Method for rapidly designing target gene sgRNA in large scale

Abstract

The invention provides a method for rapidly designing target gene sgRNA on a large scale, and belongs to the technical field of biological information. According to the method, allelic genes are subjected to matching judgment, gene sequences and CDS sequences are extracted, a Muscle tool is used for carrying out multi-sequence comparison on the extracted gene sequences, conserved sequences in comparison results are extracted, and sgRNA design and specificity evaluation are carried out according to the conserved sequences. Therefore, the requirements of localized large-scale design and target gene sgRNA evaluation are met. According to the method, limitation of species genome information recorded in a database of an existing online tool can be effectively avoided, sgRNA design and sgRNA specificity evaluation can be rapidly carried out on a large batch of target genes, the efficiency of editing the target genes through sgRNA is high, and the editing efficiency is 72% or above.

Description

Technical Field

[0001] The present invention belongs to the field of bioinformatics, and in particular relates to a method for rapidly designing target gene sgRNA in large quantities. Background Art

[0002] Gene editing is a targeted modification of the genetic material of an organism. It uses efficient and precise gene editing technology to insert, delete or replace the target gene, thereby changing the genetic information and phenotypic characteristics of the organism. The rapid development of gene editing technology has brought great changes and hope to the fields of medicine, agriculture and biotechnology. In recent years, the continuous emergence and continuous improvement of technologies such as zinc finger nuclease (ZFN), transcription activator-like effector nuclease (TALEN), CRISPR / Cas9, IS transposon nuclease (TnpB) and single base editing (BE) have made gene editing more accurate, efficient and controllable. The continuous evolution of these technologies has provided new tools and methods for disease treatment, molecular breeding and biotechnology innovation. In particular, CRISPR / Cas9 technology has become a star in the field of gene editing due to its convenient operation, high efficiency and strong specificity. At the same time, as the ancestor of CRISPR-Cas9 nuclease, TnpB has a smaller reRNA skeleton and may have better delivery performance. In the fields of medicine and agriculture, TnpB may play an important role and provide ideas and directions for the further development and application of gene editing technology.

[0003] As a carrier of species genetic information, genome sequences are of great significance and can help researchers better understand important information such as the genetic characteristics, evolutionary history, and gene functions of different species. In addition, genome sequences also provide a basic template for gene editing technology, enabling researchers to accurately edit genes, thereby achieving precise genome modification and research. Since the emergence of high-accuracy long-length sequencing technology represented by PacBio HiFi sequencing in 2019, it has completely changed the field of genome sequence assembly. Scientists have successfully completed a complete human genome assembly for the first time using the characteristics of high accuracy and long length, so that each chromosome has been fully assembled from telomere to telomere (Telomere-to-Telomere, T2T). Subsequently, more and more species have also achieved complete genome assembly from telomere to telomere. Many scientists believe that genome assembly has entered the T2T era, marking a more accurate understanding of the genes and sequence characteristics, including alleles. In addition to the genome research of a single species, the concept of the pan-genome has gradually attracted attention. The pan-genome takes into account the genomic variation and diversity between multiple individuals of a species, and it genotypes the genomes of multiple individuals. Through the assembly of the pan-genome, we can better capture the richer genetic variation information within the population. In summary, with the rapid development of T2T genome and pan-genome assembly, gene editing based on species T2T genome and pan-genome as basic templates will become a method, especially in the field of molecular breeding. In this process, designing sgRNA (small guide RNA) with better specificity to achieve the goal of homozygous editing of alleles will also be an important part.

[0004] As one of the important components of the gene editing tool system, sgRNA is used to guide the gene editing nuclease to accurately locate the specific position of the target gene, thereby achieving the purpose of gene editing. Therefore, it is crucial to ensure the specificity of sgRNA. sgRNA with good specificity helps to improve the efficiency and accuracy of gene editing, especially for alleles. Designing sgRNA with good specificity is particularly critical. At present, although there are some online tools available for designing sgRNA, they also have a good visual operation interface. However, few tools are specifically designed for alleles. In addition, the existing online tools are limited by the species genome information contained in their databases. If the genome information of the species under study is not included in its database, the tool cannot be used for sgRNA design. In addition, the specificity evaluation of sgRNA is also very important. The candidate sgRNA needs to be compared with the whole gene of the species to determine its possible targeting position, which will take a lot of time. In addition, if gene editing is used to construct a species mutant library, it will require sgRNA design for a large number of target genes. If the server can be used for batch parallel operation, it will save a lot of time and labor costs. Therefore, it would be very beneficial to develop a tool with local sgRNA design capabilities that is not limited by the inclusion of species gene information in databases. Summary of the invention

[0005] In view of this, the object of the present invention is to provide a method for rapidly designing target gene sgRNA in large quantities.

[0006] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0007] The present invention provides a method for rapidly designing target gene sgRNA in large quantities, characterized in that it comprises the following steps:

[0008] Step 1, input necessary parameters, which include gene ID, nuclease, length of sgRNA containing PAM motif, whole genome sequence of species, CDS sequence of all species, bed files of all genes of species, allele database and sgRNA database for designing target gene sgRNA;

[0009] Step 2: Search the allele database to see if the input target gene has an allele. If the allele exists, extract the gene sequence and CDS sequence of the allele according to the bed information and gene ID of the allele; if the input target gene does not have an allele, extract the gene sequence and CDS sequence of the target gene according to the bed information and gene ID of the input target gene;

[0010] Step 3, if the input target gene has alleles, the extracted allele sequences are aligned using the Muscle tool to identify the conserved sequences, obtain the output results of the Muscle alignment, and convert the output results of the Muscle alignment into text in Phylip format to obtain the alignment results in Phylip format, and then the conserved sequences are cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse-complemented K-mer fragments are used as candidate sgRNAs; if the input target gene does not have alleles, the target gene sequence is directly cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse-complemented K-mer fragments are used as candidate sgRNAs;

[0011] Step 4, screening the candidate sgRNAs in step 3 according to the PAM motif of the nuclease to obtain the screened sgRNAs that meet the nuclease requirements;

[0012] Step 5: Use the sgRNA database to evaluate the specificity of the sgRNA screened in step 4, sort the sgRNA evaluation results from best to worst according to their specificity, and obtain the gene and exon information targeted by the sgRNA and the percentage content of the bases "G" and "C" in the sgRNA to obtain the final designed sgRNA;

[0013] Step 6: Output the results of Step 2 to Step 5 to the corresponding result files. At the same time, create a Readme file in the output directory to provide detailed descriptions of each output file.

[0014] Preferably, in step 1, the nuclease is Cas9 nuclease or TnpB nuclease; and the length of the sgRNA containing the PAM motif is 19 to 25 nt.

[0015] Preferably, in step 1, the method for constructing the bed file of all genes of the species comprises the following steps: extracting the chromosome number, gene ID, the starting position and ending position of the gene on the chromosome, and the positive and negative chain information of the chromosome where the gene is located from the species genome annotation file to obtain the bed file of all genes of the species.

[0016] Preferably, in step 1, the method for constructing the allele database comprises the following steps:

[0017] (S1) dividing the species genome annotation file according to different homologous chromosomes to obtain divided homologous chromosome annotation files, extracting CDS sequences using the divided homologous chromosome annotation files and the whole genome sequence, and then translating the CDS sequences into amino acid sequences;

[0018] (S2) using the diamond tool to align the amino acid sequences obtained in step (S1) to obtain an alignment result, filtering the alignment result with an amino acid sequence identity value greater than 80 as a threshold, obtaining amino acid sequences with a sequence identity value greater than 80, and saving the results into a file with a suffix of blast to obtain a file with a suffix of blast;

[0019] (S3) extracting the chromosome number, gene ID, and the starting and ending position information of the gene on the chromosome from the annotation file of the divided homologous chromosomes obtained in step (S1) to obtain a file with a suffix of gff;

[0020] (S4) using the MCScanX tool to identify alleles of the divided homologous chromosome genes, wherein the file with the suffix blast in step (S2) and the file with the suffix gff in step (S3) are used as input files, the number of genes required for calling the collinear block is set to 10, the maximum allowed gaps is set to 45, and the output result file is in html format, thereby obtaining an html format file;

[0021] (S5) Convert the HTML format file obtained in step (S4) into a tab-delimited text, remove redundancy and merge the alleles of homologous chromosomes, and construct a species allele database, in which each row is a group of alleles, to obtain an allele database.

[0022] Preferably, in step 1, the method for constructing the sgRNA database comprises the following steps:

[0023] (1) Extract the exon sequences of all genes based on the whole genome sequence and annotation files of the target species. The exon sequence description information includes the gene ID, exon number, chromosome number, the starting and ending positions of the exon sequence on the chromosome, and the positive and negative chains;

[0024] (2) using the Jellyfish tool to cut the exon sequences of all genes in step (1) into K-mer fragments of the required length of the sgRNA containing the PAM motif, and perform redundancy removal to obtain the K-mer fragments after redundancy removal;

[0025] (3) Using the batmap tool, the de-redundant K-mer fragments obtained in step (2) are aligned with the whole genome sequence of the target species, and the number of positions where each K-mer fragment is aligned to the whole genome is counted, where the maximum number of mismatched bases is set to 4 and the maximum number of alignments is set to all. The parallel operation method is used during the alignment to obtain the statistical results of the alignment between the K-mer fragment and the whole genome;

[0026] (5) using the prepared Python script to cut the exon sequence obtained in step (1) into K-mer fragments of the required length of the sgRNA containing the PAM motif, and obtain the K-mer fragments cut by the prepared Python script;

[0027] (6) Using the statistical results of the K-mer fragments and the whole genome alignment and the correspondence between the K-mer fragments cut by the Python script, the number of positions and fragment description information of the whole genome of the species are aligned to obtain the sgRNA database.

[0028] Preferably, in step (S2), the parameter e-value threshold of the comparison is set to 1e-10, and the maximum number of sequences to be compared is set to 5;

[0029] In step (S3), the suffix is ​​gff file separated by tabs, the first column is the chromosome number, the second column is the gene ID, the third column is the starting position information of the gene on the chromosome, and the fourth column is the ending position information of the gene on the chromosome;

[0030] In step (2) or step (5), the length of the sgRNA containing the PAM motif is 19 to 25 nt;

[0031] In step (5), the description information of the K-mer fragment is the gene ID of the exon sequence where the K-mer fragment is located, the exon number of the exon sequence, the chromosome number of the exon sequence, the starting position and the ending position of the K-mer fragment on the chromosome; the starting position of the K-mer fragment on the chromosome is equal to the total distance of the exon sequence on the chromosome plus the window sliding; the ending position of the K-mer fragment on the chromosome is the starting position plus the length of the sgRNA containing the PAM motif;

[0032] In step (5), the written Python script starts window scanning from the first base of the exon sequence obtained in step (1), and cuts each time with the length of the sgRNA containing the PAM motif as the window unit until the last base of the sequence is covered. Each window sliding will generate a K-mer fragment of the required length of the sgRNA containing the PAM motif, and create the description information of the above K-mer fragment according to the exon sequence description information in step (1);

[0033] In step (6), the sgRNA database contains the location information, base sequence and statistical number of comparisons with the whole genome of the K-mer fragment, and constructs an index for the K-mer fragment description information in the sgRNA database.

[0034] The present invention provides a computer-readable storage medium, which is used to store computer instructions, programs, code sets or instruction sets. When the computer-readable storage medium is run on a computer, the computer executes the above-mentioned method for rapidly designing target gene sgRNA in large quantities.

[0035] The present invention provides 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 to implement the above-mentioned method for rapidly and massively designing target gene sgRNA.

[0036] The present invention provides an sgRNA obtained according to the above method.

[0037] Preferably, the nucleotide sequence of the sgRNA is as shown in SEQ ID NO.1; the sgRNA is the sgRNA of the alfalfa gene MsPALM1 gene.

[0038] The present invention provides a CRISPR / Cas9 system, wherein the above-mentioned sgRNA is connected to a pYLCRISPR / Cas9P35S-H backbone vector.

[0039] The present invention provides an application of the above-mentioned sgRNA or CRISPR / Cas9 system in target gene editing or studying target gene function.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention provides a method for rapidly designing target gene sgRNA in large quantities. The method of the present invention integrates five processes, including matching judgment of alleles, extracting gene sequences and CDS sequences, performing multiple sequence alignment of the extracted gene sequences using the Muscle tool, extracting conservative sequences in the alignment results, and designing sgRNA and specificity evaluation according to the conservative sequences, to meet the needs of localized large-scale design and evaluation of target gene sgRNA. The method of the present invention can effectively avoid the limitation of the existing online tools by the species genome information included in its database, and can also widely use the map pan-genome, homologous or allopolyploid species genome and diploid species genome to design sgRNA and specificity evaluation of sgRNA, and can also rapidly design sgRNA and specificity evaluation of sgRNA for large quantities of target genes. The method has high efficiency in editing target genes using sgRNA, and the editing efficiency is above 72%. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flow chart of the method for rapidly designing target gene sgRNA in large quantities according to the present invention;

[0043] Figure 2 This is an example diagram of the constructed alfalfa allele database;

[0044] Figure 3 This is an example of the bed file for all genes of alfalfa constructed;

[0045] Figure 4 This is an example diagram of the exon sequences of all genes extracted from alfalfa;

[0046] Figure 5 This is an example of a file that uses the Jellyfish tool to cut the exon sequence of alfalfa into 23nt K-mer fragments and remove redundancy.

[0047] Figure 6 This is an example of using the Python script to cut alfalfa into 23nt K-mer fragments and a file example of K-mer fragment description information;

[0048] Figure 7 This is an example diagram of the sgRNA database with a K-mer fragment length of 23nt;

[0049] Figure 8 Write logic code in Python to execute the method described in Example 1;

[0050] Fig. 9 This is an example diagram of the operation of designing sgRNA for the alfalfa gene MsPALM1 (MS51G4442v1) according to the present invention. DETAILED DESCRIPTION

[0051] The present invention provides a method for rapidly designing target gene sgRNA in large quantities, characterized in that it comprises the following steps:

[0052] Step 1, input necessary parameters, which include gene ID, nuclease, length of sgRNA containing PAM motif, whole genome sequence of species, CDS sequence of all species, bed files of all genes of species, allele database and sgRNA database for designing target gene sgRNA;

[0053] Step 2: Search the allele database to see if the input target gene has an allele. If the allele exists, extract the gene sequence and CDS sequence of the allele according to the bed information and gene ID of the allele; if the input target gene does not have an allele, extract the gene sequence and CDS sequence of the target gene according to the bed information and gene ID of the input target gene;

[0054] Step 3, if the input target gene has alleles, the extracted allele sequences are aligned using the Muscle tool to identify the conserved sequences, obtain the output results of the Muscle alignment, and convert the output results of the Muscle alignment into text in Phylip format to obtain the alignment results in Phylip format, and then the conserved sequences are cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse-complemented K-mer fragments are used as candidate sgRNAs; if the input target gene does not have alleles, the target gene sequence is directly cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse-complemented K-mer fragments are used as candidate sgRNAs;

[0055] Step 4, screening the candidate sgRNAs in step 3 according to the PAM motif of the nuclease to obtain the screened sgRNAs that meet the nuclease requirements;

[0056] Step 5: Use the sgRNA database to evaluate the specificity of the sgRNA screened in step 4, sort the sgRNA evaluation results from best to worst according to their specificity, and obtain the gene and exon information targeted by the sgRNA and the percentage content of the bases "G" and "C" in the sgRNA to obtain the final designed sgRNA;

[0057] Step 6: Output the results of Step 2 to Step 5 to the corresponding result files. At the same time, create a Readme file in the output directory to provide detailed descriptions of each output file.

[0058] In the present invention, the nuclease is Cas9 nuclease or TnpB nuclease; the length of the sgRNA containing the PAM motif is 19 to 25 nt. The PAM motif of the Cas9 nuclease is NGG, where N represents any one of the four bases A, T, C, and G. The PAM motif of the TnpB nuclease is TTTAA or TTGAT.

[0059] In the present invention, in step 1, the method for constructing the bed file of all genes of the species comprises the following steps: extracting the chromosome number, gene ID, the starting position and the ending position of the gene on the chromosome, and the positive and negative chain information of the chromosome where the gene is located from the species genome annotation file to obtain the bed file of all genes of the species.

[0060] In the present invention, a strategy of pre-constructing an isoallele database is adopted to determine whether the target gene contains an allele, and the corresponding sgRNA design operation is performed on the actual judgment result, so that sgRNAs with good specificity can be designed for multiple alleles at the same time to achieve the goal of homozygous editing.

[0061] As a preferred embodiment, the method for constructing the allele database comprises the following steps:

[0062] (S1) dividing the species genome annotation file according to different homologous chromosomes to obtain divided homologous chromosome annotation files, extracting CDS sequences using the divided homologous chromosome annotation files and the whole genome sequence, and then translating the CDS sequences into amino acid sequences;

[0063] (S2) using the diamond tool to align the amino acid sequences obtained in step (S1) to obtain an alignment result, filtering the alignment result with an amino acid sequence identity value greater than 80 as a threshold, obtaining amino acid sequences with a sequence identity value greater than 80, and saving the results into a file with a suffix of blast to obtain a file with a suffix of blast;

[0064] (S3) extracting the chromosome number, gene ID, and the starting and ending position information of the gene on the chromosome from the annotation file of the divided homologous chromosomes obtained in step (S1) to obtain a file with a suffix of gff;

[0065] (S4) using the MCScanX tool to identify alleles of the divided homologous chromosome genes, wherein the file with the suffix blast in step (S2) and the file with the suffix gff in step (S3) are used as input files, the number of genes required for calling the collinear block (-s) is set to 10, the maximum allowed gaps (-m) is set to 45, and the output result file is in html format, thereby obtaining an html format file;

[0066] (S5) Convert the HTML format file obtained in step (S4) into a tab-delimited text, remove redundancy and merge the alleles of homologous chromosomes, and construct a species allele database, in which each row is a group of alleles, to obtain an allele database.

[0067] In the present invention, in step (S2), the parameter e-value threshold of the comparison is set to 1e-10, and the maximum number of sequences compared (-k) is set to 5; in step (S3), the suffix gff file is separated by tabs, the first column is the chromosome number, the second column is the gene ID, the third column is the starting position information of the gene on the chromosome, and the fourth column is the ending position information of the gene on the chromosome.

[0068] In the present invention, the strategy of pre-constructing sgRNA data is adopted, and the step of aligning with the whole genome is omitted when evaluating the specificity of candidate sgRNAs, which significantly shortens the time required for sgRNA specificity evaluation and fundamentally achieves the goal of efficiently designing sgRNAs.

[0069] As a preferred embodiment, the method for constructing the sgRNA database comprises the following steps:

[0070] (1) Extract the exon sequences of all genes based on the whole genome sequence and annotation files of the target species. The exon sequence description information includes the gene ID, exon number, chromosome number, the starting and ending positions of the exon sequence on the chromosome, and the positive and negative chains;

[0071] (2) using the Jellyfish tool to cut the exon sequences of all genes in step (1) into K-mer fragments of the required length of the sgRNA containing the PAM motif, and perform redundancy removal to obtain the K-mer fragments after redundancy removal;

[0072] (3) Using the batmap tool, the de-redundant K-mer fragments obtained in step (2) are aligned with the whole genome sequence of the target species, and the number of positions where each K-mer fragment is aligned to the whole genome is counted, where the maximum number of mismatched bases is set to 4 and the maximum number of alignments is set to all. The parallel operation method is used during the alignment to obtain the statistical results of the alignment between the K-mer fragment and the whole genome;

[0073] (5) using the prepared Python script to cut the exon sequence obtained in step (1) into K-mer fragments of the required length of the sgRNA containing the PAM motif, and obtain the K-mer fragments cut by the prepared Python script;

[0074] (6) Using the statistical results of the K-mer fragments and the whole genome alignment and the correspondence between the K-mer fragments cut by the Python script, the number of positions and fragment description information of the whole genome of the species are aligned to obtain the sgRNA database.

[0075] When constructing the sgRNA database, in step (2) or step (5), the length of the sgRNA containing the PAM motif is 19 to 25 nt. In step (3), the parallel operation method is used during the comparison, which can improve the comparison efficiency. In step (5), the description information of the K-mer fragment is the gene ID of the exon sequence where the K-mer fragment is located, the exon number of the exon sequence, the chromosome number of the exon sequence, the starting position and the ending position of the K-mer fragment on the chromosome; the starting position of the K-mer fragment on the chromosome is equal to the total distance of the exon sequence on the chromosome plus the window sliding; the ending position of the K-mer fragment on the chromosome is the starting position plus the length of the sgRNA containing the PAM motif; the written Python script is the first exon sequence obtained from step (1). A window scan is started from one base, and each time the window unit is cut with the length of the sgRNA containing the PAM motif, until the last base of the sequence is covered, each window sliding will generate a K-mer fragment of the required length of the sgRNA containing the PAM motif, and the description information of the above K-mer fragment is created according to the exon sequence description information in step (1); in step (6), the sgRNA database is the position information, base sequence and statistical number of comparisons with the whole genome of the K-mer fragment, and the K-mer fragment description information in the sgRNA database is indexed. The indexing of the present invention can improve the query speed of the sgRNA database.

[0076] In step 6, as a preferred embodiment, if the input target gene has alleles, the output directory contains 8 files, namely, the allele ID file (recorded as 01.Gene_id.txt), the allele bed information file (recorded as 02.Gene_bed.txt), the allele sequence file (recorded as 03.Gene_sequence.fasta), the allele CDS sequence file (recorded as 04.Gene_cds_sequence.fasta), the Muscle multiple sequence alignment result file (recorded as 05.Muscle_multiple_sequence_alignment.fasta), the alignment result file in phylip format (recorded as 06.Muscle2phylip.phy), the candidate sgRNA file (recorded as 07.Candidate_sgRNAs.fasta) and the final designed sgRNA file (recorded as 08.sgRNA_primer_result.txt). If the input target gene does not have alleles, the output directory contains 6 files, namely the target gene ID file (recorded as 01.Gene_id.txt), the target gene bed information file (recorded as 02.Gene_bed.txt), the allele sequence file (recorded as 03.Gene_sequence.fasta), the target gene CDS sequence file (recorded as 04.Gene_cds_sequence.fasta), the candidate sgRNA file (recorded as 05.Candidate_sgRNAs.fasta) and the final designed sgRNA file (recorded as 06.sgRNA_primer_result.txt).

[0077] In the present invention, the default parameters can be used when using the above-mentioned Muscle tool to perform sequence alignment on alleles.

[0078] The present invention provides a computer-readable storage medium, which is used to store computer instructions, programs, code sets or instruction sets. When the computer-readable storage medium is run on a computer, the computer executes the above-mentioned method for rapidly designing target gene sgRNA in large quantities.

[0079] The present invention provides 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 to implement the above-mentioned method for rapidly and massively designing target gene sgRNA.

[0080] The present invention provides an sgRNA obtained according to the above method.

[0081] In the present invention, as an embodiment, the nucleotide sequence of the sgRNA is shown in SEQ ID NO.1; the sgRNA is the sgRNA of the alfalfa gene MsPALM1. The gene MsPALM1 encodes a Cys-2 / His-2 zinc finger transcription factor, which plays a key role in compound leaf morphogenesis. When the gene is knocked out and silenced, alfalfa has a multi-leaf phenotype instead of the original three-leaf phenotype, and the multi-leaf phenotype can significantly increase the yield of alfalfa.

[0082] The present invention provides a CRISPR / Cas9 system, wherein the above-mentioned sgRNA is connected to the pYLCRISPR / Cas9P35S-H backbone vector. As one embodiment, the method for constructing the CRISPR / Cas9 system comprises the following steps: a. using BsrGⅠ and AhdⅠ enzymes to digest the pYLCRISPR / Cas9P35S-H backbone vector to obtain an expression vector after digestion; b. connecting the sgRNA to the expression vector after digestion to obtain the CRISPR / Cas9 system. The pYLCRISPR / Cas9P35S-H backbone vector was purchased from Addgene, and the item number is Plasmid#66189.

[0083] The present invention provides an application of the above-mentioned sgRNA or CRISPR / Cas9 system in target gene editing or studying target gene function.

[0084] The method of the present invention can quickly obtain sgRNA, and the sgRNA has high editing efficiency and good specificity for the target gene. The method of the present invention for quickly designing target gene sgRNA in large quantities is also applicable to the design of sgRNA for polyploid species.

[0085] In the present invention, unless otherwise specified, all raw material components are commercially available products well known to those skilled in the art.

[0086] The technical solutions provided by the present invention are described in detail below in conjunction with the embodiments, but they should not be construed as limiting the protection scope of the present invention.

[0087] Example 1

[0088] A method for rapidly designing sgRNAs for target genes in large quantities, such as Figure 1 , which is a flow chart of the method for rapid and large-scale design of sgRNAs of the present invention.

[0089] The specific operation steps of the method for rapidly designing target gene sgRNA in large quantities are as follows:

[0090] Step 1: Construction of sgRNA database, allele database and bed files of all genes of the species

[0091] 1.1. The construction method of sgRNA database is as follows:

[0092] The exon sequences of all genes were extracted based on the whole genome sequence and annotation files of the target species. The exon sequence description information included the gene ID, exon number, chromosome number, the starting and ending positions of the exon sequence on the chromosome, and the positive and negative chains.

[0093] The exon sequences of all genes in step (1) were respectively cut into K-mer fragments of the required length of sgRNA containing PAM motif (the length of sgRNA containing PAM motif is 19 to 25 nt) using the Jellyfish tool, and redundancy was removed to obtain K-mer fragments after redundancy removal.

[0094] The de-redundant K-mer fragments obtained in step (2) were aligned with the whole genome sequence of the target species using the batmap tool, and the number of positions where each K-mer fragment was aligned to the whole genome was counted. The maximum number of mismatched bases (-n) was set to 4, and the maximum number of alignments (-m) was set to all. The parallel operation method was used to improve the alignment efficiency, and the statistical results of the alignment between the K-mer fragment and the whole genome were obtained.

[0095] The exon sequence obtained in step (1) is cut into K-mer fragments of the required length of sgRNA containing PAM motif (the length range is 19 to 25 nt) using the prepared Python script, wherein the description information of the K-mer fragment is the gene ID of the exon sequence where the K-mer fragment is located, the exon number of the exon sequence, the chromosome number of the exon sequence, the starting position and the ending position of the K-mer fragment on the chromosome; the starting position of the K-mer fragment on the chromosome is equal to the total distance of the exon sequence on the chromosome plus the window sliding; the ending position of the K-mer fragment on the chromosome is the starting position plus the length of the sgRNA containing PAM motif, and the K-mer fragment cut by the prepared Python script is obtained.

[0096] The Python script is written to perform window scanning starting from the first base of the exon sequence obtained in step (1), and each time the window is cut with the length of the sgRNA containing the PAM motif as the window unit until the last base of the sequence is covered. Each time the window slides, a K-mer fragment of the required length of the sgRNA containing the PAM motif is generated, and the description information of the above K-mer fragment is created according to the exon sequence description information in step (1);

[0097] The statistical results of the above-mentioned K-mer fragments and the whole genome alignment and the correspondence between the K-mer fragments cut by the written Python script are used to align the position number and fragment description information of the whole genome of the species to construct an sgRNA database, wherein the sgRNA database is the position information, base sequence and statistical number of alignments with the whole genome of the K-mer fragments, and an index is constructed for the K-mer fragment description information in the sgRNA database to improve the query speed of the database, thereby obtaining the sgRNA database.

[0098] 1.2. The construction method of the allele database is as follows:

[0099] (S1) Dividing the species genome annotation file according to different homologous chromosomes to obtain divided homologous chromosome annotation files, extracting CDS sequences using the divided homologous chromosome annotation files and the whole genome sequence, and then translating the CDS sequences into amino acid sequences.

[0100] (S2) using the diamond tool to align the amino acid sequences obtained in step (S1), wherein the parameter e-value threshold is set to 1e-10, and the maximum number of aligned sequences (-k) is set to 5, to obtain an alignment result, and the alignment output file format is a table. The above alignment results are filtered based on the amino acid sequence consistency value greater than 80 as a threshold to obtain amino acid sequences with a sequence consistency value greater than 80, and the amino acid sequences are saved in a file with a suffix of blast to obtain a file with a suffix of blast.

[0101] (S3) Extracting the chromosome number, gene ID, and the starting and ending position information of the gene on the chromosome from the annotation file of the divided homologous chromosomes obtained in step (S1) to form a file with a suffix of gff, wherein the composed gff file is separated by tabs, the first column is the chromosome number, the second column is the gene ID, the third column is the starting position information of the gene on the chromosome, and the fourth column is the ending position information of the gene on the chromosome, to obtain a file with a suffix of gff.

[0102] (S4) using the MCScanX tool to identify alleles of the divided homologous chromosome genes, wherein the file with the suffix blast in step (S2) and the file with the suffix gff in step (S3) are used as input files, the number of genes required for calling the collinear block (-s) is set to 10, the maximum allowed gaps (-m) is set to 45, and the output result file is in html format to obtain an html format file.

[0103] (S5) Convert the HTML format file obtained in step (S4) into a tab-delimited text, remove redundancy and merge the alleles of homologous chromosomes, and construct a species allele database, in which each row is a group of alleles, to obtain an allele database.

[0104] 1.3. Construction of bed files for all genes of a species:

[0105] The chromosome number, gene ID, the starting and ending positions of the gene on the chromosome, and the positive and negative strand information of the chromosome where the gene is located are extracted from the species genome annotation file to obtain the bed file of all genes of the species.

[0106] Step 2. First, enter the necessary parameters: the necessary parameters are the gene ID of the target gene sgRNA, the nuclease Cas9 used, the sgRNA length containing the PAM motif is 23 nt, the allele database, the bed files of all genes of the species, the whole genome sequence of the species, all CDS sequences of the species, and the sgRNA database.

[0107] Step 3: Search the allele database to see if there is an allele for the input target gene. If there is an allele, extract the gene sequence and CDS sequence of the allele according to the bed information and gene ID of the allele. If there is no allele for the input target gene, extract the gene sequence and CDS sequence of the target gene according to the bed information and gene ID of the input target gene.

[0108] Step 4: If the input target gene has alleles, the extracted allele sequences are aligned using the Muscle tool to identify the conserved sequences, obtain the output results of the Muscle alignment, and convert the output results of the Muscle alignment into text in Phylip format to obtain the alignment results in Phylip format, and then the conserved sequences are cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse complemented K-mer fragments are used as candidate sgRNAs. If the input target gene does not have alleles, the target gene sequence is directly cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse complemented K-mer fragments are used as candidate sgRNAs.

[0109] Step 5, according to the PAM motif of Cas9 nuclease, the PAM motif is NGG, where N represents any one of the four bases A, T, C, and G, the candidate sgRNA in step 4 is screened, and if the three bases at the 3' end of the candidate sgRNA sequence are any one of AGG, TGG, CGG, and GGG, the screened sgRNA that meets the Cas9 nuclease is obtained.

[0110] Step 6: Use the sgRNA database to evaluate the specificity of the sgRNA screened in step 5, sort the sgRNA evaluation results from good to bad according to their specificity, and obtain the gene and exon information targeted by the sgRNA and the percentage content of the bases "G" and "C" in the sgRNA to obtain the final designed sgRNA.

[0111] Step 7, output the results of the above steps 2 to 6 to the corresponding result files, and create a Readme file in the output directory to explain each output file in detail. If the input target gene has alleles, the output directory contains 8 files, namely, the allele ID file (01.Gene_id.txt), the allele bed information file (02.Gene_bed.txt), the allele sequence file (03.Gene_sequence.fasta), the allele CDS sequence file (04.Gene_cds_sequence.fasta), the Muscle multiple sequence alignment result file (05.Muscle_multiple_sequence_alignment.fasta), the phylip format alignment result file (06.Muscle2phylip.phy), the candidate sgRNA file (07.Candidate_sgRNAs.fasta) and the final designed sgRNA file (08.sgRNA_primer_result.txt). If the input target gene does not have alleles, the output directory contains 6 files, namely the target gene ID file (01.Gene_id.txt), the target gene bed information file (02.Gene_bed.txt), the target gene sequence file (03.Gene_sequence.fasta), the target gene CDS sequence file (04.Gene_cds_sequence.fasta), the candidate sgRNA file (05.Candidate_sgRNAs.fasta) and the final designed sgRNA file (06.sgRNA_primer_result.txt).

[0112] Example 2

[0113] A method for rapidly designing target gene sgRNA in large quantities, the specific steps are as follows:

[0114] Step 1: Construction of sgRNA database, allele database and bed files of all genes of the species

[0115] 1.1. The construction method of sgRNA database is as follows:

[0116] (1) Based on the whole genome sequence and annotation file of the target species, the exon sequences of all genes are extracted. The exon sequence description information includes the gene ID where the exon sequence is located, the exon number, the chromosome number, the starting and ending positions of the exon sequence on the chromosome, and the positive and negative chains.

[0117] (2) Using the Jellyfish tool, the exon sequences of all genes in step (1) are respectively cut into K-mer fragments of the required length of sgRNA containing the PAM motif (the length of the sgRNA containing the PAM motif is 19 to 25 nt), and redundancy is removed to obtain K-mer fragments after redundancy removal.

[0118] (3) Use the batmap tool to align the de-redundant K-mer fragments obtained in step (2) with the whole genome sequence of the target species, and count the number of positions where each K-mer fragment is aligned to the whole genome. The maximum number of mismatched bases (-n) is set to 4, and the maximum number of alignments (-m) is set to all. The parallel operation method is used to improve the alignment efficiency, and the statistical results of the alignment between the K-mer fragment and the whole genome are obtained.

[0119] (4) Using the prepared Python script, the exon sequence obtained in step (1) is cut into K-mer fragments of the required length of the sgRNA containing the PAM motif (the length range is 19 to 25 nt), wherein the description information of the K-mer fragment is the gene ID of the exon sequence where the K-mer fragment is located, the exon number of the exon sequence, the chromosome number of the exon sequence, and the starting position and ending position of the K-mer fragment on the chromosome; the starting position of the K-mer fragment on the chromosome is equal to the total distance of the exon sequence on the chromosome plus the window sliding; the ending position of the K-mer fragment on the chromosome is the starting position plus the length of the sgRNA containing the PAM motif, and the K-mer fragment cut by the prepared Python script is obtained.

[0120] The Python script is written to perform window scanning starting from the first base of the exon sequence obtained in step (1), and each time the window is cut with the length of the sgRNA containing the PAM motif as the window unit until the last base of the sequence is covered. Each time the window slides, a K-mer fragment of the required length of the sgRNA containing the PAM motif is generated, and the description information of the above K-mer fragment is created according to the exon sequence description information in step (1);

[0121] (5) Using the statistical results of the above-mentioned K-mer fragments and the whole genome alignment and the correspondence between the K-mer fragments cut by the written Python script, the position number and fragment description information of the whole genome of the species are aligned to construct an sgRNA database, wherein the sgRNA database is the position information, base sequence and statistical number of alignments with the whole genome of the K-mer fragments, and an index is constructed for the K-mer fragment description information in the sgRNA database to improve the query speed of the database, thereby obtaining the sgRNA database.

[0122] 1.2. The construction method of the allele database is as follows:

[0123] (S1) Dividing the species genome annotation file according to different homologous chromosomes to obtain divided homologous chromosome annotation files, extracting CDS sequences using the divided homologous chromosome annotation files and the whole genome sequence, and then translating the CDS sequences into amino acid sequences.

[0124] (S2) using the diamond tool to align the amino acid sequences obtained in step (S1), wherein the parameter e-value threshold is set to 1e-10, and the maximum number of aligned sequences (-k) is set to 5, to obtain an alignment result, and the alignment output file format is a table. The above alignment results are filtered based on the amino acid sequence consistency value greater than 80 as a threshold to obtain amino acid sequences with a sequence consistency value greater than 80, and the amino acid sequences are saved in a file with a suffix of blast to obtain a file with a suffix of blast.

[0125] (S3) Extracting the chromosome number, gene ID, and the starting and ending position information of the gene on the chromosome from the annotation file of the divided homologous chromosomes obtained in step (S1) to form a file with a suffix of gff, wherein the composed gff file is separated by tabs, the first column is the chromosome number, the second column is the gene ID, the third column is the starting position information of the gene on the chromosome, and the fourth column is the ending position information of the gene on the chromosome, to obtain a file with a suffix of gff.

[0126] (S4) using the MCScanX tool to identify alleles of the divided homologous chromosome genes, wherein the file with the suffix blast in step (S2) and the file with the suffix gff in step (S3) are used as input files, the number of genes required for calling the collinear block (-s) is set to 10, the maximum allowed gaps (-m) is set to 45, and the output result file is in html format to obtain an html format file.

[0127] (S5) Convert the HTML format file obtained in step (S4) into a tab-delimited text, remove redundancy and merge the alleles of homologous chromosomes, and construct a species allele database, in which each row is a group of alleles, to obtain an allele database.

[0128] 1.3. Construction of bed files for all genes of a species:

[0129] The chromosome number, gene ID, the starting and ending positions of the gene on the chromosome, and the positive and negative strand information of the chromosome where the gene is located are extracted from the species genome annotation file to obtain the bed file of all genes of the species.

[0130] Step 2. First, enter the necessary parameters: the necessary parameters are the gene ID of the designed sgRNA, the nuclease TnpB used, the sgRNA length containing the PAM motif is 25 nt, the allele database, the bed files of all genes of the species, the whole genome sequence of the species, all CDS sequences of the species, and the sgRNA database.

[0131] Step 3: Search the allele database for the input target gene ID to see if there is an allele. If there is an allele, use the bedtools tool to extract the gene sequence of the allele according to the bed information of the allele, and use the faSomeRecords tool to extract the CDS sequence of the allele according to the allele ID. If there is no allele for the input target gene, use the bedtools tool to extract the gene sequence of the target gene according to the bed information of the input target gene, and use the faSomeRecords tool to extract the CDS sequence of the target gene according to the gene ID of the target gene.

[0132] Step 4: If the input target gene has alleles, the extracted allele sequences are aligned using the Muscle tool to identify the conserved sequences, obtain the output results of the Muscle alignment, and convert the output results of the Muscle alignment into text in Phylip format to obtain the alignment results in Phylip format, and then the conserved sequences are cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse complemented K-mer fragments are used as candidate sgRNAs. If the input target gene does not have alleles, the target gene sequence is directly cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse complemented K-mer fragments are used as candidate sgRNAs.

[0133] Step 5, according to the PAM motif of TnpB nuclease, the PAM motif is one of TTTAA and TTGAT, the candidate sgRNA obtained in step 4 is screened, if the 5 bases at the 5' end of the candidate sgRNA obtained in step 4 are any one of TTTAA and TTGAT, then the sgRNA meets the requirements of the PAM motif of nuclease TnpB, and the screened sgRNA is obtained.

[0134] Step 6: Use the sgRNA database to evaluate the specificity of the screened sgRNA obtained in step 5, sort the sgRNA evaluation results from good to bad according to their specificity, and obtain the gene and exon information targeted by the sgRNA and the percentage content of the bases "G" and "C" in the sgRNA to obtain the final designed sgRNA.

[0135] Step 7, output the results of the above steps 2 to 6 to the corresponding result files, and create a Readme file in the output directory to explain each output file in detail. If the input target gene has alleles, the output directory contains 8 files, namely, the allele ID file (01.Gene_id.txt), the allele bed information file (02.Gene_bed.txt), the allele sequence file (03.Gene_sequence.fasta), the allele CDS sequence file (04.Gene_cds_sequence.fasta), the Muscle multiple sequence alignment result file (05.Muscle_multiple_sequence_alignment.fasta), the phylip format alignment result file (06.Muscle2phylip.phy), the candidate sgRNA file (07.Candidate_sgRNAs.fasta) and the final designed sgRNA file (08.sgRNA_primer_result.txt). If the input target gene does not have alleles, the output directory contains 6 files, namely the target gene ID file (01.Gene_id.txt), the target gene bed information file (02.Gene_bed.txt), the target gene sequence file (03.Gene_sequence.fasta), the target gene CDS sequence file (04.Gene_cds_sequence.fasta), the candidate sgRNA file (05.Candidate_sgRNAs.fasta) and the final designed sgRNA file (06.sgRNA_primer_result.txt).

[0136] Application Example 1

[0137] Taking tetraploid alfalfa as an example, the method for rapid and large-scale design of target gene sgRNA in Example 1 was used to design and specifically evaluate the sgRNA of the target gene of the tetraploid alfalfa:

[0138] First, construct a tetraploid alfalfa allele database:

[0139] (S1) dividing the alfalfa genome annotation file into 8 annotation files according to homologous chromosomes to obtain divided homologous chromosome annotation files, extracting CDS sequences according to the divided homologous chromosome annotation files and the whole genome sequence, and translating the CDS sequences into amino acid sequences.

[0140] (S2) Using the diamond tool to align the amino acid sequences obtained in step (S1), and filtering with an amino acid sequence identity greater than 80 as a threshold, to obtain amino acid sequences with a sequence identity greater than 80, and saving the results in a file with a suffix of blast to obtain a file with a suffix of blast.

[0141] (S3) Extracting the chromosome number, gene ID, and the starting and ending position information of the gene on the chromosome from the divided homologous chromosome annotation file obtained in step (S1) to form a file with a suffix of gff, wherein the composed gff file is separated by a tab character, the first column is the chromosome number, the second column is the gene ID, the third column is the starting position information of the gene on the chromosome, and the fourth column is the ending position information of the gene on the chromosome, to obtain a file with a suffix of gff.

[0142] (S4) using the MCScanX tool to identify alleles of homologous chromosome genes divided in alfalfa, wherein the file with the suffix blast in step (S2) and the file with the suffix gff in step (S3) are used as input files, the number of genes required for calling the collinear block (-s) is set to 10, the maximum allowed gaps (-m) is set to 45, and the output result file format is html, thereby obtaining an html format file.

[0143] (S5) Convert the html format file into a tab-delimited text, remove redundancy and merge the alleles of homologous chromosomes, and construct a species allele database, in which each row is a group of alleles, to obtain an alfalfa allele database.

[0144] Among them, the alfalfa allele database can be found in Figure 2 .

[0145] Secondly, construct the bed file of all genes of alfalfa: extract the chromosome number, gene ID, the start and end positions of the gene on the chromosome, and the positive and negative strand information of the chromosome where the gene is located from the alfalfa genome annotation file to form a bed file of all genes of alfalfa. The bed file of all genes of alfalfa can be found in Figure 3 .

[0146] Next, construct the alfalfa sgRNA database:

[0147] (1) Based on the whole genome sequence and annotation file of alfalfa, the exon sequences of all genes of alfalfa were extracted (see Figure 4 ), the exon sequence description information includes the gene ID where the exon sequence is located, the exon number, the chromosome number, the starting position and ending position of the exon sequence on the chromosome, and the positive and negative chains.

[0148] (2) Using the Jellyfish tool, the exon sequences of all genes in step (1) were cut into 23 nt K-mer fragments and de-redundant to obtain de-redundant K-mer fragments (see Figure 5 ).

[0149] (3) The de-redundant K-mer fragments obtained in step (2) were aligned with the whole genome of alfalfa using the batmap tool, and the number of positions where each K-mer fragment was aligned to the whole genome was counted. The maximum number of mismatched bases (-n) was set to 4, and the maximum number of alignments (-m) was set to all. The parallel operation method was used to improve the alignment efficiency, and the statistical results of the alignment between the K-mer fragment and the whole genome were obtained.

[0150] (4) Using the Python script written in Example 1, the above-mentioned alfalfa exon sequence was cut into 23 nt K-mer fragments, wherein the K-mer fragment description information needs to be the gene ID, exon number, chromosome number, start position and end position on the chromosome of the K-mer fragment, and the K-mer fragment cut by the Python script was obtained (see Figure 6 ).

[0151] (5) Using the statistical results of the above-mentioned K-mer fragments and the whole genome alignment and the correspondence between the K-mer fragments cut by the written Python script, the position number and fragment description information of the whole genome of alfalfa are aligned to construct an sgRNA database, wherein the sgRNA database is the position information, base sequence and statistical number of alignments with the whole genome of the K-mer fragments, and an index is constructed for the K-mer fragment description information in the sgRNA database to improve the query speed of the database, thereby obtaining the sgRNA database.

[0152] The alfalfa sgRNA database is a sub-database, which is a sgRNA database with a K-mer fragment length of 23 nt (see Figure 7 ).

[0153] Then, sgRNA design was performed for alfalfa MsPALM1 (MS51G4442v1): the design was performed according to the method described in step 2 to step 7 of Example 1. The Python logic code for executing the method described in Example 1 is shown in Figure 8 .

[0154] Fig. 9 The results show that it takes a total of 3.08 seconds to design sgRNA for the gene MsPALM1 (MS51G4442v1) using the method for rapid and large-scale design of target gene sgRNA of the present invention.

[0155] In this example, the sgRNA sequence of the gene MsPALM1 (MS51G4442v1) obtained by the method described in Example 1 is GGAGACGAGCACGGTCGCGGCGG (SEQ ID NO. 1), and the sgRNA can knock out and silence MsPALM1 (MS51G4442v1).

[0156] Evaluation of specificity and editing efficiency of sgRNA with nucleotide sequence as shown in SEQ ID NO.1:

[0157] The pYLCRISPR / Cas9P35S-H vector was digested with endonucleases BsrGⅠ and AhdⅠ to obtain an expression vector after digestion, and the sgRNA with a nucleotide sequence as shown in SEQ ID NO.1 was connected to the expression vector after digestion to obtain a pYLCRISPR / Cas9P35S-H-sgRNA plasmid, i.e., a recombinant plasmid. The recombinant plasmid was transformed into Ar1193 Agrobacterium tumefaciens, and then the plasmid was extracted and sequenced to verify whether it was a positive Agrobacterium tumefaciens. The sgRNA of the MsPALM1 gene was inserted into the cotyledons of the seedlings containing the hypocotyls of the alfalfa variety "Aohan" by the positive Agrobacterium tumefaciens-mediated transformation method, and the cotyledons of the seedlings were placed on a co-culture medium and co-cultured at 25°C in the dark for 3 days. Then, 15 cotyledons of the seedlings after co-culture were selected and placed on a hairy root induction medium, and hairy roots were induced under light conditions at 25°C. After the hairy roots grew out, 5 hairy root samples with good growth (26-2, 26-4, 26-8, 26-10 and 26-12) were selected for NGS sequencing. Based on the NGS sequencing data, the total number of reads and the number of insertion and deletion reads of the sgRNA designed by the present invention at its potential off-target position and the actual target position were counted respectively. The specificity and editing efficiency of the sgRNA were evaluated by the ratio of the number of insertion and deletion reads to the total number of reads. The results are shown in Tables 1 and 2.

[0158] Table 1 sgRNA off-target evaluation of gene MsPALM1

[0159]

[0160] Table 2 Evaluation of gene editing efficiency of gene MsPALM1

[0161] sample Total reads Insert reads Delete reads Editing efficiency 26-2 46977 19412 14538 72.26％ 26-4 47654 16984 21826 81.44％ 26-8 74682 21267 36962 77.96％ 26-10 74375 25280 33709 79.31％ 26-12 26490 4144 21979 98.61％

[0162] The results in Table 1 show that no reads editing occurred at the potential off-target positions of the gene MsPALM1 in the five hairy root samples, indicating that the sgRNA designed in the present invention has good specificity.

[0163] The results in Table 2 show that the editing efficiency of the gene MsPALM1 in the five hairy root samples was 72.26% to 98.61%, indicating that the sgRNA designed in the present invention has a high editing efficiency.

[0164] The present invention designs a method for rapidly and massively designing target gene sgRNA, which solves the problem that existing online sgRNA design tools are limited by the genomic information included in their databases and cannot efficiently design and specifically evaluate sgRNA for alleles, polyploid species and large quantities of target genes.

[0165] 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 rapidly designing target gene sgRNA in large quantities, characterized in that: The following steps are involved: Step 1, input necessary parameters, which include gene ID, nuclease, length of sgRNA containing PAM motif, whole genome sequence of species, CDS sequence of all species, bed files of all genes of species, allele database and sgRNA database for designing target gene sgRNA; Step 2: Search the allele database to see if the input target gene has an allele. If the allele exists, extract the gene sequence and CDS sequence of the allele according to the bed information and gene ID of the allele; if the input target gene does not have an allele, extract the gene sequence and CDS sequence of the target gene according to the bed information and gene ID of the input target gene; Step 3, if the input target gene has alleles, the extracted allele sequences are aligned using the Muscle tool to identify the conserved sequences, obtain the output results of the Muscle alignment, and convert the output results of the Muscle alignment into text in Phylip format to obtain the alignment results in Phylip format, and then the conserved sequences are cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse-complemented K-mer fragments are used as candidate sgRNAs; if the input target gene does not have alleles, the target gene sequence is directly cut into K-mer fragments according to the input sgRNA length parameters, and the K-mer fragments are reverse complemented, and the original K-mer fragments and the reverse-complemented K-mer fragments are used as candidate sgRNAs; Step 4, screening the candidate sgRNAs in step 3 according to the PAM motif of the nuclease to obtain the screened sgRNAs that meet the nuclease requirements; Step 5: Use the sgRNA database to evaluate the specificity of the sgRNA screened in step 4, sort the sgRNA evaluation results from best to worst according to their specificity, and obtain the gene and exon information targeted by the sgRNA and the percentage content of the bases "G" and "C" in the sgRNA to obtain the final designed sgRNA; Step 6: Output the results of Step 2 to Step 5 to the corresponding result files. At the same time, create a Readme file in the output directory to provide detailed descriptions of each output file.

2. The method according to claim 1, characterized in that In step 1, the nuclease is Cas9 nuclease or TnpB nuclease; the length of the sgRNA containing the PAM motif is 19 to 25 nt; In step 1, the method for constructing the bed file of all genes of the species comprises the following steps: extracting the chromosome number, gene ID, the starting position and the ending position of the gene on the chromosome, and the positive and negative chain information of the chromosome where the gene is located from the species genome annotation file to obtain the bed file of all genes of the species; In step 1, the method for constructing the allele database comprises the following steps: (S1) dividing the species genome annotation file according to different homologous chromosomes to obtain divided homologous chromosome annotation files, extracting CDS sequences using the divided homologous chromosome annotation files and the whole genome sequence, and then translating the CDS sequences into amino acid sequences; (S2) using the diamond tool to align the amino acid sequences obtained in step (S1) to obtain an alignment result, filtering the alignment result with an amino acid sequence identity value greater than 80 as a threshold, obtaining amino acid sequences with a sequence identity value greater than 80, and saving the results into a file with a suffix of blast to obtain a file with a suffix of blast; (S3) extracting the chromosome number, gene ID, and the starting and ending position information of the gene on the chromosome from the annotation file of the divided homologous chromosomes obtained in step (S1) to obtain a file with a suffix of gff; (S4) using the MCScanX tool to identify alleles of the divided homologous chromosome genes, wherein the file with the suffix blast in step (S2) and the file with the suffix gff in step (S3) are used as input files, the number of genes required for calling the collinear block is set to 10, the maximum allowed gaps is set to 45, and the output result file is in html format, thereby obtaining an html format file; (S5) converting the HTML format file obtained in step (S4) into a tab-delimited text, and removing redundancy and merging the alleles of homologous chromosomes to construct a species allele database, wherein each row is a group of alleles, thereby obtaining an allele database; In step 1, the method for constructing the sgRNA database comprises the following steps: (1) Extract the exon sequences of all genes based on the whole genome sequence and annotation files of the target species. The exon sequence description information includes the gene ID, exon number, chromosome number, the starting and ending positions of the exon sequence on the chromosome, and the positive and negative chains; (2) using the Jellyfish tool to cut the exon sequences of all genes in step (1) into K-mer fragments of the required length of the sgRNA containing the PAM motif, and perform redundancy removal to obtain the K-mer fragments after redundancy removal; (3) Using the batmap tool, the de-redundant K-mer fragments obtained in step (2) are aligned with the whole genome sequence of the target species, and the number of positions where each K-mer fragment is aligned to the whole genome is counted, where the maximum number of mismatched bases is set to 4 and the maximum number of alignments is set to all. The parallel operation method is used during the alignment to obtain the statistical results of the alignment between the K-mer fragment and the whole genome; (5) using the prepared Python script to cut the exon sequence obtained in step (1) into K-mer fragments of the required length of the sgRNA containing the PAM motif, and obtain the K-mer fragments cut by the prepared Python script; (6) Using the statistical results of the K-mer fragments and the whole genome alignment and the correspondence between the K-mer fragments cut by the Python script, the number of positions and fragment description information of the whole genome of the species are aligned to obtain the sgRNA database.

3. The method according to claim 2, characterized in that In step (S2), the parameter e-value threshold of the comparison is set to 1e-10, and the maximum number of sequences to be compared is set to 5; In step (S3), the suffix is ​​gff file separated by tabs, the first column is the chromosome number, the second column is the gene ID, the third column is the starting position information of the gene on the chromosome, and the fourth column is the ending position information of the gene on the chromosome; In step (2) or step (5), the length of the sgRNA containing the PAM motif is 19 to 25 nt; In step (5), the description information of the K-mer fragment is the gene ID of the exon sequence where the K-mer fragment is located, the exon number of the exon sequence, the chromosome number of the exon sequence, the starting position and the ending position of the K-mer fragment on the chromosome; the starting position of the K-mer fragment on the chromosome is equal to the total distance of the exon sequence on the chromosome plus the window sliding; the ending position of the K-mer fragment on the chromosome is the starting position plus the length of the sgRNA containing the PAM motif; In step (5), the written Python script starts window scanning from the first base of the exon sequence obtained in step (1), and cuts each time with the length of the sgRNA containing the PAM motif as the window unit until the last base of the sequence is covered. Each window sliding will generate a K-mer fragment of the required length of the sgRNA containing the PAM motif, and create the description information of the above K-mer fragment according to the exon sequence description information in step (1); In step (6), the sgRNA database contains the location information, base sequence and statistical number of comparisons with the whole genome of the K-mer fragment, and constructs an index for the K-mer fragment description information in the sgRNA database.

4. A computer-readable storage medium, characterized in that: The computer storage medium is used to store computer instructions, programs, code sets or instruction sets, which, when run on a computer, enable the computer to execute the method for rapid and large-scale design of target gene sgRNA according to any one of claims 1 to 3.

5. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory is used to store a program; and the processor is used to run the stored program to implement the method for rapidly designing target gene sgRNA in large quantities as described in any one of claims 1 to 3.

6. An sgRNA obtained according to the method according to any one of claims 1 to 3.

7. The sgRNA according to claim 6, characterized in that The nucleotide sequence of the sgRNA is shown in SEQ ID NO.1; the sgRNA is the sgRNA of the alfalfa gene MsPALM1.

8. A CRISPR / Cas9 system, characterized in that: The sgRNA according to claim 6 or 7 is connected to the pYLCRISPR / Cas9P35S-H backbone vector.

9. Use of the sgRNA according to claim 6 or 7 or the CRISPR / Cas9 system according to claim 8 in target gene editing or studying target gene function.

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