A method for detecting somatic mutations in plant genomes

Abstract

The present invention discloses a method for detecting somatic mutations in plant genomes. The method comprises (1) evaluation of resequencing data; (2) assembly of resequencing data; (3) identification of mutation sites; (4) evaluation of mutation sites; (5) screening of mutation sites; (6) classification and labeling of candidate mutation sites; and (7) manual inspection of mutation sites. The method provided by the present invention can efficiently and comprehensively detect somatic mutations in various plant genomes, including single nucleotide variant sites (SNVs) and insertion and deletion sites (INDELs), providing a powerful analytical tool for evaluating plant somatic mutation rates, thereby providing a molecular basis for studying the effects of somatic mutations on plant development and evolution.

Description

Technical Field

[0001] The present invention relates to the field of microbial technology, and in particular to a method for detecting somatic mutations in plant genomes, and in particular to splicing and evaluating original plant genome data and identifying and screening somatic mutation sites. Background Art

[0002] Plant genome variation, including mutation and recombination, is the basis of plant diversity and the intrinsic driving force of plant adaptive evolution. How genome variation is generated, retained and inherited, and what factors affect it, have always been important scientific issues in the field of life sciences. Among them, mutation is one of the important scientific issues that classical genetics has long focused on. With the development of resequencing technology, research on mutation has made great progress in recent years. At present, research on mutation mainly focuses on three aspects: evaluation of meiotic mutation rate, identification of mutations in cancer cells, and evaluation of somatic mutations. Somatic mutations are mainly concentrated in bacteria, fungi and animals.

[0003] At present, the methods used to detect somatic mutations in plants generally borrow research methods in humans and mice, but the plant genome has unique characteristics, and the detection methods in animals cannot be fully applicable to the plant genome. In order to ensure the screening of reliable mutations, existing detection methods often shield complex sequences in the genome first, such as repetitive sequences, transposable elements, etc., which will cause a large amount of genomic information to be ignored. In addition, although the existing somatic mutation detection methods can effectively remove false negative results, they cannot effectively evaluate false positive results. Therefore, the identified somatic mutations often have a high false positive rate. In summary, it is necessary to develop a method that is applicable to plant genomes and can efficiently and quickly detect reliable somatic mutations. Summary of the invention

[0004] In view of the above-mentioned defects of the prior art, the present invention provides a method for detecting somatic mutations in a plant genome.

[0005] In order to solve the problems of the prior art, the present invention provides the following technical solution: A method for detecting somatic mutations in a plant genome of the present invention comprises the following steps:

[0006] (1) Evaluation of resequencing data: Use software such as FastQC to perform quality inspection and evaluation on resequencing data and filter to obtain clean data;

[0007] (2) Assembly of resequencing data: Use the default parameters of BWA-mem to align the clean data to the reference genome to obtain the original SAM file, and then use Picard's SortSam module to sort the SAM file and convert it into BAM format;

[0008] (3) Identification of mutation sites: Using two different algorithms for identifying mutations in GATK, UG and HC, the “-rfMappingQuality -mmq 20” parameter was selected to filter reads with a mapping quality less than 20 to obtain a VCF file containing all the original mutation sites.

[0009] (4) Evaluation of mutation sites: Use the mpileup module and VarScan of samtools to count the actual number of aligned reads and alignment quality information of candidate single nucleotide variation sites (SNVs); use the joint-calling mode of HC to re-identify the insertion and deletion sites (INDELs) to further clarify the site information;

[0010] (5) Screening of mutation sites: Based on the developmental topology-based relationship of each sample and the frequency-based occurrence of alleles, candidate mutation sites can be screened;

[0011] (6) Classification and labeling of candidate mutation sites: Label all candidate mutations for various features, including but not limited to: the number of reads supporting the mutation site, whether there is a preference for the sequencing chain of the mutation reads, the base quality value of the mutation, the occurrence of the mutation-like site in the control sample, the regional sequencing quality, the regional alignment quality, whether there are insertion and deletion sites nearby, the distribution status of the site in samples of different developmental relationships, and the sequence difference. Sort and grade them according to the reliability indicated by the mutation characteristics to reduce the false positive and false negative rates;

[0012] (7) Manual inspection of mutation sites: Use IGV Integrative Genomics Viewer for manual inspection to exclude some cases where the program fails to correctly add labels, such as some false positive results caused by exogenous contamination or assembly errors.

[0013] Furthermore, in step (1), the resequencing data was quality checked by MD5 value verification based on the GC content of the sequencing data, sequencing depth, etc., and reads adapters, reads with an N ratio greater than 10%, and reads with low-quality bases accounting for more than 50% of the reads were removed. Clean data was obtained after filtering.

[0014] Furthermore, in step (2), the MarkDuplicates module was used to mark abnormally amplified reads, and the insertion and deletion regions were realigned using the RealignerTargetCreator and IndelRealigner of GATK.

[0015] Furthermore, in step (5), for somatic mutations, samples with closer genetic relationships or closer physical locations are more likely to contain the same mutation.

[0016] Furthermore, in step (5), leaf samples from the same branch often share somatic mutations, while common mutations screened from leaf samples from different branches are often false. More reliable mutation sites can be screened based on the topology-based relationship of mutations in each sample.

[0017] Furthermore, in step (5), based on the frequency-based alleles, the mutations common to all samples or only contained in a certain sample with a read number < 5 can be deleted, and these unfixed variant sites and sites with significant skewness can be deleted to remove some false positive mutation sites.

[0018] Beneficial effects: The method provided by the present invention can efficiently and comprehensively detect somatic mutations in various plant genomes, including single nucleotide variant sites (SNV) and insertion and deletion sites (INDEL), providing a powerful analytical tool for evaluating the somatic mutation rate of plants, thereby providing a molecular basis for studying the effects of somatic mutations on plant development and evolution. The present invention can efficiently identify and screen somatic mutations in different plant species, not only providing a powerful analytical tool for evaluating the mutation rate of plant somatic cells, thereby providing a molecular basis for studying the effects of somatic mutations on plant development and evolution; at the same time, it can be compared with the method of analyzing somatic mutations in animals, finding similarities and differences, and optimizing the detection process of integrated somatic mutations.

[0019] Compared with the prior art, the present invention has the following advantages: (1) The present invention provides a method for detecting somatic mutations in plants for the first time. In recent years, there has been an increasing number of studies on somatic mutations in plants, but the existing methods for detecting somatic mutations in plants are generally based on humans and mice.

[0020] (2) Since most plant genomes have undergone genome doubling events (WGD) and have many repetitive sequences, the analysis methods used in animals are not only inaccurate, but also lose a lot of effective information. The emergence of the present invention makes up for the lack of research methods in this regard and provides a very good way for the rapid identification of somatic mutations in different plant species.

[0021] (3) Mutantsmsh6 Characteristics of the mutation site: Compared with the wild type, the mutant msh6 The single base substitution mutation rate increased by 6.46 times, and the insertion and deletion mutation rate increased by 12.09 times. msh6 The skewness in transversions is more significant, with a conversion-to-transversion ratio of approximately 0.12. The number of transversion mutations accounts for 82.7% of the total number of mutations, of which mutations in the C>A direction are the majority, accounting for 62.2% of the total mutations, reflecting that the gene may have a preference for repairing transversion mutations. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the process of the present invention.

[0023] Figure 2 This is a schematic diagram of sampling the Arabidopsis mutant msh6 of the present invention, in which the rosette leaves are below the dotted line and the branch leaves are above the dotted line.

[0024] Figure 3 The schematic diagram of the manual inspection of the variant site by IGV in the present invention is shown in the figure. The variant site is Chr2:14340548, and the mutation type is a single base substitution from C to A.

[0025] Figure 4 The phylogenetic tree of the somatic mutation (single nucleotide variation site) of the Arabidopsis mutant msh6 of the present invention is shown in FIG. In the figure, the circles represent rosette leaf samples, the triangles represent branch leaf samples, and different colors represent different branches. DETAILED DESCRIPTION

[0026] The following will describe the implementation methods of the present invention in detail with reference to the embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0027] Example 1

[0028] A method for detecting somatic mutations in a plant genome of the present invention ( Figure 1 ), including the following steps:

[0029] (1) Evaluation of resequencing data: Use FastQC and other software to perform quality inspection and evaluation on resequencing data, and filter to obtain clean data; use MD5 value verification to check the quality of resequencing data based on the GC content of the sequencing data, sequencing depth, etc., remove reads adapters, reads with an N ratio greater than 10%, and reads with low-quality bases accounting for more than 50% of the reads, and obtain clean data after filtering.

[0030] (2) Assembly of resequencing data: Use the default parameters of BWA-mem to align the clean data to the reference genome to obtain the original SAM file, and then use Picard's SortSam module to sort the SAM file and convert it into BAM format; use the MarkDuplicates module to mark abnormally amplified reads, and use GATK's RealignerTargetCreator and IndelRealigner to realign the insertion and deletion regions.

[0031] (3) Identification of mutation sites: Using two different algorithms for identifying mutations in GATK, UG and HC, the “-rfMappingQuality -mmq 20” parameter was selected to filter reads with a mapping quality less than 20 to obtain a VCF file containing all the original mutation sites.

[0032] (4) Evaluation of mutation sites: Use the mpileup module and VarScan of samtools to count the actual number of aligned reads and alignment quality information of candidate single nucleotide variation sites (SNVs); use the joint-calling mode of HC to re-identify the insertion and deletion sites (INDELs) to further clarify the site information;

[0033] (5) Screening of mutation sites: Based on the developmental topology-based relationship corresponding to each sample and the frequency-based occurrence of alleles, candidate mutation sites can be screened out; for somatic mutations, the closer the relationship or the closer the physical location, the greater the possibility that the samples contain the same mutation. Somatic mutations are often shared between leaf samples from the same branch, while common mutations screened from leaf samples from different branches are often false. More reliable mutation sites can be screened out based on the topology-based relationship of mutations in each sample. Based on the frequency-based alleles, mutations shared by all samples or mutations contained in only one sample and the number of reads <5 can be deleted. These unfixed variant sites and sites with significant skewness can be deleted to remove some false positive mutation sites.

[0034] (6) Classification and labeling of candidate mutation sites: Label all candidate mutations for various features, including but not limited to: the number of reads supporting the mutation site, whether there is a preference for the sequencing chain of the mutation reads, the base quality value of the mutation, the occurrence of the mutation-like site in the control sample, the regional sequencing quality, the regional alignment quality, whether there are insertion and deletion sites nearby, the distribution status of the site in samples of different developmental relationships, and the sequence difference. Sort and grade them according to the reliability indicated by the mutation characteristics to reduce the false positive and false negative rates;

[0035] (7) Manual inspection of mutation sites: Use IGV Integrative Genomics Viewer for manual inspection to exclude some cases where the program fails to correctly add labels, such as some false positive results caused by exogenous contamination or assembly errors.

[0036] Example 2

[0037] Identification and screening of somatic mutations of Arabidopsis msh6 mutants of the present invention

[0038] Resequencing of msh6 mutant leaf samples: Leaves of the Arabidopsis T-DNA mutant msh6 were cut ( Figure 2 ), genomic DNA was extracted using the CTAB (Cetyltrimethylammonium bromide) method and sent to Wuhan BGI Gene Technology Co., Ltd. for quality inspection. Samples (21 in total) that met the sequencing requirements were selected for whole genome sequencing. The sequencing platform was the HiSeq4000 platform, using conventional library construction, the fragment size was 350bp, the fragment read length was 150bp, and the double-end sequencing method was used. The sequencing depth of each sample exceeded 30 times, ensuring that the sequencing quality was greater than 20.

[0039] Evaluation of resequencing data: The returned sequencing data was quality checked. The MD5 value was first verified to check whether the downloaded data was missing. The GC content of the mutant resequencing data was counted using FastQC software. The GC content of Arabidopsis thaliana was about 40%. If the GC content was abnormally high, there might be contamination by foreign bacteria. Finally, the amount of sequencing data was preliminarily counted, the sequencing depth was estimated, and whether the sample met the requirements of subsequent analysis. Because the returned data was raw data, it was manually filtered to remove reads connectors, reads with N ratio greater than 10%, and reads with low-quality bases accounting for more than 50% of the reads. The clean data obtained after filtering was used for subsequent analysis. Preliminary statistics showed that the sequencing coverage of each sample ranged from 19.20% to 43.92%, with an average coverage of 32.94%; the genome coverage of samples with reads ≥ 5 was 91.7-98.4%, with an average of 96.89%.

[0040] Assembly of resequencing data: The Arabidopsis reference genome and annotation files were downloaded from TAIR (The Arabidopsis Information Resource (TAIR, http: / / www.arabidopsis.org / , version 10). The clean data were aligned to the Arabidopsis reference genome using the default parameters of BWA-mem to obtain the original SAM file. The SAM file was sorted and converted to BAM format using the SortSam module of Picard. In order to exclude the influence of abnormal amplification of reads during sequencing library construction, these abnormally amplified reads were marked using the MarkDuplicates module of Picard. During the assembly process, reads near insertions and deletions may have assembly errors. In order to reduce the occurrence of such errors, the RealignerTargetCreator and IndelRealigner of GATK were used for local realignment to exclude the insertion and deletion regions that cause sequence alignment misalignment.

[0041] Re-evaluation of resequencing data: The BAM file obtained after the above steps needs to be evaluated for quality again. First, the coverage and depth of the BAM file are counted to check whether the amount of data contained in the BAM file meets the requirements of subsequent analysis. Secondly, the proportion of bases with base quality above Q20 and Q30 in the BAM file is counted. The results show that the average proportion of bases with base quality above Q20 is 95.12%, and the average proportion of bases with base quality above Q30 is 89.71%, indicating that the overall quality level of the BAM file is high. At the same time, TDNAscan (https: / / bio.tools / TDNAscan) is used to re-check whether T-DNA is inserted and the specific location of the insertion. Arabidopsis thaliana msh6 The T-DNA insertion position in the mutant is Chr4: 911675..911819, which is located in the CDS.

[0042] Identification of mutation sites: In order to prevent the skewness of the screening results caused by the algorithm, two algorithms in GATK, UnifiedGenotyper (UG) and HaplotypeCaller (HC), were used for mutation identification at the same time, and the results of the two algorithms were used to verify and complement each other. "-rf MappingQuality -mmq 20" was selected to filter reads with a mapping quality less than 20 to obtain the original VCF file;

[0043] Evaluation of mutation sites: In order to improve the accuracy of mutation sites, the quality and depth of candidate mutation sites need to be further evaluated. The mpileup module of samtools (version 1.3.1) and VarScan (version 2.3.6) were used to count the number of reads and quality information of candidate single nucleotide variant sites (SNV); the joint-calling module of HC was used to re-identify the insertion and deletion sites (INDEL) to further clarify the site information.

[0044] Screening of mutation sites: For somatic mutations, the closer the relationship or the closer the physical location, the greater the possibility that the samples contain the same mutation. For example, leaf samples from the same branch often share somatic mutations, while common mutations screened from leaf samples from different branches are often false. Therefore, more reliable mutation sites can be screened based on the developmental (topology-based) relationship corresponding to each sample. At the same time, based on the frequency of alleles (frequency-based), unfixed variant sites and sites with significant skewness can be deleted to remove some false positive sites. Specifically, (1) If only one sample contains a certain mutation, it is necessary to ensure that the number of reads of the mutation is not less than 5; (2) For the same mutation contained in more than one sample, it is necessary to ensure that at least one sample reaches a read number ≥ 5, and the number of reads of the mutation in other samples is at least 3; (3) Shared mutations in a sample as a whole or mutations shared by all samples between different samples will be excluded.

[0045] Collection of mutation site feature labels: Label the various features of all candidate mutations, sort and grade them according to the reliability indicated by the labels, and use trial and error to reduce the false positive and false negative rates.

[0046] Manual inspection of mutation sites: Manual inspection was performed using IGV (Integrative Genomics Viewer) ( Figure 3 ), excluding some cases where the program failed to add labels correctly, such as some false positive results caused by exogenous contamination or assembly errors. After manual detection and proofreading of the mutation sites by IGV, a total of 96 SNV mutation sites were screened in the Arabidopsis msh6 mutant ( Figure 4 ) and 3 INDEL loci.

[0047] The above description shows and describes several preferred embodiments of the present invention, but as mentioned above, it should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications, and can be modified within the scope of the present invention through the above teachings or the technology or knowledge of the relevant field. And the changes and modifications made by those skilled in the art do not depart from the spirit and scope of the present invention, and should be protected by the claims attached to the present invention.

Claims

1. A method for detecting somatic mutations in a plant genome, characterized in that The steps include: (1) Evaluation of resequencing data: Use FastQC and other software to perform quality inspection and evaluation on resequencing data and filter to obtain clean data; (2) Assembly of resequencing data: Use the default parameters of BWA-mem to align the clean data to the reference genome to obtain the original SAM file, and then use Picard's SortSam module to sort the SAM file and convert it into BAM format; (3) Identification of mutation sites: Using two different algorithms for identifying mutations in GATK, UG and HC, the "-rfMappingQuality-mmq 20" parameter was selected to filter reads with a mapping quality less than 20 to obtain a VCF file containing all original mutation sites; (4) Evaluation of mutation sites: The mpileup module and VarScan of samtools were used to count the actual number of aligned reads and alignment quality information of candidate single nucleotide variation sites (SNVs); the joint-calling mode of HC was used to re-identify the insertion and deletion sites (INDELs) to further clarify the site information; (5) Screening of mutation sites: Based on the developmental topology-based relationship of each sample and the frequency-based occurrence of alleles, candidate mutation sites can be screened; (6) Classification and labeling of candidate mutation sites: Label all candidate mutations for various features, including but not limited to: the number of reads supporting the mutation site, whether there is a preference for the sequencing chain of the mutation reads, the base quality value of the mutation, the occurrence of similar mutation sites in control samples, regional sequencing quality, regional alignment quality, whether there are nearby insertion and deletion sites, the distribution status of the site in samples of different developmental relationships, and sequence differences. Sort and grade them according to the reliability indicated by the mutation characteristics to reduce the false positive and false negative rates; (7) Manual inspection of mutation sites: Use IGV Integrative Genomics Viewer for manual inspection to exclude some cases where the program fails to correctly add labels, exogenous contamination, or some false positive results caused by assembly errors.

2. The method for detecting somatic mutations in a plant genome according to claim 1, characterized in that: In step (1), the resequencing data is quality checked by MD5 value verification based on the GC content of the sequencing data, sequencing depth, etc., and reads adapters, reads with an N ratio greater than 10%, and reads with low-quality bases accounting for more than 50% of the reads are removed. Clean data is obtained after filtering.

3. The method for detecting somatic mutations in a plant genome according to claim 1, characterized in that: In step (2), the MarkDuplicates module was used to mark abnormally amplified reads, and the insertion and deletion regions were realigned using the RealignerTargetCreator and IndelRealigner of GATK.

4. The method for detecting somatic mutations in a plant genome according to claim 1, characterized in that: In step (5), for somatic mutations, samples with closer genetic relationships or closer physical locations are more likely to contain the same mutation.

5. The method for detecting somatic mutations in a plant genome according to claim 4, characterized in that: In step (5), leaf samples from the same branch often share somatic mutations, while common mutations screened from leaf samples from different branches are often false. More reliable mutation sites can be screened based on the topology-based relationship of mutations in each sample.

6. The method for detecting somatic mutations in a plant genome according to claim 1, characterized in that: In step (5), based on the frequency-based alleles, mutations common to all samples or mutations contained only in a certain sample with a read number < 5 can be deleted. These unfixed variant sites and sites with significant skewness can be deleted to remove some false positive mutation sites.

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