Gene variation confidence coefficient determination method and system, electronic equipment and storage medium
Through sequencing and alignment techniques, the error alignment coefficients in gene variant detection are calculated and compared with the reference range, the problem of pseudogene interference is solved and the accuracy of gene variant judgment is improved.
Patent Information
- Application Number
- CN202411632085.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-15
- Publication Date
- 2025-03-07
AI Technical Summary
The prior art is difficult to effectively reduce the interference of pseudogenes on detection in gene variant detection, resulting in a decrease in judgment accuracy.
By sequencing the samples to be tested, the coverage ratio of functional genes and non-functional genes at gene-specific nucleotide (GSN) sites was determined, the error alignment coefficient was calculated, and compared with the reference error alignment coefficient range was compared to the confidence level of gene mutation.
This method can effectively judge whether gene mutations are located on functional genes, reduce the impact of pseudogenes on judgments, and improve the accuracy of gene mutation judgments.
Smart Images
Figure CN119580832A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of gene detection, and in particular to a gene variation confidence determination method, system, electronic device and storage medium. Background Art
[0002] The second generation sequencing technology is widely used in clinical diagnosis scenarios of genetic diseases associated with gene mutations due to its advantages such as high throughput, high sensitivity and low cost. There are about 22,000 annotated protein-coding genes (called functional genes and 7,000 pseudogenes) in human genes. The high similarity between functional genes and pseudogenes (homologous regions / regions) poses a huge challenge to detection.
[0003] How to reduce the interference of pseudogenes on gene variation detection is an urgent problem to be solved. Summary of the invention
[0004] The embodiments of the present disclosure provide a method, system, electronic device and storage medium for determining confidence of gene variation.
[0005] According to a first aspect of an embodiment of the present disclosure, a method for determining confidence of a gene variation is proposed, comprising:
[0006] For samples predicted to have genetic mutations
[0007] The method performs sequencing to determine a first coverage ratio of a first base corresponding to a functional gene and a second base corresponding to a non-functional gene at a gene-specific nucleotide (GSN) site in a sample to be tested; wherein the non-functional gene includes a pseudogene and / or a homologous region of the functional gene;
[0008] Determining an error alignment coefficient according to the first coverage ratio;
[0009] Based on the comparison result of the error alignment coefficient and the reference error alignment coefficient range, the confidence level of the predicted gene variation is determined, wherein the reference error alignment coefficient range is determined based on the reference error alignment coefficient obtained based on the second coverage ratio, wherein the second coverage ratio is determined based on the first base corresponding to the functional gene and the second base corresponding to the non-functional gene at the GSN site in a predetermined number of reference samples, wherein the reference samples correspond to the functional gene.
[0010] In some embodiments, the determining of the confidence level of the predicted gene variation based on the comparison result of the error alignment coefficient and the reference error alignment coefficient range comprises at least one of the following:
[0011] The error comparison coefficient is within the reference error comparison coefficient range, and it is determined that the gene variation is located on the functional gene of the sample to be tested;
[0012] The error comparison coefficient is less than the minimum value of the reference error comparison coefficient range, and the gene variation is determined to be a false negative;
[0013] The error alignment coefficient is greater than the maximum value of the reference error alignment coefficient range, and the gene variation is determined to be a false positive.
[0014] In some embodiments, the method further comprises:
[0015] Determine a second coverage ratio corresponding to each reference sample;
[0016] Determine a reference error alignment coefficient corresponding to each reference sample according to the determined second coverage ratio corresponding to each reference sample;
[0017] A reference error comparison coefficient range is determined based on the reference error comparison coefficients respectively corresponding to the predetermined number of reference samples.
[0018] In some embodiments, determining the reference error comparison coefficient range based on the reference error comparison coefficients corresponding to the predetermined number of reference samples respectively includes:
[0019] Determine the lower quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples as the minimum value of the reference error comparison coefficient range; and / or
[0020] The upper quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples are determined as the maximum value of the reference error comparison coefficient range.
[0021] In some embodiments, the method further comprises:
[0022] In response to the presence of heterozygous deletion in the functional gene of the sample to be tested, the reference misalignment coefficient is adjusted based on the ratio of the functional gene in the sample to be tested to the non-functional gene in the sample to be tested.
[0023] In some embodiments, the false alignment coefficient comprises the natural logarithm of the first coverage ratio;
[0024] The reference misalignment coefficient comprises the natural logarithm of the second coverage ratio.
[0025] According to a second aspect of the embodiments of the present disclosure, a gene variation confidence determination device is proposed, comprising: the device comprises: a processing module, wherein:
[0026] The processing module is used to sequence the sample to be tested that is predicted to have a gene variation, and determine a first coverage ratio of a first base corresponding to a functional gene and a second base corresponding to a non-functional gene at a gene-specific nucleotide GSN site in the sample to be tested; wherein the non-functional gene includes a pseudogene and / or a homologous region of the functional gene;
[0027] The processing module is further used to determine an error alignment coefficient according to the first coverage ratio;
[0028] The processing module is also used to determine the confidence level of the predicted gene variation based on the comparison result of the error alignment coefficient and the reference error alignment coefficient range, wherein the reference error alignment coefficient range is determined based on the reference error alignment coefficient obtained based on the second coverage ratio, wherein the second coverage ratio is determined based on the first base corresponding to the functional gene and the second base corresponding to the non-functional gene at the GSN site in a predetermined number of reference samples, wherein the reference samples correspond to the functional gene.
[0029] In some embodiments, the processing module is specifically used for at least one of the following:
[0030] The error comparison coefficient is within the reference error comparison coefficient range, and it is determined that the gene variation is located on the functional gene of the sample to be tested;
[0031] The error comparison coefficient is less than the minimum value of the reference error comparison coefficient range, and the gene variation is determined to be a false negative;
[0032] The error alignment coefficient is greater than the maximum value of the reference error alignment coefficient range, and the gene variation is determined to be a false positive.
[0033] In some embodiments, the processing module is further configured to:
[0034] Determine a second coverage ratio corresponding to each reference sample;
[0035] Determine a reference error alignment coefficient corresponding to each reference sample according to the determined second coverage ratio corresponding to each reference sample;
[0036] A reference error comparison coefficient range is determined based on the reference error comparison coefficients respectively corresponding to the predetermined number of reference samples.
[0037] In some embodiments, the processing module is specifically used to:
[0038] Determine the lower quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples as the minimum value of the reference error comparison coefficient range; and / or
[0039] The upper quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples are determined as the maximum value of the reference error comparison coefficient range.
[0040] In some embodiments, the processing module is further configured to:
[0041] In response to the presence of heterozygous deletion in the functional gene of the sample to be tested, the reference misalignment coefficient is adjusted based on the ratio of the functional gene in the sample to be tested to the non-functional gene in the sample to be tested.
[0042] In some embodiments, the false alignment coefficient comprises the natural logarithm of the first coverage ratio;
[0043] The reference misalignment coefficient comprises the natural logarithm of the second coverage ratio.
[0044] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, the electronic device comprising:
[0045] one or more processors;
[0046] The processor is used to call instructions so that the electronic device executes the gene variation confidence determination method described in the first aspect.
[0047] According to a fourth aspect of an embodiment of the present disclosure, a storage medium is proposed, wherein the storage medium stores instructions, and when the instructions are executed on an electronic device, the electronic device executes the method for determining the confidence of a genetic variation described in the first aspect.
[0048] According to the embodiment of the present disclosure, the method includes: sequencing a sample to be tested that is predicted to have a gene variation, and determining a first coverage ratio of a first base corresponding to a functional gene and a second base corresponding to a non-functional gene at a gene-specific nucleotide GSN site in the sample to be tested; wherein the non-functional gene includes a pseudogene and / or a homologous region of the functional gene; determining an error alignment coefficient according to the first coverage ratio; determining a confidence level of the predicted gene variation based on a comparison result of the error alignment coefficient and a reference error alignment coefficient range, wherein the reference error alignment coefficient range is determined based on a reference error alignment coefficient obtained based on a second coverage ratio, wherein the second coverage ratio is determined based on a first base corresponding to a functional gene and a second base corresponding to a non-functional gene at the GSN site in a predetermined number of reference samples, wherein the reference samples correspond to the functional gene. In this way, by comparing the error comparison coefficient of the sample to be tested that is predicted to have a gene mutation and the reference error comparison coefficient range, the change of the first base and the second base at the GSN site caused by the gene mutation can be determined, and then it can be determined whether the gene mutation is located in the functional gene, reducing the impact of non-functional genes on the gene mutation judgment, and improving the accuracy of gene mutation judgment. On the other hand, the reference error comparison coefficient range is determined using the reference sample corresponding to the functional gene as a benchmark for comparison with the error comparison coefficient, so as to reduce the impact of the error comparison coefficients of different functional genes, detection errors, detection errors, etc. on the comparison results, improve the accuracy of the comparison, and thus improve the accuracy of the gene mutation judgment. BRIEF DESCRIPTION OF THE DRAWINGS
[0049] Figure 1 is a flow chart of a method for determining confidence of a gene variation according to an exemplary embodiment;
[0050] Figure 2 is a flow chart of another method for determining confidence of gene variation according to an exemplary embodiment;
[0051] Figure 3 is a baseline schematic diagram of CYP21A2 gene MI value of a control population according to an exemplary embodiment;
[0052] Figure 4 is a schematic diagram of gene sequence comparison according to an exemplary embodiment;
[0053] Figure 5 is a schematic diagram of the structure of a gene variation confidence determination device according to an exemplary embodiment;
[0054] Figure 6 The diagram is a schematic structural diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION
[0055] In order to make the technical solutions and beneficial effects of the present invention more clearly understandable, the following is a detailed description by listing specific embodiments. The drawings are not necessarily drawn to scale, and local features may be enlarged or reduced to more clearly show the details of the local features; unless otherwise defined, the technical and scientific terms used herein have the same meanings as those in the technical field to which this application belongs.
[0056] The embodiments of the present disclosure are not exhaustive, but are only illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined, for example, some or all of the steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.
[0057] In each embodiment of the present disclosure, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between the embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form a new embodiment based on their internal logical relationships.
[0058] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.
[0059] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular form, such as "a", "an", "the", "above", "said", "aforementioned", "this", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun after the article may be understood as a singular expression or a plural expression.
[0060] In the embodiments of the present disclosure, “plurality” refers to two or more.
[0061] In some embodiments, the terms "at least one", "one or more", "aplurality of", "multiple", etc. can be used interchangeably.
[0062] In some embodiments, "at least one of A and B", "A and / or B", "A in one case, B in another case", "A in one case, B in another case", etc., may include the following technical solutions according to the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). When there are more branches such as A, B, C, etc., the above is also similar.
[0063] In some embodiments, the recording method of "A or B" may include the following technical solutions according to the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). When there are more branches such as A, B, C, etc., the above is also similar.
[0064] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects, and do not constitute restrictions on the position, order, priority, value or content of the description objects. The statement of the description object refers to the description in the context of the claims or embodiments, and should not constitute redundant restrictions due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields", and the "first" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the numerical value of the description object is not limited by the ordinal number, and can be one or more. Taking the "first device" as an example, the numerical value of the "device" can be one or more. In addition, the objects modified by different prefixes may be the same or different. For example, if the description object is "device", then the "first device" and the "second device" may be the same device or different devices, and their types may be the same or different. For another example, if the description object is "information", then the "first information" and the "second information" may be the same information or different information, and their contents may be the same or different.
[0065] In some embodiments, “including A”, “comprising A”, “used to indicate A”, and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.
[0066] In some embodiments, terms such as “…”, “determine…”, “in the case of…”, “at the time of…”, “when…”, “if…”, “if…”, etc. can be used interchangeably.
[0067] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not lower than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "no more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.
[0068] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.
[0069] In addition, each element, each row, or each column in the table of the embodiments of the present disclosure may be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns may also be implemented as an independent embodiment.
[0070] Traditional detection methods such as DNA microarrays or first-generation sequencing, due to the limitations of probe / primer design, usually lead to the simultaneous capture or amplification of target genes and corresponding pseudogenes, thereby generating mixed DNA signals. In contrast, next-generation sequencing (NGS) does not output mixed DNA signals, and each read length generated by NGS will be aligned to the human reference genome. Due to the high similarity between functional genes and pseudogenes, the algorithm that aligns read lengths to the reference genome may mistakenly align pseudogene read lengths to functional gene regions in the reference genome, thereby complicating the diagnosis. Currently, multiplex probe ligation-dependent amplification is often used for functional gene detection in the presence of pseudogenes and / or homologous regions.
[0071] (MultiplexLigationdependentProbeAmplification, MLPA) Method and long-fragment polymerase chain reaction (PCR). MLPA uses specially designed probes to detect the target gene and its pseudogene to identify the existing variant sites. Because MLPA requires a specific probe combination for each target, it has the limitations of low throughput, complex operation and high cost. Long-fragment PCR can amplify up to 15Kb of products in a specific region at a time. For most genes, long-fragment PCR is the best solution to eliminate the influence of pseudogenes and / or homologous regions. However, this method still has defects such as long time consumption, low efficiency and the need for manual gene-specific primer design.
[0072] From a clinical perspective, the next-generation sequencing technology with paired-end reads is still the best choice for whole-genome clinical diagnosis of pseudogenes and / or homologous regions. However, when changes occur at certain specific sites, NGS testing may miss variants, generate incorrect variant sites, or output incorrect copy number variations (CNVs) due to incorrect alignment of reads. In some highly homologous sequence regions, incorrect alignments occur more frequently. How to reduce the interference of pseudogenes and / or homologous regions on gene variation detection is an urgent problem to be solved.
[0073] like Figure 1 As shown, the disclosed embodiment relates to a method for determining gene variation confidence, comprising:
[0074] Step 101: sequencing a sample to be tested that is predicted to have a gene mutation, and determining a first coverage ratio of a first base corresponding to a functional gene and a second base corresponding to a non-functional gene at a gene-specific nucleotide GSN site in the sample to be tested; wherein the non-functional gene includes a pseudogene and / or a homologous region of the functional gene;
[0075] Step 102: determining an error alignment coefficient according to the first coverage ratio;
[0076] Step 103: Based on the comparison result of the error alignment coefficient and the reference error alignment coefficient range, determine the confidence level of the predicted gene variation, wherein the reference error alignment coefficient range is determined based on the reference error alignment coefficient obtained based on the second coverage ratio, wherein the second coverage ratio is determined based on the first base corresponding to the functional gene and the second base corresponding to the non-functional gene at the GSN site in a predetermined number of reference samples, wherein the reference samples correspond to the functional gene.
[0077] Here, the functional gene and the non-functional gene correspond to each other, that is, the non-functional gene is the non-functional gene corresponding to the functional gene.
[0078] In a possible implementation, sequencing the sample to be tested that is predicted to have a gene mutation may include sequencing the sample to be tested using NGS.
[0079] GSN is the different nucleotides between a functional gene and its corresponding non-functional gene. For example, the first base at the chromosome coordinates Chr2 (GRCh37): g.73830362 of the gene ALMS1 is G, and the second base at the chromosome coordinates Chr2 (GRCh37): g.73830362 of the pseudogene corresponding to the gene ALMS1 is C. It is usually possible to determine whether a gene is a functional gene or a non-functional gene based on the bases at the GSN site in the gene.
[0080] In a possible implementation, the first coverage ratio is the ratio of the number of reads of the first base to the number of reads of the second base in the sample to be tested. The number of reads generally refers to the number of base sequences obtained by a single sequencing of a sequencer in high-throughput sequencing technology.
[0081] In a possible implementation, the second coverage ratio is a ratio of the number of reads of the first base to the number of reads of the second base in a predetermined number of reference samples.
[0082] Determining a misalignment index (MI) based on the first coverage ratio may include: calculating the misalignment index for the first coverage. For example, the first coverage ratio may be normalized to obtain the misalignment index.
[0083] Here, the reference sample corresponding to the functional gene may include a control population sample for the functional gene.
[0084] In some embodiments, the false alignment coefficient comprises the natural logarithm of the first coverage ratio; and the reference false alignment coefficient comprises the natural logarithm of the second coverage ratio.
[0085] Specifically, in the process of determining the error alignment coefficient, the ratio of the number of reads of the first base to the number of reads of the second base in the sample to be tested, that is, the first coverage ratio, can be first determined, and then the natural logarithm of the first coverage ratio is taken to obtain the error alignment coefficient. Similarly, in the process of determining the reference error alignment coefficient, the ratio of the number of reads of the first base to the number of reads of the second base in the reference sample, that is, the second coverage ratio, can be first determined, and then the natural logarithm of the second coverage ratio is taken to obtain the reference error alignment coefficient.
[0086] The error matching coefficient relative to the coverage ratio can better quantify the change in the coverage ratio, and it is more intuitive when comparing the error matching coefficient with the reference error matching coefficient range.
[0087] In a possible implementation, for different functional genes, the coverage ratios of the bases corresponding to the functional genes and the bases corresponding to the non-functional genes at the GSN site are not completely the same.
[0088] In a possible implementation, for the same functional gene, the coverage ratio of the bases corresponding to the functional gene and the bases corresponding to the non-functional gene at the GSN site in different samples may also be different. Therefore, the reference error alignment coefficient range can be determined by a predetermined number of reference samples.
[0089] In some embodiments, the method further comprises:
[0090] Determine a second coverage ratio corresponding to each reference sample;
[0091] Determine a reference error alignment coefficient corresponding to each reference sample according to the determined second coverage ratio corresponding to each reference sample;
[0092] A reference error comparison coefficient range is determined based on the reference error comparison coefficients respectively corresponding to the predetermined number of reference samples.
[0093] Here, the reference error comparison coefficient range may be determined in advance through reference samples.
[0094] Due to the presence of additional pseudogenes between reference genomes, the different detection reagents have different abilities to capture functional genes and pseudogenes, and the random errors and errors that may occur in sequencing, the coverage ratios between different regions of genes and their non-functional genes vary. Therefore, in order to accurately distinguish functional genes from their corresponding non-functional genes, the reference error comparison coefficient range corresponding to different genes, or the MI threshold baseline, can be established based on the control population data.
[0095] In a possible implementation, for the same functional gene, a predetermined number of reference samples may be taken to determine the second coverage ratio. Here, the predetermined number of reference samples may be taken from different sampling individuals.
[0096] In a possible implementation, each reference sample may obtain a second coverage ratio, and thus each reference sample may obtain a reference error comparison coefficient. A reference error comparison coefficient range may be determined based on reference error comparison coefficients corresponding to a predetermined number of reference samples, respectively. For example, the reference error comparison coefficient range is determined based on the distribution of all reference error comparison coefficients.
[0097] Taking into account measurement errors, etc., for the same functional gene, the error comparison coefficient determined by the coverage ratio of the functional gene and the non-functional gene is usually within a certain range. If the point of gene variation occurs at the GSN site, then the error comparison coefficient determined by the coverage ratio of the functional gene and the non-functional gene will be offset. Therefore, a reference error comparison coefficient in the absence of gene variation can be determined by a predetermined number of reference samples as a benchmark for comparison with the error comparison coefficient. Comparing the error comparison coefficient with the reference error comparison coefficient range determined based on the reference error comparison coefficient can reflect the change in the coverage ratio of the first base corresponding to the functional gene and the second base corresponding to the non-functional gene at the GSN site. Then determine whether the gene variation occurs on the functional gene or the non-functional gene, and improve the accuracy of judgment.
[0098] In some embodiments, determining the reference error comparison coefficient range based on the reference error comparison coefficients corresponding to the predetermined number of reference samples respectively includes:
[0099] Determine the lower quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples as the minimum value of the reference error comparison coefficient range; and / or
[0100] The upper quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples are determined as the maximum value of the reference error comparison coefficient range.
[0101] Exemplarily, the reference error comparison coefficients corresponding to the predetermined number of reference samples may be sorted from small to large, and the value at the 25% position may be determined as the minimum value of the reference error comparison coefficient range.
[0102] Exemplarily, the reference error comparison coefficients corresponding to the predetermined number of reference samples may be sorted from large to small, and the value at the 75% position may be determined as the minimum value of the reference error comparison coefficient range.
[0103] In practical applications, a large number of reference samples collected from the control population can be sequenced for the second generation, and the sequencing data can be compared with the reference genome to obtain the number of functional gene reads and non-functional gene reads covering the GSN site. The natural logarithm of the ratio is taken as the reference error matching coefficient (MI value). Based on different regions of the gene (exon region), the MI values are classified and statistically analyzed to obtain the MI value threshold range of different regions of the gene, and the maximum and minimum values of the reference error matching coefficient range (or the upper and lower limits of the baseline) of different regions in the gene are determined respectively. The lower quartile value of the control population MI value can be used as the lower limit of the baseline of the control population of the gene, and the upper quartile value can be used as the upper limit of the baseline of the control population of the gene.
[0104] In this way, by comparing the error comparison coefficient of the sample to be tested that is predicted to have a gene mutation and the reference error comparison coefficient range, the change of the first base and the second base at the GSN site caused by the gene mutation can be determined, and then it can be determined whether the gene mutation is located in the functional gene, reducing the impact of non-functional genes on the gene mutation judgment, and improving the accuracy of gene mutation judgment. On the other hand, the reference error comparison coefficient range is determined using the reference sample corresponding to the functional gene as a benchmark for comparison with the error comparison coefficient, so as to reduce the impact of the error comparison coefficients of different functional genes, detection errors, detection errors, etc. on the comparison results, improve the accuracy of the comparison, and thus improve the accuracy of the gene mutation judgment.
[0105] In some embodiments, the determining of the confidence level of the predicted gene variation based on the comparison result of the error alignment coefficient and the reference error alignment coefficient range comprises at least one of the following:
[0106] The error comparison coefficient is within the reference error comparison coefficient range, and it is determined that the gene variation is located on the functional gene of the sample to be tested;
[0107] The error comparison coefficient is less than the minimum value of the reference error comparison coefficient range, and the gene variation is determined to be a false negative;
[0108] The error alignment coefficient is greater than the maximum value of the reference error alignment coefficient range, and the gene variation is determined to be a false positive.
[0109] In a possible implementation, a judgment result for the predicted gene variation may be reported based on the comparison result.
[0110] If the error matching coefficient is within the reference error matching coefficient range, it means that the coverage ratio of the first base of the functional gene and the second base of the non-functional gene at the GSN site in the test sample is similar to the coverage ratio of the first base of the functional gene and the second base of the non-functional gene at the GSN site in the reference sample, then it can be considered that the predicted gene variation has a high confidence level. Here, the gene variation site can be reported to be on the true gene.
[0111] The error comparison coefficient is less than the minimum value of the reference error comparison coefficient range, indicating that the proportion of functional genes obtained by sequencing the sample to be tested is relatively small relative to the reference sample. The reason for the small proportion of functional genes is most likely that a gene mutation occurs at the GSN site on the functional gene, and the GSN site on the functional gene changes from the first base to the second base, which makes the proportion of functional genes obtained by sequencing relatively small. When predicting gene mutation, since the GSN site where the gene mutation occurs is on the gene of the second base, it will be misjudged as a gene mutation in a non-functional gene, and no gene mutation occurs in the functional gene, resulting in a false negative. Therefore, the error comparison coefficient is less than the minimum value of the reference error comparison coefficient range, and the predicted gene mutation can be reported as a false negative.
[0112] The error comparison coefficient is greater than the maximum value of the reference error comparison coefficient range, indicating that the proportion of functional genes obtained by sequencing the sample to be tested is larger than that of the reference sample. The reason for the larger proportion of functional genes is most likely that a gene mutation occurs at the GSN site on the non-functional gene, and the GSN site on the non-functional gene changes from the second base to the first base, which makes the proportion of functional genes obtained by sequencing larger. When predicting a gene mutation, since the GSN site where the gene mutation occurs is on the gene of the first base, it will be misjudged as a gene mutation in a functional gene, resulting in a false positive. Therefore, if the error comparison coefficient is greater than the maximum value of the reference error comparison coefficient range, the predicted gene mutation can be reported as a false positive.
[0113] In one possible implementation, the predicted genetic variation is determined to be a false positive and / or a false negative, and verification can be performed using long-range polymerase chain reaction (Long-Range Polymerase Chain Reaction) technology / multiplex ligation-dependent probe amplification (Multiplex Ligation-dependent Probe Amplification, MLPA) technology and the like.
[0114] In some embodiments, the method further comprises:
[0115] In response to the presence of heterozygous deletion in the functional gene of the sample to be tested, the reference misalignment coefficient is adjusted based on the ratio of the functional gene in the sample to be tested to the non-functional gene in the sample to be tested.
[0116] When there is a heterozygous deletion in the functional gene of the sample to be tested, the ratio of the number of functional gene reads to the number of non-functional gene reads will decrease, that is, the first coverage ratio will decrease, and then the error matching coefficient will also decrease.
[0117] Here, the reference misalignment coefficient can be adjusted based on the change in the ratio of the number of functional gene reads to the number of non-functional gene reads.
[0118] In a possible implementation, the reference misalignment coefficient may be adjusted based on the difference between the misalignment coefficient when the functional gene has a heterozygous deletion and the misalignment coefficient when the functional gene does not have a heterozygous deletion.
[0119] For example, when there is no heterozygous loss in the functional gene, the logarithm of the ratio of functional gene to non-functional gene is ln(1:1) = 0; when there is a heterozygous loss in the functional gene, theoretically, the logarithm of the ratio of functional gene to non-functional gene is ln(1:2) = -0.69. At this time, the reference error comparison coefficient can be adjusted downward by 0.69, and then the error comparison coefficient can be compared with the reference error comparison coefficient to determine the predicted gene variation.
[0120] The reference error comparison coefficient is adjusted by the ratio of functional genes in the sample to be tested to non-functional genes in the sample to be tested; taking into account the change in the ratio of functional genes to non-functional genes when heterozygous deletion occurs, a more accurate judgment can be made on gene variation.
[0121] The following provides multiple specific examples in combination with any of the above embodiments:
[0122] This example provides a method for identifying variant sites of functional genes and their pseudogenes (homologous genes) based on second-generation sequencing data. Figure 2 The specific flow chart of this method specifically includes:
[0123] Step 201: Samples to be tested and population control samples.
[0124] Step 202: High-throughput sequencing. Perform high-throughput sequencing on the sample to be tested and the human control sample.
[0125] Step 203: Bioinformatics analysis
[0126] Step 204: Ratio of read coverage of functional gene and pseudogene GSN sites
[0127] Step 205: Determine the MI value of the control population
[0128] Step 206: Determine the MI value of the sample to be tested
[0129] Step 207: Determine the baseline MI value of the control population. If the copy number of the sample to be tested is abnormal, calibrate the baseline MI value of the control population.
[0130] Step 208: Compare the MI value of the sample to be tested with the baseline MI value of the control population (or the calibrated baseline MI value of the control population).
[0131] Exemplarily, it can be carried out based on the following specific implementation manner.
[0132] 1. Confirmation of functional genes and their pseudogenes
[0133] In this example, pseudogenes / homologous regions are collected as shown in Table (1). The coverage ratio (gene-to-pseudogene, G2P) of the functional genes and the corresponding non-functional genes (pseudogenes or homologous regions) in the table is close to 1, which is the ideal gene for the error alignment model in this example.
[0134] Table (1)
[0135] Gene Highly homologous exons Transcript ALMS1 17、21 NM_015120.4 C4A 1-41 NM_007293.2 CFC1 1-6 NM_032545.3 CYP11B1 1-8 NM_000497.4 CYP21A2 1-10 NM_000500.7 DCLRE1C 4 NM_001033855.2 DUOX2 6-8,19,32 NM_014080.4 GBA 2,6,11-12 NM_001005742.2 HBA1 1-2 NM_000558.4 HBA2 1-2 NM_000517.4 NOTCH2 1-4 NM_024408.3 OTOA 20-28 NM_144672.3 PMS2 2-4,9,12-15 NM_000535.5 PRSS1 1-5 NM_002769.4 TNXB 32-34,36-44 NM_019105.6 VWF 23-34 NM_000552.3 SMN1 1-9 NM_000344.4
[0136] 2. Establishment of baseline thresholds based on control population data
[0137] Due to the presence of additional pseudogenes between reference genomes, the differences in the ability of different detection reagents to capture functional genes and pseudogenes, and the possibility of random errors in sequencing, there are large differences in the ratio of coverage between different regions of a gene and its pseudogene or homologous region, G2P. Therefore, in order to accurately distinguish between genes and their pseudogenes or homologous regions, it is necessary to establish a MI threshold baseline (that is, the reference error matching coefficient range) for different genes based on population data. The main method is: collect a large number of control populations for second-generation sequencing, align the sequencing data with the reference genome, obtain the true gene reads and pseudogene reads covering the GSN site, and take the natural logarithm of their ratio (second coverage ratio), which is the MI value (reference error matching coefficient). Based on different regions (exon regions) of the gene, the MI values were classified and statistically analyzed to obtain the MI value threshold ranges for different regions of the gene (the minimum value of the reference error matching coefficient range, the maximum value of the reference error matching coefficient range), and the upper and lower limits of the baselines for different regions within the gene were determined respectively, that is, the lower quartile value of the MI value of the control population was used as the lower limit of the baseline of the control population of the gene (the minimum value of the reference error matching coefficient range), and the upper quartile value was used as the upper limit of the baseline of the control population of the gene (the maximum value of the reference error matching coefficient range).
[0138] 3. Construction of sequencing data analysis software process
[0139] Exome libraries were constructed for the samples to be tested. The insert fragments of the libraries were 300 to 500 bp in size and double-end 150 bp sequencing was performed on the Illumina platform. Quality control was performed on the raw sequencing data, including removal of low-quality sequencing reads and filtering out possible contamination sequences. The quality-controlled sequencing data was compared with the reference genome. The variant detection tool GATK (Genome Analysis Toolkit) was used to identify and filter variants.
[0140] 4. Genetic analysis process
[0141] The algorithm identifies misalignments based on a comparison of the sequencing depth of reads between highly homologous regions. The different nucleotides between a gene and its pseudogene or homologous region are called gene-specific nucleotides (GSN), as shown in Table (2). When a mutation in a GSN occurs due to gene conversion or a single nucleotide mutation (SNV), an incorrect alignment may occur. The method of this embodiment is to measure the coverage ratio (gene-to-pseudogene, G2P) of the GSN site of the target gene and its corresponding pseudogene. The misalignment index (MI) of the site is then calculated, MI=ln G2P, and compared with the threshold baseline. Copy number variation (CNV) has a significant impact on the MI baseline. Therefore, in order to ensure the readiness of variation detection, it is first necessary to evaluate the copy number of the gene and adjust the MI baseline according to the specific situation. When there is no deletion or duplication of the true gene, the judgment method is as follows:
[0142] a) If the MI value is within the threshold baseline range of the region, it indicates that the variant site in the region is located in the true gene;
[0143] b) If the MI value is lower than the lower limit of the threshold baseline of the region, it indicates that the region may have false negative sites due to the incorrect alignment of true genes to pseudogenes, and long-fragment PCR is needed for verification;
[0144] c) If the MI value is higher than the upper limit of the threshold baseline of the region, the region may have false positive sites due to incorrect alignment of pseudogenes to true genes, which requires further verification by long-fragment PCR.
[0145] d) When a true gene has a heterozygous deletion, the logarithm of the true gene: pseudogene ratio is theoretically ln(1:2)
[0146] =-0.69. At this time, the baseline of the control population corresponding to the heterozygous deletion region needs to be adjusted downward by 0.69, and the gene is judged based on the judgment method when there is no deletion or duplication of the true gene.
[0147] Table (2)
[0148]
[0149]
[0150] 5. Evaluation of the accuracy of the genetic analysis process
[0151] The performance of gene identification by the detection system can be further evaluated, as follows: 24 samples with pathogenic variant sites of genes with homologous regions are selected for exome sequencing, and analyzed according to the steps in the present embodiment. The performance evaluation results of the method of the present embodiment are shown in Table (3). A total of 24 samples were analyzed this time, wherein the genes of 7 samples were prompted by the method of the present embodiment to have false negative conditions. After comparison and data review, it was found that the pseudogene / homologous region of the gene had reportable variant sites, and it was clearly identified by LR-PCR combined with first-generation sequencing that the variant site was located on a functional gene. In addition, the method of the present embodiment also prompted that 6 samples had false positive sites, and further LR-PCR analysis found that the method of the present embodiment was consistent with the verification results. As can be seen from the above, the method of the present embodiment is for those that can identify pseudogenes / homologous regions, and prompts are performed to increase the accuracy of variant site identification.
[0152] Table (3)
[0153]
[0154]
[0155] 6. Example of CYP21A2 gene analysis
[0156] CYP21A2 gene mutations are associated with 21-hydroxylase deficiency, which is caused by congenital adrenal hyperplasia due to 21-hydroxylase deficiency (21-OHD), a genetic disease that affects the production of adrenal cortical hormones. Elevated serum 17-OHP levels can be used to diagnose 21-hydroxylase deficiency, but this method has the problem of high false negatives and false positives. Therefore, testing the CYP21A2 gene can be used as a supplementary means to improve the diagnosis rate of 21-hydroxylase deficiency.
[0157] In this example, human genomic nucleic acid was extracted from peripheral blood of female patients diagnosed with "congenital adrenal hyperplasia", and the obtained human genomic DNA was used to construct a whole exome library and sequenced. The sequencing data was filtered out after removing low-quality sequencing reads and filtering out possible contaminating sequences. The sequencing data after quality control was compared with the reference genome. The variant detection tool GATK (Genome Analysis Toolkit) was used to identify and filter variants. The MI value baseline of CYP21A2 was established using random 511 control second-generation sequencing data (see Figure 3). After analysis by the method of this embodiment, the MI values of each exon are shown in Table (4). As shown in Table (4), the MI value of exon 8 is abnormal. This may be because the true gene is mistakenly matched to the pseudogene, resulting in a false negative.
[0158] Table (4)
[0159]
[0160]
[0161] Further check the sequence alignment file (BAM file) of the pseudogene corresponding to the target region, such as Figure 4 As shown in the figure below, it was found that there were reportable pathogenic sites in this region. After LR-PCR verification, it was confirmed that there were two pathogenic sites NM_000500.7:c.1069C>T (p.Arg357Trp) and NM_000500.7:c.1391C>T (p.Pro464Leu) on the CYP21A2 gene of this patient, which could explain the clinical phenotype of the patient.
[0162] like Figure 5 As shown, the embodiment of the present disclosure proposes a gene variation confidence determination device 10, comprising: the device 10 comprises: a processing module 100, wherein:
[0163] The processing module is used to sequence the sample to be tested that is predicted to have a gene variation, and determine a first coverage ratio of a first base corresponding to a functional gene and a second base corresponding to a non-functional gene at a gene-specific nucleotide GSN site in the sample to be tested; wherein the non-functional gene includes a pseudogene and / or a homologous region of the functional gene;
[0164] The processing module is further used to determine an error alignment coefficient according to the first coverage ratio;
[0165] The processing module is also used to determine the confidence level of the predicted gene variation based on the comparison result of the error alignment coefficient and the reference error alignment coefficient range, wherein the reference error alignment coefficient range is determined based on the reference error alignment coefficient obtained based on the second coverage ratio, wherein the second coverage ratio is determined based on the first base corresponding to the functional gene and the second base corresponding to the non-functional gene at the GSN site in a predetermined number of reference samples, wherein the reference samples correspond to the functional gene.
[0166] In some embodiments, the processing module is specifically used for at least one of the following:
[0167] The error comparison coefficient is within the reference error comparison coefficient range, and it is determined that the gene variation is located on the functional gene of the sample to be tested;
[0168] The error comparison coefficient is less than the minimum value of the reference error comparison coefficient range, and the gene variation is determined to be a false negative;
[0169] The error alignment coefficient is greater than the maximum value of the reference error alignment coefficient range, and the gene variation is determined to be a false positive.
[0170] In some embodiments, the processing module is further configured to:
[0171] Determine a second coverage ratio corresponding to each reference sample;
[0172] Determine a reference error alignment coefficient corresponding to each reference sample according to the determined second coverage ratio corresponding to each reference sample;
[0173] A reference error comparison coefficient range is determined based on the reference error comparison coefficients respectively corresponding to the predetermined number of reference samples.
[0174] In some embodiments, the processing module is specifically used to:
[0175] Determine the lower quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples as the minimum value of the reference error comparison coefficient range; and / or
[0176] The upper quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples are determined as the maximum value of the reference error comparison coefficient range.
[0177] In some embodiments, the processing module is further configured to:
[0178] In response to the presence of heterozygous deletion in the functional gene of the sample to be tested, the reference misalignment coefficient is adjusted based on the ratio of the functional gene in the sample to be tested to the non-functional gene in the sample to be tested.
[0179] In some embodiments, the false alignment coefficient comprises the natural logarithm of the first coverage ratio;
[0180] The reference misalignment coefficient comprises the natural logarithm of the second coverage ratio.
[0181] Figure 6: is a schematic diagram of the structure of an electronic device 9100 provided in an embodiment of the present disclosure. The electronic device 9100 may be a network device, a terminal, a chip, a chip system, or a processor that supports any of the above methods, or a chip, a chip system, or a processor that supports the terminal to implement any of the above information transmission methods. The electronic device 9100 may be used to implement the gene variation confidence determination method described in the above method embodiment, and the details may refer to the description in the above method embodiment.
[0182] like Figure 6 As shown, the electronic device 9100 includes one or more processors 9101. The processor 9101 may be a general-purpose processor or a dedicated processor, etc. The processor 9101 is used to call instructions so that the electronic device 9100 executes any of the above gene variation confidence determination methods.
[0183] In some embodiments, the electronic device 9100 further includes one or more memories 9102 for storing instructions. Optionally, all or part of the memory 9102 may also be outside the electronic device 9100.
[0184] In some embodiments, the electronic device 9100 further includes one or more transceivers 9103. When the electronic device 9100 includes one or more transceivers 9103, the steps of sending, receiving and / or acquiring in the above method are performed by the transceiver 9103, and the other steps are performed by the processor 9101.
[0185] In some embodiments, the steps of obtaining and the like in the above method may also be executed by the processor 9101 , for example, obtaining information from the memory 9102 and the like.
[0186] In some embodiments, the transceiver may include a receiver and a transmitter, and the receiver and the transmitter may be separate or integrated. Optionally, the terms such as transceiver, transceiver unit, transceiver, transceiver circuit, etc. may be replaced with each other, the terms such as transmitter, transmission unit, transmitter, transmission circuit, etc. may be replaced with each other, and the terms such as receiver, receiving unit, receiver, receiving circuit, etc. may be replaced with each other.
[0187] Optionally, the electronic device 9100 further includes one or more interface circuits 9104, which are connected to the memory 9102. The interface circuit 9104 can be used to receive signals from the memory 9102 or other devices, and can be used to send signals to the memory 9102 or other devices. For example, the interface circuit 9104 can read instructions stored in the memory 9102 and send the instructions to the processor 9101.
[0188] The electronic device 9100 described in the above embodiments may be a network device or a terminal, but the scope of the electronic device 9100 described in the present disclosure is not limited thereto, and the structure of the electronic device 9100 may not be limited thereto. Figure 6 The electronic device may be an independent device or may be part of a larger device. For example, the electronic device may be: (1) an independent integrated circuit IC, or a chip, or a chip system, or a subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.
[0189] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program commands, and the aforementioned program can be stored in a storage medium, and the storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.
[0190] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several commands to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.
[0191] It should be understood that the above embodiments are exemplary and are not intended to include all possible implementations included in the claims. Various modifications and changes may be made on the basis of the above embodiments without departing from the scope of the present disclosure. Similarly, the various technical features of the above embodiments may be arbitrarily combined to form other embodiments of the present invention that may not be explicitly described. Therefore, the above embodiments only express several implementations of the present invention and do not limit the scope of protection of the patent of the present invention.
Claims
1. A method for determining confidence of gene variation, characterized in that: include: The method comprises: Sequencing the sample to be tested that is predicted to have a gene mutation, and determining a first coverage ratio of a first base corresponding to a functional gene and a second base corresponding to a non-functional gene at a gene-specific nucleotide GSN site in the sample to be tested; wherein the non-functional gene includes a pseudogene and / or a homologous region of the functional gene; Determining an error alignment coefficient according to the first coverage ratio; Based on the comparison result of the error alignment coefficient and the reference error alignment coefficient range, the confidence level of the predicted gene variation is determined, wherein the reference error alignment coefficient range is determined based on the reference error alignment coefficient obtained based on the second coverage ratio, wherein the second coverage ratio is determined based on the first base corresponding to the functional gene and the second base corresponding to the non-functional gene at the GSN site in a predetermined number of reference samples, wherein the reference samples correspond to the functional gene.
2. The method according to claim 1, characterized in that The step of determining the confidence level of the predicted gene variation based on the comparison result of the error comparison coefficient and the reference error comparison coefficient range comprises at least one of the following: The error comparison coefficient is within the reference error comparison coefficient range, and it is determined that the gene variation is located on the functional gene of the sample to be tested; The error comparison coefficient is less than the minimum value of the reference error comparison coefficient range, and the gene variation is determined to be a false negative; The error alignment coefficient is greater than the maximum value of the reference error alignment coefficient range, and the gene variation is determined to be a false positive.
3. The method according to claim 1 or 2, characterized in that: The method further comprises: Determine a second coverage ratio corresponding to each reference sample; Determine a reference error alignment coefficient corresponding to each reference sample according to the determined second coverage ratio corresponding to each reference sample; A reference error comparison coefficient range is determined based on the reference error comparison coefficients respectively corresponding to the predetermined number of reference samples.
4. The method according to claim 3, characterized in that The determining of the reference error comparison coefficient range based on the reference error comparison coefficients respectively corresponding to the predetermined number of reference samples includes: Determine the lower quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples as the minimum value of the reference error comparison coefficient range; and / or The upper quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples are determined as the maximum value of the reference error comparison coefficient range.
5. The method according to claim 1 or 2, characterized in that: The method further comprises: In response to the presence of heterozygous deletion in the functional gene of the sample to be tested, the reference misalignment coefficient is adjusted based on the ratio of the functional gene in the sample to be tested to the non-functional gene in the sample to be tested.
6. The method according to claim 1 or 2, characterized in that: The false alignment coefficient comprises the natural logarithm of the first coverage ratio; The reference misalignment coefficient comprises the natural logarithm of the second coverage ratio.
7. A gene variation confidence determination device, characterized in that: include: The device comprises: a processing module, wherein: The processing module is used to sequence the sample to be tested that is predicted to have a gene variation, and determine a first coverage ratio of a first base corresponding to a functional gene and a second base corresponding to a non-functional gene at a gene-specific nucleotide GSN site in the sample to be tested; wherein the non-functional gene includes a pseudogene and / or a homologous region of the functional gene; The processing module is further used to determine an error alignment coefficient according to the first coverage ratio; The processing module is also used to determine the confidence level of the predicted gene variation based on the comparison result of the error alignment coefficient and the reference error alignment coefficient range, wherein the reference error alignment coefficient range is determined based on the reference error alignment coefficient obtained based on the second coverage ratio, wherein the second coverage ratio is determined based on the first base corresponding to the functional gene and the second base corresponding to the non-functional gene at the GSN site in a predetermined number of reference samples, wherein the reference samples correspond to the functional gene.
8. The device according to claim 7, characterized in that The processing module is specifically used for at least one of the following: The error comparison coefficient is within the reference error comparison coefficient range, and it is determined that the gene variation is located on the functional gene of the sample to be tested; The error comparison coefficient is less than the minimum value of the reference error comparison coefficient range, and the gene variation is determined to be a false negative; The error alignment coefficient is greater than the maximum value of the reference error alignment coefficient range, and the gene variation is determined to be a false positive.
9. The device according to claim 7 or 8, characterized in that The processing module is also used for: Determine a second coverage ratio corresponding to each reference sample; Determine a reference error alignment coefficient corresponding to each reference sample according to the determined second coverage ratio corresponding to each reference sample; A reference error comparison coefficient range is determined based on the reference error comparison coefficients respectively corresponding to the predetermined number of reference samples.
10. The device according to claim 9, characterized in that The processing module is specifically used for: Determine the lower quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples as the minimum value of the reference error comparison coefficient range; and / or The upper quartile values of the reference error comparison coefficients corresponding to the predetermined number of reference samples are determined as the maximum value of the reference error comparison coefficient range.
11. The device according to claim 7 or 8, characterized in that The processing module is further used for: In response to the presence of heterozygous deletion in the functional gene of the sample to be tested, the reference misalignment coefficient is adjusted based on the ratio of the functional gene in the sample to be tested to the non-functional gene in the sample to be tested.
12. The device according to claim 7 or 8, characterized in that The false alignment coefficient comprises the natural logarithm of the first coverage ratio; The reference misalignment coefficient comprises the natural logarithm of the second coverage ratio.
13. An electronic device, characterized in that: The electronic device comprises: one or more processors; The processor is used to call instructions so that the electronic device executes the gene variation confidence determination method described in any one of claims 1 to 6.
14. A storage medium, characterized in that: The storage medium stores instructions, and when the instructions are executed on an electronic device, the electronic device executes the method for determining the confidence of a genetic variation according to any one of claims 1 to 6.