Variation detection method, detection index processing method, device, equipment and medium

Abstract

The invention provides a variation detection method, a detection index processing method, a device, equipment and a medium, the variation detection method comprises the following steps: acquiring variation information of each site, the variation information being used for describing a comparison condition of a basic group of a corresponding site and a basic group of a reference genome; according to the variation information, a first detection index corresponding to the site is determined, the first detection index is used for describing the variation complexity of the site, and the technical problem that the variation complexity of the site cannot be rapidly evaluated in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of biotechnology, and particularly relates to a variant detection method, a detection index processing method, device, equipment and medium. BACKGROUND

[0002] Variant detection is a key application in high-throughput resequencing technology. However, the instability of high-throughput sequencing data quality and the existence of complex regions in the reference genome make the process of aligning sequencing data to the reference genome and subsequent variant detection complicated. In the alignment process, a site may have multiple complex variant situations.

[0003] In the related art, a third-party variant detection software is used to analyze and determine the variant situation of a site by using an advanced algorithm.

[0004] In this way, the variant complexity of a site cannot be quickly evaluated. SUMMARY

[0005] The present disclosure aims to at least partially solve one of the technical problems in the related art.

[0006] To this end, the present disclosure provides a variant detection method, a detection index processing method, device, electronic equipment, computer readable storage medium and computer program product, which can quickly evaluate the variant complexity of a site.

[0007] To achieve the above purpose, the first aspect of the present disclosure provides a variant detection method, comprising: obtaining variant information of each site, wherein the variant information is used to describe the alignment of the base of the corresponding site and the base of the reference genome; determining a first detection index corresponding to the site according to the variant information, wherein the first detection index is used to describe the variant complexity of the site.

[0008] To achieve the above purpose, the second aspect of the present disclosure provides a detection index processing method, wherein the first detection index is determined based on the variant detection method of the first aspect; comprising: determining the site type to which the site belongs according to the first detection index; determining a second detection index according to the site type, wherein the second detection index is used to represent the accuracy of variant detection.

[0009] To achieve the above objectives, a third aspect of this disclosure provides a method for processing detection indicators, wherein a first detection indicator is determined based on the variation detection method of the first aspect described above; the method includes: acquiring a third detection indicator, wherein the third detection indicator is used to describe the variation complexity of other sites, and the sites described by the first detection indicator and the third detection indicator are different; determining first sequencing data aligned to a site and second sequencing data aligned to other sites; and determining variation difference information between the first sequencing data and the second sequencing data based on the first detection indicator and the third detection indicator.

[0010] To achieve the above objectives, a fourth aspect of this disclosure provides a variant detection device, comprising: a first acquisition module for acquiring variant information at each site, wherein the variant information is used to describe the alignment of the bases at the corresponding site with the bases of a reference genome; and a first determination module for determining a first detection index corresponding to the site based on the variant information, wherein the first detection index is used to describe the complexity of the variant at the site.

[0011] To achieve the above objectives, a fifth aspect of this disclosure provides a detection index processing apparatus, wherein the first detection index is determined based on the variant detection apparatus of the fourth aspect described above; the apparatus includes: a second determining module for determining the site type to which the site belongs based on the first detection index; and a third determining module for determining a second detection index based on the site type, wherein the second detection index is used to represent the accuracy of variant detection.

[0012] To achieve the above objectives, a sixth aspect of this disclosure provides a detection index processing apparatus, wherein the first detection index is determined based on the variation detection apparatus of the fourth aspect described above; the apparatus includes: a second acquisition module for acquiring a third detection index, wherein the third detection index is used to describe the variation complexity of other sites, and the sites described by the first detection index and the third detection index are different; a fourth determination module for determining first sequencing data aligned to a site and second sequencing data aligned to other sites; and a fifth determination module for determining variation difference information between the first sequencing data and the second sequencing data based on the first detection index and the third detection index.

[0013] To achieve the above objectives, a seventh aspect of this disclosure provides an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method proposed in the above aspects of this disclosure.

[0014] To achieve the above objectives, an eighth aspect of this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the methods proposed in the above-described aspects of this disclosure.

[0015] To achieve the above objectives, a ninth aspect of this disclosure provides a computer program product including a computer program that, when executed by a processor, implements the methods proposed in the above aspects of this disclosure.

[0016] The variation detection method, detection index processing method, apparatus, electronic device, computer-readable storage medium, and computer program product provided in this disclosure acquire variation information for each site, wherein the variation information is used to describe the alignment of the bases at the corresponding site with the bases of the reference genome, and determine a first detection index corresponding to the site based on the variation information, wherein the first detection index is used to describe the variation complexity of the site, thereby enabling rapid assessment of the variation complexity of the site.

[0017] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0018] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0019] Figure 1 This is a schematic flowchart of a mutation detection method provided in an embodiment of this disclosure;

[0020] Figure 2 This is a schematic diagram of the variation information of the site in the embodiments of this disclosure;

[0021] Figure 3 This is a schematic flowchart of another mutation detection method provided in an embodiment of this disclosure;

[0022] Figure 4 This is a schematic diagram illustrating the calculation of the Entropy value in an embodiment of this disclosure;

[0023] Figure 5 This is a schematic diagram illustrating the Entropy value and corresponding variant site information in an embodiment of this disclosure;

[0024] Figure 6 This is a schematic diagram of the variation information of the site in the embodiments of this disclosure;

[0025] Figure 7 This is a schematic flowchart of a detection index processing method provided in an embodiment of the present disclosure;

[0026] Figure 8 This is a schematic diagram illustrating the association between Entropy values ​​and different site types in an embodiment of this disclosure;

[0027] Figure 9 A flowchart illustrating another detection index processing method provided in this embodiment of the present disclosure;

[0028] Figure 10 This is a schematic diagram showing the distribution of Entropy values ​​for different sequencing data in this embodiment of the present disclosure;

[0029] Figure 11 This is a schematic diagram illustrating the calculation speed of the Entropy value in an embodiment of this disclosure;

[0030] Figure 12 This is a schematic diagram illustrating one variation of an embodiment of this disclosure;

[0031] Figure 13 This is a schematic diagram illustrating another variation in the embodiments of this disclosure;

[0032] Figure 14 This is a schematic diagram illustrating the variation differences of different sequencing data in the embodiments of this disclosure;

[0033] Figure 15 This is a schematic diagram of the structure of a mutation detection device provided in an embodiment of the present disclosure;

[0034] Figure 16 This is a schematic diagram of the structure of a detection index processing device provided in an embodiment of the present disclosure;

[0035] Figure 17 This is a schematic diagram of another detection index processing device provided in an embodiment of the present disclosure;

[0036] Figure 18 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Detailed Implementation

[0037] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0038] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0039] Figure 1 This is a schematic flowchart of a mutation detection method provided in an embodiment of this disclosure.

[0040] like Figure 1 As shown, this mutation detection method includes the following steps:

[0041] Step S101: Obtain the variation information for each site, wherein the variation information is used to describe the alignment of the bases at the corresponding site with the bases of the reference genome.

[0042] Each locus can correspond to a variant information, which describes the variant characteristics of the locus. For example, the variant characteristics include whether the alignment of the locus is consistent with the reference genome, whether it is a single base change inconsistent with the reference genome, or whether it is an insertion or deletion inconsistent with the reference genome.

[0043] In some embodiments, such as Figure 2 As shown, Figure 2 This is a schematic diagram of the variation information of the site in this embodiment of the present disclosure. The first column (chr) is the reference sequence name, the second column is the position (pos), the third column (ref) is the base of the reference genome, the fourth column (cov) is the number of sequencing fragments aligned to this site, the fifth column (pileup) is the specific alignment details, * indicates fuzzy bases, uppercase letters indicate that the bases at the site do not match the positive strand bases of the reference genome, lowercase letters indicate that the bases at the site do not match the negative strand bases of the reference genome, the regular expression "+[0-9]+[ACGTNacgtn]+" represents the number and amount of bases inserted after this site, the regular expression "-[0-9]+[ACGTNacgtn]+" represents the number and amount of bases deleted after this site, and the sixth column (base qualities) is the alignment quality of the sequencing fragments aligned to this site.

[0044] Step S102: Based on the variation information, determine the first detection index corresponding to the site, wherein the first detection index is used to describe the variation complexity of the site.

[0045] The variation complexity describes the degree of variation at a given site. Variation complexity can also be referred to as variation complexity or variation complexity situation, without any restrictions.

[0046] In this embodiment of the disclosure, the various possible variation information described above can be analyzed to achieve quantitative analysis of the variation at the locus, obtaining a quantitative index. This quantitative index can be used to summarize the variation complexity of each locus. This quantitative index can be referred to as the first detection index (e.g., entropy). In other words, the first detection index is used to describe the variation complexity of the locus.

[0047] For example, the variation complexity of each site can be quickly estimated based on a quantitative metric—entropy. The distribution of this first detection metric can support accurate estimation of its impact on subsequent variation detection, thereby effectively supporting rapid comparison of variation detection performance differences between different sequencing technologies.

[0048] In some embodiments, the various possible variations described above can be quantitatively analyzed to determine the first detection index; or the various possible variations described above can be statistically analyzed to determine the first detection index; or any other possible method can be used to analyze the various possible variations described above to determine the first detection index.

[0049] In this embodiment, variation information for each site is obtained, wherein the variation information is used to describe the alignment of the bases at the corresponding site with the bases of the reference genome, and a first detection index corresponding to the site is determined based on the variation information, wherein the first detection index is used to describe the variation complexity of the site, thereby enabling rapid assessment of the variation complexity of the site.

[0050] In some embodiments of this disclosure, the variation information includes at least one of the following: first information, wherein the first information indicates that the base at the corresponding site is an ambiguous base; second information, wherein the second information indicates that the base at the corresponding site does not match the positive strand base of the reference genome; third information, wherein the third information indicates that the base at the corresponding site does not match the negative strand base of the reference genome; fourth information, wherein the fourth information indicates the number of bases inserted after the corresponding site and / or the number of inserted bases; and fifth information, wherein the fifth information indicates the number of bases deleted after the corresponding site and / or the number of deleted bases. This effectively supports improving the accuracy of analysis of variation complexity.

[0051] For example, the first, second, third, fourth, and fifth pieces of information can be detailed as described above. Figure 2 The content shown in "Specific details of the fifth column (pileup) comparison" is not limited.

[0052] Figure 3 This is a schematic flowchart of another mutation detection method provided in an embodiment of this disclosure.

[0053] like Figure 3 As shown, this mutation detection method includes the following steps:

[0054] Step S301: Obtain the variation information for each site, wherein the variation information is used to describe the alignment of the bases at the corresponding site with the bases of the reference genome.

[0055] For a detailed description of step S301, please refer to the above embodiments, which will not be repeated here.

[0056] Step S302: Based on the variation information, determine at least one reference variation type for the site.

[0057] In some embodiments, at least one sequencing fragment may be aligned to the site, and the site may correspond to at least one variant type. The variant type corresponding to the site can be referred to as the reference variant type.

[0058] In some embodiments, the reference variant type is any of the following: single-base variant; insertion and / or deletion variant. This improves the simplicity of variant type classification, thereby effectively reducing the computational complexity of site variant complexity.

[0059] Step S303: Determine the reference frequency of the reference variant type, where the reference frequency represents the frequency of occurrence of the reference variant type among the variant types involved in the reference sequencing fragment, and the reference sequencing fragment is the sequencing fragment aligned to the location.

[0060] After determining at least one reference variant type at the site, the frequency of the reference variant type occurring in the variant types involved in the reference sequencing fragment can be statistically analyzed, and the statistically obtained frequency can be used as the reference frequency.

[0061] In some embodiments, the i-th reference variant type can be represented by x. i This indicates that the i-th reference variant type x i The reference frequency can be represented by p(x). i (This indicates that no restrictions are imposed on this.)

[0062] Step S304: Determine the first detection index based on the reference frequency, wherein the first detection index is used to describe the variation complexity of the site.

[0063] After determining the reference frequency of at least one reference variant type at a site, the above-mentioned calculation can be performed on at least one reference frequency, and the result of the calculation can be used as the first detection index.

[0064] In some embodiments, in the process of determining the first detection index based on the reference frequency, the reference frequency may be processed to obtain a processed value, and the product value between the reference frequency and the processed value may be determined, and the first detection index may be determined based on the product value, so as to accurately calculate the variation complexity of the site.

[0065] In some embodiments, when determining the first detection index based on the product value, the product value can be used as the first detection index if there is only one reference variant type, and if there are multiple reference variant types, the multiple product values ​​are summed and the sum is used as the first detection index. This allows for accurate quantitative analysis of the variation complexity of the site.

[0066] In some embodiments, the process of determining the first detection index based on the reference frequency can be based on processing the reference frequency using the following formula to determine the first detection index:

[0067]

[0068] Where Entropy represents the first detection metric, p(x) i ) indicates the reference variant type x i The reference frequency, x i Represents the i-th reference mutation type, log a () represents a logarithmic function, a represents the base, a is any one of 2, 10, and e, e represents a constant, and N represents the number of reference variant types.

[0069] For example, it can be extracted Figure 2 The alignment results for each site in the fifth column of the variant information shown are categorized into single-base variants and insertion / deletion variants. An Entropy value (p(x)) is calculated for each variant type (refer to an optional example of variant types). i )*log a (p(x i The specific process is as follows: Figure 4 As shown, Figure 4 This is a schematic diagram of the Entropy value calculation in an embodiment of this disclosure. Then, if there are multiple reference variant types, the Entropy values ​​of multiple variant types are summed, and the summed value is used as the first detection index.

[0070] For example, you can verify the calculated Entropy value against... Figure 2 The one-to-one correspondence between the complexity of variation information at the shown sites demonstrates that the Entropy value effectively indicates the complexity of site variations. For example... Figure 5 As shown, Figure 5This is a schematic diagram illustrating the entropy values ​​and corresponding variant sites in an embodiment of this disclosure. For a site, if all bases are A, then the entropy value of that site is 0, indicating that the variant at that site is highly consistent. However, as the complexity of the variant at the site increases, i.e., from... Figure 5 As shown in the table, the Entropy value gradually increases from top to bottom, accurately reflecting the complexity of site variation. This demonstrates that the Entropy value can be effectively used to quantify and assess the complexity of site variation.

[0071] In this embodiment, variation information for each site is obtained, wherein the variation information is used to describe the alignment of the bases at the corresponding site with the bases of the reference genome, and a first detection index corresponding to the site is determined based on the variation information, wherein the first detection index is used to describe the variation complexity of the site, thereby enabling rapid assessment of the variation complexity of the site.

[0072] In some embodiments of this disclosure, the variation information of each site can be associated with a first detection index. This allows for a more intuitive presentation of the complexity of site variations.

[0073] like Figure 6 As shown, Figure 6 This is a schematic diagram illustrating the variation information of the site in an embodiment of this disclosure. Figure 6 In this context, the first detection indicator for each site can be located in the last column of each row.

[0074] Figure 7 This is a flowchart illustrating a detection index processing method provided in an embodiment of the present disclosure.

[0075] The detection index in this embodiment refers to the first detection index determined based on the mutation detection method in the above embodiments. For example... Figure 7 As shown, the method for processing this detection index includes the following steps:

[0076] Step S701: Determine the site type based on the first detection index.

[0077] In some embodiments, the locus type includes at least one of the following: true positive locus; false positive locus; false negative locus. This effectively improves the accuracy of locus type classification.

[0078] In this context, a true positive locus indicates that the locus actually contains a variant and was correctly detected. A false positive locus indicates that the locus was incorrectly detected as a variant. A false negative locus indicates that the locus contains a variant but was not detected.

[0079] In some embodiments, a threshold, which may be referred to as a set threshold, can be set, and a first detection index can be compared with the set threshold to analyze the site type based on the comparison result. For example, the set threshold may be 1.

[0080] In some embodiments, during the process of determining the site type based on a first detection index, if the first detection index is less than or equal to a set threshold, the site type is determined to be a true positive site; if the first detection index is greater than the set threshold, the site type is determined to be a false positive or false negative site. This allows for the effective and accurate determination of the site type.

[0081] Step S702: Determine a second detection index based on the site type, wherein the second detection index is used to represent the accuracy of variant detection.

[0082] In this embodiment of the disclosure, the calculated Entropy value (an optional example of the first detection index) can be correlated with the variant sites detected by third-party software. The results are compared with ground truth data. Variant sites can be categorized into three types: ground truth sites (sites that do indeed exhibit variants and are correctly detected), false positive sites (sites that are incorrectly detected as variants), and false negative sites (sites that exhibit variants but are not detected). Analysis results show that the Entropy value can effectively predict the detection status of sites. Figure 8 As shown, Figure 8 This is a schematic diagram illustrating the relationship between Entropy values ​​and different site types in an embodiment of this disclosure. Figure 8 The vertical axis represents the number of loci corresponding to a certain entropy value, and the horizontal axis represents the entropy value. True positive loci have low entropy values, with many even having an entropy value of 0, indicating that the variation at true positive loci is very stable, thus leading to accurate variant detection results. In contrast, false positive and false negative loci typically have high entropy values, with many exceeding 1, indicating that the variation at these loci is more complex. This may cause variant detection software to be unable to accurately determine whether a variant exists at a locus or whether the variant is genuine, resulting in detection errors. Therefore, the entropy value, as a quantitative indicator, can effectively predict the accuracy of variant detection.

[0083] In some embodiments, in determining a second detection index based on the locus type, whereby the second detection index represents the accuracy of variant detection, the accuracy of variant detection can be determined as a first level of accuracy when the locus type is a true positive locus; and as a second level of accuracy when the locus type is a false positive or false negative locus, where the first level of accuracy is higher than the second level of accuracy. This allows for a reasonable and accurate determination of the accuracy of variant detection.

[0084] In other words, if the locus type is a true positive locus, it indicates that the accuracy of variant detection is relatively high; if the locus type is a false positive locus or a false negative locus, it indicates that the accuracy of variant detection is relatively low, thus achieving an accurate assessment of the accuracy of variant detection.

[0085] In this embodiment, the site type to which the site belongs is determined based on the first detection index, and a second detection index is determined based on the site type. The second detection index is used to represent the accuracy of variant detection, which can effectively determine the accuracy of variant detection, thereby effectively expanding the application scenarios of the detection index.

[0086] In some embodiments, a reference alignment quality of the sequencing fragment aligned to the target location can be obtained, and the first detection indicator can be processed based on the reference alignment quality. For example, the first detection indicator can be determined.

[0087] The reference comparison quality can be the comparison quality output by some testing tools. For example, the reference comparison quality is... Figure 2 The "alignment quality of the sixth column to the site" shown refers to the alignment quality of the fragment. In other words, in this embodiment of the disclosure, a reference alignment quality can also be used to process the first detection index. For example, the reference alignment quality can be used to analyze the accuracy of the calculation of the first detection index, or to assess whether the first detection index is suitable for other qualitative analyses; there are no limitations on this.

[0088] Figure 9 This is a schematic flowchart of another detection index processing method provided in an embodiment of this disclosure.

[0089] The detection index in this embodiment refers to the first detection index determined based on the mutation detection method in the above embodiments. For example... Figure 9 As shown, the method for processing this detection index includes the following steps:

[0090] Step S901: Obtain the third detection index, which is used to describe the variation complexity of other sites. The sites described by the first detection index and the third detection index are different.

[0091] In other words, the third detection indicator is the complexity of the variation obtained from the analysis of other sites.

[0092] In this embodiment of the disclosure, the variation differences between different sequencing data can be analyzed by referring to the first detection index and the second detection index.

[0093] Step S902: Determine the first sequencing data aligned to the locus and the second sequencing data aligned to other loci.

[0094] The first sequencing data consists of the sequencing data belonging to the reference sequencing fragment aligned to the target site. The second sequencing data consists of the sequencing data belonging to sequencing fragments aligned to other sites. "Site" and "other sites" refer to different sites.

[0095] Step S903: Determine the variation difference information between the first sequencing data and the second sequencing data based on the first detection index and the third detection index.

[0096] In some embodiments, the distribution differences between the first detection index and the third detection index can be determined, and the variation difference information between the first sequencing data and the second sequencing data can be qualitatively analyzed based on the distribution differences.

[0097] In some embodiments, the variant detection performance on first sequencing data can be predicted based on a first detection indicator; and / or the variant detection performance on second sequencing data can be predicted based on a third detection indicator. This enables effective prediction of variant detection performance on sequencing data and greatly expands the application scenarios of the detection indicators.

[0098] For example, the Entropy value (an optional example of a first or third detection metric) can also be used to compare variation differences between different sequencing data and predict the variability in variation detection results. Figure 10 As shown, Figure 10 This is a schematic diagram illustrating the distribution of entropy values ​​for different sequencing data in this embodiment. The black bars represent the original long-read data, while the gray bars represent the optimized and corrected long-read data. After variant detection on the corrected data, the variant detection results (represented by the F-value) were significantly improved. The F-value for variant detection increased from 57% to 74% (a higher F-value indicates better accuracy and sensitivity in variant detection). Based on... Figure 10As can be seen, the corrected data shows a significant increase in the number of sites with entropy values ​​less than 1, and the number of sites with entropy values ​​of 0 is more than twice that of the original long-read data. This indicates that the entropy value can serve as an accurate indicator for comparing differences between different sequencing data and predicting variant detection performance. The closer the entropy value distribution is to 0, the stronger its variant detection capability.

[0099] In this embodiment, by obtaining a third detection index, which is used to describe the variation complexity of other sites, and the sites described by the first detection index and the third detection index are different, the first sequencing data aligned to the site and the second sequencing data aligned to other sites are determined. Based on the first detection index and the third detection index, the variation difference information between the first sequencing data and the second sequencing data is determined. This can effectively determine the variation difference between different sequencing data, thereby effectively expanding the application scenarios of the detection index.

[0100] This disclosure proposes a novel quantitative metric: the Entropy value, calculated based on the variant information of each site output by a third-party tool. This metric can assess the variant complexity of a site directly from the alignment results of BAM files (BAM files are binary versions of Sequence Alignment / Map (SAM) files used to store the alignment results of sequencing data) without relying on any third-party variant detection software. Calculating the Entropy value for 10 million variant entries takes less than 5 minutes, significantly reducing computation time and resources consumed. By analyzing the distribution of Entropy values, variant detection performance can be accurately predicted: the Entropy value for true positive sites is typically close to 0, while the Entropy values ​​for false positive and false negative sites are usually larger. Furthermore, the closer the distribution of Entropy values ​​is to 0, the more reliable the variant detection results. Therefore, the Entropy value can not only be used to assess the variant complexity of a site but also to predict the variant detection performance of different sequencing data.

[0101] Examples of the above embodiments are illustrated below:

[0102] The testing process may include:

[0103] 1. Download sequencing data and reference genomes from different platforms, and download the necessary third-party analysis software (such as samtools).

[0104] 2. Align different sequencing data to the reference genome.

[0105] 3. Calculate the variation information for each site using third-party analysis software.

[0106] 4. Extract the alignment details for each site from the mutation information, calculate the entropy value for each site based on the alignment details, and add the entropy value to the last column of the mutation information.

[0107] 5. Use plotting code to output the distribution of entropy values ​​and evaluate the distribution of entropy values ​​for different sequencing data to predict performance differences in downstream variant detection.

[0108] In this embodiment, the entropy value for each locus can be obtained and added to the last line of the output (variation information) of the third-party analysis software. This allows for the rapid assessment of the variation complexity of the variant locus using the entropy value. A lower entropy value indicates a simpler variation, while a higher entropy value indicates a more complex variation. Furthermore, an entropy value distribution map can be quickly plotted, and based on this map, differences in variant detection from different sequencing data can be predicted and compared.

[0109] like Figure 11 As shown, Figure 11 This is a schematic diagram illustrating the calculation speed of the Entropy value in an embodiment of this disclosure. Figure 11 In the diagram, the horizontal axis represents the number of rows in the output file of the third-party analysis software (unit: 10 million), and the vertical axis represents the running time (time, unit: seconds (s)). The three broken lines represent sequencing data with coverage of 10x, 20x, and 30x, respectively. The analysis of 10 million data points can be completed in less than 5 minutes.

[0110] like Figure 12 As shown, Figure 12 This is a schematic diagram of a variation in an embodiment of this disclosure. Figure 12 The example shown is an Entropy value of 0, demonstrating that the variation at this site is relatively consistent. The variation at this site is GgggGgggGgGgggggGggGggGggGgg, with 27 consistent single G base variations, indicating a single variation pattern that corresponds to the Entropy value.

[0111] like Figure 13 As shown, Figure 13 This is a schematic diagram of another variation in the embodiments of this disclosure. Figure 13The example shown is an entropy value of 1.79, demonstrating the complex and varied variation at this site. The variation at this site includes {'C': 10, 'C+2AA': 10, 'C+1A': 5, 'C-1A': 3, 'C-11AAAAAAAAAAA': 1, 'C+5AAAAA': 1, 'C+3AAA': 2, 'C-8AAAAAAAA': 1, 'C+4AAAA': 1}, exhibiting a complex and varied variation that corresponds to a high entropy value.

[0112] like Figure 14 As shown, Figure 14 This is a schematic diagram illustrating the variation differences of different sequencing data in this embodiment. One type of sequencing data can be represented as PACBI data, and the other type can be represented as ONT R10 data. The Entropy values ​​of the PACBI and ONT R10 data are distributed as follows: Figure 14 As shown, the entropy value can be used to compare the differences in the complexity of mutations in different sequencing data. The entropy value distributions of the two are quite similar, and their mutation performance is also quite close.

[0113] In this embodiment, an entropy value can be calculated based on the variation information of a site. This entropy value characterizes the variation complexity of the site. Specifically, the entropy value can be used to estimate the variation complexity of a site, predict downstream variation detection performance, and evaluate the performance differences in variation detection between different sequencing technologies. Furthermore, the calculated entropy value can achieve accurate estimation of variation complexity without relying on third-party variation detection software.

[0114] Figure 15 This is a schematic diagram of a mutation detection device provided in an embodiment of the present disclosure.

[0115] like Figure 15 As shown, the mutation detection device 150 includes:

[0116] The first acquisition module 1501 is used to acquire the variation information of each site, wherein the variation information is used to describe the alignment of the bases at the corresponding site with the bases of the reference genome.

[0117] The first determining module 1502 is used to determine a first detection index corresponding to the site based on the variation information, wherein the first detection index is used to describe the variation complexity of the site.

[0118] It should be noted that the foregoing explanation of the mutation detection method also applies to the mutation detection device of this embodiment, and will not be repeated here.

[0119] In this embodiment, variation information for each site is obtained, wherein the variation information is used to describe the alignment of the bases at the corresponding site with the bases of the reference genome, and a first detection index corresponding to the site is determined based on the variation information, wherein the first detection index is used to describe the variation complexity of the site, thereby enabling rapid assessment of the variation complexity of the site.

[0120] Figure 16 This is a schematic diagram of a detection index processing device provided in an embodiment of the present disclosure.

[0121] The first detection index is determined based on the variation detection device described above.

[0122] like Figure 16 As shown, the detection index processing device 160 includes:

[0123] The second determining module 1601 is used to determine the site type to which the site belongs based on the first detection index.

[0124] The third determining module 1602 is used to determine a second detection index based on the site type, wherein the second detection index is used to represent the accuracy of variant detection.

[0125] It should be noted that the foregoing explanation of the detection index processing method also applies to the detection index processing device of this embodiment, and will not be repeated here.

[0126] In this embodiment, the site type to which the site belongs is determined based on the first detection index, and a second detection index is determined based on the site type. The second detection index is used to represent the accuracy of variant detection, which can effectively determine the accuracy of variant detection, thereby effectively expanding the application scenarios of the detection index.

[0127] Figure 17 This is a schematic diagram of another detection index processing device provided in an embodiment of the present disclosure.

[0128] The first detection index is determined based on the variation detection device described above.

[0129] like Figure 17 As shown, the detection index processing device 170 includes:

[0130] The second acquisition module 1701 is used to acquire a third detection index, wherein the third detection index is used to describe the variation complexity of other sites, and the sites described by the first detection index and the third detection index are different.

[0131] The fourth determination module 1702 is used to determine the first sequencing data aligned to the site and the second sequencing data aligned to other sites.

[0132] The fifth determination module 1703 is used to determine the variation difference information between the first sequencing data and the second sequencing data based on the first detection index and the third detection index.

[0133] It should be noted that the foregoing explanation of the detection index processing method also applies to the detection index processing device of this embodiment, and will not be repeated here.

[0134] In this embodiment, by obtaining a third detection index, which is used to describe the variation complexity of other sites, and the sites described by the first detection index and the third detection index are different, the first sequencing data aligned to the site and the second sequencing data aligned to other sites are determined. Based on the first detection index and the third detection index, the variation difference information between the first sequencing data and the second sequencing data is determined. This can effectively determine the variation difference between different sequencing data, thereby effectively expanding the application scenarios of the detection index.

[0135] To implement the above embodiments, this disclosure also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0136] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0137] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0138] Figure 18 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 18 The electronic device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0139] like Figure 18 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, memory 28, and bus 18 connecting different system components (including memory 28 and processing unit 16).

[0140] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0141] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0142] Memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and / or cache 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 18 Not shown; usually referred to as a "hard drive".

[0143] although Figure 18 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0144] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0145] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable human interaction with electronic device 12, and / or with any device that enables electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0146] The processing unit 16 executes various functional applications and data processing by running programs stored in the memory 28, such as implementing the methods mentioned in the foregoing embodiments.

[0147] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0148] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0149] This disclosure is intended to provide implementation schemes for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0150] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0151] Furthermore, 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 at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0152] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0154] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0155] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0156] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0157] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A mutation detection method, characterized in that, The method includes the following steps: Obtain variation information for each site, wherein the variation information is used to describe the alignment of the bases at the corresponding site with the bases in the reference genome; Based on the mutation information, a first detection index corresponding to the site is determined, wherein the first detection index is used to describe the mutation complexity of the site.

2. The method according to claim 1, characterized in that, The mutation information includes at least one of the following: First information, wherein the first information indicates that the base at the corresponding site is an ambiguous base; The second information indicates that the bases at the corresponding site do not match the positive strand bases of the reference genome; The third information indicates that the base at the corresponding site does not match the negative strand base of the reference genome; The fourth information, wherein the fourth information represents the base inserted after the corresponding site and / or the number of the inserted bases; The fifth information, wherein the fifth information represents the bases missing after the corresponding site and / or the number of the missing bases.

3. The method according to claim 1, characterized in that, The step of determining the first detection indicator corresponding to the site based on the mutation information includes: Based on the variation information, at least one reference variation type is determined for the site; Determine the reference frequency of the reference variant type, wherein the reference frequency represents the frequency of occurrence of the reference variant type among the variant types involved in the reference sequencing fragment, and the reference sequencing fragment is the sequencing fragment aligned to the site; The first detection index is determined based on the reference frequency.

4. The method according to claim 3, characterized in that, Determining the first detection index based on the reference frequency includes: The reference frequency is processed to obtain the processed value; Determine the product between the reference frequency and the processed value; The first detection index is determined based on the product value.

5. The method according to claim 4, characterized in that, Determining the first detection index based on the product value includes: If the number of reference variant types is one, the product value is used as the first detection index; When there are multiple reference variant types, the multiple product values ​​are summed, and the summed value is used as the first detection index.

6. The method according to claim 3, characterized in that, Determining the first detection index based on the reference frequency includes: The reference frequency is processed based on the following formula to determine the first detection index: Wherein, Entropy represents the first detection index, p(x i ) indicates the reference variant type x i The reference frequency, x i Represents the i-th reference mutation type, log a () represents a logarithmic function, a represents the base, a is any one of 2, 10, and e, e represents a constant, and N represents the number of reference variant types.

7. A method for processing detection indicators, characterized in that, The first detection index is determined based on the variation detection method as described in any one of claims 1-6; wherein the detection index processing method includes the following steps: Based on the first detection index, determine the site type to which the site belongs; Based on the site type, a second detection index is determined, wherein the second detection index is used to represent the accuracy of variant detection.

8. The method according to claim 7, characterized in that, The step of determining the site type to which the site belongs based on the first detection index includes: If the first detection index is less than or equal to a set threshold, the site type to which the site belongs is determined to be a true value site; or If the first detection index is greater than the set threshold, the site type is determined to be a false positive site or a false negative site.

9. The method according to claim 7, characterized in that, The step of determining a second detection index based on the site type, wherein the second detection index is used to represent the accuracy of variant detection, includes: When the site type is a true site, the second detection index is determined to be the first level of accuracy in representing the accuracy of variant detection; In the case where the locus type is a false positive or a false negative locus, the second detection index is determined to be a second level of accuracy, wherein the first level of accuracy is higher than the second level of accuracy.

10. A method for processing detection indicators, characterized in that, The first detection index is determined based on the variation detection method as described in any one of claims 1-6; wherein the detection index processing method includes the following steps: Obtain a third detection index, wherein the third detection index is used to describe the variation complexity of other sites, and the sites described by the first detection index and the third detection index are different. Determine the first sequencing data aligned to the stated site, and the second sequencing data aligned to the other sites; Based on the first detection index and the third detection index, the variation difference information between the first sequencing data and the second sequencing data is determined.

11. A mutation detection device, characterized in that, The device includes: The first acquisition module is used to acquire the variation information of each site, wherein the variation information is used to describe the alignment of the bases at the corresponding site with the bases of the reference genome; The first determining module is used to determine a first detection index corresponding to the site based on the mutation information, wherein the first detection index is used to describe the mutation complexity of the site.

12. A detection index processing device, characterized in that, The first detection index is determined based on the variation detection device as described in claim 11; wherein, the detection index processing device includes: The second determining module is used to determine the site type to which the site belongs based on the first detection index; The third determining module is used to determine a second detection index based on the site type, wherein the second detection index is used to represent the accuracy of variant detection.

13. A detection index processing device, characterized in that, The first detection index is determined based on the variation detection device as described in claim 11; wherein the detection index processing device includes the following steps: The second acquisition module is used to acquire a third detection index, wherein the third detection index is used to describe the variation complexity of other sites, and the sites described by the first detection index and the third detection index are different. The fourth determining module is used to determine the first sequencing data aligned to the said site and the second sequencing data aligned to the other sites; The fifth determining module is used to determine the variation difference information between the first sequencing data and the second sequencing data based on the first detection index and the third detection index.

14. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-10.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-10.

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