical reference amplification operations of the PCR amplification operation is: Determine the deviation of the control target of the PCR amplification operation and different historical amplification operation times at different control factors based on the analysis result of the control target, and use the deviation to determine the average value of the deviation of the control target at different control factors as the control target deviation; Determining data deviations from different historical amplification operation times according to the data volume of the target sample; A historical reference amplification operation number in the historical amplification operation number is determined based on the control target deviation and the data deviation.

5. The method for detecting ultra-low frequency mutations in high-throughput sequencing data according to claim 4, characterized in that ,, the historical reference amplification operation number in the historical amplification operation number is the historical amplification operation number in which the control target deviation and the data deviation are both within a preset range.

6. The method for detecting ultra-low frequency mutations in high-throughput sequencing data according to claim 1, characterized in that: The contamination types include misidentification, base mismatch, and gene mutation.

7. The method for detecting ultra-low frequency mutations in high-throughput sequencing data according to claim 1, characterized in that: The occurrence data includes the occurrence times of different pollution types and the number of fragments corresponding to the pollution types with different occurrence times.

8. The method for detecting ultra-low frequency mutations in high-throughput sequencing data according to claim 1, characterized in that: The method for determining the initial sequencing depth of the target sample is: Using the occurrence data of different contamination types, determine the number of fragments corresponding to different contamination types under different historical reference amplification operation times; Determining the number of contamination operations in different historical reference amplification operation numbers based on the number of fragments; The initial sequencing depth of the target sample is determined according to the number of contamination operations.

9. The method for detecting ultra-low frequency mutations in high-throughput sequencing data according to claim 1, characterized in that: Determine that no adjustment to sequencing depth is needed, including: Determine the change quantity of the mutation point of the target sample according to the change of the distribution data of the ultra-low frequency mutation detection result; The change in the number of mutation points of the target sample is used to determine whether the sequencing depth needs to be adjusted.

10. The method for detecting ultra-low frequency mutations in high-throughput sequencing data according to claim 9, characterized in that: When the change amount of the mutation point of the target sample is not within the preset change amount range, it is determined that the sequencing depth needs to be adjusted.

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