Base recognition method based on double-end sequencing, sequencing data quality evaluation method, program product and equipment

Abstract

The invention discloses a base recognition method based on double-end sequencing, a sequencing data quality evaluation method, a program product and equipment, and the method comprises the steps: obtaining a double-end sequencing file, and obtaining first sequence data and second sequence data corresponding to each sequencing fragment from the double-end sequencing file; based on the first sequence data and the second sequence data corresponding to each sequencing fragment, determining an overlapping region of each sequencing fragment; obtaining a comparison result of the first base type and the second base type corresponding to each base position obtained from the first sequence data; and determining a correct base type corresponding to the base position according to a comparison result of the obtained estimated base mass fraction corresponding to the first base type and the estimated base mass fraction corresponding to the second base type with a preset mass fraction threshold.

Description

Technical Field

[0001] The present invention relates to the field of gene technology, and in particular to a base category recognition method based on double-end sequencing, a sequencing data quality assessment method based on double-end sequencing, a computer program product and a computer device. Background Art

[0002] In double-end sequencing, if the length of an insert fragment is less than twice the sequencing length / read length, an overlap will occur in the middle of the insert fragment. In this overlapping region, each base is detected twice. In the overlapping region, the sequencer actually detects each base of the same insert fragment twice. In the prior art, if the two detection results of the base are the same, then the detected base type is the correct base type. If the two detection results of the base are different, the base with a mutation frequency or allele frequency greater than or equal to 99% at the position is used as the correct base type of the site, that is, the method considers that all sites on the genome in the sample are homozygous. This is also inconsistent with common sense, because even a healthy human genome has a large number of heterozygous sites. Taking the standard HG001NA12878 human genome as an example, it has at least two million heterozygous sites. If the sample is a non-healthy tissue, such as a tumor or a sample containing a genetic disease (aborted fetal tissue, etc.), the number of heterozygous sites will be higher. The existing base classification method cannot determine the correct base type of the heterozygous site, because there is no base with a mutation frequency greater than or equal to 99% at the heterozygous site position in the genome. Therefore, if the two detection results of the base are different, the base with a mutation frequency or allele frequency greater than or equal to 99% at the position is used as the correct base type of the site, which cannot adapt to the heterozygous site position in the genome.

[0003] In high-throughput sequencing, for each detected base (base call), the sequencer not only outputs the type of the base, but also outputs a quality value, called the base quality score, also known as the Q value (Q score). Each base corresponds to a base quality score, and the base quality score value reflects the sequencer's estimate of the error rate of base recognition. It is very important for the sequencer to output the accurate quality score value of the base, because almost all downstream analyses of high-throughput sequencing data rely on the base quality score value. Algorithms including data quality control, sequence alignment, variation detection (short insertion and deletion, copy number, structural variation), etc. are based on the calculation of base quality score values. But in fact, the output process of the base quality score value is that the sequencer first collects the optical signal or electrical signal from the sensor detecting a certain base, and then infers the corresponding base quality score value through the empirical relationship between the signal intensity and the base quality score value. It can be seen that the sequencer cannot directly calculate the error rate of base recognition, but can only estimate the base quality score value, that is, after the sequencing is completed, the base quality score of each base that can be read from the test file is the estimated base quality score of each base. This leads to the fact that the base quality score value output by the sequencer in most cases cannot accurately reflect the recognition error rate, so a data quality inspection technology is needed to inspect the quality of the sequencing data output by the sequencer.

[0004] The existing data quality inspection technology assumes that the two pieces of information output by the sequencer, namely the type of base and the base quality score, are real, so the accuracy and stability of the base quality score value (i.e., the reported base quality score value) in the sequencing result FASTQ file itself are not tested, and the error rate and preference of the sequencing base type (i.e., the reported base type) in the sequencing result FASTQ file are not tested. It is a common phenomenon that the sequencing base type is detected incorrectly (also known as misdetection or mismatch, i.e., it does not match the real base type). Therefore, the existing technology cannot accurately evaluate whether the sequencing data quality meets the requirements, which will affect the subsequent use of the sequencing data. Summary of the invention

[0005] In order to solve the existing technical problems, the embodiments of the present invention provide a sequencing data quality assessment method and device based on double-end sequencing, which can accurately identify the base type and can more accurately assess whether the sequencing data quality meets the requirements.

[0006] In a first aspect, a method for identifying base categories based on double-end sequencing is provided, comprising: obtaining a double-end sequencing file, and obtaining first sequence data and second sequence data corresponding to each sequencing fragment from the double-end sequencing file; wherein the first sequence data is base sequence data obtained by sequencing from the first end to the second end of the sequencing fragment, and the second sequence data is base sequence data obtained by sequencing from the second end to the first end of the sequencing fragment; based on the first sequence data and the second sequence data corresponding to each sequencing fragment, determining an overlapping region of each sequencing fragment; for each base position in the overlapping region, obtaining from the first sequence data a first base type and an estimated base quality score corresponding to each base position, respectively, and obtaining from the second sequence data a second base type and an estimated base quality score corresponding to each base position, respectively; for each base position, determining a correct base type corresponding to the base position based on a comparison result of the first base type and the second base type corresponding to the base position, and a comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold.

[0007] In a second aspect, a sequencing data quality assessment method based on double-end sequencing is provided, comprising determining the correct base type corresponding to each base position in the overlapping region of each sequencing fragment based on the base category identification method based on double-end sequencing described in any embodiment of the present application; for each sequencing fragment, obtaining the base information of the base in the overlapping region from the double-end sequencing file; and assessing the sequencing data quality in the double-end sequencing file based on the correct base type corresponding to the base in the overlapping region and the base information of the base in the overlapping region.

[0008] In a third aspect, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the base category identification method based on double-end sequencing described in any embodiment of the present application, or implements the sequencing data quality assessment method based on double-end sequencing described in any embodiment of the present application.

[0009] In a fourth aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the base category identification method based on double-end sequencing described in any embodiment of the present application, or executes the sequencing data quality assessment method based on double-end sequencing described in any embodiment of the present application.

[0010] In the embodiment of the present application, the first sequence data and the second sequence data of the same sequencing fragment are obtained from the double-end sequencing file. The first base type, the estimated base quality score corresponding to the first base type, the corresponding second base type and the estimated base quality score corresponding to the second base type corresponding to each base position in the overlapping region can be determined through the first sequence data and the second sequence data of the same sequencing fragment. Then, based on the comparison result of the first base type and the second base type corresponding to the base position, and the comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold, the correct base type corresponding to the base position is determined, so that the base type in the overlapping region can be accurately identified; and based on at least one of the correct base type corresponding to each base, the sequencing base type corresponding to the base position, and the fitted base quality score, the sequencing data quality in the double-end sequencing file is evaluated. Since the sequencing data quality can be evaluated based on at least one relatively accurate base information, whether the sequencing data quality meets the requirements can be accurately evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a brief schematic diagram of a gene sequencer in one embodiment;

[0012] Figure 2 A schematic diagram of the correlation between the reported Q value and the true Q value based on the Salus Evo sequencer and the Illumina sequencer in one embodiment;

[0013] Figure 3 A schematic diagram of sequencing an inserted fragment in double-end sequencing in one embodiment;

[0014] Figure 4 Schematic diagram of the correlation between the nominal Q value and the logarithm corresponding to the error rate under different c cycle numbers for an Illumina sequencer under the same doublet base AC in an embodiment;

[0015] Figure 5 is a schematic diagram of mismatched bases at certain fixed positions in the genome in one embodiment;

[0016] Figure 6 Flow chart of a method for identifying base categories based on double-end sequencing in one embodiment;

[0017] Figure 7 Flow chart of a method for evaluating sequencing data quality based on paired-end sequencing in one embodiment;

[0018] Figure 8 is a schematic diagram of error detection types in one embodiment;

[0019] Fig. 9A schematic diagram showing a comparison of the preferences of mismatched bases in two sequencers in one embodiment;

[0020] Fig.10 is a flowchart of a sequencing data quality assessment method based on paired-end sequencing in another embodiment;

[0021] Fig.11 Schematic diagram of the target straight line fitted under different sequencing platforms in one embodiment;

[0022] Fig.12 Schematic diagram of fitting straight lines fitted under different sequencing platforms in one embodiment;

[0023] Fig.13 is a schematic diagram of significance values ​​corresponding to various candidate features in one embodiment;

[0024] Fig.14 A schematic diagram of preliminary statistics of valid information in one embodiment;

[0025] Fig.15 This is a schematic diagram of grouped information in one embodiment;

[0026] Fig.16 is a schematic diagram of the original base quality scores of each group in one embodiment;

[0027] Fig.17 is a schematic diagram of a base type identification device based on double-end sequencing in one embodiment;

[0028] Fig.18 is a schematic diagram of a sequencing data quality assessment device based on paired-end sequencing in one embodiment;

[0029] Fig.19 Schematic diagram of the structure of a gene sequencer in one embodiment. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the technical field of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0032] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it should be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0033] Gene sequencing refers to the analysis of the base sequence of the DNA fragment of the data to be tested, that is, the arrangement of adenine (A), thymine (T), cytosine (C) and guanine (G). Currently, the fluorescent labeling method is commonly used for gene sequencing. The gene sequencing optical system uses laser to excite the fluorescent markers on the sequencing chip to produce fluorescence and collect the fluorescent signals. The four bases combine with different fluorescent markers to produce four different fluorescence bands, thereby identifying the bases.

[0034] The second generation sequencing technology, taking the Illumina sequencer as an example, uses different fluorescent molecules with different fluorescence emission wavelengths. When these fluorescent molecules are irradiated by laser, they will emit fluorescent signals of corresponding wavelengths. By using filters to selectively filter out non-specific wavelengths of light after laser irradiation, fluorescent signals of specific wavelengths can be obtained. Therefore, by obtaining fluorescent signals and analyzing them, the base type can be identified. It mainly includes sample preparation, cluster generation, sequencing and data analysis.

[0035] Sample preparation: The DNA sample to be sequenced is extracted and purified, and then DNA fragmentation and adapter connection are performed. In the optional example, the DNA sample is usually cut by ultrasound or restriction endonuclease to cut the DNA sample into a large number of smaller DNA fragments. Then, adapters are connected to both ends of the DNA fragments. The adapters contain specific sequences for subsequent connection and sequencing reactions.

[0036] Cluster generation: This process is to amplify DNA fragments to form fixed DNA fragments so that a DNA fragment can be formed into a base cluster later. In an optional example, specifically, the DNA fragments are amplified by methods such as polymerase chain reaction (PCR) or bridge amplification, so that each DNA fragment forms millions of copies, and the amplified DNA fragments are fixed on a fixed plate. Each DNA fragment forms an independent cluster on the fixed plate.

[0037] Sequencing refers to sequencing reads for each base cluster on the flow cell. Sequencing adds a fluorescently labeled dNTP sequencing primer. One end of the dNTP chemical formula is connected to an azide group, which can prevent polymerization during the sequencing chain extension, ensuring that only one base can be extended in a cycle, and a corresponding sequencing read is generated, that is, sequencing while synthesizing. In one cycle, a base is identified by a fluorescently labeled dNTP for each base cluster, and the fluorescence signal of a specific color corresponds to the sequencing signal response of different base types. Through laser scanning, the color of the emitted fluorescence can be used to determine which base corresponds to each base cluster in the current cycle. In one cycle, tens of millions of base clusters are sequenced simultaneously in the flow cell. A fluorescent spot represents the fluorescence emitted by a base cluster, and a base cluster corresponds to a read in fastq. In the sequencing stage, the infrared camera is used to capture the fluorescence image of the flow cell surface, and the fluorescence image is processed and the fluorescence point position is located to detect the base cluster. The template is constructed based on the base cluster detection results of multiple fluorescence images corresponding to the sequencing signal response of different base types, and the position of all base cluster template points (cluster) on the flow cell is constructed. According to the template, the fluorescence intensity of the filtered image is extracted, and then the fluorescence intensity is corrected. Finally, the base is identified and the score is calculated based on the maximum intensity of each base cluster template point position, and the fastq base sequence file is output.

[0038] The gene sequencer may also include an optical platform, which may include an operating table and a camera, wherein the sequencing chip may be placed on the operating table, and the gene sequencer uses a laser to excite the fluorescent markers on the sequencing chip to produce fluorescence, and collects the fluorescence signal, and the four bases combine with different fluorescent markers to produce four different fluorescence bands. That is, the fluorescence images of the four base types. The sequencing chip is photographed by a camera to capture the fluorescence image of the fluorescence signal generated on the charge coupled device (CCD) on the test chip. There are many fluorescent spots in a fluorescent image, and a fluorescent spot in the fluorescent image represents the fluorescence emitted by a base cluster.

[0039] The imaging method of the gene sequencer can be a four-channel imaging system or a two-channel imaging system. For the two-channel imaging system, each camera needs to be exposed twice at the same position of the test chip. For the four-channel imaging system, the camera of each channel shoots once at the same position of the sample, and obtains fluorescent images of four base types respectively. For example, the fluorescent image of the A base type, the fluorescent image representing the A base type, the fluorescent image of the C base type, the fluorescent image of the G base type and the fluorescent image of the T base type are obtained respectively. Since the non-specific wavelength of light is selectively filtered out by using a filter after laser irradiation to obtain a fluorescent signal of a specific wavelength, each base type corresponds to a different fluorescent signal. In the same cycle reaction, the brightness of the same type of base cluster in its corresponding category base type is much greater than that of other categories of bases, and the base clusters emitting light in each channel will not be repeated in theory.

[0040] After the gene sequencer obtains the fluorescent image, it will reconstruct the gene image, align the gene image, and identify the gene base (gene basecall) to obtain the gene sequence.

[0041] Gene image reconstruction is used to improve the resolution of the fluorescence image, to improve the clarity of the image, and to reduce the crosstalk between samples. Gene image reconstruction includes but is not limited to conventional operations such as deconvolution.

[0042] Gene image registration is to correct the fluorescence images of the four base types so that they can overlap, so as to extract the fluorescence brightness of the four channels at the same position, which is convenient for subsequent base recognition. Gene image registration includes but is not limited to image registration of the same channel, global or local affine registration.

[0043] The gene recognition process is to determine whether the base cluster in the image belongs to one of the four bases A, C, G, and T based on the registered image. After the test data undergoes gene recognition, the test data information is converted from a digital image to the sequence information of the four bases A, C, G, and T, which is the DNA sequence result of the sample for subsequent analysis and evaluation.

[0044] Data analysis: Analyze and interpret sequencing data based on image data and sequence information. Compare sequence information with reference genome for mutation identification.

[0045] The process of sequencing a piece of data to be tested is called a run. The sequencing process of a piece of data to be tested is composed of multiple cycles. One cycle corresponds to one reaction cycle, that is, it corresponds to the identification of one base type in the sequencing chip. During sequencing, the synthesis is performed while sequencing. In one cycle, tens of millions of base clusters are sequenced simultaneously.

[0046] A test data includes many DNA fragments. In the above sequencing process, a base is added to each DNA fragment, so the length of the base sequence of the DNA of the test data determines the number of cycles. In each cycle, the gene sequencer can obtain a fluorescence image of each of the four base types ACGT. When sequencing the test data, the gene sequencer can obtain fluorescence images of the ACGT channel for multiple cycles.

[0047] It should be noted that the above is an explanation of the sequencing process using Illumina sequencing technology as an example of massively parallel sequencing technology (MPS), in which the DNA molecules to be tested are amplified through a specific amplification technology, and base clusters are amplified for each DNA fragment (single-stranded library molecule). The base cluster detection results are used to construct template points of the base clusters on the sequencing chip, so that subsequent operations such as base recognition can be performed based on the template points of the base clusters, thereby improving the efficiency and accuracy of base recognition. It can be understood that the base recognition method based on fluorescently labeled dNTP gene sequencing provided in the embodiment of the present application is based on the positioning detection and base type recognition of the base clusters after amplification of the single-stranded library molecules on the sequencing chip. Here, each base cluster refers to a base signal acquisition unit, so that it is not limited to which amplification technology is used for the single-stranded library molecules. That is, the sequencing data quality assessment method based on double-end sequencing provided in the embodiment of the present application can also be applied to the positioning detection and base type recognition of the base signal acquisition unit of the sequencing chip in other large-scale parallel sequencing technologies. For example, the base signal acquisition unit can refer to the base cluster obtained by the bridge amplification technology in the Illumina sequencing technology, and also includes the nanospheres obtained by the rolling circle amplification technology (RCA, Rolling Circle Amplification), etc., and the present application does not limit this. In the following embodiments, for ease of understanding, the base signal acquisition unit is taken as an example of a base cluster for explanation.

[0048] See also Figure 1 , is a brief schematic diagram of a gene sequencer in one embodiment. The gene sequencer may also include an operating table and a camera, wherein a sequencing chip may be placed on the operating table, and the gene sequencing chip has a number of base clusters arranged in an array or randomly distributed. Through a dyeing reagent, different types of base clusters will be connected to one of the different fluorescent markers during the sequencing reaction, and these fluorescent markers will emit fluorescent signals after being irradiated by a laser, and the fluorescent signals of non-specific wavelengths are selectively filtered out by a filter to obtain fluorescent signals of specific wavelengths. The fluorescent molecules in different fluorescent markers have different fluorescence emission wavelengths, so that different base clusters correspond to different fluorescent signals. The fluorescent image is obtained by a camera, and the fluorescent image is analyzed to identify the base category of each base cluster. The camera may be an optical microscope.

[0049] In a gene sequencing process, the process of sequencing a gene sample to be tested is called a run. A gene sample to be tested will be broken into M base sequences to be tested, which can also be called short chains. Each base sequence to be tested includes N base clusters. In one cycle, the top base clusters of these M short chains are sequenced on the sequencing chip at the same time. On the sequencing chip, each base cluster being sequenced corresponds to a position. In one cycle, tens of millions of base clusters are sequenced simultaneously. N determines the number of cycles of the test. The larger N is, the more cycles there are. In different cycles, the base clusters in the M base sequences to be tested are sequenced respectively. For example, if a gene sample to be tested is broken into 10,000 short chains, each of which is 100 bases long, then 100 cycles of sequencing reactions are required to identify the base type. In each cycle, the top base clusters of these 10,000 short chains are sequenced on the sequencing chip. After a gene sequencing is completed, multiple sequencing sequences that have been sequenced can be obtained from the sequencing result file, that is, many short chains that have been sequenced can be obtained. A sequencing sequence is a short chain or a read or a sequencing fragment.

[0050] For any base, the conversion relationship between the base recognition error rate and the base quality score value is as follows: E = 10 -Q / 10 , where E is the recognition error rate of the base by the sequencer, and Q is the quality score of the base. For example, if the Q value output by the sequencer for a certain base is 30, it means that the sequencer believes that the recognition error rate of the base is 10 -30 / 10 =0.001.

[0051] The most widely used base quality score correction technologies at present have the same base grouping method. The existing technology believes that the following four characteristics of the base are the characteristics that significantly affect the accuracy of the base quality score, and the bases in the sample are grouped (bin) according to the following characteristics: the order of the read (read order), the number of sequencing cycles (cycle), the double base (dinucleotide context) composed of the base and the base 1bp upstream of the base, and the estimated base quality score (qual) reported by the sequencer. However, these four characteristics are preset and are not suitable for all samples. Due to factors such as different samples and different instruments, some characteristics may not be the main factors affecting the base quality score, which will affect the accuracy of the grouping and thus affect the correction effect. Grouping the bases based on the above four major categories of features is based on the characteristics of the Illumina NovaSeq sequencer. For sequencers of other brands, the significant features may be different from those of the Illumina platform sequencers. Appropriate significant features should be selected according to their specific characteristics to group the bases, rather than using fixed four categories of feature groupings. For example, Figure 2 As shown in the figure, the Q value of the Salus Evo sequencer is not sensitive to the read order, where the red line is the Salus Evo sequencer data, the blue line is the Illumina sequencer data, and the black dotted line is the diagonal line. It can be seen that when grouped by read order, the data points of the Salus Evo sequencer are closer to the diagonal line, indicating that its nominal base quality score value is very close to the actual base quality score value. If the same significant feature grouping as Illumina NovaSeq is used, the grouping will be inaccurate, affecting the final correction effect.

[0052] They first group all the bases involved in the calculation in the sample according to the above four features, and then calculate the error rate and corresponding base quality score of each group by comparing the base type of each base with the correct base at the genomic position. At this time, the base quality score of each group contains errors, which is called the raw base quality score (raw Q). In order to reduce the error, the raw base quality scores of several groups will be fitted with new base quality scores using local weighted regression (lowess), which is called the fitted base quality score (fitted Q). This base quality score is considered to be the true base quality score of the bases in the group. Finally, the base quality scores of all bases in each group will be set to the fitted base quality score in the group.

[0053] When calculating the error rate of a group, it is necessary to count the number of incorrect bases in the group, so it is necessary to determine the correct base type of each base in the sequencing file. The method for determining the correct base type in the prior art can be based on the correction technology of known standard sites. As the name implies, the user compares the base at a base position with the reference genome sequence. The reference genome sequence is a list of known standard sites. The content of the list is a series of genome coordinates and the correct base type of each coordinate position. The corresponding coordinate position will be determined in the reference genome according to the base position, and the base at the corresponding coordinate position will be determined as the correct base type at the base position. At present, GATK-BQSR uses the known standard sites of humans in the NCBI dbSNP database. However, the current method of using known standard sites to determine the correct base type is only applicable to some species that have been widely studied, such as humans. Most other species do not have known standard sites. This limitation makes the GATK-BQSR function basically unable to be applied to sequencing data of samples from other species. Secondly, this method assumes that all base types in the sequencing results that do not match the known standard sites are detection errors of the sequencer, which is inconsistent with common sense. Because in the high-throughput sequencing experiment, various external factors such as high concentration of formaldehyde reagents, high-intensity mechanical force, inaccurate endonucleases and DNA synthases, and inappropriate sample storage environments may change the base types in the sample during sample preparation and library preparation, making some sites in the sample inconsistent with the base types of the known standard sites, and these are not caused by sequencer detection errors. Attributing them all to detection errors of the sequencer will underestimate the base quality score.

[0054] In paired-end sequencing, if the length of an insert fragment is less than twice the sequencing length / read length, read1 and read2 will overlap in the middle of the insert fragment. Figure 3 As shown in the figure, the schematic diagram of sequencing an inserted fragment in double-end sequencing. If the inserted fragment is small enough, the results of read1 and read2 will overlap partially.

[0055] When calculating the fitted base quality scores, the local weighted regression (lowess) method is used to fit the model. Function values ​​outside the numerical range of the model independent variables cannot be fitted. Only values ​​within a partial range are used. For example, when the group with estimated base quality scores of 11 to 30 is used, their error rates are E(11), E(12)...E(30). We cannot fit the error rate E(31) of the group with an estimated base quality score of 31. In addition, this method does not take into account the consistency of the continuous variable grouping. It only uses values ​​within a partial range, which causes overfitting in some cases. For example, Figure 4Figure 1 is a schematic diagram of the correlation between the nominal Q value and the logarithm of the error rate under different c cycle numbers for the same dinucleotide context AC in an Illumina sequencer in an embodiment. For the Salus Pro sequencer, when the dinucleotide context is AC and the read order is 1st read, the relationship between the reported Q value, i.e., the estimated base quality score (Qual), and the true error rate should be a quadratic linear relationship. Using local weighted regression to fit different cycle cycles, when the cycle number is large (such as Figure 4 The left figure in the figure shows a quadratic linear relationship at a cycle number of 140, while at a medium cycle number ( Figure 4 In the right figure, the number of cycles is 88) there is overfitting.

[0056] In sequencing, mismatch bias refers to the tendency of a sequencer to detect a base as another base type when the sequencer mistakenly detects a base on the read sequence fragment. This is not a random misdetection, but a tendency to occur on a specific base type. For example, a certain brand of sequencer has 54 misdetections of T as A and 32 misdetections of A as T for every 100 misdetections. If the sequencer has no mismatch bias, the probability of the twelve misdetections should be similar.

[0057] At present, the quality control software only checks the number or percentage of base types that do not match the actual base types (number of false positives or false positive rates) output by the sequencer, without considering the preference of the mismatched bases. In fact, when the number or percentage of false positives of two sequencers is the same, the data quality of the sequencer with a high preference for mismatched bases is worse, and it is more likely to produce errors in downstream analysis. Because when a sequencer with a high preference for mismatched bases makes a sequencing error, the mismatched bases tend to cluster at certain fixed positions in the genome (as shown below). Figure 5 As shown in the figure, this may lead to incorrect variant calling. If the bias of mismatched bases is ignored, the data quality cannot be accurately measured.

[0058] like Figure 6 As shown, Figure 6 The flowchart of a base category recognition method based on double-end sequencing in one embodiment is applied to a gene sequencer. The base category recognition method based on double-end sequencing includes the following steps:

[0059] S11, obtaining a double-end sequencing file, and obtaining first sequence data and second sequence data corresponding to each sequencing fragment from the double-end sequencing file.

[0060] In this embodiment, the bidirectional sequencing file is a sequencing result file output by the sequencer, and the bidirectional sequencing file includes the results of sequencing at both ends of each sequencing fragment, that is, the first sequence data and the second sequence data. The first sequence data is the base sequence data obtained by sequencing from the first end to the second end of the sequencing fragment, and the second sequence data is the base sequence data obtained by sequencing from the second end to the first end of the sequencing fragment. For example, Figure 3 The sequencing sequence data corresponding to read1 and the sequencing sequence data corresponding to read2.

[0061] In this embodiment, the double-end sequencing file is a file directly output from the sequencer, and the double-end sequencing file needs to be preprocessed. The preprocessing includes but is not limited to filtering out data that does not meet the preset conditions, thereby improving the quality of the data. The processed double-end sequencing file is aligned to the reference genome using alignment software. Each base position has a corresponding unique reference position in the reference genome. Therefore, after obtaining the alignment result, the first sequence data and the second sequence data corresponding to each sequencing fragment can be obtained.

[0062] The first sequence data and the second sequence data include at least one of the following: base position of a base, base sequence of each sequencing fragment, estimated base score of each base in each sequencing fragment, number of sequencing cycles corresponding to each base, identification of the sequencing fragment, length of the sequencing fragment, and order (read order) of the sequencing fragment. The base position indicates the position of the base in the sequencing fragment, the base sequence indicates the base type of each base in each sequencing fragment output by the sequencer, and the order indicates the number of the sequencing fragment and the sequencing direction.

[0063] S12. Determine the overlapping region of each sequencing fragment based on the first sequence data and the second sequence data corresponding to each sequencing fragment.

[0064] In this embodiment, the processed double-end sequencing file is aligned to the reference genome using alignment software. Each base position has a corresponding unique reference position in the reference genome, and the first sequence data and the second sequence data corresponding to each sequencing fragment are obtained. For the same reference position, the positions of the two base sequences are corresponding, and the position of the overlapping region is determined. By traversing the reference positions, the overlapping region can be obtained.

[0065] S13. For each base position in the overlapping region, obtain from the first sequence data the first base type corresponding to each base position and the estimated base quality score corresponding to the first base type, and obtain from the second sequence data the second base type corresponding to each base position and the estimated base quality score corresponding to the second base type.

[0066] S14. For each base position, determine the correct base type corresponding to the base position based on a comparison result of the first base type and the second base type corresponding to the base position, and a comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold.

[0067] In the above embodiment, the first sequence data and the second sequence data of the same sequencing fragment are obtained from the double-end sequencing file, and the first base type corresponding to each base position in the overlapping region, the estimated base quality score corresponding to the first base type, the corresponding second base type and the estimated base quality score corresponding to the second base type can be determined through the first sequence data and the second sequence data of the same sequencing fragment. Then, based on the comparison result of the first base type and the second base type corresponding to the base position, and the comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold, the correct base type corresponding to the base position is determined, so that the base type in the overlapping region can be accurately identified.

[0068] In some embodiments, for each of the base positions, based on a comparison result of a first base type corresponding to the base position and a comparison result of an estimated base quality score corresponding to the first base type and an estimated base quality score corresponding to the second base type with a preset quality score threshold, determining a correct base type corresponding to the base position comprises:

[0069] For each base position in the overlapping region of each sequencing fragment, if the first base type corresponding to the base position in the first sequence data is the same as the second base type corresponding to the base position in the second sequence data, then the correct base type corresponding to the base position is the first base type or the second base type;

[0070] If the first base type is different from the second base type, and the obtained estimated base quality score corresponding to the first base type and the obtained estimated base quality score corresponding to the second base type are both greater than the preset quality score threshold, the base type corresponding to the estimated base quality score with the higher estimated base quality score is used as the correct base type corresponding to the base position;

[0071] If the first base type is different from the second base type, the obtained estimated base quality score corresponding to the first base type and the obtained estimated base quality score corresponding to the second base type are both less than or equal to a preset quality score threshold, and when a major allele base exists at a reference position corresponding to the base position in the reference genome, the major allele base is used as the correct base type corresponding to the base position;

[0072] If the first base type is different from the second base type, the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type obtained are both less than or equal to a preset quality score threshold, and when there is no major allele base at the reference position corresponding to the base position in the reference genome, the base type at the reference position is used as the correct base type corresponding to the base position.

[0073] In this embodiment, for a base at a base position in the overlapping region, the base position will correspond to two base types, namely, the first base type in the first sequence data and the second base type in the second sequence data. If the two are the same, it means that the sequencing is correct, and the first base type or the second base type is set as the correct base type corresponding to the base position. If the two are different, it means that there may be inaccurate sequencing or the position may be a heterozygous site position. Therefore, it is necessary to further determine whether the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type are both greater than the preset quality score threshold. If both are greater than the preset quality score threshold, it means that the sequencing result is correct, and the base type corresponding to the higher one is used as the correct base type corresponding to the base position; if both are less than, it means that the base position may be a heterozygous site position, and it is further determined that there is a major allele base at the reference position corresponding to the base position in the reference genome. If there is a major allele base at the reference position corresponding to the base position in the reference genome, When a base is located at a certain position, the major allele existing at the reference position corresponding to the base position in the reference genome is used as the correct base type corresponding to the base position. For example, the sequencing depth corresponding to the reference position is 100x, that is, a total of 100 measurements are made at the base position, and there are 60 A, 30 T, 8 C, and 2 G in the sequencing results. Then A is the only major allele base, and A is set as the correct base type at the base position. When it is determined after further judgment that the major allele does not exist at the reference position corresponding to the base position in the reference genome, the base type at the reference position corresponding to the base position in the reference genome sequence is used as the correct base type corresponding to the base position.

[0074] In the above embodiment, the double sequencing results of the overlapping region in the double-end sequencing are used to identify the correct base. When the double sequencing results are different, the base with a mutation frequency greater than 99% is no longer simply considered to be the correct base. Instead, a series of conditions are analyzed, such as combining the analysis of whether the base types of the double-end sequencing in the overlapping region are the same, analyzing the estimated base quality scores corresponding to the double-end sequencing, analyzing whether there is a major allele base at the base position, etc., and comprehensively considering multiple factors to confirm the correct base type of the base. Therefore, the base quality score correction method based on double-end sequencing provided by the present application is applicable to many species, has a large recognition range, and has high recognition accuracy. Moreover, the method does not require the input of a species-dependent standard site list, and is basically applicable to determining the correct base type corresponding to any species and any sample type, thereby improving the accuracy of base category recognition.

[0075] See also Figure 7 , is a flow chart of a sequencing data quality assessment method based on double-end sequencing provided in an embodiment of the present application. The sequencing data quality assessment method based on double-end sequencing is applied to a gene sequencer, and the sequencing data quality assessment method based on double-end sequencing includes the following steps:

[0076] S11, obtaining a double-end sequencing file, and obtaining first sequence data and second sequence data corresponding to each sequencing fragment from the double-end sequencing file.

[0077] S12. Determine the overlapping region of each sequencing fragment based on the first sequence data and the second sequence data corresponding to each sequencing fragment.

[0078] S13. For each base position in the overlapping region, obtain from the first sequence data the first base type corresponding to each base position and the estimated base quality score corresponding to the first base type, and obtain from the second sequence data the second base type corresponding to each base position and the estimated base quality score corresponding to the second base type.

[0079] S14. For each base position, determine the correct base type corresponding to the base position based on a comparison result of the first base type and the second base type corresponding to the base position, and a comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold.

[0080] S15. For each sequencing fragment, obtain the base information of the bases in the overlapping region from the double-end sequencing file.

[0081] In this embodiment, the base information includes but is not limited to one or more of the following: the type of sequencing base corresponding to the base position, the estimated base quality score corresponding to the sequencing base type, and the correct base type corresponding to the base position.

[0082] S16. Evaluate the sequencing data quality in the double-end sequencing file based on the correct base types corresponding to the bases in the overlapping region and the base information of the bases in the overlapping region.

[0083] In this embodiment, for the same base position in any sequencing fragment, the correct base type corresponding to the base position is the real base type at the base position, and the sequencing base type corresponding to the base position is the sequencing base type output by the sequencer. The sequencing base type may not necessarily be the real base type, and misdetection may occur. The fitted base quality score corresponding to the base is the base quality score estimated and calculated based on the base information of the base, which is more accurate than the estimated base quality score corresponding to the base.

[0084] In the above embodiment, the first sequence data and the second sequence data of the same sequencing fragment are obtained from the double-end sequencing file, and the base information of the bases in the overlapping region can be determined through the first sequence data and the second sequence data of the same sequencing fragment. The correct base types corresponding to the same base position are determined based on the base information of the bases in the overlapping region, and the sequencing data quality in the double-end sequencing file is evaluated based on at least one of the correct base type corresponding to each base, the sequencing base type corresponding to the base position, and the fitted base quality score. Since the sequencing data quality can be evaluated based on at least one relatively accurate base information, whether the sequencing data quality meets the requirements can be accurately evaluated.

[0085] In some embodiments, based on the correct base types corresponding to the bases in the overlapping regions and the base information of the bases in the overlapping regions, evaluating the sequencing data quality in the double-end sequencing file includes:

[0086] Determine error detection data according to the correct base type and the corresponding sequencing base type corresponding to each base in the overlapping region, wherein the error detection data includes each error detection type and the total number of error detections corresponding to each error detection type;

[0087] Calculate the false detection ratio corresponding to each false detection type;

[0088] Based on the error detection ratio corresponding to each error detection type, calculating the similarity between the error detection type of the double-end sequencing file and the preset average error detection ratio;

[0089] If the similarity is within the preset similarity range, it is determined that the quality of the sequencing data in the double-end sequencing file meets the requirements;

[0090] If the similarity is not within the preset similarity range, it is determined that the quality of the sequencing data in the double-end sequencing file does not meet the requirements.

[0091] In this embodiment, the error detection type indicates that a correct base type is detected as another base type, such as Figure 8 As shown, Figure 8 is a schematic diagram of error detection types in one embodiment, from Figure 8 It can be seen that there are 12 types of misdetection. In the case of random misdetection, these 12 types of misdetection should occur on average, that is, the probability of each type of misdetection is the same, that is, 1 / 12≈0.08333. If the misdetection ratio corresponding to the misdetection type is closer to 0.08333, it means that the preference for mismatched bases is smaller and the sequencing data quality is higher. Conversely, if the misdetection ratio corresponding to the misdetection type is closer to 0.08333, it means that the preference for mismatched bases is higher and the sequencing data quality is worse. Fig. 9 As shown, it is a comparative schematic diagram of the preference for mismatched bases in two sequencers in one embodiment, red and cyan represent two sequencers respectively, the X-axis represents twelve types of misdetection, and the Y-axis represents percentage. It can be seen that the red sequencer has a higher proportion of A->C and T->G misdetection (similarity is 0.732), while the cyan sequencer has a higher proportion of A->T misdetection (similarity is 0.856), and the blue sequencer has a smaller preference for mismatched bases.

[0092] Optionally, the error detection ratio corresponding to each error detection type is the ratio of the total number of error detections corresponding to each error detection type to the total number of bases, and the formula for calculating the similarity is as follows:

[0093]

[0094] Where R i is the false detection ratio of the i-th false detection type, and similarity represents the similarity.

[0095] In the above embodiment, based on the correct base type and the corresponding sequencing base type corresponding to each base in the overlapping region of each sequencing fragment, the error detection data is determined, and the similarity between the error detection type of the double-end sequencing file and the preset average error detection ratio is calculated, wherein the preset average error detection ratio represents the average probability of each error detection type occurring under random probability. The calculated similarity can measure the preference of the mismatched bases in the double-end sequencing file, thereby evaluating the quality of the sequencing data according to the preference of the mismatched bases, thereby being able to more accurately measure the data quality.

[0096] In some embodiments, the base information includes at least an estimated base quality score of each base obtained from a double-end sequencing file and a candidate feature associated with the estimated base quality score; Fig.10 As shown, it is a flowchart of a sequencing data quality assessment method based on double-end sequencing in another embodiment of the present application, and S16 also includes:

[0097] S161, based on the base information of the bases in the overlapping region, determining, from the candidate features, a significant feature whose correlation with the estimated base quality score meets a preset condition;

[0098] In this embodiment, the candidate features only represent features related to the base quality score, but may not be the features that mainly affect the base quality score. Therefore, it is necessary to select significant features from the candidate features. The significant features represent features that are highly correlated with the base quality score. The significant features are selected through the base information of the bases in the overlapping region of each sequencing fragment. These significant features are determined based on the actual sequencing results of the sequencing fragments of the actual sample. Therefore, the selected significant features can truly reflect the features that affect the base quality score, thereby reducing the influence of external factors such as samples and sequencing platforms on the base quality score correction.

[0099] S162. Based on the significant features, the correct base types corresponding to the base positions and the estimated base quality scores, the base data in the double-end sequencing file are grouped to obtain a plurality of base data groups, base information of the bases in each base data group is obtained, and an original base quality score corresponding to each base data group is calculated based on the base information of the bases in each base data group.

[0100] In this embodiment, because the significant features can truly reflect the features that affect the base quality scores, the base data groups are analyzed according to the significant features and the estimated base quality scores to obtain more accurate base data groups, and then based on the base information of each base data group, a more accurate original base quality score corresponding to each base data group can be calculated.

[0101] S163. Based on the original base quality scores corresponding to each base data group and the significant features in the base information of each base data group, a base quality score correction model is fitted, and based on the base quality score correction model, a fitted base quality score corresponding to each base data group is calculated, and the fitted base quality score corresponding to each base is determined as the fitted base quality score corresponding to the base data group where the base is located.

[0102] In this embodiment, after the base quality score correction model is obtained by the fitting method, the data of the significant features in the base information of each base data group is used as the independent variable of the base quality score correction model, so that the fitted base quality score of each base data group can be obtained, and then the fitted base quality score of the base in each base data group is the fitted base quality score of each base data group, and then the estimated base quality score of each base is replaced by the fitted base quality score. The base quality score correction model is obtained by fitting the original base quality score corresponding to each base data group and the data of the significant features in the base information of each base data group, that is, the data of all groups are combined, and the model is not obtained by fitting the data of some base data groups, and the consistency of the continuous variable base data group is comprehensively considered, thereby avoiding overfitting of the base quality score correction model, thereby improving the correction effect of the base quality score of the base.

[0103] S164. Evaluate the quality of the sequencing data in the double-end sequencing file based on the fitted base quality score corresponding to each base data group.

[0104] In the above embodiment, the embodiment of the present application obtains the first sequence data and the second sequence data of the same sequencing fragment from the double-end sequencing file, and the first base type, the estimated base quality score corresponding to the first base type, the corresponding second base type and the estimated base quality score corresponding to the second base type corresponding to each base position in the overlapping region can be determined through the first sequence data and the second sequence data of the same sequencing fragment, and then the correct base type corresponding to the base position is determined based on the comparison result of the first base type and the second base type corresponding to the base position, and the comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold, so that the base type in the overlapping region can be accurately identified; and the sequencing data quality in the double-end sequencing file is evaluated based on at least one of the correct base type corresponding to each base, the sequencing base type corresponding to the base position, and the fitted base quality score. Since the sequencing data quality can be evaluated based on at least one relatively accurate base information, whether the sequencing data quality meets the requirements can be accurately evaluated.

[0105] In some embodiments, the step of evaluating the quality of sequencing data in the double-end sequencing file based on the fitted base quality score corresponding to each base data group includes:

[0106] A fitting point is formed by using the estimated base quality score corresponding to each base data group and the corresponding fitted base quality score, and the target straight line is obtained by fitting;

[0107] Calculating the weighted distance between the target straight line and the preset straight line;

[0108] If the weighted distance between the target straight line and the preset straight line is less than or equal to the preset weighted distance, it is determined that the quality of the sequencing data in the double-end sequencing file meets the requirements;

[0109] If the weighted distance between the target straight line and the preset straight line is greater than the preset weighted distance, it is determined that the quality of the sequencing data in the double-end sequencing file does not meet the requirements.

[0110] In this embodiment, the estimated base quality score corresponding to each base data group and the corresponding fitted base quality score are formed into a fitting point, and a target straight line is fitted. When the weighted distance between the target straight line and the preset straight line is calculated, the accuracy and stability of the estimated base quality score output by the sequencer can be measured. The preset straight line is a diagonal line passing through the origin at the same coordinate as the target straight line. The smaller the weighted distance between the target straight line and the preset straight line, the closer the fitted target straight line is to the diagonal line, and the higher the accuracy of the estimated base quality score of the base output by the sequencer, the farther the fitted target straight line is from the diagonal line, and the lower the accuracy of the estimated base quality score of the base output by the sequencer.

[0111] In an optional implementation, the formula for calculating the weighted distance between the target straight line and the preset straight line is as follows:

[0112]

[0113] Where D represents the weighted distance between the target line and the preset line, A and B are the slope and intercept of the target line. Q is the set of estimated base quality score values ​​in the double-end sequencing file, N is the total number of Q, and R n It is the ratio of the number of bases corresponding to the estimated base quality score value n to the total number of bases. For most sequencers, Q is a sequence starting from 1 and increasing by 1 to a positive integer. For the Illumina platform, Q∈{11,25,37}, that is, the total number of Q is 3.

[0114] like Fig.11 As shown, it is a schematic diagram of the target straight line fitted under different sequencing platforms in one embodiment. The curves of different colors are the accuracy linear fitting lines of two different sequencing platforms. The red dotted line is the diagonal line. The closer the fitted target straight line is to the diagonal line, the smaller the distance D is, indicating that the accuracy of the reported Q value (i.e., the estimated base quality score) of the sequencer is higher. After calculation, the distance D of the red straight line is 0.94, and the distance D of the blue straight line is 1.30, indicating that the accuracy of the reported Q value of the red sequencer platform is relatively high.

[0115] In the above embodiment, the first sequence data and the second sequence data of the same sequencing fragment are obtained from the double-end sequencing file, and the base information of the bases in the overlapping region can be determined by the first sequence data and the second sequence data of the same sequencing fragment. The significant features that affect the base quality score are determined based on the base information of the bases in the overlapping region, and grouping is performed based on the significant features to obtain more accurate grouping. Then, based on the base information of each group, a more accurate original base quality score corresponding to each group can be calculated, so that in the subsequent fitting step, a more accurate fitting result can be obtained, thereby improving the accuracy of the corrected base quality score. In addition, in the fitting step, the data of all groups are used instead of the model obtained by fitting the data of some groups. The consistency of the continuous variable grouping is comprehensively considered, thereby avoiding the overfitting of the base quality score correction model, thereby improving the correction effect of the base quality score of the base. After obtaining a more accurate base quality score, a fitting point is formed with the corresponding estimated base quality score of each group and the corresponding fitted base quality score, and a target straight line is obtained by fitting. The quality of sequencing data is measured by the weighted distance between the target straight line and the preset straight line, so that the quality of sequencing data can be quantitatively analyzed, and the quality of sequencing data can be more accurately measured.

[0116] In some embodiments, the evaluating the quality of the sequencing data in the double-end sequencing file based on the fitted base quality score corresponding to each base data group further includes:

[0117] Obtaining the total number of bases corresponding to each estimated base quality score, and determining a target estimated base quality score;

[0118] Obtaining base sequence data corresponding to each target estimated base quality score, wherein the total number of bases of the target estimated base quality score is ranked in the first preset number of digits;

[0119] For the base sequence data corresponding to each target estimated base quality score, all cycle sequencing numbers and the fitted base quality score corresponding to each cycle sequencing number under each target estimated base quality score are obtained, a fitting point is formed by the cycle sequencing number and the fitted base quality score corresponding to the cycle sequencing number, and a fitting straight line corresponding to each target estimated base quality score is obtained by fitting;

[0120] Calculate the weighted distance between the fitting straight line corresponding to each target estimated base quality score and the preset horizontal line to obtain the weighted distance corresponding to each target estimated base quality score;

[0121] If the weighted distance corresponding to each target estimated base quality score is less than the preset distance, it is determined that the quality of the sequencing data in the double-end sequencing file meets the requirements;

[0122] If there is a weighted distance corresponding to the target estimated base quality score that is greater than or equal to the preset distance, it is determined that the quality of the sequencing data in the double-end sequencing file does not meet the requirements.

[0123] In this embodiment, the target estimated base quality score represents the estimated base quality score with a relatively high proportion in the sequencing results. Subsequent calculations based on the target estimated base quality score can better reflect the overall sequencing data quality. Then, in the double-end sequencing file, the target estimated base quality score is used as the classification standard to obtain the base sequence data corresponding to each target estimated base quality score, thereby obtaining the fitting straight line corresponding to each target estimated base quality score, wherein the closer the fitting straight line corresponding to each target estimated base quality score is to the preset horizontal line, the more stable the estimated base quality score in the double-end sequencing file is, and vice versa, the farther the fitting straight line corresponding to each target estimated base quality score is from the preset horizontal line, the more unstable the estimated base quality score in the double-end sequencing file is.

[0124] In an optional implementation, the formula for calculating the weighted distance between the fitting straight line corresponding to each target estimated base quality score and the preset horizontal line is as follows:

[0125]

[0126] DD is the weighted distance between the fitting straight line corresponding to the target estimated base quality score and the preset horizontal line, Q out is a reported Q value, i.e., an estimated base quality score, and C is out The most common C is a positive integer sequence starting from 1 and increasing from 1 to 150, N is the number of C, R is the number of n Q is the ratio of the total number of bases in sequencing cycle number n to the total number of bases. n Indicates the fitted base quality score corresponding to the sequencing cycle number n.

[0127] like Fig.12 As shown, Fig.12 It is a schematic diagram of the fitted straight line fitted under different sequencing platforms in one embodiment; the left and right figures represent the stability of the base quality scores output by two different sequencing instrument platforms. In the figure, the X-axis is the sequencing cycle number (cycle), the Y-axis is the fitted base quality score, the curves of different colors are the fitting curves of different reported Q values, and the red dotted line is the horizontal line. The closer the fitting curve is to the horizontal line, the error between the reported Q value and the true Q value of the sequencer is independent of the sequencing cycle number, and the higher the stability. It can be seen that the reported Q value stability of the blue sequencer platform on the left is relatively high, while the Q value of the sequencer on the right decreases rapidly with the increase of cycle, and the error increases.

[0128] In the above embodiment, the first sequence data and the second sequence data of the same sequencing fragment are obtained from the double-end sequencing file, and the base information of the bases in the overlapping region can be determined by the first sequence data and the second sequence data of the same sequencing fragment. The significant features affecting the base quality score are determined based on the base information of the bases in the overlapping region, and grouping is performed based on the significant features to obtain more accurate grouping. Then, based on the base information of each group, a more accurate original base quality score corresponding to each group can be calculated, so that in the subsequent fitting step, a more accurate fitting result can be obtained, thereby improving the effect of correcting the base quality score. In addition, in the fitting step, the data of all groups are used, not the model obtained by fitting the data of some groups, and the consistency of the continuous variable grouping is comprehensively considered, thereby avoiding overfitting of the base quality score correction model, thereby improving the correction effect of the base quality score of the base. After obtaining a relatively accurate base quality score, an estimated base quality score with a relatively large proportion is selected from the double-end sequencing file as the target estimated base quality score. Then, for a target estimated base quality score, a fitting point is formed with the cycle sequencing number and the fitting base quality score corresponding to the cycle sequencing number, and a fitting straight line corresponding to each target estimated base quality score is obtained by fitting. The weighted distance between the fitting straight line corresponding to each target estimated base quality score and a preset horizontal line is calculated to obtain the weighted distance corresponding to each target estimated base quality score. The stability of the base quality score of the base output by the sequencer is evaluated through the weighted distance corresponding to each target estimated base quality score, so that the accuracy of the sequencing data quality can be quantitatively analyzed through the stability of the base quality score of the base, and the accuracy of the sequencing data quality can be more accurately measured.

[0129] In some embodiments, based on the base information of the bases in the overlapping region, determining from the candidate features a significant feature whose correlation with the estimated base quality score satisfies a preset condition, comprises:

[0130] Taking each candidate feature as a feature to be checked, obtaining a target feature corresponding to the feature to be checked, traversing the bases in the overlapping regions of all sequencing fragments, and performing statistics on the traversed bases based on the target feature corresponding to the feature to be checked, to obtain valid information corresponding to the feature to be checked; wherein the valid information includes multiple pieces of data, each piece of data includes a target feature;

[0131] Taking the feature to be checked and the estimated base quality score as grouping criteria, grouping multiple pieces of data in the valid information corresponding to the feature to be checked, and calculating the original base quality score and error rate of each group in the valid information corresponding to the feature to be checked;

[0132] Based on the target feature, the original base quality score and the error rate of each group in the valid information corresponding to the feature to be checked, the significance value between the feature to be checked and the estimated base quality score is calculated by using a statistical analysis method;

[0133] The candidate features whose significance values ​​are lower than the preset significance value are determined as significant features.

[0134] In this embodiment, the significance value indicates the degree of association between the candidate feature and the base quality score. The lower the significance value, the higher the degree of association, and the higher the significance value, the lower the degree of association. For example, the preset significance value is set to 0.05, such as Fig.13 As shown, Fig.13 : is a schematic diagram of the significance values ​​corresponding to each candidate feature in an embodiment. The reported Q value is the estimated base quality score. It can be seen from the figure that the significance values ​​corresponding to features 2, 3, 5, 6, 9, 10, and 11 are all less than 0.05, and features 2, 3, 5, 6, 9, 10, and 11 are significant features.

[0135] Optionally, the significant features include at least one of the following: the sequencing cycle number where the base is located, the content ratio of G bases and C bases in the sequencing fragment, the estimated base quality score of the base, the estimated base quality score corresponding to 2bp upstream of the base, the estimated base quality score corresponding to 1bp upstream of the base, the estimated base quality score corresponding to 2bp downstream of the base, the estimated base quality score corresponding to 1bp downstream of the base, the base type corresponding to 2bp upstream of the base, the base type corresponding to 1bp upstream of the base, the base type corresponding to 2bp downstream of the base, the base type corresponding to 1bp downstream of the base, and the correct base type at the base position where the base is located. Depending on the sample, appropriate significant features can be determined based on the base information of the bases in the overlapping regions of the sequencing fragments in the double-end sequencing file.

[0136] During the correction process, fixed base features are not used. Instead, multiple candidate features are collected, and significant features are found through statistical methods. Based on the base information of the bases in the overlapping regions of the sequencing fragments, the significance values ​​corresponding to each candidate feature are calculated respectively, and the significant features are screened out. Then, grouping is performed according to the significant features and the estimated base quality scores, so that more accurate grouping can be obtained. Then, based on the base information of each group, a more accurate original base quality score corresponding to each group can be calculated, so that in the subsequent fitting step, a more accurate fitting result can be obtained, thereby improving the effect of correcting the base quality score. Since it is based on the actual sample sequencing results and the significant features are obtained by statistical methods, it can meet the needs of more types of sequencers without being limited to a specific sequencer platform.

[0137] In this embodiment, for each feature to be checked, the target feature may be the same or different, and the target feature may be part or all of the candidate feature. The significance value between the feature to be checked and the base quality score is subsequently calculated based on the data of the target feature. For example, taking the estimated base quality score value 1 bp upstream of the base as an example of a feature to be checked, the correct base type of the base, the estimated base quality score 1 bp upstream of the base, the estimated base quality score of the base, the base type, and the number can be selected as the target feature, wherein the base type is the base result output by the sequencer, and the correct base type of the base, the estimated base quality score 1 bp upstream of the base, the estimated base quality score of the base, and the base type are used as statistical rules to traverse the bases in the overlapping regions of all sequencing fragments, merge the correct base type of the base, the estimated base quality score 1 bp upstream of the base, the estimated base quality score of the base, and the base type with the same base, and count the number in each case, so as to obtain the following: Fig.14 As shown in the figure, Fig.14 Schematic diagram of preliminary statistical effective information in one embodiment; then the estimated base quality score 1 bp upstream of the base and the estimated base quality score are used as grouping criteria, Fig.14 The multiple data are grouped, and the original base quality score and error rate of each group in the effective information corresponding to the estimated base quality score 1 bp upstream of the base are calculated. The specific data are as follows Fig.15 As shown, Fig.15 The data in the statistical analysis were Fig.16 The raw base quality scores for each group are shown in .

[0138] Optionally, calculating the original base quality score and error rate of each group in the valid information corresponding to the feature to be checked includes: in each group obtained above, counting the number of erroneous bases and the total number of bases in each group, calculating the error rate of each group based on the number of erroneous bases and the total number of bases in each group, and calculating the original base quality score corresponding to each group based on the error rate of each group,

[0139]

[0140] Original base quality score = -10×log 10 (error rate),

[0141] Then, if the feature to be checked is a two-level variable, the Mann–Whitney U test method is used to calculate the significance value between the feature to be checked and the base quality score. If the feature to be checked is a three-level or more variable, variance analysis is used to calculate the significance value between the feature to be checked and the base quality score.

[0142] In the above embodiment, during the correction process, fixed base features are not used, but multiple candidate features are collected, and significant features are found through statistical methods. Based on the base information of the bases in the overlapping regions of the sequencing fragments, the significance values ​​corresponding to each candidate feature are calculated respectively, and the significant features are screened out. Then, grouping is performed according to the significant features and the estimated base quality scores, so that more accurate base data groups can be obtained. Then, based on the base information of each base data group, more accurate original base quality scores corresponding to each base data group can be calculated, so that in the subsequent fitting step, more accurate fitting results can be obtained, thereby improving the effect of correcting the base quality scores. Since it is based on the actual sample sequencing results and the significant features are obtained by statistical methods, it can meet the needs of more types of sequencers without being limited to a specific sequencer platform.

[0143] In some embodiments, fitting a base quality score correction model based on the original base quality score corresponding to each base data set and the data of significant features in the base information of each base data set comprises:

[0144] Each base data group is taken as a sample point to form multiple sample points; the significant features are taken as independent variables and the original base quality scores are taken as dependent variables, and based on multiple sample points, a hierarchical linear model is used to fit the base quality score correction model.

[0145] In this embodiment, after obtaining the significant features, the base data in the double-end sequencing file can be grouped according to the significant features and the estimated base quality scores, so as to obtain multiple base data groups. In each base data group, the number of erroneous bases and the total number of bases in each base data group are counted according to the above method, and the error rate of each base data group is calculated, and the original base quality score corresponding to each base data group is calculated based on the error rate of each base data group. In this way, each base data group can be understood as a sample point, and multiple base data groups form multiple sample points. The significant features are used as independent variables and the original base quality scores are used as dependent variables. Based on multiple sample points, a base quality score correction model is fitted, wherein the base quality score correction model is a hierarchical linear model. For example, if there are 11 significant features, then these 11 significant features are independent variables, and multiple sample points are sample points formed by all base data groups obtained based on the double-end sequencing file, not local data, that is, the data of all groups are used in combination, and the model is not obtained by fitting the data of all base data groups. The consistency of the continuous variable base data group is comprehensively considered, thereby avoiding overfitting of the base quality score correction model, thereby improving the correction effect of the base quality score of the base.

[0146] In the above embodiment, the base quality score correction model is obtained by fitting the original base quality score corresponding to each base data group and the data of significant features in the base information of each base data group, that is, the data of all groups are combined, and the model is not obtained by fitting the data of some base data groups. The consistency of the continuous variable base data groups is comprehensively considered, thereby avoiding overfitting of the base quality score correction model, thereby improving the correction effect of the base quality score of the base.

[0147] In another aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the base category identification method based on double-end sequencing described in any embodiment of the present application. Among them, in the computer program product, the optional implementation form of the program module architecture of the computer program that implements each step of the method can be a base category identification device based on double-end sequencing. Please refer to Fig.17 An embodiment of the present application provides a base category identification device based on double-end sequencing, including: an acquisition module 1601, used to acquire a double-end sequencing file, and acquire first sequence data and second sequence data corresponding to each sequencing fragment from the double-end sequencing file; wherein the first sequence data is base sequence data obtained by sequencing from the first end to the second end of the sequencing fragment, and the second sequence data is base sequence data obtained by sequencing from the second end to the first end of the sequencing fragment; a determination module 1602, used to determine the overlapping region of each sequencing fragment based on the first sequence data and the second sequence data corresponding to each sequencing fragment; the acquisition module 1601 is also used to obtain, for each base position in the overlapping region, a first sequence data and a second sequence data corresponding to each sequencing fragment; The first base type corresponding to each base position and the estimated base quality score corresponding to the first base type are obtained from the first sequence data, and the second base type corresponding to each base position and the estimated base quality score corresponding to the second base type are obtained from the second sequence data; the determination module 1602 is further used to determine, for each base position, the correct base type corresponding to the base position based on the comparison result of the first base type and the second base type corresponding to the base position, and the comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold.

[0148] Optionally, the determination module 1602 is further configured to:

[0149] For each base position in the overlapping region of each sequencing fragment, if the first base type corresponding to the base position in the first sequence data is the same as the second base type corresponding to the base position in the second sequence data, then the correct base type corresponding to the base position is the first base type or the second base type;

[0150] If the first base type is different from the second base type, and the obtained estimated base quality score corresponding to the first base type and the obtained estimated base quality score corresponding to the second base type are both greater than the preset quality score threshold, the base type corresponding to the estimated base quality score with the higher estimated base quality score is used as the correct base type corresponding to the base position;

[0151] If the first base type is different from the second base type, the obtained estimated base quality score corresponding to the first base type and the obtained estimated base quality score corresponding to the second base type are both less than or equal to a preset quality score threshold, and when a major allele base exists at a reference position corresponding to the base position in the reference genome, the major allele base is used as the correct base type corresponding to the base position;

[0152] If the first base type is different from the second base type, the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type obtained are both less than or equal to a preset quality score threshold, and when there is no major allele base at the reference position corresponding to the base position in the reference genome, the base type at the reference position is used as the correct base type corresponding to the base position.

[0153] In another aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the electrode array positioning method described in any embodiment of the present application. In the computer program product, the optional implementation form of the program module architecture of the computer program that implements each step of the method can be a base category recognition device based on double-end sequencing. Fig.17An embodiment of the present application provides a base category identification device based on double-end sequencing, including: an acquisition module 1601, used to acquire a double-end sequencing file, and acquire first sequence data and second sequence data corresponding to each sequencing fragment from the double-end sequencing file; wherein the first sequence data is base sequence data obtained by sequencing from the first end to the second end of the sequencing fragment, and the second sequence data is base sequence data obtained by sequencing from the second end to the first end of the sequencing fragment; a determination module 1602, used to determine the overlapping region of each sequencing fragment based on the first sequence data and the second sequence data corresponding to each sequencing fragment; the acquisition module 1601 is also used to acquire, for each base position in the overlapping region, the first base type and the estimated base quality score corresponding to each base position respectively from the first sequence data, and obtain the first base type and the estimated base quality score corresponding to the first base type from the second sequence data The second base type corresponding to each base position and the estimated base quality score corresponding to the second base type are obtained; the determination module 1602 is also used to determine, for each base position, the correct base type corresponding to the base position based on the comparison result of the first base type and the second base type corresponding to the base position, and the comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold; the acquisition module 1601 is also used to obtain, for each sequencing fragment, the base information of the base in the overlapping region from the double-end sequencing file; the evaluation module 1603 is used to evaluate the sequencing data quality in the double-end sequencing file based on the correct base type corresponding to the base in the overlapping region and the base information of the base in the overlapping region.

[0154] It can be understood by those skilled in the art that Fig.17 The structure of the base classification identification device based on double-end sequencing does not constitute a limitation on the base classification identification device based on double-end sequencing. Fig.18 The structure of the sequencing data quality assessment device based on double-end sequencing does not constitute a limitation on the sequencing data quality assessment device based on double-end sequencing, and each module can be implemented in whole or in part by software, hardware, and a combination thereof. The above modules can be embedded in or independent of the controller in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the controller can call and execute the operations corresponding to the above modules. In other embodiments, more or fewer modules than shown in the figure can be included.

[0155] See also Fig.18In another aspect of the embodiment of the present application, a computer device 200 is provided, including a memory 3011 and a processor 3012, wherein the memory 3011 stores a computer program, and when the computer program is executed by the processor, the processor 3012 executes the steps of the base category identification method based on double-end sequencing provided in any of the above embodiments of the present application, and / or the steps of the sequencing data quality assessment method based on double-end sequencing. The computer device 200 may include a gene sequencer, a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smart phone, a wireless phone, etc.), a wearable device (e.g., a pair of smart glasses or a smart watch), or a similar device.

[0156] The processor 3012 is the control center, which uses various interfaces and lines to connect various parts of the entire computer device, and executes various functions of the computer device and processes data by running or executing software programs and / or modules stored in the memory 3011, and calling data stored in the memory 3011. Optionally, the processor 3012 may include one or more processing cores; preferably, the processor 3012 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user pages and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 3012.

[0157] The memory 3011 can be used to store software programs and modules. The processor 3012 executes various functional applications and data processing by running the software programs and modules stored in the memory 3011. The memory 3011 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 3011 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 3011 may also include a memory controller to provide the processor 3012 with access to the memory 3011.

[0158] On the other hand, an embodiment of the present application further provides a storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the base category identification method based on double-end sequencing and / or the steps of the sequencing data quality assessment method based on double-end sequencing provided in any of the above embodiments of the present application.

[0159] Those skilled in the art can understand that all or part of the processes in the methods provided in the above embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0160] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for identifying base types based on double-end sequencing. It is characterized in that include: Obtaining a double-end sequencing file, and obtaining first sequence data and second sequence data corresponding to each sequencing fragment from the double-end sequencing file; wherein the first sequence data is base sequence data obtained by sequencing from the first end to the second end of the sequencing fragment, and the second sequence data is base sequence data obtained by sequencing from the second end to the first end of the sequencing fragment; Determining the overlapping region of each sequencing fragment based on the first sequence data and the second sequence data corresponding to each sequencing fragment; For each base position in the overlapping region, obtaining from the first sequence data a first base type corresponding to each base position and an estimated base quality score corresponding to the first base type, and obtaining from the second sequence data a second base type corresponding to each base position and an estimated base quality score corresponding to the second base type; For each of the base positions, the correct base type corresponding to the base position is determined based on a comparison result of the first base type and the second base type corresponding to the base position, and a comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold.

2. The method for identifying base types based on double-end sequencing according to claim 1, It is characterized in that The determining, for each of the base positions, based on a comparison result of the first base type corresponding to the base position and the second base type, and a comparison result of the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type with a preset quality score threshold, a correct base type corresponding to the base position includes: For each base position in the overlapping region of each sequencing fragment, if the first base type corresponding to the base position in the first sequence data is the same as the second base type corresponding to the base position in the second sequence data, then the correct base type corresponding to the base position is the first base type or the second base type; If the first base type is different from the second base type, and the obtained estimated base quality score corresponding to the first base type and the obtained estimated base quality score corresponding to the second base type are both greater than the preset quality score threshold, the base type corresponding to the estimated base quality score with the higher estimated base quality score is used as the correct base type corresponding to the base position; If the first base type is different from the second base type, the obtained estimated base quality score corresponding to the first base type and the obtained estimated base quality score corresponding to the second base type are both less than or equal to a preset quality score threshold, and when a major allele base exists at a reference position corresponding to the base position in the reference genome, the major allele base is used as the correct base type corresponding to the base position; If the first base type is different from the second base type, the estimated base quality score corresponding to the first base type and the estimated base quality score corresponding to the second base type obtained are both less than or equal to a preset quality score threshold, and when there is no major allele base at the reference position corresponding to the base position in the reference genome, the base type at the reference position is used as the correct base type corresponding to the base position.

3. A sequencing data quality assessment method based on paired-end sequencing, It is characterized in that Based on the base type identification method based on double-end sequencing according to claim 1 or 2, determining the correct base type corresponding to each base position in the overlapping region of each sequencing fragment; For each sequencing fragment, obtaining base information of the bases in the overlapping region from the double-end sequencing file; The sequencing data quality in the double-end sequencing file is evaluated based on the correct base types corresponding to the bases in the overlapping region and the base information of the bases in the overlapping region.

4. The method for evaluating sequencing data quality based on paired-end sequencing according to claim 3, It is characterized in that The step of evaluating the sequencing data quality in the double-end sequencing file based on the correct base types corresponding to the bases in the overlapping region and the base information of the bases in the overlapping region includes: Determine error detection data according to the correct base type and the corresponding sequencing base type corresponding to each base in the overlapping region, wherein the error detection data includes each error detection type and the total number of error detections corresponding to each error detection type; Calculate the false detection ratio corresponding to each false detection type; Based on the error detection ratio corresponding to each error detection type, calculating the similarity between the error detection type of the double-end sequencing file and the preset average error detection ratio; If the similarity is within the preset similarity range, it is determined that the quality of the sequencing data in the double-end sequencing file meets the requirements; If the similarity is not within the preset similarity range, it is determined that the quality of the sequencing data in the double-end sequencing file does not meet the requirements.

5. The method for evaluating sequencing data quality based on paired-end sequencing according to claim 3, It is characterized in that The base information at least includes an estimated base quality score of each base obtained from the double-end sequencing file and a candidate feature associated with the estimated base quality score; the evaluating the sequencing data quality in the double-end sequencing file based on the correct base type corresponding to the base in the overlapping region and the base information of the base in the overlapping region also includes: Based on the base information of the bases in the overlapping region, determining, from the candidate features, a significant feature whose correlation with the estimated base quality score meets a preset condition; Based on the significant features, the correct base types corresponding to the base positions and the estimated base quality scores, the base data in the double-end sequencing file are grouped to obtain a plurality of base data groups, base information of the bases in each base data group is obtained, and the original base quality score corresponding to each base data group is calculated based on the base information of the bases in each base data group; Based on the original base quality score corresponding to each base data group and the significant features in the base information of each base data group, a base quality score correction model is fitted, and based on the base quality score correction model, a fitted base quality score corresponding to each base data group is calculated, and the fitted base quality score corresponding to each base is determined as the fitted base quality score corresponding to the base data group where the base is located; The quality of the sequencing data in the double-end sequencing file is evaluated based on the fitted base quality score corresponding to each base data group.

6. The method for evaluating sequencing data quality based on paired-end sequencing according to claim 5, It is characterized in that The step of evaluating the quality of sequencing data in the double-end sequencing file based on the fitted base quality score corresponding to each base data group includes: A fitting point is formed by using the estimated base quality score corresponding to each base data group and the corresponding fitted base quality score, and the target straight line is obtained by fitting; Calculating the weighted distance between the target straight line and the preset straight line; If the weighted distance between the target straight line and the preset straight line is less than or equal to the preset weighted distance, it is determined that the quality of the sequencing data in the double-end sequencing file meets the requirements; If the weighted distance between the target straight line and the preset straight line is greater than the preset weighted distance, it is determined that the quality of the sequencing data in the double-end sequencing file does not meet the requirements.

7. The method for evaluating sequencing data quality based on paired-end sequencing according to claim 5, It is characterized in that The step of evaluating the quality of sequencing data in the double-end sequencing file based on the fitted base quality score corresponding to each base data group further includes: Obtaining the total number of bases corresponding to each estimated base quality score, and determining a target estimated base quality score; Obtaining base sequence data corresponding to each target estimated base quality score, wherein the total number of bases of the target estimated base quality score is ranked in the first preset number of digits; For the base sequence data corresponding to each target estimated base quality score, all cycle sequencing numbers and the fitted base quality score corresponding to each cycle sequencing number under each target estimated base quality score are obtained, a fitting point is formed by the cycle sequencing number and the fitted base quality score corresponding to the cycle sequencing number, and a fitting straight line corresponding to each target estimated base quality score is obtained by fitting; Calculate the weighted distance between the fitting straight line corresponding to each target estimated base quality score and the preset horizontal line to obtain the weighted distance corresponding to each target estimated base quality score; If the weighted distance corresponding to each target estimated base quality score is less than the preset distance, it is determined that the quality of the sequencing data in the double-end sequencing file meets the requirements; If there is a weighted distance corresponding to the target estimated base quality score that is greater than or equal to the preset distance, it is determined that the quality of the sequencing data in the double-end sequencing file does not meet the requirements.

8. The method for evaluating sequencing data quality based on paired-end sequencing according to claim 5, It is characterized in that The determining, based on the base information of the bases in the overlapping region, from the candidate features, a significant feature whose correlation with the estimated base quality score satisfies a preset condition, comprises: Taking each candidate feature as a feature to be checked, obtaining a target feature corresponding to the feature to be checked, traversing the bases in the overlapping regions of all sequencing fragments, and performing statistics on the traversed bases based on the target feature corresponding to the feature to be checked, to obtain valid information corresponding to the feature to be checked; wherein the valid information includes multiple pieces of data, each piece of data includes a target feature; Taking the feature to be checked and the estimated base quality score as grouping criteria, grouping multiple pieces of data in the valid information corresponding to the feature to be checked, and calculating the original base quality score and error rate of each group in the valid information corresponding to the feature to be checked; Based on the target feature, the original base quality score and the error rate of each group in the valid information corresponding to the feature to be checked, the significance value between the feature to be checked and the estimated base quality score is calculated by using a statistical analysis method; The candidate features whose significance values ​​are lower than the preset significance value are determined as significant features.

9. A computer program product, It is characterized in that It comprises a computer program, which, when executed by a processor, implements the base category identification method based on double-end sequencing as described in claim 1 or 2, or implements the sequencing data quality assessment method based on double-end sequencing as described in any one of claims 3 to 8.

10. A computer device, It is characterized in that It comprises a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the base category identification method based on double-end sequencing as described in claim 1 or 2, or executes the sequencing data quality assessment method based on double-end sequencing as described in any one of claims 3 to 8.

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