An auxiliary method for judging stress resistance of plant individual

Abstract

The application discloses an auxiliary method for judging the stress resistance of a plant individual, which is composed of the following steps: target sequence determination and acquisition, characteristic subsequence extraction, target sample small RNA sequencing, sample stress resistance evaluation and the like, wherein the target sequence is determined based on the alignment result of an artificial nucleic acid sequence A, the characteristic subsequence is derived from 26 subsegment sequences on the target sequence, and the sample stress resistance evaluation result is determined by the product of the expression behavior of the 26 characteristic subsequences in the sample and the probability value of the stress or non-stress sample to which the characteristic subsequences belong. The method has high accuracy, can assist a breeder in removing candidate individuals which exhibit stress behaviors under non-stress conditions in advance, can also assist the breeder in determining key candidate objects which exhibit non-stress characteristics under stress conditions in advance, and is beneficial to saving the time, energy, financial resources and material resources of the breeder and improving the stress resistance breeding efficiency.

Description

Technical Field

[0001] This invention belongs to the field of crop breeding, specifically relating to an auxiliary method for determining the stress resistance of individual plants. Background Technology

[0002] Seed industry is the source of agricultural development, and successful crop breeding is key to its revitalization. my country has a vast territory, and various crops face varying degrees of adverse environmental stresses such as pests and diseases, low temperatures, high temperatures, drought, and salinity. Therefore, stress-resistant breeding is an enduring theme in crop breeding.

[0003] The basic breeding procedure for crop breeders involves selecting stress-resistant individual plants from wild resources, from naturally sown seedlings, from plants with vegetative line variations, and from the offspring of hybrid seeds. Each plant is then subjected to large-scale propagation, varietal comparison trials, and multi-regional, multi-year trait stability assessments. In recent years, transgenic and gene-editing technologies have also been used in the breeding of stress-resistant new varieties. However, regardless of the method used, the stress resistance of the initially selected superior individual plants must be evaluated.

[0004] Evaluation of stress resistance is a labor-intensive, time-consuming, costly, and resource-intensive task, especially for woody fruit trees with long juvenile stages and tall stature. To reduce the workload, identifying a number of preferred individual trees and decreasing the number of candidate plants is the first step in stress resistance evaluation. Traditional methods often use morphological, physiological, biochemical, and molecular indicators such as stomatal conductance, net photosynthetic rate, vegetation index, and trait linkage markers to identify early-stage stress traits in crops, including drought resistance, flood tolerance, heat tolerance, salt tolerance, and disease and pest resistance. However, these methods have limitations, including low accuracy and applicability only to specific breeding objectives.

[0005] A search of existing patents revealed the following applications: Patent application CN202111469828.8 discloses the application of a CRK22 gene and its encoded protein in breeding for potato resistance to blight, bacterial wilt, and high salt stress. Patent application CN202010187022.9 discloses the application of a grape VyLhcb4 gene and its encoded protein in drought-resistant variety breeding. Patent application CN202011128242.0 discloses a SyDcw module that responds to stress signals and its application in breeding crops to resist high salt and drought stress. Patent application CN201510916723.0 discloses the application of a maize mZmDEP gene and its expression repression structure in maize stress resistance breeding. All of these patents are developed based on gene modules consisting of a single stress resistance gene encoding a protein or a small number of genes, and lack evaluation of their application effects in early identification of stress resistance traits.

[0006] The plant TAS3 gene is a highly conserved gene in the plant kingdom. The production of small RNAs on its transcripts is characterized by multiple sites, co-expression, and strong control by the stress response factor MIR390. Therefore, small RNA accumulation maps based on the TAS3 gene can help determine external stress conditions, thus providing a better method for identifying plant stress resistance traits, especially for early identification. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide an auxiliary method for judging the stress resistance of individual plants, in order to overcome the shortcomings of the prior art.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] An auxiliary method for determining the stress resistance of an individual plant includes the following steps:

[0010] (1) Determination and acquisition of target sequence: The target sequence is determined based on the alignment results of artificial nucleic acid sequence A in the whole genome sequence of the target species according to the double sequence alignment method;

[0011] (2) Feature subsequence extraction: 26 feature subsequences are extracted from the target sequence in step (1) in order from left to right;

[0012] (3) Target sample small RNA sequencing: Collect individual plant samples to be tested and perform small RNA sequencing on them to obtain the target sample small RNA sequencing library;

[0013] (4) Evaluation of sample resilience: In the small RNA sequencing library of the target sample obtained in step (3), the presence or absence of the 26 feature subsequences extracted in step (2) is detected, and the sample resilience is evaluated.

[0014] In step (1), the artificial nucleic acid sequence A is a consistent sequence generated by multiple sequence alignment of the TAS3 gene of Arabidopsis thaliana, strawberry, blueberry, grape, apple, soybean, peach and tomato.

[0015] Specifically, in step (1), the artificial nucleic acid sequence A has the following nucleotide sequence: TCTTGACCTTGTAAGACCTTTTCTTGACCTTGTAAGACCTCATGCCGTGTCTNTTGTCTTTGTTTTTTNTTTTGTTACAAATTCTGTATCAGAATTNNCTGATATAGAGTTTGGCCNTCGTTCTTCCTCGTTCCCGCCCAACTCATNTTCTCCTTCCTTGTCTATCCCTCCTGAGCT (SEQ ID No. 1)

[0016] In step (1), the determination of the target sequence based on the alignment results of the artificial nucleic acid sequence A specifically involves: searching and determining the target genome sequence position S1 corresponding to the left start point of the artificial nucleic acid sequence A and the target genome sequence position S1 corresponding to the right end point of the artificial nucleic acid sequence A in the whole genome sequence of the target species. 177 And extract S1 to S 177 and S 177 The genome sequence corresponding to the next 21 nucleotide positions is used as the target sequence.

[0017] In step (2), the 26 characteristic subsequences are selected from 356 long subsequences of 21 nucleotides each of the target sequence and its reverse complementary sequence. The basic selection criteria are: the probability of the characteristic subsequence appearing in adverse or non-adverse samples is 30-70%, and it is unbalanced, that is, the sample size difference is greater than 2 times or less than 0.5 times.

[0018] Specifically, in step (2), the 26 characteristic sub-sequences are: the reverse complementary sequences of positions 6-26, 10-30, 17-37, 18-38, 23-43, 25-45, 29-49, 37-57, 40-60, 41-61, 42-62, 59-79, and 71-91 in the target sequence. The following sequences are listed: 84-104 bit reverse complementary sequence, 85-105 bit sequence, 114-134 bit reverse complementary sequence, 123-143 bit reverse complementary sequence, 124-144 bit sequence, 124-144 bit reverse complementary sequence, 126-146 bit sequence, 128-148 bit sequence, 130-150 bit sequence, 138-158 bit reverse complementary sequence, 143-163 bit reverse complementary sequence, 148-168 bit reverse complementary sequence, and 149-169 bit reverse complementary sequence.

[0019] In step (4), the evaluation of sample resilience is based on the expression behavior of the 26 feature subsequences of the target sample and the product of the probability values ​​of the adverse or non-adversity samples to which they belong. The probability of being judged as an adverse sample and a non-adversity sample is calculated according to the product probability method. When the probability of an adverse sample is greater than the probability of a non-adversity sample, the sample is judged as adverse. When the probability of a non-adversity sample is greater than the probability of an adverse sample, the sample is judged as non-adversity.

[0020] Specifically, the expression behavior of the 26 characteristic subsequences and the probability values ​​of their respective adverse or non-adverse samples are generated by a mixture of 2,584 samples from 11 species, including Arabidopsis thaliana, rice, grape, strawberry, blueberry, apple, soybean, peach, tomato, corn, and wheat.

[0021] Specifically, the expression behavior of the 26 feature subsequences and the probability values ​​of their respective adverse or non-adversity samples are shown in Table 1.

[0022] Table 1. Probability values ​​of adversity state judgment for feature sequences.

[0023]

[0024]

[0025] Beneficial effects: This method has high accuracy and can help breeders eliminate candidate individuals that exhibit adverse behavior under non-adverse stress conditions in advance, thereby saving breeders' time, energy, financial resources, and material resources, and improving the efficiency of stress-resistant breeding. This method can also help breeders identify key candidate individuals that exhibit non-adverse characteristics under adverse conditions in advance, thereby clarifying the key breeding targets. Attached Figure Description

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0027] Figure 1 Search results were obtained by using the BLAST double sequence alignment tool in the strawberry genome database to search for the corresponding region of the artificial nucleic acid sequence A.

[0028] Figure 2 Search results were obtained by using the BLAST double sequence alignment tool in the Arabidopsis thaliana genome database to search for the corresponding region of the artificial nucleic acid sequence A. Detailed Implementation

[0029] Unless otherwise specified, the experimental methods described in the following examples are conventional methods; the reagents and materials described are commercially available unless otherwise specified.

[0030] Example 1: An auxiliary method for assessing plant stress resistance

[0031] (1) Determination and acquisition of target sequence

[0032] Using the double sequence alignment tool BLAST (https: / / blast.ncbi.nlm.nih.gov), the left start position S1 and the right end position S2 of the artificial nucleic acid sequence A were searched and identified in the whole genome sequence of the target species. 177 And extract S1 to S 177 and S 177The genomic sequence corresponding to the next 21 nucleotide positions is the target sequence, where artificial nucleic acid sequence A is a consistent sequence generated by multiple sequence alignment of the TAS3 gene of Arabidopsis thaliana, strawberry, blueberry, grape, apple, soybean, peach and tomato using EMBOSS Cons software.

[0033] TCTTGACCTTGTAAGACCTTTTCTTGACCTTGTAAGACCTCATGCCGTGTCTNTTGTCTTTGTTTTTTNTTTTGTTACAAATTCTGTATCAGAATTNNCTGATATAGAGTTTGGCCNTCGTTCTTCCTCGTTCCCGCCCAACTCATNTTCTCCTTCCTTGTCTATCCCTCCTGAGCT (SEQ ID No. 1)

[0034] (2) Feature subsequence extraction

[0035] A self-developed program was used to extract feature subsequences, and then, from left to right, the following reverse complementary sequences were extracted from the target sequence: positions 6-26, 10-30, 17-37, 18-38, 23-43, 25-45, 29-49, 37-57, 40-60, 41-61, 42-62, 59-79, 71-91, and 8... There are 26 characteristic subsequences, including the reverse complementary sequence of bits 4-104, bit 85-105, bit 114-134, bit 123-143, bit 124-144, bit 124-144, bit 126-146, bit 128-148, bit 130-150, bit 138-158, bit 143-163, bit 148-168, and bit 149-169.

[0036] The 26 characteristic subsequences mentioned above were selected from 356 21-nucleotide long subsequences of the target sequence and its reverse complementary sequence. The basic screening criteria were: the probability of occurrence of the sequence in adverse or non-adverse samples was moderate (30-70%) and unbalanced (greater than 2 times or less than 0.5 times).

[0037] The custom program was written in Perl, and the code is as follows:

[0038] #$MySequence stores the target sequence, which varies in different implementations.

[0039] my

[0040] $MySequence='TCTTGACCTTGTAAGACCTTTTCTTGACCTTGTAAGATCTCACGCC GCGTCTGTTGTCTTCCTTCGCATTTCTAACACACCCTGCACCGGAATTCTCCGA TGTAGAGTTTGGCCTCCGCTTCTCCCCGCTCCCGCCATACCCTTCTTCTCCTTCCTT GTCTATCCCTCCTGAGCTAATCTCCACATATATCTTTTG';

[0041] my@a=('6-26 bit reverse complementary sequence','10-30 bit sequence','17-37 bit reverse complementary sequence','18-38 bit sequence','23-43 bit sequence','25-45 bit sequence','29-49 bit reverse complementary sequence','37-57 bit reverse complementary sequence','40-60 bit reverse complementary sequence','41-61 bit reverse complementary sequence','42-62 bit sequence','59-79 bit sequence','71-91 bit reverse complementary sequence','84-104 bit reverse complementary sequence') Sequences ','85-105 bit sequence','114-134 bit reverse complementary sequence','123-143 bit reverse complementary sequence','124-144 bit sequence','124-144 bit reverse complementary sequence','126-146 bit sequence','128-148 bit sequence','130-150 bit sequence','138-158 bit reverse complementary sequence','143-163 bit reverse complementary sequence','148-168 bit reverse complementary sequence','149-169 bit reverse complementary sequence');

[0042] for(my$i=0;$i<=$#a;$i++)

[0043] {chomp($a[$i]);

[0044] if($a[$i]=~ / (\d+)\-(\d+) / )

[0045] {my$a=substr($MySequence,$1-1,($2-$1+1));

[0046] if($a[$i]=~ / reverse / )

[0047] {my$ac=reverse($a);

[0048] $ac=~tr / acgtACGT / tgcaTGCA / ;

[0049] print$a[$i],"\t",uc($ac),"\n";

[0050] }

[0051] else

[0052] {print$a[$i],"\t",uc($a),"\n";

[0053] }

[0054] }}

[0055] (3) Target sample small RNA sequencing

[0056] Plant samples to be tested were collected, and small RNAs were sequenced using existing sequencing technologies to obtain a sample small RNA sequencing library.

[0057] (4) Evaluation of sample resilience

[0058] The expression of the 26 characteristic sequences obtained in step (2) was detected in the small RNA sequencing library. It was assumed that the sample came from either adverse or non-adverse conditions. Based on the probability values ​​of expression and non-expression of each sequence under adverse and non-adverse conditions given in Table 1, the product of the probabilities of the 26 characteristic sequences under adverse and non-adverse conditions (i.e., the product probability method) was calculated. Then, the adverse or non-adverse condition was determined by taking the larger of the two probability products.

[0059] in:

[0060] The formula for calculating the product probability under adverse conditions is: P 逆境 =P1·P2·......·P i ·......P 25 ·P 26

[0061] P i Let be the probability value of whether feature sequence i is expressed or not under the adverse conditions given in Table 1 below.

[0062] The formula for calculating the product probability under non-adversity conditions is: P 非逆境 =P1'·P2'·......·P i '·......P 25 '·P 26 '

[0063] P i ' represents the probability value of whether feature sequence i is expressed or not under the non-adversity conditions given in Table 1 below.

[0064] The probability values ​​of the 26 characteristic sequences in Table 1 below were calculated from 2584 sets of samples from 11 species, including Arabidopsis thaliana, rice, grape, strawberry, blueberry, apple, soybean, peach, tomato, corn, and wheat: (1) The occurrence times n1 and n2 of each characteristic sequence in the adverse and non-adverse samples, and the non-occurrence times n3 and n4 were counted respectively; (2) The total number of samples in which each sequence appeared and the total number of samples in which it did not appear were proportionally increased or decreased to 1000 sets, and the occurrence times N1′ and N2′ of each characteristic sequence in the adverse and non-adverse samples, and the non-occurrence times N3′ and N4′ were generated accordingly; (3) The probability value of each characteristic sequence was calculated according to the following formula:

[0065] Probability of appearing in adversity samples:

[0066] Probability of appearing in non-adversity samples:

[0067] The probability of not appearing in adversity samples:

[0068] The probability of not appearing in non-adversity samples:

[0069] Table 1. Probability values ​​of adversity state judgment for feature sequences.

[0070]

[0071]

[0072] Example 2: Stress Assessment of Strawberry Samples

[0073] (1) Determination of the target sequence

[0074] Using the BLAST double sequence alignment tool in the Strawberry Genome Database (http: / / sequenceserver.njau.edu.cn / ), the corresponding regions of similar sequences to the following artificial nucleic acid sequence A were searched:

[0075] TCTTGACCTTGTAAGACCTTTTCTTGACCTTGTAAGACCTCATGCCGTGTCTNTTGTCTTTGTTTTTTNTTTTGTTACAAATTCTGTATCAGAATTNNCTGATATAGAGTTTGGCCNTCGTTCTTCCTCGTTCCCGCCCAACTCATNTTCTCCTTCCTTGTCTATCCCTCCTGAGCT (SEQ ID No. 1)

[0076] The search results are as follows Figure 1 As shown.

[0077] The first search result shows that the left boundary of the target sequence begins at position 23,838,219 of Fvb4, and the right boundary ends at position 23,838,395 of Fvb4. The target sequence was obtained by extracting positions 23,838,219-23,838,395 and the following 21 nucleotides from the Fvb4 sequence in the strawberry genome, as follows:

[0078] TCTTGACCTTGTAAGACCTTTTCTTGACCTTGTAAGATCTCACGCCGCGTCTG

[0079] TTGTCTTTCTCTTCGCATTTCTAACACACCCTGCACCGGAATTCTCCGATGTAGAG

[0080] TTTGGCCTCCGCTTCTCCCCGCTCCCGCCATACCCTTCTCTCCTTCCTTGTCTATC

[0081] CCTCCTGAGCTAATCTCCACATATATCTTTTG (SEQ ID No. 2)

[0082] (2) Feature subsequence segment extraction and processing

[0083] Extract the following reverse complementary sequences from the target sequence of the strawberry, in order from left to right: 6-26 bits, 10-30 bits, 17-37 bits, 18-38 bits, 23-43 bits, 25-45 bits, 29-49 bits, 37-57 bits, 40-60 bits, 41-61 bits, 42-62 bits, 59-79 bits, 71-91 bits, and 84-104 bits. The results of 26 characteristic subsequences, including the sequence of bits 85-105, 114-134, 123-143, 124-144, 124-144, 126-146, 128-148, 130-150, 138-158, 143-163, 148-168, and 149-169, are shown in Table 2.

[0084] Table 2. 26 characteristic subsequences of strawberry

[0085]

[0086] (3) Target sample small RNA sequencing

[0087] This example directly uses the third-party small RNA sequencing library GSE61798 (https: / / ngdc.cncb.ac.cn / gsa / browse / insdc / SRA185932) from the National Genomics Data Center of China. This dataset contains eight strawberry small RNA sequencing libraries, derived from non-stressed conditions: 4-day ovary wall (GSM1513950), 10-day ovary wall (GSM1513951), 4-day seed (GSM1513952), 10-day seed (GSM1513953), receptacle (GSM1513954), flower bud (GSM1513955), leaf (GSM1513956), and seedling (GSM1513957).

[0088] (4) Evaluation of sample resilience

[0089] The results of the 26 characteristic sequences obtained in step (2) were detected in 8 sets of small RNA sequencing libraries. Based on the expression behavior of each characteristic sequence and the probability value of belonging to adversity or non-adversity sample given in Table 1, the probability of it being judged as adversity sample and non-adversity sample was calculated by the product probability method. The larger of the two was used to determine whether the sample was adversity or non-adversity. The results showed (Table 3) that all of them were non-adversity samples, which was consistent with the sample annotation and the accuracy rate was 100%.

[0090] Table 3. Evaluation results of this method on 8 sets of small RNA sequencing libraries from different strawberry organs.

[0091]

[0092] Example 3: Assessing the Stress Condition in Arabidopsis thaliana Samples

[0093] (1) Determination of the target sequence

[0094] The BLAST double sequence alignment tool in the Arabidopsis genome database (https: / / www.arabidopsis.org / Blast / index.jsp) was used to search for the corresponding region of the artificial nucleic acid sequence A below:

[0095] TCTTGACCTTGTAAGACCTTTTCTTGACCTTGTAAGACCTCATGCCGTGTCTNTTGTCTTTGTTTTTTNTTTTGTTACAAATTCTGTATCAGAATTNNCTGATATAGAGTTTGGCCNTCGTTCTTCCTCGTTCCCGCCCAACTCATNTTCTCCTTCCTTGTCTATCCCTCCTGAGCT (SEQ ID No. 1)

[0096] The search results are as follows Figure 2 As shown.

[0097] The first search result shows that the left boundary of the target sequence begins at position 1366 on chromosome 3, and the right boundary ends at position 1542 on chromosome 3. The target sequence was obtained by extracting positions 1366-1542 and the subsequent 21 nucleotides from the Arabidopsis thaliana chromosome 3 sequence as follows:

[0098] TCTTGACCTTGTAAGGCCTTTTCTTGACCTTGTAAGACCCCATCTCTTTCTAA

[0099] ACGTTTTATTATTTTCTCGTTTTACAGATTCTATTCTATTCTCTTCTCAATATAGAAT

[0100] AGATATCTATCTCTACCTCTAATTCGTTCGAGTCATTTTCTCCTACCTTGTCTATCC

[0101] CTCCTGAGCTAATCTCCACATATATCTTTTG(SEQ ID No.3)

[0102] (2) Feature subsequence segment extraction and processing

[0103] Extract the following reverse complementary sequences from the target sequence in order from left to right: 6-26 bits, 10-30 bits, 17-37 bits, 18-38 bits, 23-43 bits, 25-45 bits, 29-49 bits, 37-57 bits, 40-60 bits, 41-61 bits, 42-62 bits, 59-79 bits, 71-91 bits, and 84-104 bits. The results of 26 characteristic sequences, including the 85-105 bit sequence, the 114-134 bit reverse complementary sequence, the 123-143 bit reverse complementary sequence, the 124-144 bit sequence, the 124-144 bit reverse complementary sequence, the 126-146 bit sequence, the 128-148 bit sequence, the 130-150 bit sequence, the 138-158 bit reverse complementary sequence, the 143-163 bit reverse complementary sequence, the 148-168 bit reverse complementary sequence, and the 149-169 bit reverse complementary sequence, are shown in Table 4.

[0104] Table 4. 26 characteristic subsequences of Arabidopsis thaliana

[0105]

[0106]

[0107] (3) Target sample small RNA sequencing

[0108] This example directly uses the third-party small RNA sequencing library GSE66599 (https: / / ngdc.cncb.ac.cn / gsa / browse / insdc / SRA245746) from the National Genomics Data Center of China. This dataset contains 39 Arabidopsis small RNA sequencing libraries, of which 3 are for non-abiotic stress and 36 are for various abiotic stresses such as drought, high temperature, high salinity, copper excess, copper deficiency, cadmium excess, and sulfur deficiency.

[0109] (4) Evaluation of sample resilience

[0110] The 26 characteristic sequences obtained in step (2) were detected in 39 Arabidopsis thaliana small RNA sequencing libraries. The product probability P(adversity) of the sequence being identified as an adverse sample and the product probability P(non-adversity) of the sequence being identified as a non-adversity sample were calculated according to the criteria in Table 1. The maximum of the two probabilities was used to determine whether the sample was adverse or non-adversity. The results (Table 5) showed that all samples were adverse samples. Compared with the sample annotation, the results of 36 libraries were correct, with an accuracy rate of 92.3%.

[0111] Table 5. Evaluation results of this method on 39 Arabidopsis thaliana small RNA sequencing libraries.

[0112]

[0113]

[0114] This invention provides an auxiliary method for determining the stress resistance of individual plants. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. An auxiliary method for determining the stress resistance of an individual plant, characterized in that, Includes the following steps: (1) Determination and acquisition of target sequence: The target sequence is determined based on the alignment results of artificial nucleic acid sequence A in the whole genome sequence of the target species according to the double sequence alignment method; (2) Feature subsequence extraction: 26 feature subsequences are extracted from the target sequence in step (1) in order from left to right; (3) Target sample small RNA sequencing: Collect individual plant samples to be tested and perform small RNA sequencing on them to obtain the target sample small RNA sequencing library; (4) Evaluation of sample resilience: In the small RNA sequencing library of the target sample obtained in step (3), the presence or absence of the 26 feature subsequences extracted in step (2) is detected, and the sample resilience is evaluated. The artificial nucleic acid sequence A is as follows: TCTTGACCTTGTAAGACCTTTTCTTGACCTTGTAAGACCTCATGCCGTGTCTNTTGTCTTTGTTTTTTNTTTTGTTACAAATTCTGTATCAGAATTNNCTGATATAGAGTTTGGCCNTCGTTCTTCCTCGTTCCCGCCCAACTCATNTTCTCCTTCCTTGTCTATCCCTCCTGAGCT; In step (1), the determination of the target sequence based on the alignment results of the artificial nucleic acid sequence A specifically involves: searching and determining the target genome sequence position S1 corresponding to the left start point of the artificial nucleic acid sequence A and the target genome sequence position S1 corresponding to the right end point of the artificial nucleic acid sequence A in the whole genome sequence of the target species. 177 And extract S1 to S 177 and S 177 The genome sequence corresponding to the next 21 nucleotide positions is used as the target sequence; In step (2), the 26 characteristic sub-sequences are as follows: the reverse complementary sequences of bits 6-26, 10-30, 17-37, 18-38, 23-43, 25-45, 29-49, 37-57, 40-60, 41-61, 42-62, 59-79, and 71-91 in the target sequence. 84-104 bit reverse complementary sequence, 85-105 bit sequence, 114-134 bit reverse complementary sequence, 123-143 bit reverse complementary sequence, 124-144 bit sequence, 124-144 bit reverse complementary sequence, 126-146 bit sequence, 128-148 bit sequence, 130-150 bit sequence, 138-158 bit reverse complementary sequence, 143-163 bit reverse complementary sequence, 148-168 bit reverse complementary sequence, 149-169 bit reverse complementary sequence; In step (4), the evaluation of sample resilience is based on the expression behavior of the 26 feature subsequences of the target sample and the product of the probability values ​​of the adverse or non-adversity samples to which they belong. The probability of being judged as an adverse sample and a non-adversity sample is calculated according to the product probability method. When the probability of an adverse sample is greater than the probability of a non-adversity sample, the sample is judged as adverse. When the probability of a non-adversity sample is greater than the probability of an adverse sample, the sample is judged as non-adversity.

2. The auxiliary method according to claim 1, characterized in that, In step (1), the artificial nucleic acid sequence A is a consistent sequence generated by multiple sequence alignment of the TAS3 gene of Arabidopsis thaliana, strawberry, blueberry, grape, apple, soybean, peach and tomato.

3. The auxiliary method according to claim 1, characterized in that, In step (2), the 26 characteristic subsequences are selected from 356 long subsequences of 21 nucleotides each of the target sequence and its reverse complementary sequence. The basic selection criteria are: the probability of the characteristic subsequence appearing in adverse or non-adverse samples is 30-70%, and it is unbalanced, that is, the sample size difference is greater than 2 times or less than 0.5 times.

4. The auxiliary method according to claim 1, characterized in that, The expression behavior of the 26 characteristic subsequences and the probability values ​​of their respective adverse or non-adverse samples were generated by a mixture of 2,584 samples from 11 species, including Arabidopsis thaliana, rice, grape, strawberry, blueberry, apple, soybean, peach, tomato, corn, and wheat.

5. The auxiliary method according to claim 1 or 4, characterized in that, The expression behavior of the 26 feature subsequences and the probability values ​​of their respective adverse or non-adversity samples are shown in Table 1. Table 1. Probability values ​​of adversity state judgment for feature sequences 。

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