Method for detecting insertion site of agrobacterium T-DNA on plant genome
By generating the merged genome and performing sequence alignment and classification processing, combining secondary alignment and blacklist filtering, the problem of insufficient sensitivity and false positive risk of detection of Agrobacterium T-DNA insertion site in the prior art is solved, and the problem of insufficient sensitivity and false positive risk of detection of Agrobacterium T-DNA insertion site in the prior art is achieved, and the High detection sensitivity and accuracy.
Patent Information
- Application Number
- CN202411436645.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-15
- Publication Date
- 2025-02-07
AI Technical Summary
The existing second-generation sequencing method is insufficient in detecting the insertion sites of Agrobacterium T-DNA on the plant genome, and it is difficult to accurately locate in the repeating sequence, and it is easy to introduce false positives when the reads originate from the repeating region. There may be a problem in the T-DNA sequence that the target plant origin leads to the risk of false positives.
By combining the T-DNA genome of the labeled vector and the plant genome sequence of the plant host, the merged genome is generated, and the reads are aligned onto the merged genome using sequence alignment software, and SAM files with softclip and softclip are classified and processed, and the SAM files without softclip are performed. Quadratic comparison and similarity scoring, combined with blacklist filtering and repeated region marking, output the number of final integration sites and real integration events.
It improves the sensitivity of insertion site detection, enhances the accuracy of T-DNA insertion position in repeat sequences, reduces the risk of false positives, and improves the credibility and accuracy of the detection.
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Abstract
Description
Technical Field
[0001] The present invention relates to the technical field related to second generation sequencing data analysis in the field of bioinformatics, and specifically to a method for detecting an insertion site of Agrobacterium T-DNA in a plant genome. Background Art
[0002] Carrying out molecular breeding innovation of crop germplasm resources is an important measure to improve the practice of modern seed industry engineering, and the biosafety risks brought by modern molecular breeding cannot be ignored. Therefore, the prevention and control of biosafety risks brought by molecular breeding and the construction of a governance system are one of the four key development areas in the plan.
[0003] T-DNA is the transfer DNA in the Ti-plasmid of Agrobacterium. It is a type of vector commonly used in plant molecular breeding technology. It is used to carry exogenous DNA fragments and integrate them into the plant host genome by insertion. When the integration vector is integrated into a certain site of the host genome, it may affect the normal function of genes near the site. In particular, when there are key genes near the integration site, the integration event may cause cell function disorders, thereby affecting the stable inheritance and safety of genetically modified crops.
[0004] The existing effective method for detecting T-DNA insertion sites is to directional amplify the t-DNA border sequence at the junction of the T-DNA insertion sequence and the plant genome, and then perform high-throughput sequencing on the amplified junction DNA sequence. These junction DNA sequences are theoretically sequence fragments where the plant genome and t-DNA are connected, so they contain both plant genome sequence fragments and t-DNA sequence fragments. Finally, the location of the plant genome source sequence is analyzed by bioinformatics methods to determine the insertion position of the t-DNA.
[0005] At present, the second-generation sequencing-based method only considers finding reads with both T-DNA and host genome sequences at one end in the read segment. In actual detection, if the sequence segment is too long, the double-end sequencing will be completely mapped to the vector and host genome respectively ( Figure 1 ), and this type of reads also has a certain indicative effect on the insertion site, but the current bioinformatics methods do not take this type of reads into account, which has a certain detrimental effect on the detection sensitivity.
[0006] Plant genomes usually contain a large number of repetitive sequences. For example, the maize genome contains about 85% of its total genome length. Among them, there are not only abundant short tandem repeat sequences, but also 45S rDNA repeat sequences as long as 9349bp. If the t-DNA integration event occurs in the repetitive region of the above genome, it is difficult to determine the true location of the t-DNA integration on the genome using the current bioinformatics method based on short sequence alignment, which brings certain challenges to the accurate positioning of the integration event.
[0007] In addition, the T-DNA vector contains various transcriptional regulatory elements and target gene fragments. In practical applications, in order to make the target gene efficiently expressed in plant cells, the above-mentioned gene elements are usually derived from plant host cells. In 2022, Shaofang Li and colleagues from the Chinese Academy of Agricultural Sciences explored the integration patterns of a large number of T-DNAs and found that the integration patterns of T-DNA in plant genomes are diverse. In addition to normal single-copy T-DNA insertions with clear boundaries, T-DNA also exists in the form of tandem duplications, reverse splicing, partial truncation, or partial truncation + tandem duplications. Therefore, in abnormal insertion events, the T-DNA boundaries are not necessarily connected to the plant genome, but to part of the sequence of the vector itself. If some elements of the vector are derived from host cells, a false positive phenomenon will occur in which the T-DNA boundaries are connected to DNA derived from the host cell. At present, there is no bioinformatics method to judge such events.
[0008] In summary, the current methods based on second-generation sequencing have the following problems:
[0009] (1) Only the case where the insertion site is on R1 or R2 is considered, and the case where the insertion site is between R1 and R2 is not considered, which has a certain damage to the detection sensitivity of the insertion site;
[0010] (2) When T-DNA is inserted into the repetitive sequence of the plant genome, it is difficult to determine the actual insertion position of T-DNA in the repetitive sequence using current methods based on short sequence alignment;
[0011] (3) If the reads come from the repetitive regions of the genome, one read may output multiple alignment sites during alignment, which may introduce the risk of false positives.
[0012] (4) The T-DNA sequence may contain promoter elements and target gene fragments from the target plant, which may introduce a risk of false positive detection when unconventionally inserted.
[0013] Therefore, accurate detection of the integration position of T-DNA in the host genome is crucial to the stability of molecular breeding lines and the construction of a safety control system. Summary of the invention
[0014] In order to solve the defect in the prior art that the current method based on second-generation sequencing only considers the situation where the insertion site is on R1 or R2, but does not consider the situation where the insertion site is between R1 and R2, which has a certain damage to the detection sensitivity of the insertion site, the present invention provides a method for detecting the insertion site of Agrobacterium T-DNA on the plant genome.
[0015] In order to solve the above technical problems, the present invention provides the following technical solutions:
[0016] A method for detecting the insertion site of Agrobacterium T-DNA in a plant genome comprises the following steps:
[0017] Step 1: merging the T-DNA genome of the marked vector and the plant genome sequence of the plant host to generate a combined genome G, in which the sources of the marked vector and the plant genome sequence can be obtained;
[0018] Step 2: Align the sequencing reads to the merged genome G using sequence alignment software to obtain a SAM file with the alignment results;
[0019] Step 3, classify the SAM files according to whether there is softclip, and divide them into SAM files with softclip at either end and SAM files without softclip;
[0020] Step 4: Classify the reads in the SAM file with softclip generated in step 3 to obtain the Usam without duplicate matches and the Dsam with duplicate matches; for Usam, merge the alignment records according to the genomic site and output the precise integration site data set; perform a secondary alignment for Dsam, merge all alignment results according to the genomic site and output the site sorting data set;
[0021] The reads in the SAM file without softclip generated in step 3 are screened to obtain the reads whose double-end matching results are on the plant genome and T-DNA genome respectively, and the sites are recorded to generate a fuzzy integration site dataset;
[0022] Step 5, filtering the fuzzy integration site dataset and the precise integration site dataset respectively to generate a filtered fuzzy integration site dataset and a filtered precise integration site dataset;
[0023] Merge the filtered fuzzy integration site dataset and the filtered precise integration site dataset with the site ranking dataset in step 4, and output the merged integration site dataset M;
[0024] Step 7: Mark and classify the integration site dataset M for repeated regions, and output the final number of integration sites and true integration events.
[0025] According to the present invention, step 1 also includes the generation of a blacklist B, and the specific steps are:
[0026] The T-DNA genome and the plant genome of the plant host are locally sequenced, and the region information of the plant genome having a local match on the T-DNA genome is recorded. A blacklist is created based on the information to generate a blacklist B.
[0027] Furthermore, the step 5 of generating a filtered fuzzy integration site dataset comprises the following specific steps: filtering the fuzzy integration site dataset using the sites recorded in the blacklist B to generate a filtered fuzzy integration site dataset.
[0028] Furthermore, the step 5 of generating a filtered precise integration site dataset comprises the following specific steps: filtering the precise integration site dataset using the sites recorded in the blacklist B to generate a filtered precise integration site dataset.
[0029] According to the present invention, step 4 classifies the read segments in the SAM file with softclip generated in step 3 to obtain a portion Usam without repeated matches and a portion Dsam with repeated matches, specifically:
[0030] The read segments in the SAM file with softclip generated in step 3 are screened according to whether the read segments have two or more genome alignment results, and the partial Usam without duplicate matching and the partial Dsam with duplicate matching are obtained.
[0031] According to the present invention, the step 4 of generating a fuzzy integration site dataset comprises the following specific steps:
[0032] The reads in the SAM file without softclip generated in step 3 were screened to obtain the reads with double-end matching results on the plant genome and T-DNA genome respectively, and the matching positions of the reads on the plant genome were recorded; then, based on a certain threshold, the alignment records of the genomic sites within the threshold range were merged to generate a fuzzy integration site dataset, which contained the number of supporting reads for each site.
[0033] Furthermore, the fuzzy integration site dataset is generated in step 4, and the specific steps are as follows:
[0034] For each alignment record in the SAM file without the softclip part generated in step 3, screening is performed according to the alignment positions at both ends of R1 and R2 to obtain the reads of the double-end matching results on the plant genome and T-DNA genome respectively, and the matching positions of the reads on the plant genome are recorded; based on a certain threshold, the alignment records of the genomic sites within the threshold range are merged to generate a fuzzy integration site data set, which contains the number of supporting reads for each site.
[0035] According to the present invention, the output site sorting data set of step 4 comprises the following specific steps:
[0036] For each supporting read in the partial Dsam with repeated matches generated in step 4, these reads are then aligned twice, with the alignment target being the merged genome G; all alignment results are then merged according to the genomic sites, and the site-sorted data set is output.
[0037] Furthermore, the output site sorting data set of step 4 is specifically as follows:
[0038] For each supporting read in the partial Dsam with repeated matches generated in step 4, these reads are then aligned twice, with the alignment target being the merged genome G. After the alignment results are obtained, they are sorted according to the matching scores of multiple alignment results of each read, and for each read, the alignment result with the highest score is recorded. If there are more than one alignment results with the same score, they are recorded randomly. All alignment results are then merged according to the genomic site, and the site-sorted data set is output.
[0039] According to the present invention, step 7 marks and classifies the integration site dataset M for repeated regions, and outputs the final number of integration sites and true integration events. The specific steps are:
[0040] In the merged integration site dataset M generated in step 6, sequences near all integration sites are extracted and multiple sequence alignment is performed, integration sites with sequence similarity higher than a certain threshold are classified, and the final integration sites and the number of true integration events are output.
[0041] The beneficial effects of the present invention are:
[0042] 1. This method for detecting the insertion site of Agrobacterium T-DNA in the plant genome takes into account the situation where the insertion site is between R1 and R2 in the analysis, which increases the number of reads carrying valid information by approximately 27.3%-200%, greatly improving the credibility of the detected site with the same amount of data.
[0043] 2. Method for detecting the insertion site of the Agrobacterium T-DNA in the plant genome When T-DNA is inserted into the repetitive sequence of the plant genome, it is difficult to determine the true insertion site of T-DNA in the repetitive sequence based on the current short sequence alignment method. This method introduces secondary alignment and similarity scoring functions to determine the most likely T-DNA insertion site, which greatly improves the site accuracy.
[0044] 3. In the detection method of the insertion site of Agrobacterium T-DNA in the plant genome, if the read segment comes from the repeated region of the genome, one read segment will output multiple alignment sites during the alignment, which may introduce the risk of false positives. This method extracts the sequence near the insertion site in the final result and performs multiple sequence similarity alignment to test the sequence similarity between the insertion sites and eliminate the risk of false positives caused by repeated matching.
[0045] 4. In the method for detecting the insertion site of Agrobacterium T-DNA on the plant genome, the T-DNA sequence may contain promoter elements and target gene fragments from the target plant, which can easily introduce false positive detection during unconventional insertion. This method sets a blacklist for the local sequence comparison between the two genomes, filters out false positives introduced by abnormal insertion events, and reduces the false positive rate of detection. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:
[0047] Figure 1 Schematic diagram of fuzzy matches and exact matches mapped to vector and host genomes for existing paired-end sequencing.
[0048] Figure 2 It is a flow chart of the method for detecting the insertion site of Agrobacterium T-DNA on the plant genome of the present invention; first, two genomes are merged, then reads are aligned to the merged genome, and classification analysis is performed according to the matching of the read segments, and then blacklist filtering, merging, repeated region marking and other analyses are performed, and finally the final integration site and the number of true integration events are output.
[0049] Figure 3 Integration site 2 has a comparison of the number of preferred reads supported by each of the 17 output result sites. It can be seen that the site with the highest number of supported reads is chr2:80959381.
[0050] Figure 4This is a diagram of sequence similarity check and classification results near the integration sites in all original results in Example 3 of the present invention; the X-axis and Y-axis in the figure represent the integration sites respectively, and the higher the similarity of the sequences near the two integration sites, the darker the color at the intersection of the two sequences. From the classification results, the 19 integration sites of the original results can be divided into 2 categories by similarity, corresponding to 2 real integration events, and the remaining sites are false positives caused by repeated sequences.
[0051] Figure 5 This is a graph showing the PCR verification results of the detection method of the present invention for the insertion event of the site chr2:80959381 with the highest number of supported preferred reads. DETAILED DESCRIPTION
[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.
[0053] The specific meanings of the terms used in the present invention are as follows.
[0054] IS:Integration site
[0055] TIAT: TIAT (T-DNA Integration Analysis Toolkit) T-DNA integration site detection toolkit
[0056] Softclip: A read that has both T-DNA and host genome sequences at one end.
[0057] SAM file: SAM (Sequence Alignment / Map) is a file format for storing sequence alignment results.
[0058] Sample set: The sample set contains 3 samples with known T-DNA integration sites, and the 3 samples contain 1, 2, and 2 integration sites respectively. Among them, sample 1 has 1 integration site, which is not located in the repeat region; the 2 integration sites in sample 2 are both located in the repeat region; sample 3 has 2 integration sites, which are not located in the repeat region.
[0059] Example 1 A method for detecting the insertion site of Agrobacterium T-DNA in a plant genome (i.e., TIAT algorithm)
[0060] like Figure 1 As shown, a method for detecting the insertion site of Agrobacterium T-DNA in a plant genome of the present invention comprises the following steps:
[0061] Step 1: merging the T-DNA genome of the marked vector and the plant genome sequence of the plant host to generate a combined genome G, in which the sources of the marked vector and the plant genome sequence can be obtained;
[0062] Step 2: Align the sequencing reads to the merged genome G using sequence alignment software to obtain a SAM file with the alignment results;
[0063] Step 3, classify the SAM files according to whether there is softclip, and divide them into SAM files with softclip at either end and SAM files without softclip (i.e., the complete matching part at both ends);
[0064] Step 4: Classify the reads in the SAM file with softclip generated in step 3 to obtain the part Usam without duplicate matches and the part Dsam with duplicate matches (i.e., the part with 2 or more alignment results); for Usam, merge the alignment records according to the genomic site, and output the precise integration site data set, which includes the number of supporting reads for each site; perform a secondary alignment for Dsam, merge all alignment results according to the genomic site, and output the site sorting data set;
[0065] The reads in the SAM file without softclip generated in step 3 are screened to obtain the reads whose double-end matching results are on the plant genome and T-DNA genome respectively, and the sites are recorded to generate a fuzzy integration site dataset;
[0066] Step 5, filtering the fuzzy integration site dataset and the precise integration site dataset respectively to generate a filtered fuzzy integration site dataset and a filtered precise integration site dataset;
[0067] Merge the filtered fuzzy integration site dataset and the filtered precise integration site dataset with the site ranking dataset in step 4, and output the merged integration site dataset M;
[0068] Step 7: Mark and classify the integration site dataset M for repeated regions, and output the final number of integration sites and true integration events.
[0069] Furthermore, step 1 also includes the generation of a blacklist B, specifically:
[0070] The T-DNA genome and the plant genome of the plant host are locally sequenced, and the region information of the plant genome having a local match on the T-DNA genome is recorded. A blacklist is created based on the information to generate a blacklist B.
[0071] Furthermore, the step 5 of generating a filtered fuzzy integration site dataset comprises the following specific steps: filtering the fuzzy integration site dataset using the sites recorded in the blacklist B to generate a filtered fuzzy integration site dataset.
[0072] Furthermore, the step 5 of generating a filtered precise integration site dataset comprises the following specific steps: filtering the precise integration site dataset using the sites recorded in the blacklist B to generate a filtered precise integration site dataset.
[0073] Furthermore, in step 4, the read segments in the SAM file with softclip generated in step 3 are classified to obtain the part Usam without repeated matches and the part Dsam with repeated matches, specifically:
[0074] The read segments in the SAM file with softclip generated in step 3 are screened according to whether the read segments have two or more genome alignment results, and the partial Usam without duplicate matching and the partial Dsam with duplicate matching are obtained.
[0075] Furthermore, the fuzzy integration site dataset is generated in step 4, and the specific steps are as follows:
[0076] The reads in the SAM file without softclip generated in step 3 are screened to obtain the reads with double-end matching results on the plant genome and T-DNA genome respectively, and the site closest to the site on the plant genome is recorded; then, based on a certain threshold, the alignment records of the genomic sites within the threshold range are merged to generate a fuzzy integration site dataset, which contains the number of supporting reads for each site.
[0077] Furthermore, the fuzzy integration site dataset is generated in step 4, and the specific steps are as follows:
[0078] For each alignment record in the SAM file without the softclip part generated in step 3, screening is performed according to the alignment positions at both ends of R1 and R2 to obtain the reads of the double-end matching results on the plant genome and T-DNA genome respectively, and the matching positions of the reads on the plant genome are recorded; based on a certain threshold, the alignment records of the genomic sites within the threshold range are merged to generate a fuzzy integration site data set, which contains the number of supporting reads for each site.
[0079] Furthermore, the output site sorting data set of step 4 is specifically as follows:
[0080] For each supporting read in the partial Dsam with repeated matches generated in step 4, these reads are aligned twice using refined parameters. The alignment software is blat or other similar functional software, and the alignment target is the merged genome G. After obtaining the alignment results, sort them according to the matching scores of multiple alignment results of each read, and for each read, record the alignment result with the first score. If there are more than one alignment results with the same score, record them randomly. Then merge all the alignment results according to the genomic site, and output the site sorting data set, which contains the number of supporting reads for each site.
[0081] Furthermore, in step 7, the integration site dataset M is marked and classified for repeated regions, and the final integration sites and the number of true integration events are output. The specific steps are:
[0082] In the merged integration site dataset M generated in step 6, sequences near all integration sites are extracted and multiple sequence alignment is performed, integration sites with sequence similarity higher than a certain threshold are classified, and the final integration sites and the number of true integration events are output.
[0083] Example 2 Sensitivity Detection of TIAT Algorithm
[0084] This example detects three samples with known integration sites in the sample set (sample 1, sample 2, and sample 3) and compares the changes in the read support number corresponding to each integration site after the TIAT algorithm considers the situation where the integration site is between R1 and R2.
[0085] Sample 1 has 1 integration site that is not located in the repetitive region; sample 2 has 2 integration sites that are both located in the repetitive region; sample 3 has 2 integration sites that are not located in the repetitive region.
[0086] After sequencing samples 1, 2, and 3, the TIAT algorithm was used to detect the three samples. The reads with softclip and the reads without softclip detected by the TIAT algorithm were compared to verify whether the TIAT algorithm can improve the sensitivity of detection. First, the TIAT detection algorithm was used to detect the three samples respectively, and then the number of reads supporting the integration site in each sample was recorded. The reads were classified according to whether there was a softclip in the reads, and finally the number of the two types of reads in the integration site detection was compared and recorded. The results are shown in Table 1.
[0087] The results showed that among all the reads supporting the integration site in sample 1, there were 5 reads containing softclip and 3 reads without softclip, and the number of reads supporting increased from 5 to 8;
[0088] In integration site 1 of sample 2, there are 11 reads containing softclip and 3 reads without softclip, and the number of read support increases from 11 to 14. In integration site 2 of sample 2, there is only 1 read containing softclip and 1 read without softclip, and the number of read support increases from 1 to 2.
[0089] In integration site 1 and integration site 2 of sample 3, there were 11 and 9 reads containing softclip, respectively, and 6 and 5 reads without softclip, respectively. The number of read supports increased from 11 and 9 to 17 and 14, respectively.
[0090] As can be seen from Table 1, after adding the situation where the integration site is between the paired-end sequencing reads, the number of supporting reads for the detected integration site increased by an average of 48.6%, and the detection credibility of the integration site was greatly improved. It can be seen that in the TIAT algorithm, the reads without softclip are used as a supplementary detection for the reads with softclip, which greatly improves the credibility of the integration site detection.
[0091] Conclusion: The TIAT algorithm can more effectively detect integration sites on repetitive regions, amplify the signals, and increase the sensitivity of detection; for existing detection sites, the credibility of the sites is increased by increasing the number of reads supported by the integration sites.
[0092] Table 1 Changes in the number of supporting reads at the integration site after optimization by the TIAT algorithm
[0093]
[0094] Example 3 Verification of whether the TIAT algorithm can improve the accuracy of repeated region detection
[0095] The purpose of this embodiment is to analyze sample 2 in the sample set whose known insertion site is in the repetitive region of the plant genome using the TIAT algorithm, to further confirm whether the processing logic of the TIAT algorithm for repetitive regions can improve the accuracy of repetitive region detection, and at the same time identify false positive events caused by repeated alignment.
[0096] Sample 2 is known to have two integration sites, but because both integration sites are located in the repetitive region of the plant genome, before optimization, the program analysis results output a total of 19 insertion sites (see Table 2).
[0097] Table 2 The number of true integration events found by integration site sequence alignment
[0098]
[0099]
[0100] The TIAT algorithm optimized the 19 sites in two aspects. First, the TIAT algorithm performed multiple sequence similarity comparisons on the sequences of the 19 insertion sites in the final results to determine whether each site originated from the same integration event.
[0101] from Figure 4 It can be seen that after the TIAT algorithm performed a similarity comparison, the 19 sites were divided into two categories according to the similarity, of which the sites corresponding to chr8:141778585 and chr2:176646432 were divided into one cluster, and the other sites were divided into another cluster, which is completely consistent with the fact that sample 2 has two known integration sites. Secondly, when the first alignment result of a read segment is more than one site, the TIAT algorithm will perform a secondary match on the read segment and optimize it based on the matching score of the secondary matching result. Therefore, for multiple output result sites from the same integration event, the more supporting reads the TIAT algorithm gives to the site based on the matching score, the more likely it is that the site is a true integration site among all the reported sites of the repetitive sequence.
[0102] from Figure 3 It can be seen that after classification, integration site 2 has 17 output result sites, and the site with the highest number of supported preferred reads is chr2:80959381.
[0103] from Figure 5 It can be seen that PCR verification was performed on chr2:80959381 with the highest number of reads, confirming the authenticity of chr2:80959381. This shows that when the integration site is located in a repetitive sequence, TIAT's strategy based on secondary alignment and site optimization can help determine the true integration site.
[0104] Embodiment 4:
[0105] The purpose of this example is to verify whether T-DNA may contain sequence fragments of the host genome by aligning the vector and the plant genome, and to illustrate the necessity of establishing a filtering blacklist through analysis. First, the T-DNA vector sequence and the corn genome were aligned using the BWA sequence alignment software, and then the T-DNA vector sequence was checked based on the sequence alignment results to see whether there were sequence fragments derived from the corn genome. The results are shown in Table 3.
[0106] As can be seen from Table 3, there are 8 sequence matching regions between the vector and the corn genome. The matching sites on the corn genome are distributed on 3 different chromosomes, with lengths ranging from 101bp to 1681bp. The total length of the sequence from the plant genome and the vector sequence matching is 3400bp, accounting for 16.2% of the total length of T-DNA. If T-DNA breaks or is not fully integrated during integration, the T-DNA boundary will be connected to part of the vector's own sequence, resulting in false positives. Given that the length of plant host-derived sequences in T-DNA sequences accounts for as much as 16.2%, by identifying false positives introduced by abnormal insertion events, establishing a filtering blacklist can prevent the occurrence of such false positive detection events.
[0107] Table 3
[0108]
[0109] In summary of the above embodiments, this method takes into account the special situation of double-end sequencing of the insertion site, establishes a blacklist, performs secondary matching optimization for the unique repetitive sequences of the plant genome, and performs multiple sequence similarity comparisons on the result sites, thereby increasing the sensitivity of T-DNA integration site detection and reducing the false positive rate of detection. It provides certain practical application value in the prevention and control of biosafety risks in molecular breeding.
[0110] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. A method for detecting the insertion site of Agrobacterium T-DNA in a plant genome, characterized in that: The following steps are involved: Step 1: merging the T-DNA genome of the labeled vector and the plant genome sequence of the plant host to generate a merged genome G; Step 2: Align the sequencing reads to the merged genome G using sequence alignment software to obtain a SAM file with the alignment results; Step 3, classify the SAM files according to whether there is softclip, and divide them into SAM files with softclip at either end and SAM files without softclip; Step 4, classify the read segments in the SAM file with softclip generated in step 3 to obtain the part Usam without repeated matching and the part Dsam with repeated matching; For Usam, the alignment records are merged according to the genomic sites, and the precise integration site data set is output; for Dsam, a secondary alignment is performed, all alignment results are merged according to the genomic sites, and the site ranking data set is output; The reads in the SAM file without softclip generated in step 3 are screened to obtain the reads whose double-end matching results are on the plant genome and T-DNA genome respectively, and the sites are recorded to generate a fuzzy integration site dataset; Step 5, filtering the fuzzy integration site dataset and the precise integration site dataset respectively to generate a filtered fuzzy integration site dataset and a filtered precise integration site dataset; Merge the filtered fuzzy integration site dataset and the filtered precise integration site dataset with the site ranking dataset in step 4, and output the merged integration site dataset M; Step 7: Mark and classify the integration site dataset M for repeated regions, and output the final number of integration sites and true integration events.
2. The method for detecting the insertion site of Agrobacterium T-DNA in a plant genome according to claim 1, characterized in that: Step 1 also includes the generation of blacklist B, and the specific steps are: The T-DNA genome and the plant genome of the plant host are locally sequenced, and the region information of the plant genome having a local match on the T-DNA genome is recorded. A blacklist is created based on the information to generate a blacklist B.
3. The method for detecting the insertion site of Agrobacterium T-DNA in a plant genome according to claim 2, characterized in that: The step 5 of generating a filtered fuzzy integration site dataset comprises the following specific steps: filtering the fuzzy integration site dataset using the sites recorded in the blacklist B to generate a filtered fuzzy integration site dataset.
4. The method for detecting the insertion site of Agrobacterium T-DNA in a plant genome according to claim 2, characterized in that: The specific steps of generating a filtered precise integration site dataset in step 5 are as follows: filtering the precise integration site dataset using the sites recorded in the blacklist B to generate a filtered precise integration site dataset.
5. The method for detecting the insertion site of Agrobacterium T-DNA in a plant genome according to claim 1, characterized in that: Step 4 classifies the read segments in the SAM file with softclip generated in step 3 to obtain the part Usam without duplicate matches and the part Dsam with duplicate matches, specifically: The read segments in the SAM file with softclip generated in step 3 are screened according to whether the read segments have two or more genome alignment results, and the partial Usam without duplicate matching and the partial Dsam with duplicate matching are obtained.
6. The method for detecting the insertion site of Agrobacterium T-DNA in a plant genome according to claim 1, characterized in that: Step 4 generates a fuzzy integration site dataset, and the specific steps are as follows: Screen the reads in the SAM file without softclip generated in step 3 to obtain the reads whose double-end matching results are on the plant genome and the T-DNA genome respectively, and record the matching positions of the reads on the plant genome; Then, according to a certain threshold, the alignment records of the genomic sites within the threshold range are merged to generate a fuzzy integration site dataset, which contains the number of supporting reads for each site.
7. The method for detecting the insertion site of Agrobacterium T-DNA in a plant genome according to claim 1 or 6, characterized in that: Step 4 generates a fuzzy integration site dataset, and the specific steps are as follows: For each alignment record in the SAM file without the softclip part generated in step 3, screening is performed according to the alignment positions at both ends of R1 and R2 to obtain the reads of the double-end matching results on the plant genome and T-DNA genome respectively, and the matching positions of the reads on the plant genome are recorded; based on a certain threshold, the alignment records of the genomic sites within the threshold range are merged to generate a fuzzy integration site data set, which contains the number of supporting reads for each site.
8. The method for detecting the insertion site of Agrobacterium T-DNA in a plant genome according to claim 1, characterized in that: The output site sorting dataset of step 4 is as follows: For each supporting read in the partial Dsam with repeated matches generated in step 4, these reads are then aligned twice, with the alignment target being the merged genome G; all alignment results are then merged according to the genomic sites, and the site-sorted data set is output.
9. The method for detecting the insertion site of Agrobacterium T-DNA in a plant genome according to claim 1 or 8, characterized in that: The output site sorting dataset of step 4 is as follows: For each supporting read in the partial Dsam with repeated matches generated in step 4, these reads are then aligned twice, with the alignment target being the merged genome G. After the alignment results are obtained, they are sorted according to the matching scores of multiple alignment results of each read, and for each read, the alignment result with the highest score is recorded. If there are more than one alignment results with the same score, they are recorded randomly. All alignment results are then merged according to the genomic site, and the site-sorted data set is output.
10. The method for detecting the insertion site of Agrobacterium T-DNA in a plant genome according to any one of claims 1 to 6 and 8, characterized in that: Step 7 marks and classifies the integration site dataset M for repeated regions, and outputs the final number of integration sites and true integration events. The specific steps are: In the merged integration site dataset M generated in step 6, sequences near all integration sites are extracted and multiple sequence alignment is performed, integration sites with sequence similarity higher than a certain threshold are classified, and the final integration sites and the number of true integration events are output.