A method, storage medium, and electronic device for accurately and efficiently assessing gene editing efficiency based on NGS data.

Abstract

This invention provides a method, storage medium, and electronic device for accurately and efficiently assessing gene editing efficiency based on NGS data, belonging to the field of bioinformatics processing technology. This invention integrates the following steps: splitting NGS pooled sequencing data according to barcode tag sequences; constructing a reference genome index and dictionary; aligning the NGS data of each sample with the reference genome; calculating the alignment rate; constructing an alignment result index; converting the alignment results to TSV format; and evaluating the editing efficiency of gene editing target sites in each sample. This approach meets the need for accurate and efficient assessment of gene editing efficiency based on NGS data. The method effectively avoids the limitations of existing tools on data file size, allows for custom barcode tag sequences for data splitting, utilizes mainstream alignment algorithms to obtain high-accuracy alignment results, and leverages the advantages of computing clusters for parallel computation, thereby achieving the goal of accurately and efficiently assessing the gene editing efficiency of multiple genes and a large number of samples.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics processing technology, specifically relating to a method, storage medium, and electronic device for accurately and efficiently evaluating gene editing efficiency based on NGS data. Background Technology

[0002] Gene editing technology has enabled more precise gene regulation. Traditional gene editing techniques include artificially mediated zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered, regularly spaced short palindromic repeat-associated protein nucleases (CRISPR / Cas9). These techniques target almost any genomic site and induce cellular DNA repair mechanisms through non-homologous end joining or homologous recombination, thereby triggering insertion or deletion mutations in DNA. However, they have limitations in terms of precision. Single-base editing technology, by precisely modifying a single base, can alter key amino acids. In addition to generating loss-of-function mutations by introducing stop codons, it can also adjust regulatory sites in promoter regions to regulate gene expression. The development of this technology is of great significance in gene therapy for genetic diseases and the modification of biological traits.

[0003] With the widespread application of gene editing technology, evaluating gene editing efficiency has become a major issue. NGS technology enables high-throughput sequencing of large numbers of samples simultaneously in a short time, significantly reducing sequencing costs and greatly improving data processing speed. Furthermore, NGS technology offers higher sequencing depth and coverage, making the detection of complex and low-frequency mutations more sensitive and accurate. This makes NGS technology an efficient and reliable technique in the systematic analysis of genome editing, especially when dealing with large numbers of samples and complex mutations.

[0004] Currently, several bioinformatics tools exist for quantitative analysis of reads inserted and deleted at gene editing target sites based on NGS data. Among these, Hi-TOM, CRISPResso2, CRISPR-GRANT, CRISprStitch, CRISPRMatch, and CRIS.py are noteworthy. Of these, Hi-TOM and CRISPResso2 are both web browser-based tools with user-friendly interfaces. However, both Hi-TOM and CRISPResso2 have limitations on the size of uploaded data files. Furthermore, Hi-TOM cannot customize barcode tag sequences to split NGS pooled sequencing data, nor can it perform batch analysis of sample sequencing data. CRISPResso2 lacks the ability to split NGS pooled sequencing data, and some laboratories storing sensitive data often lack internet connectivity, making offline use of the web browser service impossible. CRISPR-GRANT and CRISprStitch offer cross-platform graphical user interfaces for NGS data analysis, demonstrating good adaptability. However, CRISPR-GRANT cannot perform batch analysis of sample sequencing data or split NGS pooled sequencing data, and the input files must be uncompressed, resulting in significant disk space consumption. While CrisprStitch offers a cross-platform graphical user interface and good result visualization, its alignment algorithm is of poor quality, impacting the accuracy of subsequent analysis results. CRISPRMatch, although capable of batch analysis of sample sequencing data and offering good result visualization, cannot split NGS pooled sequencing data. CRIS.py cannot perform either batch analysis of sample sequencing data or split NGS pooled sequencing data. Furthermore, none of these six tools can evaluate the efficiency of single-base editing based on NGS data. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method for accurately and efficiently evaluating gene editing efficiency based on NGS data. This method integrates the following steps: splitting barcode tag sequences, constructing a reference genome index and dictionary, aligning the NGS data of each sample obtained after splitting with the reference genome, calculating the alignment rate of the alignment results and constructing an alignment result index, converting the alignment result format, and evaluating the editing efficiency of gene editing target sites. This achieves parallel computing and accurate and efficient gene editing efficiency for multiple genes and large samples.

[0006] This invention provides a method for accurately and efficiently evaluating gene editing efficiency based on NGS data, comprising the following steps:

[0007] The NGS pooled sequencing data is split based on the sequencing sample information and the barcode tag sequence of each sample to obtain the NGS data of a single sample;

[0008] Construct an index and dictionary for the reference genome;

[0009] The NGS data of the single sample is compared with the constructed reference genome index to obtain the comparison results with the reference genome index;

[0010] The alignment rate with the reference genome index was calculated, and the alignment results with the reference genome index were obtained.

[0011] The alignment results with the reference genome index are converted into TSV format alignment results;

[0012] Based on the sequencing sample information, gene editing method, and alignment results of the TSV format, the editing efficiency of gene editing target sites in each sample was evaluated:

[0013] When the gene editing method is traditional gene editing, the number of reads inserted and deleted at the gene editing target site is quantified to obtain the editing efficiency of the gene editing target site;

[0014] When the gene editing method is single-base editing, the number of reads that conform to the single nucleotide polymorphism of the gene editing target site and are mutated to the target base is quantified to obtain the editing efficiency of the gene editing target site.

[0015] Preferably, the tool for splitting the NGS pooled sequencing data includes the fastq-multx tool;

[0016] The tools used to construct the reference genome index include the bwa index tool;

[0017] The tools used to construct the reference genome dictionary include the gatk CreateSequenceDictionary tool;

[0018] The comparison tools include the bwa mem tool;

[0019] The tools used to calculate the alignment rate with the reference genome index include the samtoolsflagstat tool;

[0020] The tool used to construct the index of the alignment results with the reference genome index includes the samtools index tool;

[0021] Tools for converting TSV format include the sam2tsv tool.

[0022] Preferably, the method for constructing the index and dictionary of the reference genome is a method of parallel computing on a computing cluster;

[0023] The method of parallel computing cluster operation involves creating a task to build a reference genome index and dictionary for each reference genome in the config configuration file. Then, the PBS task management system is used to batch deliver the created tasks for each reference genome to the computing cluster. The index and dictionary for each reference genome are built simultaneously in parallel operation. The tasks delivered to the computing nodes are monitored in real time to ensure that each reference genome task is successfully built. At the same time, the operation logs of each reference genome task are recorded.

[0024] Preferably, the method for evaluating the editing efficiency of gene editing target sites in each sample includes the following steps:

[0025] (S1) Determine the editing range of the gene editing target site based on the location of the gene editing target site on the reference genome in the sequencing sample information;

[0026] (S2) Read the alignment results in TSV format line by line, filter out reads that are not aligned to the reference genome, soft-cut reads and hard-cut reads, and obtain high-quality reads that are aligned to the reference genome. Then extract the base information of each position within the editing range of the gene editing target site determined in step (S1) from the high-quality reads that are aligned to the reference genome.

[0027] (S3) Based on the base mutation information at each position in the high-quality reads aligned with the reference genome extracted in step (S2), the high-quality reads aligned with the reference genome are corrected to obtain the corrected reads;

[0028] (S4) Based on step (S3), obtain the corrected reads and count the total number of reads and the number of reads of different mutation types within the editing range of the gene editing target site determined in step (S1).

[0029] (S5) Calculate the percentage of reads of different variant types obtained in step (S4) to the total number of reads, and use the percentage of reads of different variant types to the total number of reads as the editing efficiency of the gene editing target site in each sample.

[0030] Preferably, the editing range of the gene editing target site in step (S1) is specifically determined according to different types of gene editing methods:

[0031] When the gene editing method is traditional gene editing, the editing range of the gene editing target site is from the position of the gene editing target site on the reference genome minus 10 bp to the position of the gene editing target site on the reference genome plus 10 bp, for a total range of 20 bp;

[0032] When the gene editing method is single-base editing, the editing range of the gene editing target site is the position of the target base on the reference genome.

[0033] Preferably, the method for correcting errors in the high-quality reads aligned with the reference genome in step (S2) is to count the number of different bases appearing at each position within the editing range of the high-quality reads, and take the base with the most occurrences as the correct base at that position; if different bases appear the same number of times, no error correction is performed.

[0034] Preferably, the different variant types mentioned in step (S4) include at least one of the following: insertion, deletion, and single nucleotide polymorphism;

[0035] The percentage of reads of different variant types in the total number of reads mentioned in step (S5) is used as the editing efficiency of the gene editing target site in each sample. The calculation is performed according to the different editing methods as follows:

[0036] When the editing method is traditional gene editing, the percentage of the number of inserted and deleted reads to the total number of reads is used as the editing efficiency of the gene editing target site in each sample;

[0037] When the editing method is single-base editing, the percentage of single nucleotide polymorphism reads that match the original base mutation as the target base is taken as the editing efficiency of the gene editing target site in each sample.

[0038] Preferably, the method further includes outputting the results of each step of the method to the corresponding result directory, creating a Readme file in the output directory, and providing a detailed description of each output result directory.

[0039] This invention provides a computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets; when the computer instructions, programs, code sets, or instruction sets are run on a computer, the computer performs the method for accurately and efficiently evaluating gene editing efficiency based on NGS data.

[0040] The present invention provides an electronic device, including a processor and a memory, wherein the memory is used to store a program; the processor is used to run the stored program, and the running of the stored program is to realize the method for accurately and efficiently evaluating gene editing efficiency based on NGS data.

[0041] This invention provides a method for accurate and efficient gene editing based on NGS data. It integrates six steps: splitting NGS pooled sequencing data according to barcode tag sequences, constructing a reference genome index and dictionary, aligning the split NGS data of each sample with the reference genome, calculating the alignment rate and constructing an alignment result index, converting the alignment results to TSV format, and evaluating the editing efficiency of gene editing target sites in each sample. This method is designed to meet the needs of accurate and efficient gene editing based on NGS data. The method effectively avoids the limitations of existing tools on data file size, allows for custom barcode tag sequences to split NGS pooled sequencing data, utilizes mainstream alignment algorithms to obtain high-accuracy alignment results, and leverages the advantages of computing clusters to achieve accurate and efficient gene editing of multiple genes and large numbers of samples through parallel computation. Attached Figure Description

[0042] Figure 1 This is a flowchart of the method for accurate and efficient gene editing based on NGS data according to the present invention;

[0043] Figure 2 Example diagram showing sequencing sample information for rice genes OsPDS and alfalfa genes MsPALM1;

[0044] Figure 3 Example image of the config configuration file;

[0045] Figure 4 Write Python logic code to perform the method described in Example 1;

[0046] Figure 5 This is a running example diagram of the gene editing efficiency evaluation of sequencing samples of rice genes OsPDS and alfalfa genes MsPALM1 in this invention.

[0047] Figure 6 Example visualization of the alignment results of NGS data from 12 hairy root samples with the reference genome. Detailed Implementation

[0048] This invention provides a method for accurate and efficient gene editing based on NGS data, comprising the following steps:

[0049] The NGS pooled sequencing data is split based on the sequencing sample information and the barcode tag sequence of each sample to obtain the NGS data of a single sample;

[0050] Construct an index and dictionary for the reference genome;

[0051] The NGS data of the single sample is compared with the constructed reference genome index to obtain the comparison results with the reference genome index;

[0052] The alignment rate with the reference genome index was calculated, and the alignment results with the reference genome index were obtained.

[0053] The alignment results with the reference genome index are converted into TSV format alignment results;

[0054] Based on the sequencing sample information, gene editing method, and alignment results of the TSV format, the editing efficiency of gene editing target sites in each sample was evaluated:

[0055] When the gene editing method is traditional gene editing, the number of reads inserted and deleted at the gene editing target site is quantified to obtain the editing efficiency of the gene editing target site;

[0056] When the gene editing method is single-base editing, the number of reads that conform to the single nucleotide polymorphism of the gene editing target site and are mutated to the target base is quantified to obtain the editing efficiency of the gene editing target site.

[0057] This invention splits NGS pooled sequencing data based on sequencing sample information and the barcode tag sequence of each sample to obtain NGS data for a single sample.

[0058] In this invention, the sequencing sample information is preferably presented in one row, separated by tabs. The gene editing method is either traditional gene editing or single-base editing. When the gene editing method is traditional gene editing, the sequencing sample information includes the filename of the NGS pooled sequencing data, the barcode tag sequence ID corresponding to each sample in the NGS pooled sequencing data, the reference genome corresponding to each sample in the NGS pooled sequencing data, and the position of the gene editing target site on the reference genome. When the gene editing method is single-base editing, the sequencing sample information includes the filename of the NGS pooled sequencing data, the barcode tag sequence ID corresponding to each sample in the NGS pooled sequencing data, the reference genome corresponding to each sample in the NGS pooled sequencing data, the position of the gene editing target site on the reference genome, and the single nucleotide polymorphism of the original base mutation of the gene editing target site into the target base. If a sample has only one target site, then only the location of that target site on the reference genome needs to be written into the sequencing sample information; if a sample has multiple target sites, then the locations of all multiple target sites on the reference genome should be written into the sequencing sample information, separated by commas.

[0059] In this invention, the barcode label sequence is located in a configuration file. The configuration file contains the following four configuration items:

[0060] (1) The first item is the absolute path of the configuration reference genome file. If there is only one reference genome, fill in the absolute path of the reference genome in "ref_genome_files=" in the config configuration file; if there are multiple reference genomes, separate the absolute paths of the multiple reference genomes with commas and fill them in "ref_genome_files=" in the config configuration file.

[0061] (2) The second item is to configure the absolute path of the directory where the NGS pooled sequencing data is located. Fill in the absolute path of the directory where the NGS pooled sequencing data is located in the "raw_data_path=" in the config configuration file;

[0062] (3) The third item is to configure the absolute path of the barcode label sequence file. Fill in the absolute path of the barcode label sequence file in "barcode_grouped_file=" in the config configuration file. The barcode label sequence file format is two columns. The first column is the barcode label sequence ID, and the second column is the base sequence of the F and R ends of the barcode label, connected by "-".

[0063] (4) The fourth item is to configure the resources required for parallel computing of the computing cluster. This includes the name of the computing node, the number of threads required, and the memory size. Fill in the name of the computing node in the "computing_node_name=" in the config configuration file, and fill in the number of threads and the memory size in the "threads=" and "mem=" in the config configuration file, respectively.

[0064] In this invention, the tool for splitting NGS pooled sequencing data includes the fastq-multx tool.

[0065] In this invention, the tools used to construct the reference genome index include the bwa index tool; the tools used to construct the reference genome dictionary include the gatk CreateSequenceDictionary tool; the alignment tool includes the bwa mem tool; the tool for calculating the alignment rate with the reference genome index includes the samtoolsflagstat tool; the tool for constructing the index of the alignment results with the reference genome index includes the samtoolsindex tool; and the tool for TSV format conversion includes the sam2tsv tool. All the above tools can be used with default parameters.

[0066] In this invention, the method for evaluating the editing efficiency of gene editing target sites in each sample preferably includes the following steps:

[0067] (S1) Determine the editing range of the gene editing target site based on the location of the gene editing target site on the reference genome in the sequencing sample information;

[0068] (S2) Read the alignment results in TSV format line by line, filter out reads that are not aligned to the reference genome, soft-cut reads and hard-cut reads, and obtain high-quality reads that are aligned to the reference genome. Then extract the base information of each position within the editing range of the gene editing target site determined in step (S1) from the high-quality reads that are aligned to the reference genome.

[0069] (S3) Based on the base mutation information at each position in the high-quality reads aligned with the reference genome extracted in step (S2), the high-quality reads aligned with the reference genome are corrected to obtain the corrected reads;

[0070] (S4) Based on step (S3), obtain the corrected reads and count the total number of reads within the editing range of the gene editing target site determined in step (S1) and the number of reads of different mutation types.

[0071] (S5) Calculate the percentage of reads of different variant types obtained in step (S4) to the total number of reads, and use the percentage of reads of different variant types to the total number of reads as the editing efficiency of the gene editing target site in each sample.

[0072] In this invention, the editing range of the gene editing target site in step (S1) is preferably determined specifically according to different types of gene editing methods: when the gene editing method is a traditional gene editing method, the editing range of the gene editing target site is the range from the position of the gene editing target site on the reference genome minus 10 bp to the position of the gene editing target site on the reference genome plus 10 bp, for a total of 20 bp; when the gene editing method is single-base editing, the editing range of the gene editing target site is the position of the target base on the reference genome. The traditional gene editing methods preferably include artificially mediated zinc finger nuclease (ZFN) technology, transcription activator effector nuclease (TALEN) technology, and clustered regular short palindromic repeat-associated protein nuclease (CRISPR / Cas9) technology. The method for correcting errors in high-quality reads aligned with the reference genome in step (S2) is preferably to count the number of different bases appearing at each position within the editing range of the high-quality reads, and take the base with the most occurrences as the correct base at that position; if different bases appear the same number of times, no error correction is performed. The standards for high-quality reads are: 1) no contamination; 2) no sequencing adapters; 3) no N; 4) Q20 > 90%; 5) Q30 > 85%. The different variant types mentioned in step (S4) preferably include at least one of the following: insertion, deletion, and single nucleotide polymorphism (SNP). The percentage of reads with different variant types relative to the total number of reads in step (S5) is used as the editing efficiency of the gene editing target site in each sample, preferably calculated according to the editing method as follows: when the editing method is traditional gene editing, the percentage of reads with insertions and deletions relative to the total number of reads is used as the editing efficiency of the gene editing target site in each sample; when the editing method is single-base editing, the percentage of reads with SNPs that match the original base mutation as the target base relative to the total number of reads is used as the editing efficiency of the gene editing target site in each sample.

[0073] In this invention, the method determines the evaluation method according to the different gene editing methods, which not only helps to simultaneously evaluate the editing efficiency of gene editing target sites in each sample of traditional gene editing or single base editing, but also ensures the accuracy of the editing efficiency of gene editing sites brought about by different gene editing methods.

[0074] In this invention, the method for constructing the index and dictionary of the reference genome is preferably a method of parallel computing on a computing cluster. The method of parallel computing on a computing cluster involves creating a task for constructing the index and dictionary of the reference genome for each reference genome in the config configuration file, and then using the PBS task management system to batch deliver the created tasks of each reference genome to the computing cluster. The index and dictionary of each reference genome are constructed simultaneously in parallel, and the tasks delivered to the computing nodes are monitored in real time to ensure that each reference genome task is successfully constructed. At the same time, the operation log of each reference genome task is recorded.

[0075] In this invention, a parallel computing method using a computing cluster is employed to create tasks for each sample in the sequencing sample information, including splitting the NGS pooled sequencing data, aligning the NGS data of each sample with a constructed reference genome index, calculating the alignment rate with the reference genome, constructing an index of the alignment results with the reference genome, converting the alignment results with the reference genome index into TSV format, and evaluating the editing efficiency of gene editing target sites in each sample. The tasks created in the above steps are batch-delivered to the computing cluster using the PBS task management system. The NGS data of each sample is analyzed in parallel on the computing cluster, significantly reducing the time required for computation and analysis. Furthermore, the tasks delivered to the computing nodes are monitored in real time to ensure the successful completion of the computation for each sample, and the computation log for each sample task is recorded, fundamentally achieving the goal of accurate and rapid gene editing efficiency.

[0076] In this invention, the method executes the program according to a specified starting step: if it is specified to start from splitting NGS pooled sequencing data, then the steps in the entire method are executed; if it is specified to start from constructing a reference genome index and dictionary, then the steps of constructing the reference genome index and dictionary and subsequent steps are executed; if it is specified to start from aligning NGS data with the constructed reference genome index, then the steps of aligning NGS data with the constructed reference genome index and subsequent steps are executed; if it is specified to start from evaluating the editing efficiency of gene editing target sites in each sample, then only the step of evaluating the editing efficiency of gene editing target sites in each sample is executed.

[0077] In this invention, the method preferably further includes outputting the results of each step of the method to a corresponding result directory, creating a Readme file in the output directory, and providing a detailed description of each output result directory. In one embodiment of this invention, the output directory contains four folders: the NGS data folder for each sample (denoted as 01.Barcode_data), the folder for the constructed reference genome index and dictionary (denoted as 02.Reference_genome), the folder related to the alignment results with the reference genome index (denoted as 03.Bwa_mapped), and the folder for the editing efficiency evaluation results of the gene editing target sites for each sample (denoted as 04.Edited_efficiency).

[0078] The method described in this invention can accurately and efficiently evaluate the gene editing efficiency of multi-gene, large-scale samples. This method is applicable to both traditional gene editing and single-base editing.

[0079] This invention provides a computer-readable storage medium for storing computer instructions, programs, code sets, or instruction sets; when the computer instructions, programs, code sets, or instruction sets are run on a computer, the computer performs the method for accurately and efficiently evaluating gene editing efficiency based on NGS data.

[0080] The present invention provides an electronic device, including a processor and a memory, wherein the memory is used to store a program; the processor is used to run the stored program, and the running of the stored program is to realize the method for accurately and efficiently evaluating gene editing efficiency based on NGS data.

[0081] The following detailed description, in conjunction with embodiments, illustrates a method, storage medium, and electronic device for accurately and efficiently evaluating gene editing efficiency based on NGS data, provided by the present invention. However, these descriptions should not be construed as limiting the scope of protection of the present invention.

[0082] Example 1

[0083] A method for accurately and efficiently assessing gene editing efficiency based on NGS data, such as Figure 1 The diagram shown is a flowchart of the method for accurately and efficiently evaluating gene editing efficiency based on NGS data according to the present invention.

[0084] The specific steps of the method for accurately and efficiently evaluating gene editing efficiency based on NGS data are as follows:

[0085] Step 1: Input sequencing sample information, config configuration file, and select traditional gene editing as the editing method. Specify that the starting step for the operation is to split the NGS pooled sequencing data.

[0086] 1.1 Sequencing Sample Information:

[0087] The information for the sequencing samples includes the filename of the NGS pooled sequencing data, the barcode tag sequence ID corresponding to each sample in the NGS pooled sequencing data, the reference genome corresponding to each sample in the NGS pooled sequencing data, and the location of the gene editing target site on the reference genome.

[0088] Step 2: Based on the sequencing sample information, the barcode label sequence file in the config configuration file, and the NGS pooled sequencing data, the fastq-multx tool is used to split the NGS pooled sequencing data to obtain the NGS data for each sample, and the NGS data for each sample is compressed.

[0089] Step 3: Based on the reference genome in the config configuration file, use the bwa index tool to build a reference genome index and use the gatk CreateSequenceDictionary tool to build a reference genome dictionary.

[0090] Step 4: Use the bwa mem tool to align the NGS data of each sample obtained in Step 2 with the reference genome index constructed in Step 3 to obtain the alignment results with the reference genome index.

[0091] Step 5: Use the samtools flagstat tool to calculate the alignment rate of the alignment results obtained in Step 4 with the reference genome index, and use the samtools index tool to construct an index of the alignment results obtained in Step 4 with the reference genome index.

[0092] Step 6: Use the sam2tsv tool to convert the alignment results obtained in step 4 with the reference genome index into TSV format alignment results.

[0093] Step 7: Based on the sequencing sample information, editing method, and the alignment results of the TSV format from Step 6, the number of reads inserted and deleted at the gene editing target site is quantified to obtain the editing efficiency of the gene editing target site for each sample.

[0094] Step 8: Output the results from steps 2 to 7 to the corresponding result directories. Simultaneously, create a Readme file in the output directory to provide a detailed description of each output directory. The output directory contains four folders: the NGS data folder for each sample (denoted as 01.Barcode_data), the folder containing the constructed reference genome index and dictionary (denoted as 02.Reference_genome), the folder related to the alignment results with the reference genome index (denoted as 03.Bwa_mapped), and the folder containing the evaluation results of the editing efficiency of gene editing target sites in each sample (denoted as 04.Edited_efficiency).

[0095] Example 2

[0096] A method for accurately and efficiently assessing gene editing efficiency based on NGS data, the specific steps of which are as follows:

[0097] Step 1: Input sequencing sample information, config configuration file, edit mode as single base editing, and specify the starting step of the calculation as splitting the NGS pooled sequencing data.

[0098] 1.1 Sequencing Sample Information:

[0099] The sequencing sample information includes the filename of the NGS pooled sequencing data, the barcode tag sequence ID corresponding to each sample in the NGS pooled sequencing data, the reference genome corresponding to each sample in the NGS pooled sequencing data, the location of the gene editing target site on the reference genome, and the single nucleotide polymorphism of the original base mutation of the gene editing target site into the target base.

[0100] Step 2: Based on the sequencing sample information, the barcode label sequence file in the config configuration file, and the NGS pooled sequencing data, the fastq-multx tool is used to split the NGS pooled sequencing data to obtain the NGS data for each sample, and the NGS data for each sample is compressed.

[0101] Step 3: Based on the reference genome in the config configuration file, use the bwa index tool to build a reference genome index and use the gatk CreateSequenceDictionary tool to build a reference genome dictionary.

[0102] Step 4: Use the bwa mem tool to align the NGS data of each sample obtained in Step 2 with the reference genome index constructed in Step 3 to obtain the alignment results with the reference genome index.

[0103] Step 5: Use the samtools flagstat tool to calculate the alignment rate of the alignment results obtained in Step 4 with the reference genome index, and use the samtools index tool to construct an index of the alignment results obtained in Step 4 with the reference genome index.

[0104] Step 6: Use the sam2tsv tool to convert the alignment results obtained in step 4 with the reference genome index into TSV format alignment results.

[0105] Step 7: Based on the sequencing sample information, editing method, and the alignment results of the TSV format in Step 6, the number of reads that conform to the single nucleotide polymorphism of the gene editing target site with the original base mutation to the target base is quantified to obtain the editing efficiency of the gene editing target site.

[0106] Step 8: Output the results from steps 2 to 7 to the corresponding result directories. Simultaneously, create a Readme file in the output directory to provide a detailed description of each output directory. The output directory contains four folders: the NGS data folder for each sample (denoted as 01.Barcode_data), the folder containing the constructed reference genome index and dictionary (denoted as 02.Reference_genome), the folder related to the alignment results with the reference genome index (denoted as 03.Bwa_mapped), and the folder containing the evaluation results of the editing efficiency of gene editing target sites in each sample (denoted as 04.Edited_efficiency).

[0107] Application Example 1

[0108] Using NGS pooled sequencing data from 12 root hair samples (numbered 1-1, 1-2, 1-3, 1-4, 1-5, 1-6, 5-1, 5-2, 5-3, 5-4, 5-5, and 5-6) of rice OsPDS and alfalfa MsPALM1 genes as an example, the gene editing efficiency of these 12 samples was evaluated using the method for accurately and efficiently assessing gene editing efficiency based on NGS data as described in Example 1.

[0109] First, the sequencing information of 12 hairy root samples from rice gene OsPDS and alfalfa gene MsPALM1 was input (see...). Figure 2 ), config configuration file (see Figure 3 The editing method is traditional gene editing, and the starting step for the operation is specified as splitting the NGS pooled sequencing data.

[0110] Then, the editing efficiency of 12 hairy root samples from rice gene OsPDS and alfalfa gene MsPALM1 was evaluated: the editing efficiency was evaluated according to steps 2-8 of Example 1. The Python logic code for executing the method described in Example 1 is shown below. Figure 4 .

[0111] Figure 5 The results showed that the method of the present invention for accurately and efficiently evaluating gene editing efficiency based on NGS data was used to evaluate the editing efficiency of 12 hairy root samples of genes OsPDS and MsPALM1, and the total time taken was 223.74s.

[0112] The editing efficiency of 12 hairy root samples from rice gene OsPDS and alfalfa gene MsPALM1 was evaluated using this invention. The results are shown in Table 1. To verify the accuracy of the editing efficiency evaluation results, the alignment results of the 12 hairy root samples with the reference gene were visualized using IGV software. The results are shown in Table 1 and [other tables not provided]. Figure 6 .

[0113] Table 1. Evaluation of the editing efficiency of genes OsPDS and MsPALM1

[0114]

[0115]

[0116] Table 1 shows that the editing efficiency of the rice gene OsPDS at target sites 2441 and 4834 was 0.03%–1.59%, which was extremely low; the editing efficiency of the alfalfa gene MsPALM1 at target site 50 was 0%–0.07%, which was also extremely low; and the editing efficiency of the alfalfa gene MsPALM1 at target site 89 was 53.15%–69.86%, which was relatively high.

[0117] Figure 6 The results showed that the gene editing efficiency assessment results of the 12 hairy root samples were the same as the comparison results, indicating that the gene editing efficiency assessment results obtained by the present invention are highly accurate.

[0118] This invention designs a method for accurately and efficiently evaluating gene editing efficiency based on NGS data. It solves the problems of existing tools being limited by the size of data files, unable to customize barcode label sequences for splitting NGS pooled sequencing data, and unable to utilize the advantages of computing clusters to accurately and efficiently evaluate gene editing efficiency of a large number of samples through parallel computing.

[0119] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for accurate and rapid gene editing efficiency based on NGS data, characterized in that, It consists of the following steps: The NGS pooled sequencing data is split based on the sequencing sample information and the barcode tag sequence of each sample to obtain the NGS data of a single sample; Construct an index and dictionary for the reference genome; The method for constructing the index and dictionary of the reference genome is a method of parallel computing on a computing cluster; The method of parallel computing cluster operation is to create a task to build a reference genome index and dictionary for each reference genome in the configuration file, and then use the PBS task management system to deliver the created tasks of each reference genome to the computing cluster in batches, so as to build the index and dictionary of each reference genome simultaneously in parallel operation, and monitor the tasks delivered to the computing nodes in real time to ensure that each reference genome task is successfully built, and record the operation log of each reference genome task. The NGS data of the single sample is compared with the constructed reference genome index to obtain the comparison results with the reference genome index; The alignment rate with the reference genome index was calculated, and the alignment results with the reference genome index were obtained. The alignment results with the reference genome index are converted into TSV format alignment results; Based on the sequencing sample information, gene editing method, and alignment results of the TSV format, the editing efficiency of gene editing target sites in each sample was evaluated: When the gene editing method is traditional gene editing, the number of reads inserted and deleted at the gene editing target site is quantified to obtain the editing efficiency of the gene editing target site; When the gene editing method is single-base editing, the number of reads that conform to the single nucleotide polymorphism of the gene editing target site and the mutation of the original base into the target base is quantified to obtain the editing efficiency of the gene editing target site; The method for evaluating the editing efficiency of gene editing target sites in each sample includes the following steps: (S1) Determine the editing range of the gene editing target site based on the location of the gene editing target site on the reference genome in the sequencing sample information; (S2) Read the alignment results in TSV format line by line, filter out reads that are not aligned to the reference genome, soft-cut reads and hard-cut reads, and obtain high-quality reads that are aligned to the reference genome. Then extract the base information of each position within the editing range of the gene editing target site determined in step (S1) of the obtained high-quality reads aligned to the reference genome. (S3) Based on the base mutation information at each position in the high-quality reads aligned with the reference genome extracted in step (S2), the high-quality reads aligned with the reference genome are corrected to obtain the corrected reads; (S4) Obtain the corrected reads according to step (S3), and count the total number of reads within the editing range of the gene editing target site determined in step (S1) and the number of reads of different mutation types; (S5) Calculate the percentage of reads of different mutation types obtained in step (S4) to the total number of reads, and use the percentage of reads of different mutation types to the total number of reads as the editing efficiency of the gene editing target site in each sample. The method for correcting errors in high-quality reads aligned with the reference genome in step (S3) is to count the number of different bases appearing at each position within the editing range of the high-quality reads, and take the base with the most occurrences as the correct base at that position; if different bases appear the same number of times, no error correction is performed.

2. The method according to claim 1, characterized in that, The tools used to split NGS pooled sequencing data include the fastq-multx tool; The tools used to construct the reference genome index include the bwa index tool; The tools used to construct the reference genome dictionary include the gatk CreateSequenceDictionary tool; The comparison tools include the bwa mem tool; The tools used to calculate the alignment rate with the reference genome index include the samtools flagstat tool; The tool used to construct the index of the alignment results with the reference genome index includes the samtools index tool; Tools for converting TSV format include the sam2tsv tool.

3. The method according to claim 1, characterized in that, The editing range of the gene editing target site described in step (S1) is specifically determined according to different types of gene editing methods: When the gene editing method is traditional gene editing, the editing range of the gene editing target site is from the position of the gene editing target site on the reference genome minus 10 bp to the position of the gene editing target site on the reference genome plus 10 bp, for a total range of 20 bp; When the gene editing method is single-base editing, the editing range of the gene editing target site is the position of the target base on the reference genome.

4. The method according to claim 1, characterized in that, The different variant types mentioned in step (S4) include at least one of the following: insertion, deletion, and single nucleotide polymorphism; The percentage of reads of different variant types in the total number of reads mentioned in step (S5) is used as the editing efficiency of the gene editing target site in each sample. The calculation is performed according to the different editing methods as follows: When the editing method is traditional gene editing, the percentage of the number of inserted and deleted reads to the total number of reads is used as the editing efficiency of the gene editing target site in each sample; When the editing method is single-base editing, the percentage of single nucleotide polymorphism reads that match the original base mutation as the target base is taken as the editing efficiency of the gene editing target site in each sample.

5. The method according to any one of claims 1 to 4, characterized in that, It also includes outputting the results of each step of the method to the corresponding result directory, creating a Readme file in the output directory, and providing a detailed description of each output result directory.

6. A computer-readable storage medium, characterized in that, Used to store computer instructions, programs, code sets, or instruction sets; when the computer instructions, programs, code sets, or instruction sets are run on a computer, the computer performs the method for accurate and efficient gene editing based on NGS data as described in any one of claims 1 to 5.

7. An electronic device comprising a processor and a memory, the memory for storing a program; the processor for running the stored program, characterized in that, The operation of the stored program is a method for achieving accurate and efficient gene editing based on NGS data as described in any one of claims 1 to 5.

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