A method for base sequencing quality assessment

Abstract

The application provides a base sequencing quality evaluation method and system, which are used in the field of gene sequencing. The base sequencing quality evaluation method provided by the application comprises the following steps: providing a standard nucleic acid sequence, aligning the standard nucleic acid sequence to a reference sequence, and giving a label to a base in the standard nucleic acid sequence according to an alignment result; training a classifier to fit a relationship between a sequencing signal feature of the base and the label; providing a to-be-tested nucleic acid sequence, taking the base as a basic unit, and calculating the sequencing signal feature of the base in the to-be-tested nucleic acid sequence; and giving at least two quality values to the base in the to-be-tested nucleic acid sequence.

Description

A method for assessing the quality of base sequencing Technical Field

[0001] This invention relates to a method and system for assessing the quality of base sequencing, belonging to the field of gene sequencing. Background Technology

[0002] Nucleic acid sequencing technology can determine the sequence of genetic material and is widely used in clinical tumor typing, microbial identification, and genetic disease diagnosis. Modern mainstream nucleic acid sequencing technologies, in addition to producing the sequence of the tested nucleic acid sample, also assign a quality value to each base detected to assess the accuracy of the measurement. This quality value is generally expressed in Phreed form.

[0003] q = -10log 10 (1-a)

[0004] In the formula, 'a' represents the accuracy of the base, and 'q' represents the Phred value. For example, the Phred values ​​corresponding to accuracy of 99%, 99.9%, and 99.99% are 20, 30, and 40, respectively.

[0005] In bioinformatics analysis of nucleic acid sequencing data, quality values ​​play a crucial role. For example, when identifying gene mutations, if a base in the sequenced sequence differs from its corresponding base in the reference sequence, a high quality value for that base indicates a gene mutation; conversely, a low quality value suggests a sequencing error and the absence of a gene mutation.

[0006] Insertion errors, deletion errors, and substitution errors are common concepts in bioinformatics. An insertion error refers to the presence of one or more extra bases in the sequence relative to a reference sequence. A deletion error refers to the absence of one or more bases in the sequence relative to a reference sequence. A substitution error refers to the substitution of one or more bases in the sequence relative to a reference sequence. Current techniques assign only one quality value to each base. This approach is suitable for Illumina and Ion Torrent sequencing technologies because Illumina sequencing errors are almost entirely substitution errors, while Ion Torrent sequencing errors are almost entirely insertion and deletion errors. Therefore, a single quality value is sufficient for subsequent bioinformatics analysis. Summary of the Invention

[0007] ECC sequencing faces different error patterns: insertion, deletion, and substitution errors each account for approximately one-third. Furthermore, the primary causes of these three errors in ECC sequencing differ: insertions and deletions are mainly due to ECC's lower accuracy with long homologous polymers compared to short homologous polymers, while substitutions are primarily caused by DNA damage during library construction and PCR replication errors. Therefore, separating these three error types and labeling them with quality values ​​helps downstream bioinformatics analysis distinguish between background noise and true mutations, providing more accurate identification results. Thus, in ECC sequencing, assigning only a total quality value to each base using traditional methods leads to the following problems: First, because high-throughput sequencers produce large amounts of data, both the sequencer's random software and conventional bioinformatics workflows include numerous data screening steps to select high-quality sequence data based on base quality values, improving analysis accuracy. In ECC sequencing, a single total quality value can result in many sequences having lower quality values ​​at long homologous polymers, leading to significant data loss during screening and impacting downstream bioinformatics analysis. Distinguishing between the three error types and assigning them separate quality values ​​helps improve the discriminative power of base quality and reduces loss during data screening. Secondly, DNA damage introduced during library preparation and DNA replication errors introduced by PCR are difficult to eliminate, yet they are unrelated to the sequencer. This affects the sequencer's judgment of base quality values, meaning the total quality value does not accurately reflect the sequencer's true accuracy, but only reflects the accuracy of library preparation.

[0008] Hereinafter, the present invention discloses a method for assigning multiple quality values ​​to bases to overcome the above-mentioned problems.

[0009] Specifically, the present invention provides a method for assessing the quality of base sequencing, characterized by comprising:

[0010] 1) Provide a standard nucleic acid sequence, wherein the standard nucleic acid sequence is a base sequence obtained by sequencing a standard nucleic acid sample; in the standard nucleic acid sequence, the sequencing signal characteristics of the bases are calculated using the bases as the basic units;

[0011] 2) Align the standard nucleic acid sequence to the reference sequence, and assign labels to the bases in the standard nucleic acid sequence according to the alignment results. The labels are selected from correct sequencing, insertion error, deletion error, or substitution error; 3) Train a classifier to fit the relationship between the sequencing signal features of the bases and their labels.

[0012] 4) Provide the nucleic acid sequence to be tested, and calculate the sequencing signal characteristics of the bases in the nucleic acid sequence to be tested, using bases as the basic unit;

[0013] 5) Using the trained classifier, based on the sequencing signal characteristics calculated in step 4), assign at least two quality values ​​to the bases in the nucleic acid sequence to be tested, representing the insertion and / or deletion error rate and the substitution error rate of the base, respectively.

[0014] According to the preferred embodiment, based on the sequencing signal characteristics calculated in step 4), the bases in the nucleic acid sequence to be tested are assigned three quality values, representing the insertion error rate, deletion error rate, and substitution error rate of the bases, respectively.

[0015] According to a preferred embodiment, the sequencing method includes error-correcting code sequencing, i.e., ECC sequencing.

[0016] According to a preferred embodiment, the sequencing method includes nanopore sequencing.

[0017] According to a preferred embodiment, the sequencing signal characteristics of a base refer to the characteristics of the signal generated when the base undergoes a sequencing chemical reaction during the sequencing process, including but not limited to: base type, base position in the sequence, length of the polymer in which the base is located, position of the base in the polymer, number of rounds of sequencing chemical reaction, signal intensity, degree to which the signal intensity (and its neighboring signal intensities) are close to integers, sequencing signal parameters (unit signal, background signal, lead coefficient, lag coefficient, attenuation coefficient), degree of phase loss corresponding to the base, etc.

[0018] According to a preferred embodiment, the classifier divides bases into several classes based on the sequencing signal characteristics of the bases, and calculates the accuracy of each class of bases.

[0019] According to a preferred embodiment, step 3) fits the relationship between the sequencing signal features of the bases and their labels, including converting the fitting results of the classifier into sequencing quality values ​​of the bases.

[0020] According to a preferred embodiment, the quality value refers to a numerical value that characterizes the sequencing accuracy, selected from accuracy, error rate, Phred value, etc. For example, the error rates corresponding to accuracy of 99%, 99.9%, and 99.99% are 1%, 0.1%, and 0.01%, respectively, and the corresponding Phred values ​​are 20, 30, and 40, respectively.

[0021] According to a preferred embodiment, the quality value is logarithmically based on the base detection error probability, and the quality value includes Q10, Q15, Q20, Q25, Q30, Q35, Q40, Q45, Q50, Q55, and Q60.

[0022] According to preferred embodiments, the classifiers include linear regression, multinomial regression, logistic regression, support vector machine, artificial neural network, random forest, Phred algorithm, ensemble learning, etc.

[0023] According to a preferred embodiment, the nucleic acid sample includes deoxyribonucleic acid (DNA), ribonucleic acid (RNA), peptide nucleic acid (PNA), xylononucleotide (XNA), locked nucleic acid (LNA), etc.

[0024] According to the preferred embodiment, a standard nucleic acid sample refers to a nucleic acid sample whose source and sequence have been determined and which is highly homozygous at almost all sites in the genome, including Escherichia coli DNA, Saccharomyces cerevisiae DNA, λ phage DNA, etc.

[0025] According to a preferred embodiment, the nucleic acid sequence includes a sequenced base sequence or a degenerate base sequence.

[0026] According to preferred embodiments, sequencing methods include dideoxynucleotide termination (Sanger sequencing), chemical degradation (Gilbert method), pyrosequencing, semiconductor sequencing, cyclic reversible terminator, fluorogenic sequencing, error-correction code sequencing, fuzzy sequencing, combined probe-anchor ligation, combined probe-anchor polymerization, sequencing by oligonucleotide ligation and detection, sequencing-by-binding, single-molecule fluorescence sequencing, single-molecule real-time sequencing, and nanopore sequencing.

[0027] According to a preferred embodiment, the standard nucleic acid sample and the nucleic acid sample to be tested are sequenced using the same method, for example, both using ECC sequencing or either of the aforementioned sequencing methods.

[0028] According to a preferred embodiment, the quality assessment method further includes: 6) performing bioinformatics analysis on the nucleic acid sequence after assigning a quality value.

[0029] According to a preferred embodiment, bioinformatics analysis includes screening high-quality nucleic acid sequences based on assigned quality values. Screening methods include, but are not limited to, screening nucleic acid sequences where all quality values ​​are higher than or lower than a certain threshold, screening nucleic acid sequences where the average quality value is higher than or lower than a certain threshold, screening regions within nucleic acid sequences where all quality values ​​are higher than or lower than a certain threshold, and screening regions where the average quality value is higher than or lower than a certain threshold, etc.

[0030] According to a preferred embodiment, bioinformatics analysis includes aligning a nucleic acid sequence to a reference sequence based on an assigned quality value. Alignment is a common concept in bioinformatics and can be performed using algorithms such as Smith-Waterman, Bowtie, BWA, SOAP, Needleman-Wunch, Bowtie2, BLAST, ELAND, TMAP, MAQ, minimap2, and SHRiMP.

[0031] According to a preferred embodiment, bioinformatics analysis includes identifying gene variations based on alignment results and quality values ​​assigned to the aligned sequences. Gene variation is a conventional concept in biology, including but not limited to single nucleotide polymorphisms, copy number variations, epigenetic variations, and large-scale structural variations.

[0032] According to a preferred embodiment, the bioinformatics analysis includes identifying gene variants: when identifying substitution variants, using a quality value characterizing the substitution error rate to calculate the probability of the variant's existence; when identifying insertion variants, using a quality value characterizing the insertion error rate to calculate the probability of the variant's existence; and when identifying deletion variants, using a quality value characterizing the deletion error rate to calculate the probability of the variant's existence.

[0033] According to a preferred embodiment, the bioinformatics analysis includes identifying gene variants: when identifying substitution variants, using a quality value characterizing the substitution error rate to calculate the probability of the variant's existence; when identifying insertion / deletion variants, using a quality value characterizing the insertion and / or deletion error rate to calculate the probability of the variant's existence.

[0034] According to a preferred embodiment, when identifying gene variations, certain features of the comparison results can be used to remove potential false positive or false negative results.

[0035] According to a preferred embodiment, bioinformatics analysis includes assembling nucleic acid sequences into longer nucleic acid sequences based on assigned quality values.

[0036] According to the preferred implementation, steps 1)-3) can be completed in advance, and the trained classifier can be stored in the system as a configuration file, which can be retrieved when steps 4)-5) are executed.

[0037] According to the preferred embodiment, standard nucleic acid samples and nucleic acid samples to be tested can be equipped with different molecular markers and mixed together for simultaneous sequencing. After sequencing, the two types of samples are first separated using molecular markers, completing steps 1)-3), to obtain a trained classifier, which is then applied to the nucleic acid samples to be tested.

[0038] The present invention also provides a system for evaluating the accuracy of base recognition in nucleic acid sequencing, comprising:

[0039] Machine-readable storage; and

[0040] A processor configured to execute machine-readable instructions, which, when executed by the processor, cause the system to perform a method, the method comprising: the processor receiving...

[0041] 1) Standard nucleic acid sequence, wherein the standard nucleic acid sequence is the base sequence obtained by sequencing a standard nucleic acid sample; in the standard nucleic acid sequence, the sequencing signal characteristics of the bases are calculated using the bases as the basic units;

[0042] 2) Align the standard nucleic acid sequence to the reference sequence, and assign labels to the bases in the standard nucleic acid sequence according to the alignment results. The labels are selected from correct sequencing, insertion error, deletion error, or substitution error; 3) Train a classifier to fit the relationship between the sequencing signal features of the bases and their labels.

[0043] 4) Provide the nucleic acid sequence to be tested, and calculate the sequencing signal characteristics of the bases in the nucleic acid sequence to be tested, using bases as the basic unit;

[0044] 5) Using the trained classifier, based on the sequencing signal characteristics calculated in step 4), assign at least two quality values ​​to the bases in the nucleic acid sequence to be tested, representing the insertion and / or deletion error rate and the substitution error rate of the base, respectively.

[0045] According to a preferred embodiment, based on the sequencing signal characteristics calculated in step 4), the bases in the nucleic acid sequence to be tested are assigned three quality values, representing the insertion error rate, deletion error rate, and substitution error rate of the base, respectively.

[0046] Beneficial effects of the present invention

[0047] The method for assigning multiple mass values ​​to a single base disclosed in this invention has the following significant advantages compared to existing methods that assign only one mass value:

[0048] 1. Since the sample preparation process generally makes it difficult to introduce insertion / deletion errors, the insertion / deletion quality value of ECC sequencing can be very high, easily reaching Q60, which helps to accurately identify insertion / deletion variants.

[0049] 2. Since substitution errors are less likely to occur in long DPL sequences, the substitution quality value of ECC sequencing does not decrease significantly with the increase of DPL, which helps to identify substitution variations at long DPLs. Attached Figure Description

[0050] The novel features of the invention are set forth in the appended claims. A better understanding of the features and advantages of the invention will be obtained by referring to the following detailed description and accompanying drawings, which illustrate illustrative embodiments utilizing the principles of the invention, in which:

[0051] Figure 1 illustrates three error types in sequencing reactions.

[0052] Figure 2 illustrates an example of sequencing signal characteristics.

[0053] Figure 3 illustrates the base sequencing quality values ​​according to one embodiment, including: insertion quality value, deletion quality value, substitution quality value, and total quality value.

[0054] Figure 4 illustrates four base mass values ​​corresponding to different degenerate polymer lengths according to one embodiment.

[0055] Figure 5 illustrates a schematic diagram of sequence re-alignment. Detailed Implementation

[0056] Unless otherwise defined, all scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0057] Terminology Explanation

[0058] Each

[0059] As used herein, when referring to a collection of items, the term "each" is intended to identify an individual item in the collection, but not necessarily every item in the collection. Exceptions may occur if explicitly stated otherwise or if the context otherwise specifies otherwise.

[0060] include

[0061] The term "includes" is intended to be open-ended in this document, encompassing not only the listed elements but also any additional elements.

[0062] Degenerate bases

[0063] In this invention, in accordance with the IUPAC symbol naming rules (Nucleic acid notation), the letters in Table 1 below are used to represent degenerate bases, for example, the letter M represents A and / or C.

[0064] Table 1

[0065] The letters represent MA / CKG / TRA / GYC / TWA / TSC / GBC / G / TDA / G / THA / C / TVA / C / G surface

[0066] Degenerate polymer length Polymer Length, DPL)

[0067] Degenerate polymers are polymers composed of degenerate bases. For example, the letter M represents A and / or C. The degenerate polymer MMKKK has a length of 5, that is, a DPL of 5.

[0068] The position of the base in the polymer

[0069] This refers to the distance between a base in a polymer and its nearest end. An example is shown below: the left side shows the polymer sequence, and the right side shows the position of each base within the polymer.

[0070] A:0

[0071] AA:0,0

[0072] AAA: 0, 1, 0

[0073] AAAA: 0, 1, 1, 0

[0074] AAAAA:0,1,2,1,0

[0075] AAAAAA:0,1,2,2,1,0.

[0076] Error correction code Correcting Code (ECC) sequencing and error correction

[0077] In this application, error-correcting code sequencing has the following characteristics:

[0078] This sequencing method requires multiple sequencing runs, each run yielding incomplete information, while the total information from multiple runs is redundant. This redundancy is utilized to detect and correct potential sequencing errors, resulting in highly accurate sequences. For example, in 2+2 sequencing, sequencing reagents are divided into three pairs of matched bases (e.g., MK, RY, and WS), and the DNA sequence to be tested is sequenced three times independently, generating three degenerate sequence codes. These three codes can cross-check each other, allowing not only the derivation of the actual base sequence information through decoding but also the ability to correct errors from single-run sequencing. This correction process is called error correction.

[0079] Phase correction

[0080] In high-throughput sequencing, each sequencing unit contains thousands to tens of thousands of DNA molecules, replicated from the same DNA template and possessing identical sequences, amplifying the sequencing signal to a level that can be accurately detected. However, during sequencing, some DNA molecules cannot guarantee a full response in each round, leading to lag, while others may react prematurely due to substrate doping or mismatches, leading to advance. The elongation of these DNA molecules gradually becomes out of sync as sequencing progresses; this phenomenon is called "phase loss" in sequencing. The algorithmic process of fitting and recorrecting the phase-loss sequencing signal according to a model of the sequencing response is called "phase loss correction."

[0081] Normalized signal

[0082] That is, after normalization, the original sequencing signal is attenuated and corrected to obtain a more accurate unit signal. The ratio of the attenuated and corrected sequencing signal of each sequencing site to the unit signal of that site is the normalized signal of that site in each sequencing round.

[0083] Comparison

[0084] Alignment is a common concept in bioinformatics, often used to compare the similarity between different nucleic acids or proteins. In this invention, alignment refers to comparing the sequenced base sequence with a reference sequence to determine the correctness of the sequence. Commonly used sequence alignment algorithms and software include, but are not limited to, Smith-Waterman algorithm, Bowtie, BWA, SOAP, Needleman-Wunch algorithm, Bowtie2, BLAST, ELAND, TMAP, MAQ, minimap2, and SHRiMP.

[0085] Reference sequence

[0086] A reference sequence is any specific known genome sequence of any organism, whether partial or complete, that can be used to reference an identified sequence from a subject. For example, reference genomes for human subjects and many other organisms can be found at the National Center for Biotechnology Information (NCBI). A “genome” refers to the complete genetic information of an organism or virus expressed as a nucleic acid sequence. A genome includes both genes and the non-coding sequence of DNA. Reference sequences can be larger than the reads they are aligned with. For example, a reference sequence can be at least approximately 100 times larger, or at least approximately 1000 times larger, or at least approximately 10 times larger than the aligned read. 4 Larger, or at least about 105 Larger, or at least about 10 6 Larger, or at least about 10 7 The reference sequence is doubled. Other examples of reference sequences include genomes of other species, as well as chromosomes, subchromosome regions (such as chains), etc., of any species. In this invention, the species corresponding to the reference sequence are preferably nucleic acid sequences from species including *Escherichia coli*, *λ* phage, and *Saccharomyces cerevisiae*, whose nucleic acid sequences are known and highly homozygous at almost all sites in the genome; the *λ* phage is preferably *λ* phage DNA produced by New England Biolabs.

[0087] Mutations

[0088] Somatic variants refer to nucleic acid sequences that differ from a nucleic acid reference sequence. Typical variants include, but are not limited to, single nucleotide variants (SNs), short deletions and insertions (Indels), copy number variations (CNVs), epigenetic variations, microsatellite markers or short tandem repeats, and structural variations. Somatic variant detection is the work of identifying variants present at low frequencies in DNA samples. Somatic variant detection is of interest in the context of cancer treatment. Cancer is caused by the accumulation of mutations in DNA. DNA samples from tumors are often heterogeneous, including some normal cells, some cells in the early stages of cancer progression (with fewer mutations), and some late-stage cells (with more mutations). Due to this heterogeneity, when sequencing tumors (e.g., from FFPE samples), somatic variants will typically appear at low frequencies. For example, SNVs can be seen in only 10% of reads covering a given base.

[0089] Location

[0090] The term "position" refers to the location or coordinates of one or more nucleotides within a nucleotide sequence, or the location or coordinates of one or more base pairs within a nucleotide sequence.

[0091] Classifier

[0092] Classification is a very important method in data mining. In machine learning, the role of a classifier is to determine the category to which a new observation sample belongs based on the training data with labeled categories.

[0093] The construction and implementation of a classifier generally involves the following steps:

[0094] Select samples (including positive and negative samples), and divide all samples into two parts: training samples and test samples;

[0095] The classifier algorithm is executed on the training samples to generate a classification model;

[0096] The classification model is executed on the test samples to generate prediction results.

[0097] In a preferred embodiment, the necessary evaluation indicators are calculated based on the prediction results to evaluate the performance of the classification model.

[0098] It should be noted that the terms mentioned above in this invention are not unique, first-time invention terms or definitions. They are commonly used in the applicant's previous patents or in basic knowledge in the field. These definitions need not be considered unique features. Mathematical terms are actually common terms in the field of mathematics and do not contain any special meaning.

[0099]

[0100] Insertion errors, deletion errors, and substitution errors are common concepts in bioinformatics. An insertion error refers to the presence of one or more extra bases in the sequence relative to the reference sequence. A deletion error refers to the absence of one or more bases in the sequence relative to the reference sequence. A substitution error refers to the substitution of one or more bases in the sequence relative to the reference sequence. Figure 1 illustrates examples of these three sequencing errors, from left to right: a sequence with an inserted G, a deleted T, and a G replaced by a T, respectively, relative to the reference sequence. In ECC sequencing, all three types of errors occur in roughly equal proportions, necessitating quality assessment for each. This invention discloses a base sequencing quality assessment method that assigns at least two quality values ​​to each base in the nucleic acid sequence to be tested, representing the insertion and / or deletion error rate and the substitution error rate, respectively, to improve the accuracy of base readout and enable more targeted subsequent bioinformatics analysis.

[0101] Specifically, the first aspect of the present invention discloses a method for assessing the quality of base sequencing, characterized by comprising:

[0102] 1) Provide a standard nucleic acid sequence, wherein the standard nucleic acid sequence is a base sequence obtained by sequencing a standard nucleic acid sample; in the standard nucleic acid sequence, the sequencing signal characteristics of the bases are calculated using the bases as the basic units;

[0103] 2) Align the standard nucleic acid sequence to the reference sequence, and assign a label to the bases in the standard nucleic acid sequence according to the alignment result. The label is selected from correct sequencing, insertion error, deletion error or substitution error.

[0104] 3) Train a classifier to fit the relationship between the sequencing signal features of the bases and their labels;

[0105] 4) Provide the nucleic acid sequence to be tested, and calculate the sequencing signal characteristics of the bases in the nucleic acid sequence to be tested, using bases as the basic unit;

[0106] 5) Using the trained classifier, based on the sequencing signal characteristics calculated in step 4), assign at least two quality values ​​to the bases in the nucleic acid sequence to be tested, representing the insertion and / or deletion error rate and the substitution error rate of the base, respectively.

[0107] In a preferred embodiment, the bases in the tested nucleic acid sequence are assigned three quality values, representing the insertion error rate, deletion error rate, and substitution error rate of the base, respectively.

[0108] In this invention, nucleic acids include deoxyribonucleic acid (DNA), ribonucleic acid (RNA), peptide nucleic acid (PNA), xylononucleotide (XNA), locked nucleic acid (LNA), etc. A nucleic acid sample refers to a sample containing nucleic acids or mixtures of nucleic acids, typically derived from biological fluids, cells, tissues, organs, or organisms, wherein the nucleic acid or mixture contains at least one nucleic acid sequence to be sequenced and / or phased. Such samples include, but are not limited to, blood, blood fractions, sputum / oral fluid, amniotic fluid, fine-needle biopsy samples (e.g., surgical biopsy, fine-needle biopsy, etc.), urine, peritoneal fluid, pleural fluid, tissue explants, organ cultures, and any other tissue or cell preparations, or fractions or derivatives thereof, or fractions or derivatives isolated therefrom. While samples are typically taken from human subjects (e.g., patients), samples can be taken from any organism possessing chromosomes, including but not limited to cattle, horses, pigs, sheep, dogs, cats, etc. Samples can be used directly as is from their biological source, or after pretreatment to alter their properties. For example, such pretreatment may include preparing plasma from blood, diluting viscous fluids, etc. Pretreatment methods may also include, but are not limited to, filtration, precipitation, dilution, distillation, mixing, centrifugation, freezing, lyophilization, concentration, amplification, nucleic acid fragmentation, inactivation of interfering components, addition of reagents, lysis, etc.

[0109] In this invention, the sequencing methods include dideoxynucleotide termination (Sanger sequencing), chemical degradation (Gilbert method), pyrosequencing, semiconductor sequencing, cyclic reversible terminator, fluorogenic sequencing, error-correction code sequencing, fuzzy sequencing, deletion sequencing (patent CN202210104037.3), combined probe-anchor ligation, combined probe-anchor polymerization, sequencing by oligonucleotide ligation and detection, sequencing-by-binding, single-molecule fluorescence sequencing, single-molecule real-time sequencing, and nanopore sequencing.

[0110] According to the preferred embodiment, the sequencing method is error-correcting code (ECC) sequencing. In ECC sequencing, the proportions of insertion errors, deletion errors, and substitution errors are roughly equal. If, in the traditional method, only one total quality value is assigned to each base, then ECC sequencing has two problems: First, the highest quality value can only reach Q50. This is because the sample preparation process introduces some substitution errors (PCR errors, DNA damage, etc.), making it difficult to prepare high-purity samples. Second, the quality value decreases rapidly with the increase of degenerate polymer length (DPL), because long DPLs are inaccurate and prone to insertion and deletion errors. It can be seen that the causes of substitution and insertion / deletion errors in ECC sequencing are different. Therefore, it is necessary to assign multiple quality values ​​(at least two, for example, two or three) to each base, representing the substitution error rate and insertion / deletion error rate respectively, to overcome the above-mentioned problems in ECC sequencing.

[0111] In some implementations, ECC sequencing is performed on standard nucleic acid samples and ECC sequencing is performed on nucleic acid samples to be tested. The sequencing reactions of the two samples can be performed simultaneously or sequentially.

[0112] According to a preferred embodiment, the sequencing method is nanopore sequencing. In nanopore sequencing, nanopores are immobilized on a thin film. Ions on one side of the film diffuse through the nanopores under the influence of a concentration gradient, forming a diffusion current. When the DNA to be tested passes through the nanopores, it hinders the ion penetration, causing a decrease in the diffusion current. Different base genes have different sizes and chemical properties, resulting in varying degrees of ion penetration resistance and thus different decreases in diffusion current. Therefore, nanopore sequencing can obtain the sequence information of the DNA to be tested by detecting changes in diffusion current. Due to the randomness of single-molecule motion and errors in diffusion current detection, the error rate of nanopore sequencing is very high, often exceeding 10%, with multiple error modes such as insertion, deletion, and substitution coexisting. Nanopore sequencing performs high-frequency detection of diffusion current; therefore, multiple diffusion current values ​​typically correspond to a single detected base. These multiple diffusion current values ​​not only contain information about the type of base but also information about its potential error type. Therefore, in nanopore sequencing, each base needs to be assigned multiple quality values ​​(at least two, for example, two or three) to represent the substitution error rate and insertion / deletion error rate, respectively, in order to fully extract the information contained in the detected diffusion current value, indicate possible error patterns, and improve the accuracy of downstream bioinformatics analysis.

[0113] In this invention, the process of obtaining the nucleic acid sequence is also the process of base calling. During the sequencing run, the sequencer generates raw data, which may include, for example, fluorescence image data, which can be monochrome or multicolor fluorescence images, represented by cyclic reversible terminator sequencing, pyrosequencing, fluorogenic sequencing, and error-correction code sequencing. This raw data may also include voltage changes, typically represented by IonTorrent semiconductor sequencing, where the release of hydrogen ions from base incorporation causes pH changes, which are ultimately reflected as voltage changes proportional to the number of incorporated nucleotides. Alternatively, the raw data may also include current signals from sequencing methods such as Oxford Nanopore Sequencing (ONT), which relies on nanopore sensing to use biosensors to measure the interruption of current when the analyte passes through or approaches its pore opening, while simultaneously determining the type of base.

[0114] In a preferred embodiment, the nucleic acid sequence obtained by base recognition is a defined base sequence, that is, a sequence represented by A, G, C, T, or a sequence represented by A, G, C, U.

[0115] In some implementations, the nucleic acid sequence obtained by base recognition is a degenerate base sequence, that is, a degenerate base represented by M, K, R, Y, W, S, B, D, H, V, etc. Taking MK sequencing as an example, A and C of the M substrate are labeled with two different fluorescent dyes, and G and T of the K substrate are labeled with two different fluorescent dyes. For each sequencing reaction cycle, the number of extended two bases can be obtained. This degenerate base sequence is simply referred to as a two-color degenerate base sequence.

[0116] Understandably, nucleic acid sequences can also be combinations of deterministic base sequences and degenerate base sequences. For example, when sequencing is performed using A and B as substrates, the sequence obtained from the sequencing reaction using A as substrate is a deterministic base sequence, while the sequence obtained from the reaction using B as substrate is a degenerate base sequence.

[0117] In this invention, the sequencing signal characteristics of a base refer to the characteristics of the signal generated when the base on the sequence being sequenced undergoes a sequencing chemical reaction during the sequencing process. Figure 2 shows examples of sequencing signal characteristics, including but not limited to: the type of the base, i.e., whether the base belongs to A, G, C, T (or U); the position of the base in the sequence, i.e., the position of the base in its nucleotide sequence. For example, for single-end sequencing, the sequencing quality value of bases located earlier in the sequence is usually higher than that of bases located later in the sequence; the length of the polymer in which the base is located, i.e., the number of bases in the homopolymer or degenerate polymer in which the base is located. Generally, shorter polymer lengths result in higher sequencing quality values; and the position of the base within its polymer, i.e., the position of the base within its nearest homopolymer or degenerate polymer. The distance between the ends; the number of cycles in which the base undergoes the sequencing chemical reaction, i.e., the number of cycles corresponding to the base incorporating into the nucleotide chain. Generally, a smaller number of cycles indicates a higher quality value; signal intensity, which can be the intensity of the signal directly acquired by the sequencer, including brightness, voltage level, or current level, etc. It can be a normalized signal or a phase-corrected signal; the degree to which the signal intensity (and its neighboring signal intensities) is close to an integer, i.e., the difference between the normalized signal, the phase-corrected signal, or the error-corrected signal and the nearest integer. Generally, a smaller difference indicates higher accuracy; sequencing signal parameters, i.e., unit signal, background signal, lead coefficient, lag coefficient, attenuation coefficient, etc.; the degree of phase loss when the base is detected. Generally, a lower degree of phase loss indicates higher accuracy; and so on.

[0118] In a specific implementation, a standard nucleic acid sequence is aligned to its corresponding reference sequence to obtain an alignment result. Based on the alignment result, bases are marked as either correctly sequenced or incorrectly sequenced. Preferably, high-quality aligned base sequences are further screened from the alignment results, and the bases in these high-quality aligned base sequences are marked as correctly sequenced or incorrectly sequenced, ignoring bases that cannot be determined (i.e., bases that cannot be successfully aligned to the reference sequence or bases with low alignment quality). Based on the alignment result, bases with a "match" alignment result are marked as "correctly sequenced," bases with a "mismatch" alignment result are marked as "replacement error," bases with an "insertion" alignment result are marked as "insertion error," and bases with a "deletion" alignment result are marked as "deletion error." Optionally, bases with a "mismatch" alignment result are marked as "replacement error," and bases with an "insertion" or "deletion" alignment result are marked as "insertion or deletion error." The high-quality alignment described in this invention requires the specific selection of the quality value range based on the alignment software or algorithm used. For example, when using BWA for sequence alignment, a high-quality aligned base sequence refers to a base sequence with an alignment quality greater than 0, or greater than or equal to 10, or greater than or equal to 20, or greater than or equal to 30, or greater than or equal to 40, or greater than or equal to 50, or greater than or equal to 60.

[0119] In this invention, classifier is a conventional concept in the field of pattern recognition, including linear regression, multinomial regression, logistic regression, support vector machines, artificial neural networks, random forests, Phred algorithms, ensemble learning, etc. With the development of pattern recognition, many novel classifier algorithms have been proposed in recent years. Using novel classifier algorithms does not change the essence of this invention.

[0120] According to a preferred embodiment, the classifier can divide bases into several classes based on the sequencing signal characteristics of the bases, and calculate the accuracy of each class. For example, bases located in sequences 1-20, 21-40, 41-60, 61-80, and 81-100 bp can be classified into one class, or bases located in polymers of length 1, 2, 3, 4, 5, and above can be classified into another class. When using multiple sequencing signal characteristics, orthogonal division can be performed. For example, bases located within sequences 1-20 bp and in 1-fold polymers can be classified into one class, bases located within sequences 21-40 bp and in 2-fold polymers can be classified into another class, and so on.

[0121] In a preferred embodiment, a classifier is trained to fit the relationship between the sequencing signal features of bases and their labels, including converting the classifier's fitting results into quality values. Numerous studies report on how to convert classifier predictions into quality values. Taking the well-known softmax algorithm as an example, suppose the output of a classifier is (a,b), where (1,0) represents correct and (0,1) represents incorrect. Due to factors such as the accuracy of classifier training or computational errors during prediction, the classifier's output during prediction is not always exactly (1,0) or (0,1), but rather a value closer to (1,0) or (0,1), such as (0.9,0.05) or (0.1,0.99). In this case, the softmax algorithm uses the following formula to convert the output (a,b) into an accuracy rate:

[0122]

[0123] With the development of pattern recognition, a variety of novel transformation algorithms have been proposed in recent years, including Sparse-softmax, log-softmax, Taylor softmax, log-Taylor softmax, soft-margin softmax, and SM-Taylor softmax. Using novel transformation algorithms does not change the essence of this invention.

[0124] The quality score is a numerical value characterizing the accuracy of sequencing. It can be expressed mathematically in different ways, such as accuracy, error rate, and Phred value. For example, accuracy rates of 99%, 99.9%, and 99.99% correspond to error rates of 1%, 0.1%, and 0.01%, respectively, and corresponding Phred values ​​of 20, 30, and 40. In some implementations, for ease of recording and storage, the Phred value is converted to ASCII code by adding 33; for example, Phred values ​​of 20, 30, and 40 are converted to the characters '5', '?', and 'I', respectively. The different forms of quality score representation do not affect the essence of this invention.

[0125] In a preferred embodiment, the quality value is logarithmically based on the base detection error probability, and the quality value includes Q10, Q15, Q20, Q25, Q30, Q35, Q40, Q45, Q50, Q55, and Q60.

[0126] In a preferred embodiment, steps 1)-3) can be completed in advance, and the trained classifier can be stored in the system as a configuration file, which can be retrieved when steps 4)-5) are executed.

[0127] In a preferred embodiment, the standard nucleic acid sample and the nucleic acid sample to be tested can carry different molecular markers and be mixed together for simultaneous sequencing. After sequencing, the two types of samples are first separated using molecular markers (e.g., index). For the standard nucleic acid sequence, steps 1)-3) are performed to obtain a trained classifier, which is then applied to the nucleic acid sample to be tested, i.e., steps 4)-5).

[0128] According to a preferred embodiment, the quality assessment method further includes sequence alignment. The penalty score during alignment depends on at least two quality values ​​for a single base, eliminating the need for a realignment step, thus simplifying the bioinformatics workflow and accelerating analysis. Specifically, the alignment software no longer uses a fixed penalty score rule, but instead makes the penalty score dependent on different quality values ​​of the bases. That is, if the substitution quality value of a base is low, a lower penalty score is assigned when a substitution is determined to exist; if the insertion quality value of a base is low, a lower penalty score is assigned when an insertion is determined to exist; and if the deletion quality value of a base is low, a lower penalty score is assigned when a deletion is determined to exist.

[0129] According to a preferred embodiment, the quality assessment method further includes: 6) performing bioinformatics analysis on the nucleic acid sequence after assigning a quality value.

[0130] In a preferred embodiment, bioinformatics analysis may involve screening high-quality nucleic acid sequences based on the quality values ​​assigned to the bases. Screening methods include, but are not limited to, screening nucleic acid sequences where all quality values ​​are higher than or lower than a certain threshold, screening nucleic acid sequences where the average quality value is higher than or lower than a certain threshold, screening regions within a nucleic acid sequence where all quality values ​​are higher than or lower than a certain threshold, and screening regions within a nucleic acid sequence where the average quality value is higher than or lower than a certain threshold, etc. The threshold may be changed based on empirical analysis; for example, the threshold may be set to 15, 20, 25, or 30.

[0131] In a preferred embodiment, bioinformatics analysis refers to identifying gene variations based on the alignment results and the quality value assigned to the aligned sequence.

[0132] According to a preferred embodiment, the bioinformatics analysis includes identifying gene variants: when identifying substitution variants, using a quality value characterizing the substitution error rate to calculate the probability of the variant's existence; when identifying insertion variants, using a quality value characterizing the insertion error rate to calculate the probability of the variant's existence; and when identifying deletion variants, using a quality value characterizing the deletion error rate to calculate the probability of the variant's existence.

[0133] According to a preferred embodiment, the bioinformatics analysis includes identifying gene variants: when identifying substitution variants, using a quality value characterizing the substitution error rate to calculate the probability of the variant's existence; when identifying insertion / deletion variants, using a quality value characterizing the insertion and / or deletion error rate to calculate the probability of the variant's existence.

[0134] In some implementations, bioinformatics analysis may involve assigning a null hypothesis when identifying gene variations: that no gene variation exists at the locus. Based on the quality score and alignment results, the probability of the null hypothesis being true is calculated. If this probability is greater than a given significance level, the null hypothesis is accepted; otherwise, it is rejected, and the gene variation at the locus is considered to exist. When calculating the probability of the null hypothesis being true:

[0135] 1. When identifying insertion variations, only the insertion quality value should be used;

[0136] 2. When identifying deletion variants, only the deletion quality value should be used;

[0137] 3. When identifying substitution variations, only the substitution quality value is used.

[0138] In ECC sequencing, when the DPL is large, if only one quality value is used to assess the base quality, low-frequency gene mutations may not be detected due to the low quality value. However, this invention uses multiple quality values ​​to assess the base sequencing quality. Among them, the substitution quality value is less affected by the long DPL and has a higher quality value, making it easier to successfully detect low-frequency mutations.

[0139] According to a preferred embodiment, when identifying gene variations, certain features of the comparison results can be used to remove potential false positives or false negatives. These are routine operations in bioinformatics, and their addition does not affect the essence of the present invention. Such features include, but are not limited to:

[0140] 1. This gene variation is concentrated in sequences aligned forward or backward, and less frequently appears in sequences aligned backward or forward.

[0141] 2. This gene variation is concentrated at both ends of the sequence, and less so in the middle of the sequence;

[0142] 3. When using paired-end sequencing, read1 detects that the site is mainly G changing to T, while read2 detects that the site is mainly C changing to A, or read1 detects that the site is mainly C changing to T, while read2 detects that the site is mainly G changing to A;

[0143] 4. Other different gene variations frequently appear near this gene variation.

[0144] In some implementations, insertion quality values ​​and deletion quality values ​​can be combined into a single insertion / deletion quality value. That is, each base is assigned both an insertion / deletion quality value and a substitution quality value, representing the probability of an insertion or deletion error, or a substitution error, for that base. The aforementioned operations can be naturally extended to the case of two quality values, and will not be elaborated further here.

[0145] A second aspect of the present invention provides a system for evaluating the accuracy of base recognition in nucleic acid sequencing, comprising:

[0146] Machine-readable storage; and

[0147] A processor configured to execute machine-readable instructions, which, when executed by the processor, cause the system to perform a method, the method comprising: the processor receiving...

[0148] 1) Standard nucleic acid sequence, wherein the standard nucleic acid sequence is the base sequence obtained by sequencing a standard nucleic acid sample; in the standard nucleic acid sequence, the sequencing signal characteristics of the bases are calculated using the bases as the basic units;

[0149] 2) Align the standard nucleic acid sequence to the reference sequence, and assign labels to the bases in the standard nucleic acid sequence according to the alignment results. The labels are selected from correct sequencing, insertion error, deletion error, or substitution error; 3) Train a classifier to fit the relationship between the sequencing signal features of the bases and their labels.

[0150] 4) Provide the nucleic acid sequence to be tested, and calculate the sequencing signal characteristics of the bases in the nucleic acid sequence to be tested, using bases as the basic unit;

[0151] 5) Using the trained classifier, based on the sequencing signal characteristics calculated in step 4), assign at least two quality values ​​to the bases in the nucleic acid sequence to be tested, representing the insertion and / or deletion error rate and the substitution error rate of the base, respectively.

[0152] In a preferred embodiment, based on the sequencing signal characteristics calculated in step 4), the bases in the nucleic acid sequence to be tested are assigned three quality values: insertion quality value, deletion quality value, and substitution quality value, which respectively represent the insertion error rate, deletion error rate, and substitution error rate of the base.

[0153] The features discussed in the specific embodiments of the base sequencing quality assessment method of the present invention are also applicable to specific embodiments of the system used to assess the accuracy of nucleic acid sequencing base identification. As shown above, all other features are not repeated here and should be considered as repetitions by reference. Those skilled in the art will understand how easily the features identified in these embodiments can be combined with basic feature sets identified in other embodiments.

[0154] Example 1

[0155] Genomic DNA of λ phage was purchased from New England Biolabs. After library construction, error-corrected coding sequencing was performed to obtain the base sequence (i.e., the standard nucleic acid sequence). For each base, the values ​​of the following five predictors were calculated:

[0156] 1. The position of this base in the sequence;

[0157] 2. The length of the homopolymer in which the base is located;

[0158] 3. The difference between the phase correction signal and the error correction signal at which the base is located;

[0159] 4. The Euclidean distance between the phase correction signal of the base and its rounding signal;

[0160] 5. The Euclidean distance between all phase-corrected signals and their rounded signals in the sequence containing this base.

[0161] The obtained DNA sequences were aligned to a reference genome using BWA-MEM software, ignoring unaligned sequences. Based on the alignment results, bases were labeled as "match," "substitution," "insertion," or "deletion." A quality assessment table was constructed using the Phred algorithm with these five predictors. The following four methods were employed for base labeling during construction:

[0162] 1. Only "Insert" is considered an error; all other markers are considered correct.

[0163] 2. Only "missing" is considered an error; other markers are considered correct.

[0164] 3. Only "Replace" is considered an error; other markers are considered correct.

[0165] 4. Only "match" is considered correct; other tags are considered incorrect.

[0166] This resulted in four quality assessment tables: inserted quality value, missing quality value, replaced quality value, and total quality value. The discrimination of these four quality assessment tables was statistically analyzed, as shown in Figure 3 (i.e., the proportion of bases with quality values ​​above a certain threshold).

[0167] Specifically, the proportions of quality values ​​not lower than 20, 30, 40, 50, and 60 are shown in Table 2:

[0168] Table 2

[0169] Quality Value Type | Inserted Quality Value | Missing Quality Value | Replaced Quality Value | Total Quality Value AQ20 | 100% | 91.56% | 92.56% | 85.79% AQ30 | 82.49% | 74.37% | 82.63% | 73.58% AQ40 | 73.34% | 63.56% | 65.85% | 58.08% AQ50 | 58.25% | 59.40% | 6.58% | 1.95% AQ60 | 50.78% | 49.36% | 0% | 0% surface

[0170] As can be seen, the highest total quality value only reached 1.95% Q50, while the highest insertion and deletion quality values ​​not only both reached Q60, but also accounted for nearly 50%. Although the highest substitution quality value was also Q50, its proportion increased significantly to 6.58%. Therefore, this invention can significantly improve the distinguishability of base quality values, although it is not obvious that insertion and deletion quality values ​​can reach Q60.

[0171] Figure 4 (village plot) shows the relationship between the length of the homopolymer containing the base and its insertion, deletion, substitution, and total mass values ​​(from top to bottom). It can be seen that when the homopolymer is long, the insertion, deletion, and total mass values ​​are all low, while the substitution mass value is less affected. When detecting substitution mutations in long homopolymers, using the total mass value in the traditional method will fail to detect lower-frequency mutations due to the low mass value. However, using the substitution mass value will successfully detect lower-frequency mutations due to the higher mass value. This effect is not readily apparent.

[0172] Example 2

[0173] Alignment is a process that attempts to find the optimal base correspondence between a sequence being compared to a reference sequence. In an alignment result, a match between two bases earns bonus points, while mismatches, insertions, and deletions incur penalties. The optimal base correspondence is the alignment result with the highest score after considering all bonus and penalty points. Alignment software typically uses a fixed penalty rule to calculate alignment results; this rule is only related to base pairing and not to base quality values. In addition, most alignment software suffers from another problem: they usually process each sequence alignment independently, meaning the alignment result of one sequence is unrelated to other sequences. This leads to different alignment results for different sequences when insertion / deletion mutations or multiple consecutive mutations occur, making it difficult to identify the specific mutation. In traditional methods, a "realignment" step is added after the alignment. The specific process is shown in Figure 5. Based on the alignment of all sequences covering the same site, the alignment results of each sequence are locally corrected to increase the accuracy of identifying insertions / deletions or consecutive mutations.

[0174] By using the method described in this invention, assigning multiple different quality values ​​to each base, the aforementioned problems can be avoided, eliminating the need for re-alignment steps, simplifying the bioinformatics process, and accelerating analysis speed. Specifically, the alignment software no longer uses a fixed penalty score rule, but instead makes the penalty score dependent on the different quality values ​​of the bases. That is, if a base has a low substitution quality value, a lower penalty score is assigned when a substitution is determined; if a base has a low insertion quality value, a lower penalty score is assigned when an insertion is determined; and if a base has a low deletion quality value, a lower penalty score is assigned when a deletion is determined.

[0175] Example 3

[0176] 1. Genomic DNA was extracted from the HBL101 cell line after repeated passages, and its targeted sequencing library was constructed using the Aijitaikang Hereditary Breast Cancer Panel Kit.

[0177] 2. The library was subjected to ECC sequencing, and each base sequenced was assigned one of four quality values: insertion quality value, deletion quality value, substitution quality value, and total quality value, resulting in four fastq files.

[0178] 3. Use the FASTP software to preprocess these four FASTQ files to remove low-quality data, and then use the BWA-MEM software to align the sequences to the hg19 reference genome.

[0179] 4. Use pysam to statistically analyze the coverage uniformity and background noise of the alignment results, and use GATK to identify gene variations present in the library.

[0180] 5. The analysis results are shown in Table 3. In the results using total quality value, insertion quality value, and deletion quality value, the quality value at long homologous polymers is relatively low, as most sequences in this area are removed by the FastP software, resulting in a low uniformity of coverage on the genome after alignment, only about 0.6. In the results using substitution quality value, the quality values ​​at long and short homologous polymers are equal, and both are preserved during FastP processing, resulting in a higher uniformity of coverage on the genome after alignment, reaching 0.83. Uniformity of coverage is calculated as the area enclosed by the Lorenz curve of coverage depth and the x-axis. In the GATK identification results, the detection counts of different variant types were compared. Since there are four quality values, when using insertion / deletion / substitution quality values, only the corresponding insertion / deletion / substitution variants were detected, excluding the other two variants. When using the total quality value, all three variant types were detected. The identification results are shown in Table 3. The results using substitution quality values ​​identified 20% more variants than those using total quality values.

[0181] Table 3

[0182] Quality value type coverage uniformity Inserted variance Missing variance Replacement variance Inserted quality value 0.62800 Missing quality value 0.61050 Replacement quality value 0.830030 Total quality value 0.596425 surface

[0183] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing the quality of base sequencing, characterized in that, include: 1) Provide a standard nucleic acid sequence, and calculate the sequencing signal characteristics of the bases in the standard nucleic acid sequence, using bases as the basic unit; 2) Align the standard nucleic acid sequence to a reference sequence, and assign labels to the bases in the standard nucleic acid sequence according to the alignment results. The labels are selected from correct sequencing, insertion error, deletion error, or substitution error. 3) Train a classifier to fit the relationship between the sequencing signal features of the bases and their labels. 4) Provide a nucleic acid sequence to be tested. In the nucleic acid sequence to be tested, the sequencing signal features of the bases are calculated using bases as basic units. 5) Using the trained classifier, based on the sequencing signal features calculated in step 4), assign at least three quality values ​​to the bases in the nucleic acid sequence to be tested, representing the insertion error rate, deletion error rate, and substitution error rate of the bases, respectively.

2. The method according to claim 1, characterized in that, The sequencing methods include error-correcting code sequencing and nanopore sequencing.

3. The method according to claim 2, characterized in that, The sequencing signal characteristics of the base refer to the characteristics of the signal generated when the base undergoes a sequencing chemical reaction during the sequencing process, including: base type, base position in the sequence, length of the polymer in which the base is located, position of the base in the polymer, number of rounds of sequencing chemical reaction, signal intensity, degree to which the signal intensity is close to an integer, sequencing signal parameters, and degree of phase loss corresponding to the base.

4. The method according to any one of claims 1-3, characterized in that, The classifier divides bases into several classes based on the sequencing signal characteristics of the bases and calculates the accuracy of each class of bases.

5. The method according to claim 4, characterized in that, The relationship between the sequencing signal features of the fitted bases and their labels includes converting the fitting results of the classifier into sequencing quality values ​​of the bases.

6. The method according to claim 5, characterized in that, The classifiers include linear regression, multinomial regression, logistic regression, support vector machine, artificial neural network, random forest, Phred algorithm, and ensemble learning.

7. The method according to any one of claims 1-3, 5, and 6, characterized in that, The standard nucleic acid sequence is the base sequence obtained by sequencing a standard nucleic acid sample; the standard nucleic acid sample refers to a nucleic acid sample whose source and sequence have been determined and which is highly homozygous at all sites in the genome, including λ phage DNA, Escherichia coli DNA, and Saccharomyces cerevisiae DNA.

8. The method according to claim 1, characterized in that, Also includes: 6) Perform bioinformatics analysis on the nucleic acid sequences after assigning quality values.

9. The method according to claim 8, characterized in that, The bioinformatics analysis includes identifying gene variants: when identifying substitution variants, using a quality value characterizing the substitution error rate to calculate the probability of the variant's existence; when identifying insertion variants, using a quality value characterizing the insertion error rate to calculate the probability of the variant's existence. When identifying missing variants, the quality value, which characterizes the missing error rate, is used to calculate the probability of the variant's presence.

10. The method according to claim 8, characterized in that, The bioinformatics analysis includes identifying gene variants: when identifying substitution variants, using quality values ​​characterizing the substitution error rate to calculate the probability of the variant's existence; when identifying insertion / deletion variants, using quality values ​​characterizing the insertion error rate and / or deletion error rate to calculate the probability of the variant's existence.

11. The method according to claim 1, characterized in that, It also includes sequence alignment, where the penalty score during alignment depends on three quality values ​​for a single base.

12. The method according to any one of claims 8-11, characterized in that, Steps 1)-3) are completed in advance, and the trained classifier is stored in the system as a configuration file. The configuration file is retrieved when steps 4)-5) are executed.

13. A system for evaluating the accuracy of base recognition in nucleic acid sequencing, comprising: Machine-readable storage; and a processor configured to execute machine-readable instructions, which, when executed by the processor, cause the system to perform a method, the method comprising: the processor receiving 1) a standard nucleic acid sequence, the standard nucleic acid sequence being a base sequence obtained by sequencing a standard nucleic acid sample; 1) In the standard nucleic acid sequence, the sequencing signal characteristics of the bases are calculated using bases as the basic unit; 2) The standard nucleic acid sequence is aligned to a reference sequence, and the bases in the standard nucleic acid sequence are labeled according to the alignment results. The labels are selected from correct sequencing, insertion error, deletion error, or substitution error; 3) A classifier is trained to fit the relationship between the sequencing signal characteristics of the bases and their labels; 4) A nucleic acid sequence to be tested is provided, and the sequencing signal characteristics of the bases are calculated using bases as the basic unit in the nucleic acid sequence to be tested; 5) Using the trained classifier, the bases in the nucleic acid sequence to be tested are assigned three quality values ​​according to the sequencing signal characteristics calculated in step 4), which represent the insertion error rate, deletion error rate, and substitution error rate of the bases, respectively.

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