An automated online tool for analyzing Sanger sequencing results

Abstract

The present invention relates to the field of bioinformatics, and more specifically, to an automated online tool for analyzing Sanger sequencing results. The tool comprises an input and submission unit that receives user-uploaded sequencing data and reference sequence files, and describes and labels them; a parameter setting unit that sets quality control thresholds, bimodal identification thresholds, and visualization window sizes; a quality control unit that performs quality control on the 5' and 3' ends of each ab1 file based on the parameter setting unit; a mutation identification unit that uses an alignment algorithm to identify SNPs, InDeIs, and long insertions / deletions, and identifies bimodal phenomena using a bimodal probability model; and a visualization and output unit that displays and generates a visual report of the mutation identification results. This automated online tool for analyzing Sanger sequencing results uses a bimodal probability model in combination with advanced genome alignment and variation detection algorithms to accurately identify heterozygous mutations and incomplete gene editing in Sanger sequencing chromatograms, as well as high-quality SNPs and short and long insertions / deletions.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to an automated online tool for analyzing Sanger sequencing results. Background Art

[0002] AutoVGS is a web tool designed for analysis of sequencing results for targeted genetic manipulation. It aims to simplify comprehensive, automated, and high-throughput mutation scanning. Using a comparison algorithm and a bimodal probability model, it can efficiently identify high-quality single nucleotide polymorphisms (SNPs), short insertions / deletions, long insertions / deletions, and double peaks in Sanger sequencing chromatograms.

[0003] Existing automated online tools for Sanger sequencing result analysis can only perform basic mutation identification. Due to the complexity of the sequencing result analysis process of target genetic manipulation and the hidden nature of potential mutations, it can lead to misidentification and missed detection of low-quality data. Therefore, an automated online tool for Sanger sequencing result analysis was designed. Summary of the Invention

[0004] The purpose of the present invention is to provide an automated online tool for analyzing Sanger sequencing results to solve the problems raised in the above background technology, such as the complexity of the sequencing result analysis process of target genetic manipulation and the hidden nature of potential mutations, which may lead to misidentification and missed detection of low-quality data.

[0005] To achieve the above objectives, the present invention provides an automated online tool for analyzing Sanger sequencing results, comprising:

[0006] An input and submission unit, which is used to accept sequencing data and reference sequence files uploaded by users in single and batch formats, use ab1 files for analysis, and describe and label the uploaded input files;

[0007] The system further includes a parameter setting unit, wherein the user can click Advanced Options in the Submission and Batch interfaces to expand the parameter setting interface and set the quality control threshold, bimodal identification threshold, and visualization window size parameters;

[0008] Also included is a quality control unit, which provides two quality control options based on the parameter setting unit to perform quality assessment and control on the 5' end and 3' end of each ab1 file;

[0009] The method also includes a mutation recognition unit, which uses an alignment algorithm and a variation detection algorithm to identify and mark short fragment insertions / deletions; uses an advanced alignment algorithm to detect and mark long fragment insertions / deletions; and uses a bimodal probability model to identify double peaks in Sanger sequencing chromatograms;

[0010] It also includes a visualization and output unit, which is used to perform detailed visualization and output report generation on the results of mutation identification and analysis.

[0011] As a further improvement of this technical solution, the input and submission unit includes a single submission module, a batch submission module and a task description module;

[0012] The single submission module is used for users to submit sequencing analysis targets one by one. Users can select GenBank files and directly enter target sequences through the user interface and upload the corresponding ab1 files.

[0013] The batch submission module is used for users to submit multiple sequencing tasks in batches. Users can use the batch submission module to upload a ZIP file containing all sequencing ab1 files and reference sequences, as well as an EXCEL file. The EXCEL file is used to describe the specific information of each sequencing task. The batch submission module supports two formats:

[0014] Type 1: The ZIP file contains the sequencing ab1 files of all targets and their corresponding GenBank files; the EXCEL file header line includes sample, template, target, and ab1 files;

[0015] Type 2: The ZIP file contains only the sequenced ab1 file; the EXCEL file title includes sample, sequence name, target sequence, and ab1 files;

[0016] The task description module is used for the user to describe each analysis task through the user interface, including the task name, label and remarks.

[0017] As a further improvement of the present technical solution, the parameter setting unit includes a connection region length setting module, a quality control setting module, a bimodal identification threshold setting module and a visualization window setting module;

[0018] The flanking region length setting module allows the user to enter a specified length in the "Length of Flanking Region" row to extend the reference sequence range on the upstream 5' end and downstream 3' end regions of the target sequence;

[0019] The quality control setting module provides two quality control options: quality control option Q1 and quality control option Q2, which are used to ensure the quality of sequencing data;

[0020] Among them, the bimodal identification threshold setting module is used to set the threshold for bimodal identification. The ratio of the height of the secondary peak to the height of the highest peak must exceed the threshold specified by the user;

[0021] The visualization window setting module uses the genome browser-IGV.js toolkit for visualization, providing detailed visualization of the reference sequence and the bam file after the alignment, and showing the specific position of each ab1 aligned to the genome, the alignment quality and mutation information.

[0022] As a further improvement of the present technical solution, the quality control option Q1 in the quality control setting module is a quality control option based on the continuous low-quality fragment removal rule, wherein each ab1 sequence will be quality controlled according to the continuous low-quality fragment removal rule;

[0023] The rule for removing consecutive low-quality fragments specifically removes all fragments with a quality score lower than Y for X consecutive bases. X is the value entered in the Length row, and Y is the value entered in the Quality Score row. If the user specifies a Quality Score of 0, quality control will not be triggered.

[0024] Among them, the quality control option Q2 is a user-defined trimming range, and the trimming range is specifically C bases at the 5' end and D bases at the 3' end will be trimmed; where C is the value filled in the "User-specific 5'-end length" row, and D is the value filled in the "User-specific 3'-end length" row. If the user specifies C and D as 0, quality control will not be triggered.

[0025] As a further improvement of the present technical solution, the quality control unit is used to perform quality assessment and control on the input sequencing data, and the quality control unit includes an endpoint quality trimming module, a continuous low-quality base filtering module, and an ab1 file quality assessment module;

[0026] Wherein, the endpoint quality trimming module trims low-quality bases at the 5' and 3' ends of each ab1 file based on the quality control option Q2 provided in the parameter setting unit;

[0027] The continuous low-quality base filtering module removes continuous low-quality base fragments according to the continuous low-quality fragment removal rule in the quality control option Q2 provided by the parameter setting unit;

[0028] The ab1 file quality assessment module assesses the overall quality of each ab1 file, including detecting and recording the quality control results of each file. The quality control results specifically record the number of base pairs trimmed at the 5' end and 3' end of each ab1 file.

[0029] As a further improvement of the present technical solution, the mutation identification unit includes a mutation scanning identification module and a bimodal identification module;

[0030] The mutation scanning module uses a genome alignment algorithm to identify single nucleotide polymorphisms, automatically detecting and marking SNPs by aligning sequencing data with reference sequences; uses an alignment algorithm and a variation detection algorithm to identify and mark short insertions / deletions; and uses an advanced alignment algorithm to detect and mark long insertions / deletions.

[0031] The bimodal identification module uses a bimodal probability model to compare the height ratio of the secondary peak to the highest peak, sets a user-specified threshold, and identifies bimodal phenomena in Sanger sequencing chromatograms. The specific steps involved in the bimodal probability model to identify bimodal phenomena in Sanger sequencing chromatograms are as follows:

[0032] S4.1. Convert Sanger sequencing chromatogram data into signal sequence S = {s1, s2, ..., s n}, where s i represents the peak height at the i-th position;

[0033] S4.2. Detect preliminary peaks using the first-order derivative method;

[0034] S4.3. For each detected peak p i , calculate the signal variance σ in its local area 2 and mean μ, construct a bimodal probability density function and calculate the probability of the second peak;

[0035] S4.4. Use high-order spectrum analysis to separate overlapping peak signals and separate the secondary peaks and main peaks through inverse Fourier transform;

[0036] S4.5. Re-detect the separated signals to verify the presence of double peaks;

[0037] S4.6. Visualize the positions of the identified double peaks and their probability distributions, and provide detailed ratio analysis and chromatogram comparison results.

[0038] As a further improvement of this technical solution, the mathematical formula involved in detecting the preliminary peak using the first-order derivative method in S4.2 is as follows:

[0039]

[0040] Where P is the set of all detected peak positions; p i is the position of the i-th peak; is the first-order derivative of the signal sequence S.

[0041] As a further improvement of this technical solution, the S4.3 is for each detected peak value p i , calculate the signal variance σ in its local area 2 and mean μ,

[0042] Constructing a bimodal probability density function and calculating the probability of the secondary peak involves the following mathematical formula:

[0043] Signal variance σ 2 And mean μ:

[0044]

[0045] Among them, μ i is the peak value p i The local mean of is the peak value p i The local variance of ; w is the window size, which determines the range of the local area;

[0046] Bimodal probability density function

[0047]

[0048] in, is the peak value p i The probability density function of

[0049] The probability of the second peak is P 次高峰 (p i ):

[0050]

[0051] Among them, P 次高峰 (p i ) is the probability of the secondary peak.

[0052] As a further improvement of the present technical solution, the mathematical formula involved in applying high-order spectrum analysis to separate overlapping peak signals in S4.4 and separating the secondary peak and main peak signals through inverse Fourier transform is as follows:

[0053] Apply high-order spectral analysis to separate overlapping peak signals:

[0054]

[0055] Where H(ω) is the high-order spectrum of the signal; A kis the amplitude of the signal; ω is the frequency; N is the length of the signal; j is the imaginary unit;

[0056] By inverse Fourier transform, the secondary peak and main peak signals are separated:

[0057]

[0058] Among them S 分离 (t) is the separated signal in the time domain; is the inverse Fourier transform;

[0059] The mathematical formula involved in re-detecting the separated signal in S4.5 is as follows:

[0060]

[0061] Among them, R is the final confirmed set of double peak positions; r i is the i-th position of the double peak; is the separation signal S 分离 The first derivative of .

[0062] As a further improvement of this technical solution, the visualization and output unit includes a result visualization module and a report generation module;

[0063] The result visualization module uses visualization tools such as IGV.js to provide a visual display of mutation scanning results, including chromatogram comparison, mutation position marking, and bimodal phenomenon result display;

[0064] The report generation module is used to generate a detailed analysis report, including statistical data of all mutations, bimodal analysis results and quality control information.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. This automated online tool for analyzing Sanger sequencing results uses a bimodal probability model to identify double peaks in Sanger sequencing chromatograms, which can accurately identify heterozygous mutations and incomplete gene editing.

[0067] 2. This automated online tool for Sanger sequencing result analysis uses advanced genome alignment algorithms and variation detection algorithms to efficiently identify high-quality single nucleotide polymorphisms, short insertions / deletions, and long insertions / deletions. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a flowchart of the overall process of the present invention.

[0069] The meaning of each number in the figure is:

[0070] 1. Input and submission unit; 2. Parameter setting unit; 3. Quality control unit; 4. Mutation identification unit; 5. Visualization and output unit. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0072] Example

[0073] See also Figure 1 As shown, an automated online tool for Sanger sequencing result analysis is provided, including:

[0074] Input and submission unit 1, which is used to accept sequencing data and reference sequence files uploaded by users in single and batch formats, use ab1 files for analysis, and describe and label the uploaded input files;

[0075] The input and submission unit 1 includes a single submission module, a batch submission module and a task description module;

[0076] The single submission module is used for users to submit sequencing analysis targets one by one. Users can select GenBank files and directly enter target sequences through the user interface and upload the corresponding ab1 files; ensure that the input data of each sequencing task is complete and submitted for analysis;

[0077] The batch submission module is used for users to submit multiple sequencing tasks in batches. Users can use the batch submission module to upload a ZIP file containing all sequencing ab1 files and reference sequences, as well as an EXCEL file. The EXCEL file is used to describe the specific information of each sequencing task. The batch submission module supports two formats:

[0078] Type 1: The ZIP file contains the sequencing ab1 files of all targets and their corresponding GenBank files; the EXCEL file header line includes sample, template, target, and ab1 files;

[0079] Type 2: The ZIP file contains only the sequenced ab1 file; the EXCEL file title includes sample, sequence name, target sequence, and ab1 files;

[0080] The task description module is used for the user to describe each analysis task through the user interface, including the task name, label and remarks.

[0081] The system further includes a parameter setting unit 2, wherein the parameter setting unit 2 is used to allow the user to click Advanced Options in the Submission and Batch interfaces to expand the parameter setting interface and set the quality control threshold, bimodal recognition threshold, and visualization window size parameters;

[0082] The parameter setting unit 2 includes a connection area length setting module, a quality control setting module, a bimodal recognition threshold setting module and a visualization window setting module;

[0083] The flanking region length setting module allows the user to enter a specified length in the "Length of Flanking Region" row to extend the reference sequence range on the upstream 5' end and downstream 3' end regions of the target sequence;

[0084] The quality control setting module provides two quality control options: quality control option Q1 and quality control option Q2, which are used to ensure the quality of sequencing data;

[0085] Among them, the bimodal identification threshold setting module is used to set the threshold for bimodal identification. The ratio of the height of the secondary peak to the height of the highest peak must exceed the threshold specified by the user;

[0086] Among them, the visualization window setting module uses the genome browser-IGV.js toolkit for visualization, providing detailed visualization of the reference sequence and the bam file after the alignment, showing the specific position of each ab1 aligned to the genome, the alignment quality and mutation information, and the user can enter a value greater than 100 in the Window size field to specify the number of bases around each individual mutation and doublet for drawing the visualization results.

[0087] The quality control option Q1 in the quality control setting module is a quality control option based on the continuous low-quality fragment removal rule, wherein each ab1 sequence will be quality controlled according to the continuous low-quality fragment removal rule;

[0088] The rule for removing consecutive low-quality fragments specifically removes all fragments with a quality score lower than Y for X consecutive bases. X is the value entered in the Length row, and Y is the value entered in the Quality Score row. If the user specifies a Quality Score of 0, quality control will not be triggered.

[0089] Among them, the quality control option Q2 is a user-defined trimming range, and the trimming range is specifically C bases at the 5' end and D bases at the 3' end will be trimmed; where C is the value filled in the "User-specific 5'-end length" row, and D is the value filled in the "User-specific 3'-end length" row. If the user specifies C and D as 0, quality control will not be triggered.

[0090] The quality control unit 3 is further included, and the quality control unit 3 provides two quality control options based on the parameter setting unit 2 to perform quality assessment and control on the 5' end and the 3' end of each ab1 file;

[0091] The quality control unit 3 is used to evaluate and control the quality of the input sequencing data to ensure the accuracy and reliability of the analysis results. It filters out low-quality data through quality control operations to avoid erroneous mutation identification, thereby improving the accuracy of mutation analysis. The quality control unit 3 includes an endpoint quality trimming module, a continuous low-quality base filtering module, and an ab1 file quality assessment module;

[0092] The endpoint quality trimming module trims low-quality bases at the 5' and 3' ends of each ab1 file based on the quality control option Q2 provided in the parameter setting unit 2; by trimming low-quality endpoint bases, it ensures that only high-quality data are used for subsequent analysis, thereby reducing errors;

[0093] The continuous low-quality base filtering module removes continuous low-quality base fragments according to the continuous low-quality fragment removal rule in the quality control option Q2 provided by the parameter setting unit 2; filters out base fragments with quality scores lower than the set threshold to ensure the overall quality of the data;

[0094] The ab1 file quality assessment module evaluates the overall quality of each ab1 file, including detecting and recording the quality control results of each file. The quality control results specifically record the number of base pairs trimmed at the 5' and 3' ends of each ab1 file. A detailed quality control report is provided in the analysis results. By automatically comparing the quality of target regions across different ab1 files, mutations with higher confidence are automatically identified, avoiding the misidentification of incorrect mutations.

[0095] The method further includes a mutation recognition unit 4, which recognizes and marks short fragment insertions / deletions through an alignment algorithm and a variation detection algorithm; detects and marks long fragment insertions / deletions using an advanced alignment algorithm; and recognizes double peaks in Sanger sequencing chromatograms using a double peak probability model;

[0096] The mutation identification unit 4 includes a mutation scanning identification module and a bimodal identification module;

[0097] The mutation scanning module uses a genome alignment algorithm to identify single nucleotide polymorphisms, automatically detecting and marking SNPs by aligning sequencing data with reference sequences; uses an alignment algorithm and a variation detection algorithm to identify and mark short insertions / deletions; and uses an advanced alignment algorithm to detect and mark long insertions / deletions.

[0098] Among them, the genome alignment algorithm specifically aligns the sequencing data with the reference sequence to identify the location of the variation; the variation detection algorithm specifically involves algorithms such as GATK, FreeBayes and SAMtools, which are used to identify SNPs and InDels from the alignment results; the advanced alignment algorithm is used to handle complex long-fragment insertions / deletions, combining multiple alignment strategies to ensure the accurate identification and positioning of large-scale structural variations.

[0099] The bimodal identification module uses a bimodal probability model to compare the height ratio of the secondary peak to the highest peak, sets a user-specified threshold, and identifies bimodal phenomena in Sanger sequencing chromatograms. The specific steps involved in the bimodal probability model to identify bimodal phenomena in Sanger sequencing chromatograms are as follows:

[0100] S4.1. Convert Sanger sequencing chromatogram data into signal sequence S = {s1, s2, ..., s n}, where s i represents the peak height at the i-th position;

[0101] S4.2. Detect preliminary peaks using the first-order derivative method;

[0102] S4.3. For each detected peak p i , calculate the signal variance σ in its local area 2 and mean μ, construct a bimodal probability density function and calculate the probability of the second peak;

[0103] S4.4. Use high-order spectrum analysis to separate overlapping peak signals and separate the secondary peaks and main peaks through inverse Fourier transform;

[0104] S4.5. Re-detect the separated signals to verify the presence of double peaks;

[0105] S4.6. Visualize the positions of the identified double peaks and their probability distributions, and provide detailed ratio analysis and chromatogram comparison results.

[0106] The mathematical formula involved in detecting the preliminary peak using the first-order derivative method in S4.2 is as follows:

[0107]

[0108] Where P is the set of all detected peak positions; p i is the position of the i-th peak; is the first-order derivative of the signal sequence S.

[0109] The S4.3 for each detected peak p i , calculate the signal variance σ in its local area 2 And mean μ, construct a bimodal probability density function, and calculate the probability of the secondary peak. The mathematical formula involved is as follows:

[0110] Signal variance σ 2 And mean μ:

[0111]

[0112] Among them, μ i is the peak value p i The local mean of is the peak value p i The local variance of ; w is the window size, which determines the range of the local area;

[0113] Bimodal probability density function

[0114]

[0115] in, is the peak value p i The probability density function of

[0116] The probability of the second peak is P 次高峰 (p i ):

[0117]

[0118] Among them, P 次高峰 (p i ) is the probability of the secondary peak.

[0119] The mathematical formulas involved in separating overlapping peak signals by applying high-order spectrum analysis in S4.4 and separating the secondary peaks and main peak signals by inverse Fourier transform are as follows:

[0120] Apply high-order spectral analysis to separate overlapping peak signals:

[0121]

[0122] Where H(ω) is the high-order spectrum of the signal; A k is the amplitude of the signal; ω is the frequency; N is the length of the signal; j is the imaginary unit;

[0123] By inverse Fourier transform, the secondary peak and main peak signals are separated:

[0124]

[0125] Among them S 分离 (t) is the separated signal in the time domain; is the inverse Fourier transform;

[0126] The mathematical formula involved in re-detecting the separated signal in S4.5 is as follows:

[0127]

[0128] Among them, R is the final confirmed set of double peak positions; r i is the i-th position of the double peak; is the separation signal S 分离 The first derivative of .

[0129] The system also includes a visualization and output unit 5, which is used to visualize the results of mutation identification and analysis in detail and generate an output report.

[0130] The visualization and output unit 5 includes a result visualization module and a report generation module;

[0131] The result visualization module uses visualization tools such as IGV.js to provide a visual display of mutation scanning results, including chromatogram comparison, mutation position marking, and bimodal phenomenon result display;

[0132] The report generation module is used to generate a detailed analysis report, including statistical data of all mutations, bimodal analysis results and quality control information.

[0133] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An automated online tool for analyzing Sanger sequencing results, characterized by: include: An input and submission unit (1), the input and submission unit (1) is used to accept sequencing data and reference sequence files uploaded by users in single and batches, use the ab1 files for analysis, and describe and label the uploaded input files; A parameter setting unit (2), wherein the parameter setting unit (2) is used for setting the quality control threshold, the bimodal recognition threshold and the visualization window size parameters by the user clicking Advanced Options in the Submission and Batch interfaces to expand the parameter setting interface; A quality control unit (3), wherein the quality control unit (3) provides two quality control options based on the parameter setting unit (2) to perform quality assessment and control on the 5' end and the 3' end of each ab1 file; A mutation identification unit (4), wherein the mutation identification unit (4) identifies and marks short fragment insertions / deletions through an alignment algorithm and a variation detection algorithm; detects and marks long fragment insertions / deletions using an advanced alignment algorithm; and identifies double peaks in Sanger sequencing chromatograms using a double peak probability model; The visualization and output unit (5) is used to visualize the results of mutation identification and analysis in detail and generate an output report.

2. The automated online tool for Sanger sequencing result analysis according to claim 1, characterized in that: The input and submission unit (1) includes a single submission module, a batch submission module and a task description module; The single submission module is used for users to submit sequencing analysis targets one by one. Users can select GenBank files and directly enter target sequences through the user interface and upload the corresponding ab1 files. The batch submission module is used for users to submit multiple sequencing tasks in batches. Users can use the batch submission module to upload a ZIP file containing all sequencing ab1 files and reference sequences, as well as an EXCEL file. The EXCEL file is used to describe the specific information of each sequencing task. The batch submission module supports two formats: Type 1: The ZIP file contains the sequencing ab1 files of all targets and their corresponding GenBank files; the EXCEL file header line includes sample, template, target, and ab1 files; Type 2: The ZIP file contains only the sequenced ab1 file; the EXCEL file title includes sample, sequence name, target sequence, and ab1 files; The task description module is used for the user to describe each analysis task through the user interface, including the task name, label and remarks.

3. The automated online tool for Sanger sequencing result analysis according to claim 1, characterized in that: The parameter setting unit (2) includes a connection region length setting module, a quality control setting module, a bimodal recognition threshold setting module and a visualization window setting module; The flanking region length setting module allows the user to enter a specified length in the "Length of Flanking Region" row to extend the reference sequence range on the upstream 5' end and downstream 3' end regions of the target sequence; The quality control setting module provides two quality control options: quality control option Q1 and quality control option Q2, which are used to ensure the quality of sequencing data; Among them, the bimodal identification threshold setting module is used to set the threshold for bimodal identification. The ratio of the height of the secondary peak to the height of the highest peak must exceed the threshold specified by the user; The visualization window setting module uses the genome browser-IGV.js toolkit for visualization, providing detailed visualization of the reference sequence and the bam file after the alignment, and showing the specific position of each ab1 aligned to the genome, the alignment quality and mutation information.

4. The automated online tool for Sanger sequencing result analysis according to claim 3, characterized in that: The quality control option Q1 in the quality control setting module is a quality control option based on the continuous low-quality fragment removal rule, wherein each ab1 sequence will be quality controlled according to the continuous low-quality fragment removal rule; The rule for removing consecutive low-quality fragments specifically removes all fragments with a quality score lower than Y for X consecutive bases. X is the value entered in the Length row, and Y is the value entered in the Quality Score row. If the user specifies a Quality Score of 0, quality control will not be triggered. Among them, the quality control option Q2 is a user-defined trimming range, and the trimming range specifically includes C bases at the 5' end and D bases at the 3' end, where C is the value entered in the "User-specific 5'-end length" row, and D is the value entered in the "User-specific 3'-end length" row. If the user specifies C and D as 0, quality control will not be triggered.

5. The automated online tool for Sanger sequencing result analysis according to claim 1, characterized in that: The quality control unit (3) is used to perform quality assessment and control on the input sequencing data, and the quality control unit (3) includes an endpoint quality trimming module, a continuous low-quality base filtering module and an ab1 file quality assessment module; Wherein, the endpoint quality trimming module trims the low-quality bases at the 5' end and 3' end of each ab1 file based on the quality control option Q2 provided in the parameter setting unit (2); The continuous low-quality base filtering module removes the continuous low-quality base fragments according to the continuous low-quality fragment removal rule in the quality control option Q2 provided by the parameter setting unit (2); The ab1 file quality assessment module assesses the overall quality of each ab1 file, including detecting and recording the quality control results of each file. The quality control results specifically record the number of base pairs trimmed at the 5' end and 3' end of each ab1 file.

6. The automated online tool for Sanger sequencing result analysis according to claim 1, characterized in that: The mutation recognition unit (4) includes a mutation scanning recognition module and a bimodal recognition module; The mutation scanning module uses a genome alignment algorithm to identify single nucleotide polymorphisms, automatically detecting and marking SNPs by aligning sequencing data with reference sequences; uses an alignment algorithm and a variation detection algorithm to identify and mark short insertions / deletions; and uses an advanced alignment algorithm to detect and mark long insertions / deletions. The bimodal identification module uses a bimodal probability model to compare the height ratio of the secondary peak to the highest peak, sets a user-specified threshold, and identifies bimodal phenomena in Sanger sequencing chromatograms. The specific steps involved in the bimodal probability model to identify bimodal phenomena in Sanger sequencing chromatograms are as follows: S4.

1. Convert Sanger sequencing chromatogram data into signal sequence S = {s1, s2, ..., s n }, where s i represents the peak height at the i-th position; S4.

2. Detect preliminary peaks using the first-order derivative method; S4.

3. For each detected peak p i , calculate the signal variance σ in its local area 2 and mean μ, Construct a bimodal probability density function and calculate the probability of the secondary peak; S4.

4. Use high-order spectrum analysis to separate overlapping peak signals and separate the secondary peaks and main peaks through inverse Fourier transform; S4.

5. Re-detect the separated signals to verify the presence of double peaks; S4.

6. Visualize the positions of the identified double peaks and their probability distributions, and provide detailed ratio analysis and chromatogram comparison results.

7. The automated online tool for Sanger sequencing result analysis according to claim 6, characterized in that: The mathematical formula involved in detecting the preliminary peak using the first-order derivative method in S4.2 is as follows: Where P is the set of all detected peak positions; p i is the position of the i-th peak; is the first-order derivative of the signal sequence S.

8. The automated online tool for Sanger sequencing result analysis according to claim 6, characterized in that: The S4.3 for each detected peak p i , calculate the signal variance σ in its local area 2 and mean μ, Constructing a bimodal probability density function and calculating the probability of the secondary peak involves the following mathematical formula: Signal variance σ 2 And mean μ: Among them, μ i is the peak value p i The local mean of is the peak value p i The local variance of ; w is the window size, which determines the range of the local area; Bimodal probability density function in, is the peak value p i The probability density function of The probability of the second peak is P 次高峰 (p i ): Among them, P 次高峰 (p i ) is the probability of the secondary peak.

9. The automated online tool for Sanger sequencing result analysis according to claim 6, characterized in that: The mathematical formulas involved in separating overlapping peak signals by applying high-order spectrum analysis in S4.4 and separating the secondary peaks and main peak signals by inverse Fourier transform are as follows: Apply high-order spectral analysis to separate overlapping peak signals: Where H(ω) is the high-order spectrum of the signal; A k is the amplitude of the signal; ω is the frequency; N is the length of the signal; j is the imaginary unit; By inverse Fourier transform, the secondary peak and main peak signals are separated: Among them S 分离 (t) is the separated signal in the time domain; is the inverse Fourier transform; The mathematical formula involved in re-detecting the separated signal in S4.5 is as follows: Among them, R is the final confirmed set of double peak positions; r i is the i-th position of the double peak; is the separation signal S 分离 The first derivative of .

10. The automated online tool for Sanger sequencing result analysis according to claim 1, characterized in that: The visualization and output unit (5) includes a result visualization module and a report generation module; The result visualization module uses visualization tools such as IGV.js to provide a visual display of mutation scanning results, including chromatogram comparison, mutation position marking, and bimodal phenomenon result display; The report generation module is used to generate a detailed analysis report, including statistical data of all mutations, bimodal analysis results and quality control information.

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