Method for accurately and efficiently evaluating gene editing efficiency based on NGS data, storage medium and electronic equipment

Abstract

The invention provides a method for accurately and efficiently evaluating gene editing efficiency based on NGS data, a storage medium and electronic equipment, and belongs to the technical field of biological information processing. The method comprises the following steps: splitting NGS mixed pool sequencing data according to a bar code tag sequence, constructing a reference genome index and a dictionary, comparing NGS data of each sample with a reference genome, counting a comparison rate of a comparison result, constructing a comparison result index, converting the comparison result into a tsv format, and evaluating editing efficiency integration of a gene editing target site in each sample. The requirement for accurately and efficiently evaluating the gene editing efficiency based on NGS data is met. According to the method, the limitation of an existing tool on the size of a data file can be effectively avoided, the bar code label sequence can be customized for data splitting, and a high-accuracy comparison result is obtained by utilizing a mainstream comparison algorithm tool; and parallel operation is carried out by utilizing the advantages of the computing cluster, so that the aim of accurately and efficiently evaluating the gene editing efficiency of multiple genes and a large number of samples is fulfilled.

Description

Technical Field

[0001] The present invention belongs to the technical field of biological information processing, and specifically relates to a method for accurately and efficiently evaluating gene editing efficiency based on NGS data, a storage medium, and an electronic device. Background Art

[0002] Gene editing technology makes more precise gene regulation possible. Traditional gene editing technologies include artificially mediated zinc finger nuclease (ZFN) technology, transcription activator-like effector nuclease (TALEN) technology, and clustered regularly interspaced short palindromic repeat-associated protein nuclease (CRISPR / Cas9) technology, which target almost any genomic site and induce the cell's DNA repair mechanism through non-homologous end joining or homologous recombination repair, thereby causing DNA insertion or deletion mutations, but there are certain limitations in accuracy. Single-base editing technology can achieve changes in key amino acids by precisely modifying a single base. In addition to generating gene function loss mutations by introducing stop codons, it can also adjust the regulatory sites in the promoter region to achieve regulation of gene expression. The development of this technology is of great significance in the fields of gene therapy for genetic diseases and modification of biological traits.

[0003] With the widespread application of gene editing technology, the evaluation of gene editing efficiency has become one of the main issues. NGS technology can perform high-throughput sequencing on a large number of samples at the same time in a short period of time, greatly reducing the sequencing cost and significantly improving the data processing speed. In addition, NGS technology provides higher sequencing depth and coverage, making the detection of complex mutations and low-frequency mutations more sensitive and accurate. This makes NGS technology an efficient and reliable technical means in the systematic analysis of genome editing, especially when dealing with a large number of samples and complex mutations.

[0004] At present, there are several bioinformatics tools for quantitative analysis of reads of insertions and deletions of gene editing target sites based on NGS data. Among them, the noteworthy tools are Hi-TOM, CRISPResso2, CRISPR-GRANT, CrisprStitch, CRISPRMatch and CRIS.py. Among these tools, Hi-TOM and CRISPResso2 are both developed based on web browsers and have user-friendly operation interfaces. However, both Hi-TOM and CRISPResso2 have restrictions on the size of uploaded data files. In addition, Hi-TOM cannot customize barcode tag sequences to split NGS mixed pool sequencing data, nor can it batch analyze sample sequencing data, while CRISPResso2 does not have the function of splitting NGS mixed pool sequencing data. At the same time, some laboratories often store sensitive data on computers that are not connected to the Internet, resulting in the inability to use web browser services offline. CRISPR-GRANT and CrisprStitch provide cross-platform graphical user interfaces for NGS data analysis, which have good adaptability. However, CRISPR-GRANT cannot batch analyze sample sequencing data and split NGS mixed pool sequencing data, and the input files must be uncompressed, resulting in a large amount of hard disk storage space. Although CrisprStitch provides a cross-platform graphical user interface and better result visualization, the quality of its alignment algorithm is poor, which has a certain impact on the accuracy of subsequent analysis results. Although CRISPRMatch has the ability to batch analyze sample sequencing data and better result visualization, it cannot split NGS mixed pool sequencing data. CRIS.py can neither batch analyze sample sequencing data nor split NGS mixed pool sequencing data. At the same time, none of the above 6 tools can evaluate the editing efficiency of single-base editing based on NGS data. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a method for accurately and efficiently evaluating gene editing efficiency based on NGS data, by integrating the barcode tag sequence splitting, constructing a reference genome index and dictionary, comparing the NGS data of each sample after splitting with the reference genome, counting the comparison rate of the comparison results and constructing an index of the comparison results, converting the comparison result format, evaluating the editing efficiency of gene editing target sites, etc., to achieve parallel computing and accurate and efficient gene editing efficiency of multiple genes and large samples.

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

[0007] The NGS mixed pool sequencing data is split according to the sequencing sample information and the barcode tag sequence of each sample to obtain the NGS data of a single sample;

[0008] Build an index and dictionary of reference genomes;

[0009] Comparing the NGS data of the single sample with the constructed reference genome index to obtain a comparison result with the reference genome index;

[0010] Counting the comparison rate of the comparison result with the reference genome index, and obtaining the comparison result with the reference genome index;

[0011] Convert the comparison result with the reference genome index into a comparison result in tsv format;

[0012] The editing efficiency of the gene editing target site in each sample was evaluated based on the sequencing sample information, gene editing method and the comparison results in the tsv format:

[0013] When the gene editing method is traditional gene editing, the number of reads of insertion and deletion of 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 of the single nucleotide polymorphism in the gene editing target site that meets the mutation of the original base to the target base is quantified to obtain the editing efficiency of the gene editing target site.

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

[0016] The reference genome index construction tool includes a bwa index tool;

[0017] The reference genome dictionary construction tool includes a gatk CreateSequenceDictionary tool;

[0018] The comparison tool includes a bwa mem tool;

[0019] The tool for counting the comparison rate of the comparison result with the reference genome index includes the samtoolsflagstat tool;

[0020] The tool for constructing the index of the comparison result with the reference genome index includes the samtools index tool;

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

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

[0023] The method for parallel computing of the computing cluster is to create a task of building a reference genome index and a dictionary for each reference genome in the config configuration file, and then use the PBS task management system to batch deliver the created tasks of each reference genome to the computing cluster, and simultaneously build an index and a dictionary for each reference genome in a parallel computing manner, 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 computing log of each reference genome task.

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

[0025] (S1) determining the editing range of the gene editing target site according to the position of the gene editing target site in the sequencing sample information on the reference genome;

[0026] (S2) reading the content of the alignment result in tsv format line by line, filtering reads that are not aligned to the reference genome at all, soft-cut and hard-cut reads, obtaining high-quality reads aligned to the reference genome, and then extracting the base information at each position of the high-quality reads aligned to the reference genome within the editing range of the gene editing target site determined in step (S1);

[0027] (S3) correcting the high-quality reads aligned with the reference genome according to the base mutation information at each position within the editing range of the high-quality reads aligned with the reference genome extracted in step (S2) to obtain error-corrected reads;

[0028] (S4) obtaining the corrected reads according to step (S3), and obtaining by statistics 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 variant types;

[0029] (S5) Calculate the percentage of the number of reads of different variant types obtained according to step (S4) to the total number of reads, and use the percentage of the number 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, a total 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 high-quality reads aligned with the reference genome in step (S2) is to count the number of different bases that appear at each position in the editing range of the high-quality reads, and take the base with the largest number of occurrences as the correct base for the position; if the number of occurrences of different bases is the same, no error correction is performed.

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

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

[0036] When the editing method is traditional gene editing, the percentage of the number of insertion and deletion 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 the number of single nucleotide polymorphism reads that match the original base mutation as the target base to the total number of reads is used as the editing efficiency of the gene editing target site in each sample.

[0038] Preferably, the method further includes outputting the result 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.

[0039] The present 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 executes the method for accurately and efficiently evaluating gene editing efficiency based on NGS data.

[0040] The present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store programs; 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] The present invention provides a method for accurate and efficient gene editing efficiency based on NGS data, which is to split the NGS mixed pool sequencing data according to the barcode label sequence, construct a reference genome index and dictionary, obtain the NGS data of each sample after splitting and compare it with the reference genome, statistically compare the comparison result and construct the comparison result index, convert the comparison result into a comparison result in tsv format, and evaluate the editing efficiency of the gene editing target site in each sample. These six parts of the process are integrated together to meet the needs of accurate and efficient gene editing efficiency based on NGS data. The method described in the present invention can effectively avoid the size limit of the data file on the existing tools, can customize the barcode label sequence for splitting the NGS mixed pool sequencing data, can use the mainstream comparison algorithm tool to obtain a high-accuracy comparison result, and can also use the advantages of the computing cluster to accurately and efficiently calculate the gene editing efficiency of multiple genes and a large number of samples in a parallel computing manner. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0043] Figure 2 This is an example diagram of sequencing sample information for rice gene OsPDS and alfalfa MsPALM1;

[0044] Figure 3 This is a sample diagram of the config configuration file;

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

[0046] Figure 5 This is an example diagram of the operation of the present invention for evaluating the gene editing efficiency of sequencing samples of rice gene OsPDS and alfalfa MsPALM1.

[0047] Figure 6 This is an example visualization of the comparison results between the NGS data of 12 hairy root samples and the reference genome. DETAILED DESCRIPTION

[0048] The present invention provides a method for accurately and efficiently editing genes based on NGS data, comprising the following steps:

[0049] The NGS mixed pool sequencing data is split according to the sequencing sample information and the barcode tag sequence of each sample to obtain the NGS data of a single sample;

[0050] Build an index and dictionary of reference genomes;

[0051] Comparing the NGS data of the single sample with the constructed reference genome index to obtain a comparison result with the reference genome index;

[0052] Counting the comparison rate of the comparison result with the reference genome index, and obtaining the comparison result with the reference genome index;

[0053] Convert the comparison result with the reference genome index into a comparison result in tsv format;

[0054] The editing efficiency of the gene editing target site in each sample was evaluated based on the sequencing sample information, gene editing method and the comparison results in the tsv format:

[0055] When the gene editing method is traditional gene editing, the number of reads of insertion and deletion of 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 of the single nucleotide polymorphism in the gene editing target site that meets the mutation of the original base to the target base is quantified to obtain the editing efficiency of the gene editing target site.

[0057] The present invention splits the NGS mixed pool sequencing data according to the sequencing sample information and the barcode label sequence of each sample to obtain NGS data of a single sample.

[0058] In the present invention, the sequencing sample information preferably has one sample information per line, separated by tabs. The gene editing method is traditional gene editing or single base editing. When the gene editing method is traditional gene editing, the sequencing sample information is the file name of the NGS mixed pool sequencing data, the barcode label sequence ID corresponding to each sample in the NGS mixed pool sequencing data, the reference genome corresponding to each sample in the NGS mixed pool 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 is the file name of the NGS mixed pool sequencing data, the barcode label sequence ID corresponding to each sample in the NGS mixed pool sequencing data, the reference genome corresponding to each sample in the NGS mixed pool 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 to the target base. If a sample has only one target site, only the position of the target site on the reference genome needs to be written into the sequencing sample information; if a sample has multiple target sites, all the positions of the multiple target sites on the reference genome need to be written into the sequencing sample information, with commas separating the positions of the multiple target sites on the reference genome.

[0059] In the present invention, the barcode label sequence is located in the config configuration file. The config configuration file contains the following four configuration contents:

[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=" of the config configuration file; if there are multiple reference genomes, separate the absolute paths of multiple reference genes with commas and fill them in "ref_genome_files=" of the config configuration file in sequence;

[0061] (2) The second item is to configure the absolute path of the directory where the NGS mixed pool sequencing data is located. Fill in the absolute path of the directory where the NGS mixed pool sequencing data is located in "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 end and R end of the barcode label, connected by "-":

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

[0064] In the present invention, the tool for splitting NGS mixed pool sequencing data includes the fastq-multx tool.

[0065] In the present invention, the tool for constructing the reference genome index includes the bwa index tool; the tool for constructing the reference genome dictionary includes the gatk CreateSequenceDictionary tool; the comparison tool includes the bwa mem tool; the tool for counting the comparison rate of the comparison result with the reference genome index includes the samtoolsflagstat tool; the tool for constructing the index of the comparison result with the reference genome index includes the samtoolsindex tool; the tool for converting the tsv format includes the sam2tsv tool. When using the above tools, the default parameters can be used.

[0066] In the present invention, the method for evaluating the editing efficiency of the gene editing target site in each sample preferably comprises the following steps:

[0067] (S1) determining the editing range of the gene editing target site according to the position of the gene editing target site in the sequencing sample information on the reference genome;

[0068] (S2) reading the content of the alignment result in tsv format line by line, filtering reads that are not aligned to the reference genome at all, soft-cut and hard-cut reads, obtaining high-quality reads aligned to the reference genome, and then extracting the base information at each position of the high-quality reads aligned to the reference genome within the editing range of the gene editing target site determined in step (S1);

[0069] (S3) correcting the high-quality reads aligned with the reference genome according to the base mutation information at each position within the editing range of the high-quality reads aligned with the reference genome extracted in step (S2) to obtain error-corrected reads;

[0070] (S4) obtaining the corrected reads according to step (S3), and obtaining by statistics 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 variant types;

[0071] (S5) Calculate the percentage of the number of reads of different variant types obtained according to step (S4) to the total number of reads, and use the percentage of the number 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 the present 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 position of the gene editing target site on the reference genome minus 10bp to the position of the gene editing target site on the reference genome plus 10bp, a total of 20bp; 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 method preferably includes artificially mediated zinc finger nuclease (ZFN) technology, transcription activator-like effector nuclease (TALEN) technology, and clustered regularly interspaced short palindromic repeat-associated protein nuclease (CRISPR / Cas9) technology. The method for correcting high-quality reads aligned with the reference genome in step (S2) is preferably to count the number of different bases that appear at each position in the editing range of high-quality reads, and the base with the largest number of occurrences is used as the correct base for the position; if the number of occurrences of different bases is the same, 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 types of variation described in step (S4) preferably include at least one of the following: insertion, deletion, and single nucleotide polymorphism. The percentage of the number of reads of different variation types in the total number of reads in step (S5) as the editing efficiency of the gene editing target site in each sample is preferably calculated as follows according to different editing methods: when the editing method is traditional gene editing, the percentage of the number of reads of insertion and deletion 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 the number of single nucleotide polymorphism reads that meet the original base mutation as the target base to the total number of reads is used as the editing efficiency of the gene editing target site in each sample.

[0073] In the present invention, the method determines the evaluation method according to the different gene editing methods, which is not only conducive to the simultaneous evaluation of the editing efficiency of the gene editing target site in each sample of traditional gene editing or single base editing, but also can ensure the accuracy of the editing efficiency of the gene editing site brought about by different gene editing methods.

[0074] In the present invention, the method for constructing an index and dictionary of a reference genome is preferably a method for parallel computing of a computing cluster; the method for parallel computing of a computing cluster creates a task for constructing an index and dictionary of a reference genome for each reference genome in a config configuration file, and then uses a PBS task management system to batch deliver the created tasks of each reference genome to the computing cluster, and simultaneously constructs an index and dictionary for each reference genome in a parallel computing manner, and monitors the tasks delivered to the computing nodes in real time to ensure that each reference genome task is successfully constructed, and at the same time records the computing log of each reference genome task.

[0075] In the present invention, the method of parallel computing of computing cluster is used to create NGS mixed pool sequencing data splitting for each sample in the sequencing sample information, compare the NGS data of each sample with the constructed reference genome index, calculate the comparison rate with the reference genome comparison result, construct the index with the reference genome comparison result, convert the comparison result with the reference genome index into the comparison result in tsv format, and evaluate the editing efficiency of the gene editing target site in each sample. The tasks created by the above method steps are delivered to the computing cluster in batches using the PBS task management system, and the NGS data of each sample is analyzed in parallel computing mode on the computing cluster, which significantly shortens the time required for computing analysis, and monitors the tasks delivered to the computing nodes in real time to ensure that the tasks of each sample are successfully completed, and the computing log of each sample task is recorded at the same time, fundamentally achieving the purpose of accurate and fast gene editing efficiency.

[0076] In the present invention, the method executes a program according to the steps of specifying the start of the operation: when it is specified to start from splitting the NGS mixed pool sequencing data, the steps in the entire method are executed; when it is specified to start from building a reference genome index and dictionary, the steps of building the reference genome index and dictionary and subsequent steps are executed; when it is specified to start from aligning the NGS data with the constructed reference genome index, the steps of aligning the NGS data with the constructed reference genome index and subsequent steps are executed; when it is specified to start from evaluating the editing efficiency of the gene editing target site in each sample, only the step of evaluating the editing efficiency of the gene editing target site in each sample is executed.

[0077] In the present invention, the method preferably further comprises outputting the result of each step of the method to the corresponding result directory, creating a Readme file in the output directory, and describing each output result directory in detail. In one embodiment of the present invention, the output directory contains 4 folders, namely, the NGS data folder of each sample (denoted as 01.Barcode_data), the folder of the constructed reference genome index and dictionary (denoted as 02.Reference_genome), the folder related to the reference genome index comparison result (denoted as 03.Bwa_mapped), and the editing efficiency evaluation result folder of each sample gene editing target site (denoted as 04.Edited_efficiency).

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

[0079] The present 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 executes the method for accurately and efficiently evaluating gene editing efficiency based on NGS data.

[0080] The present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store programs; 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 is a detailed description of a method for accurately and efficiently evaluating gene editing efficiency based on NGS data, a storage medium, and an electronic device provided by the present invention in conjunction with the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0082] Example 1

[0083] A method for accurately and efficiently evaluating gene editing efficiency based on NGS data, such as Figure 1 Shown is a flow chart of the method for accurately and efficiently evaluating gene editing efficiency based on NGS data of the present invention.

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

[0085] Step 1: Enter the sequencing sample information, config configuration file, and edit in traditional gene editing mode. Specify the start operation step to start with the splitting of NGS mixed pool sequencing data.

[0086] 1.1. Sequencing sample information:

[0087] The information of the sequencing samples includes the file name of the NGS mixed pool sequencing data, the barcode label sequence ID corresponding to each sample in the NGS mixed pool sequencing data, the reference genome corresponding to each sample in the NGS mixed pool sequencing data, and the position of the gene editing target site on the reference genome.

[0088] Step 2: According to the sequencing sample information, the barcode tag sequence file in the config configuration file, and the NGS mixed pool sequencing data, the fastq-multx tool is used to split the NGS mixed pool sequencing data to obtain the NGS data of each sample, and the NGS data of 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 compare the NGS data of each sample obtained in step 2 with the reference genome index constructed in step 3 to obtain the result of comparison with the reference genome index.

[0091] Step 5, using the samtools flagstat tool to count the alignment rate of the alignment result obtained in step 4 with the reference genome index, and using the samtools index tool to construct an index of the alignment result obtained in step 4 with the reference genome index;

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

[0093] Step 7: According to the sequencing sample information, editing method and the comparison results in the tsv format of step 6, the number of reads of insertion and deletion of the gene editing target site is quantified to obtain the editing efficiency of the gene editing target site of each sample.

[0094] Step 8: Output the results of steps 2 to 7 to the corresponding result directory, and create a Readme file in the output directory to explain each output result directory in detail. The output directory contains 4 folders, namely, the NGS data folder of each sample (recorded as 01.Barcode_data), the folder of the constructed reference genome index and dictionary (recorded as 02.Reference_genome), the folder related to the reference genome index comparison results (recorded as 03.Bwa_mapped), and the evaluation result folder of the editing efficiency of the gene editing target site in each sample (recorded as 04.Edited_efficiency).

[0095] Example 2

[0096] A method for accurately and efficiently evaluating gene editing efficiency based on NGS data. The specific steps are as follows:

[0097] Step 1: Enter the sequencing sample information, config configuration file, and edit in single base editing mode. Specify the start operation step to start with NGS mixed pool sequencing data splitting.

[0098] 1.1. Sequencing sample information:

[0099] The sequencing sample information includes the file name of the NGS mixed pool sequencing data, the barcode label sequence ID corresponding to each sample in the NGS mixed pool sequencing data, the reference genome corresponding to each sample in the NGS mixed pool 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 to the target base.

[0100] Step 2: According to the sequencing sample information, the barcode tag sequence file in the config configuration file, and the NGS mixed pool sequencing data, the fastq-multx tool is used to split the NGS mixed pool sequencing data to obtain the NGS data of each sample, and the NGS data of 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 compare the NGS data of each sample obtained in step 2 with the reference genome index constructed in step 3 to obtain the result of comparison with the reference genome index.

[0103] Step 5, using the samtools flagstat tool to count the alignment rate of the alignment result obtained in step 4 with the reference genome index, and using the samtools index tool to construct an index of the alignment result obtained in step 4 with the reference genome index;

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

[0105] Step 7: According to the sequencing sample information, editing method and the comparison result in the tsv format of step 6, the number of reads of the single nucleotide polymorphism in the gene editing target site that meets the mutation of the original base to the target base is quantified to obtain the editing efficiency of the gene editing target site.

[0106] Step 8: Output the results of steps 2 to 7 to the corresponding result directory, and create a Readme file in the output directory to explain each output result directory in detail. The output directory contains 4 folders, namely, the NGS data folder of each sample (recorded as 01.Barcode_data), the folder of the constructed reference genome index and dictionary (recorded as 02.Reference_genome), the folder related to the reference genome index comparison results (recorded as 03.Bwa_mapped), and the evaluation result folder of the editing efficiency of the gene editing target site in each sample (recorded as 04.Edited_efficiency).

[0107] Application Example 1

[0108] Taking the NGS mixed pool sequencing data of 12 (respectively 1-1, 1-2, 1-3, 1-4, 1-5, 1-6, 5-1, 5-2, 5-3, 5-4, 5-5, 5-6) hairy root samples of species rice gene OsPDS and alfalfa gene MsPALM1 as an example, the method for accurately and efficiently evaluating gene editing efficiency based on NGS data in Example 1 was used to evaluate the editing efficiency of 12 samples of rice gene OsPDS and alfalfa gene MsPALM1:

[0109] First, the sequencing sample information of 12 hairy root samples of 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 step of starting the operation is specified to start from the splitting of NGS mixed pool sequencing data.

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

[0111] Figure 5 The results showed that the editing efficiency of 12 hairy root samples of genes OsPDS and MsPALM1 was evaluated using the method of the present invention for accurately and efficiently evaluating gene editing efficiency based on NGS data, and it took a total of 223.74 seconds.

[0112] The editing efficiency of 12 hairy root samples of rice gene OsPDS and alfalfa gene MsPALM1 was evaluated by the present invention, and the results are shown in Table 1. In order to verify the accuracy of the editing efficiency evaluation results of the present invention, the results of the comparison of the 12 hairy root samples with the reference gene were visualized using IGV software, and the results are shown in Tables 1 and Figure 6 .

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

[0114]

[0115]

[0116] The results in Table 1 show that the editing efficiency of the target site positions 2441 and 4834 of the rice gene OsPDS is 0.03% to 1.59%, which is extremely low; the editing efficiency of the target site position 50 of the alfalfa gene MsPALM1 is 0% to 0.07%, which is extremely low; the editing efficiency of the target site position 89 of the alfalfa gene MsPALM1 is 53.15% to 69.86%, which is relatively high.

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

[0118] The present invention designs a method for accurately and efficiently evaluating gene editing efficiency based on NGS data, which solves the problems of data file size limitations of existing tools, the inability to customize barcode label sequences for splitting NGS mixed pool sequencing data, and the inability to take advantage of computing clusters to accurately and efficiently evaluate the gene editing efficiency of a large number of samples through parallel computing.

[0119] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for accurately and rapidly measuring gene editing efficiency based on NGS data, characterized in that: The following steps are involved: The NGS mixed pool sequencing data is split according to the sequencing sample information and the barcode tag sequence of each sample to obtain the NGS data of a single sample; Build an index and dictionary of reference genomes; Comparing the NGS data of the single sample with the constructed reference genome index to obtain a comparison result with the reference genome index; Counting the comparison rate of the comparison result with the reference genome index, and obtaining the comparison result with the reference genome index; Convert the comparison result with the reference genome index into a comparison result in tsv format; The editing efficiency of the gene editing target site in each sample was evaluated based on the sequencing sample information, gene editing method and the comparison results in the tsv format: When the gene editing method is traditional gene editing, the number of reads of insertion and deletion of 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 of the single nucleotide polymorphism in the gene editing target site that meets the mutation of the original base to the target base is quantified to obtain the editing efficiency of the gene editing target site.

2. The method according to claim 1, characterized in that: The tool for splitting NGS mixed pool sequencing data includes fastq-multx tool; The reference genome index construction tool includes a bwaindex tool; The reference genome dictionary construction tool includes a gatk CreateSequenceDictionary tool; The comparison tool includes a bwa mem tool; The tool for counting the comparison rate of the comparison result with the reference genome index includes the samtools flagstat tool; The tool for constructing the index of the comparison result with the reference genome index includes the samtoolsindex tool; Tools for converting tsv format include the sam2tsv tool.

3. The method according to claim 1, characterized in that: The method of constructing the index and dictionary of the reference genome is a method of parallel computing of a computing cluster; The method for parallel computing of the computing cluster is to create a task of building a reference genome index and a dictionary for each reference genome in the config configuration file, and then use the PBS task management system to batch deliver the created tasks of each reference genome to the computing cluster, and simultaneously build an index and a dictionary for each reference genome in a parallel computing manner, 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 computing log of each reference genome task.

4. The method according to claim 1, characterized in that: The method for evaluating the editing efficiency of the gene editing target site in each sample comprises the following steps: (S1) determining the editing range of the gene editing target site according to the position of the gene editing target site in the sequencing sample information on the reference genome; (S2) reading the content of the alignment result in tsv format line by line, filtering reads that are not aligned to the reference genome at all, soft-cut and hard-cut reads, obtaining high-quality reads aligned to the reference genome, and then extracting the base information at each position of the high-quality reads aligned to the reference genome within the editing range of the gene editing target site determined in step (S1); (S3) correcting the high-quality reads aligned with the reference genome according to the base mutation information at each position within the editing range of the high-quality reads aligned with the reference genome extracted in step (S2) to obtain error-corrected reads; (S4) obtaining the corrected reads according to step (S3), and obtaining by statistics 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 variant types; (S5) Calculate the percentage of the number of reads of different variant types obtained according to step (S4) to the total number of reads, and use the percentage of the number 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.

5. The method according to claim 4, characterized in that: The editing range of the gene editing target site 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, 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.

6. The method according to claim 4, characterized in that: The method for correcting errors of high-quality reads aligned with the reference genome in step (S2) is to count the number of different bases that appear at each position in the editing range of the high-quality reads, and take the base with the largest number of occurrences as the correct base at that position; If different bases appear the same number of times, no error correction is performed.

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

8. The method according to any one of claims 1 to 7, characterized in that: The method also includes outputting the result 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.

9. 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 executes the method for accurate and efficient gene editing based on NGS data as described in any one of claims 1 to 8.

10. An electronic device comprising a processor and a memory, wherein the memory is used to store a program; and the processor is used to run the stored program, wherein: The operation of the stored program is a method for achieving accurate and efficient gene editing efficiency based on NGS data as described in any one of claims 1 to 8.

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