Stress high-photosynthetic-efficiency transcription factor screening method based on gene regulatory network

Abstract

The invention discloses an adversity high-photosynthetic-efficiency transcription factor screening method based on a gene regulatory network, and relates to the technical field of biological information analys.The method comprises the steps that RNA-seq data of rice under normal and stress conditions are obtained; performing standardized quantification on the RNA-seq data to generate a gene expression matrix; obtaining a gene-gene relation matrix according to the gene expression matrix; converting the gene-gene relation matrix into a transcription factor-gene correlation coefficient matrix by predicting a rice genome transcription factor; kEGG pathway annotation is carried out on rice genes, and a transcription factor-gene correlation coefficient matrix is converted into a regulation relation matrix between transcription factor-KEGG pathways; and screening key transcription factors related to photoreaction and carbon metabolic pathways under regulation and control of stress. According to the method, a regulation and control relation matrix between transcription factor-KEGG channels is constructed, and the key transcription factors having remarkable influence on photosynthesis and carbon metabolism are screened out more accurately and efficiently.

Description

Technical Field

[0001] The invention relates to the technical field of bioinformatics analysis, and in particular to a method for screening transcription factors with high light efficiency in adverse conditions based on a gene regulatory network. Background Art

[0002] Faced with the ever-increasing global food demand and increasing environmental pressure, there is an urgent need to increase crop yields to ensure food security and sustainable agricultural development. Photosynthesis is the material basis for the formation of crop yields, and improving photosynthesis is an important way to increase crop yields. Studies have shown that transcription factors participate in various biological processes such as cell differentiation, development, metabolism, and response to environmental changes by regulating gene expression. Therefore, transcription factors have important potential in synergistically regulating photosynthesis and stress response, and increasing yields.

[0003] By constructing gene regulatory networks, researchers have identified some key transcription factors that regulate photosynthetic carbon metabolism, increase yield and stress resistance. With the rapid development of high-throughput sequencing technology, a large number of publicly available omics data resources have been accumulated, especially gene expression data. This provides an opportunity to discover new factors regulating photosynthetic carbon metabolism, but how to use appropriate algorithms to process large data sets and discover key genes that regulate high light efficiency in adverse conditions still requires the development of new bioinformatics analysis methods and processes. Summary of the invention

[0004] The purpose of the present invention is to provide a method for screening transcription factors with high light efficiency in adverse conditions based on a gene regulatory network, so as to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network, the method comprising the steps of:

[0006] S1. Obtain rice RNA-seq data under normal and stress conditions from public databases;

[0007] S2, standardize and quantify RNA-seq data, calculate Pearson correlation coefficient, and generate gene expression matrix;

[0008] S3, according to the gene expression matrix, using Fisher-Z transformation to obtain the gene-gene relationship matrix;

[0009] S4, by predicting the transcription factors of rice genome, using the inverse Gaussian algorithm, the gene-gene relationship matrix is ​​converted into a transcription factor-gene correlation coefficient matrix;

[0010] S5. By annotating rice genes with KEGG pathways, the transcription factor-gene correlation coefficient matrix is ​​converted into a regulatory relationship matrix between transcription factors and KEGG pathways;

[0011] S6. Based on the regulatory relationship matrix between rice transcription factors and KEGG pathways under normal and stress conditions, key transcription factors related to light response and carbon metabolism pathways under stress were screened.

[0012] According to the above scheme, step S1 includes:

[0013] The normal rice RNA-seq data includes raw data obtained by sequencing rice samples collected from different developmental stages and different tissue parts using RNA-seq technology;

[0014] The rice RNA-seq data under stress include raw data obtained by sequencing rice samples collected at different developmental stages and different tissue parts under different non-biological stresses using RNA-seq technology.

[0015] According to the above scheme, step S2 includes:

[0016] Download the rice reference genome sequence file and annotation file;

[0017] According to the rice reference genome sequence file and annotation file, the RNA-seq data is compared with the rice reference genome, the position information of each read segment on the genome is obtained, and gene quantitative analysis is performed;

[0018] The comparison comprises comparing the read segments obtained by RNA-seq sequencing with the rice reference genome to obtain the position information of the read segments on the reference genome;

[0019] The gene quantification, based on the aligned reads, counts the number of aligned reads on each gene and estimates the expression level of each gene, the formula is as follows:

[0020]

[0021] Among them, FPKM is expressed as the number of mapped fragments per million per kilobase of transcript; cDNA Fragments is expressed as the number of fragments mapped to a certain gene; Mapped Reads (Millions) is expressed as the total number of reads mapped to the rice reference genome, in units of millions; Transcript Length (kb) is expressed as the length of the gene transcript, in units of kilobases;

[0022] According to the gene quantification results, the Pearson correlation coefficient between biological replicate samples was calculated to determine the correlation between samples. The formula is as follows:

[0023]

[0024] Where r is the Pearson correlation coefficient; X i and Y i They are expressed as the number of aligned fragments per kilobase of transcript per million for the i-th gene in the two samples, respectively; and They are expressed as the average number of aligned fragments per kilobase of transcript per million for all genes in the two samples, respectively;

[0025] The Pearson correlation coefficient r value ranges from -1 to +1, where: r = +1 indicates a perfect positive correlation; r = -1 indicates a perfect negative correlation; r = 0 indicates no linear correlation;

[0026] Delete samples with a Pearson correlation coefficient less than 0.8 to obtain a gene expression matrix; the gene expression matrix is ​​represented by E = E gs , where g is the rice gene and s is the sample.

[0027] According to the above scheme, step S3 includes:

[0028] Through Fisher-Z transformation, the Pearson correlation coefficient is converted into a standardized correlation coefficient so that the standardized correlation coefficient conforms to the standard normal distribution N(0,1). The formula is as follows:

[0029]

[0030] Wherein, z represents the standardized correlation coefficient after Fisher-Z transformation; r represents the Pearson correlation coefficient;

[0031] Through Fisher-Z transformation, the gene-gene relationship matrix is ​​obtained; the gene-gene relationship matrix is ​​expressed as: C = C g1g2 , where g1 and g2 are rice genes.

[0032] According to the above scheme, step S4 includes:

[0033] Use the iTAK database to predict transcription factors in the rice genome;

[0034] According to the CLR algorithm, the inverse Gaussian method is used to introduce weighted information to calculate the specific correlation coefficients between all rice genes on the rice genome and each transcription factor, and obtain the transcription factor-gene relationship matrix; the transcription factor-gene relationship matrix is ​​expressed as S=S tg , where t is a transcription factor and g is a rice gene.

[0035] According to the above scheme, each transcription factor is divided into two groups according to the gene-gene relationship matrix C = C g1g2 , converted by weighted merging method, the standardized vector V of transcription factor t and rice gene is obtained t , the formula is as follows:

[0036] V t =stouffer(S t );

[0037] Among them, S t It is expressed as the correlation score between transcription factor t and all rice genes;

[0038] Each rice gene is classified according to the gene-gene relationship matrix C = C g1g2 , and the standardized vector V of rice gene g and transcription factor is obtained by weighted merging method. g , the formula is as follows:

[0039] V g =stouffer(S g );

[0040] Among them, S g It is expressed as the correlation score between rice gene g and all transcription factors;

[0041] According to the CLR algorithm, the specific correlation coefficient between each transcription factor and each rice gene was calculated. The formula is as follows:

[0042] CLR(t, g) = S tg =V t T V g ;

[0043] Wherein, CLR(t,g) represents the specific correlation coefficient between transcription factor t and rice gene g; S tg Represented as a transcription factor-gene relationship matrix.

[0044] According to the above scheme, step S5 includes:

[0045] The KEGG pathway annotation is performed on all rice genes in the rice genome using the BlastKOALA tool according to the KEGG database, each rice gene is mapped to a KEGG pathway, and the relevant pathway information is returned;

[0046] Using the Z-scores calculation method in PAGE, the regulatory relationship matrix between transcription factors and KEGG pathways was obtained. The formula is as follows:

[0047]

[0048] Among them, t represents transcription factor; k represents KEGG pathway; Z tk It is represented as the correlation matrix between transcription factor t and KEGG pathway k; Sm tk It is expressed as the average regulatory relationship coefficient of transcription factor t on all genes in KEGG pathway k; μ t Expressed as the average regulatory coefficient of transcription factor t on all genes; δ t It is expressed as the standard deviation of the regulatory relationship coefficient of transcription factor t on all genes.

[0049] According to the above scheme, the regulatory relationship matrix between the transcription factor-KEGG pathway is expressed as Z tk , where t is the transcription factor and k is the KEGG pathway;

[0050] The regulatory relationship matrix between the transcription factor-KEGG pathways includes a regulatory relationship matrix between normal rice transcription factors-KEGG pathways and a regulatory relationship matrix between rice transcription factors-KEGG pathways under stress conditions.

[0051] According to the above scheme, based on the regulatory relationship matrix between the transcription factor-KEGG pathway, a transcription factor-function association network under normal and stress conditions is constructed; the transcription factor-function association network under normal and stress conditions represents the association between each transcription factor in the rice genome and a specific biological pathway under normal and stress conditions, respectively, and is used to predict the transcription factors that regulate specific biological pathways.

[0052] According to the above scheme, step S6 includes:

[0053] The screening regulates key transcription factors related to light reaction and carbon metabolism pathways under adverse conditions, and selects pathways related to photosynthesis, carbon metabolism, carbon fixation and chlorophyll synthesis according to the KEGG database; and screens transcription factors that have a positive regulatory relationship with the KEGG pathways of light reaction and carbon metabolism under normal conditions, and a negative regulatory relationship with the KEGG pathways of light reaction and carbon metabolism under stress conditions according to the regulatory relationship matrix between transcription factors and KEGG pathways.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] 1. The present invention uses RNA-seq data to construct a regulatory relationship matrix between transcription factors and KEGG pathways, and more accurately and efficiently screens out key transcription factors that have a significant impact on photosynthesis and carbon metabolism under stress conditions;

[0056] 2. When constructing a rice gene regulatory network, the present invention converts the gene-gene relationship matrix into a transcription factor-function association network, which is beneficial for predicting key transcription factors that regulate specific biological pathways. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flowchart of the steps of a method for screening transcription factors with high light efficiency in adverse conditions based on a gene regulatory network of the present invention;

[0058] Figure 2 A flow chart for constructing a regulatory relationship matrix between transcription factors and KEGG pathways in a method for screening transcription factors with high light efficiency under adverse conditions based on a gene regulatory network of the present invention;

[0059] Figure 3 This is a rice RNA-seq data set under normal environment for a method for screening transcription factors with high light efficiency under adverse conditions based on gene regulatory network of the present invention;

[0060] Figure 4 This is a rice RNA-seq dataset under partial stress environment for a method for screening transcription factors with high light efficiency under adverse conditions based on gene regulatory network of the present invention;

[0061] Figure 5 This is a method for screening transcription factors with high light efficiency under adverse conditions based on gene regulatory networks of the present invention, and a result diagram of transcription factor screening of transcription factors and light response and carbon metabolism pathway regulatory networks under normal and adverse rice environments;

[0062] Figure 6 A method for screening transcription factors with high light efficiency under adverse conditions based on gene regulatory networks of the present invention is a heat map of the regulatory network of key transcription factors and pathways such as light response and carbon metabolism under normal and stress conditions of rice;

[0063] Figure 7 It is a scatter plot of the regulatory relationship between key transcription factors and gene sets of pathways such as photosynthesis and carbon metabolism under normal and stress conditions in a method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory networks of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Example: Figure 1-Figure 7 As shown, the present invention provides a technical solution, a method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory networks;

[0066] A rice RNA-seq data set is obtained from the NCBI database; the rice RNA-seq data set includes different developmental stages and tissue parts under normal growth conditions and multiple abiotic stress conditions; the different developmental stages and tissue parts under normal growth conditions are a control group; the different developmental stages and tissue parts under multiple abiotic stress conditions are a stress group;

[0067] The control group included 254 samples distributed in 38 data sets, and the stress group included 355 samples distributed in 13 data sets;

[0068] Download the Japanese rice reference genome sequence file and the sequencing and annotation files of the International Rice Genome Project Research Group; the Japanese rice reference genome number is IRGSP-1.0;

[0069] The acquired rice RNA-seq dataset was subjected to quality control checks using FastQC, and low-quality sequences were removed by Trimmomatic software to obtain cleaned reads;

[0070] The cleaned reads after quality control were aligned with the IRGSP-1.0 reference genome using the hi sat2 alignment tool to obtain the position information of the reads on the reference genome;

[0071] HTseq software was used to count the number of reads mapped to each gene, and the FPKM value was calculated as an indicator of gene expression level. The formula is as follows:

[0072]

[0073] Among them, FPKM is expressed as the number of mapped fragments per million per kilobase of transcript; cDNA Fragments is expressed as the number of fragments mapped to a certain gene; Mapped Reads (Millions) is expressed as the total number of reads mapped to the rice reference genome, in units of millions; Transcript Length (kb) is expressed as the length of the gene transcript, in units of kilobases;

[0074] According to the gene quantification results, the Pearson correlation coefficient between biological replicate samples was calculated using the following formula:

[0075]

[0076] Where r is the Pearson correlation coefficient; X i and Y i They are expressed as the number of aligned fragments per kilobase of transcript per million for the i-th gene in the two samples, respectively; and They are expressed as the average number of aligned fragments per kilobase of transcript per million for all genes in the two samples, respectively;

[0077] The Pearson correlation coefficient r value ranges from -1 to +1, where: r = +1 indicates a perfect positive correlation; r = -1 indicates a perfect negative correlation; r = 0 indicates no linear correlation;

[0078] Samples with a Pearson correlation coefficient less than 0.8 were deleted, and finally 238 samples were retained for subsequent analysis. An expression data matrix containing 38,993 rice genes was constructed; the gene expression matrix is ​​represented by E = E gs , where g is the rice gene and s is the sample.

[0079] Through Fisher-Z transformation, the Pearson correlation coefficient is converted into a standardized correlation coefficient so that the standardized correlation coefficient conforms to the standard normal distribution N(0,1). The formula is as follows:

[0080]

[0081] Wherein, z represents the standardized correlation coefficient after Fisher-Z transformation; r represents the Pearson correlation coefficient;

[0082] Through Fisher-Z transformation, the gene-gene relationship matrix is ​​obtained; the gene-gene relationship matrix is ​​expressed as: C = C g1g2 , where g1 and g2 are rice genes.

[0083] Use the iTAK database to predict transcription factors in the rice genome;

[0084] According to the CLR algorithm, the inverse Gaussian method is used to introduce weighted information to calculate the specific correlation coefficients between all rice genes on the rice genome and each transcription factor, and obtain the transcription factor-gene relationship matrix; the transcription factor-gene relationship matrix is ​​expressed as S=S tg , where t is a transcription factor and g is a rice gene.

[0085] Each transcription factor is divided into two groups according to the gene-gene relationship matrix C = C g1g2 , converted by weighted merging method, the standardized vector V of transcription factor t and rice gene is obtained t , the formula is as follows:

[0086] V t =stouffer(S t );

[0087] Among them, S t It is expressed as the correlation score between transcription factor t and all rice genes;

[0088] Each rice gene is classified according to the gene-gene relationship matrix C = C g1g2 , and the standardized vector V of rice gene g and transcription factor is obtained by weighted merging method. g , the formula is as follows:

[0089] V g =stouffer(S g );

[0090] Among them, S g It is expressed as the correlation score between rice gene g and all transcription factors;

[0091] According to the CLR algorithm, the specific correlation coefficient between each transcription factor and each rice gene was calculated. The formula is as follows:

[0092] CLR(t, g) = S tg =V t T V g ;

[0093] Wherein, CLR(t,g) represents the specific correlation coefficient between transcription factor t and rice gene g; S tg Represented as a transcription factor-gene relationship matrix.

[0094] The KEGG pathway annotation is performed on all rice genes in the rice genome using the BlastKOALA tool according to the KEGG database, each rice gene is mapped to a KEGG pathway, and the relevant pathway information is returned;

[0095] Using the Z-scores calculation method in PAGE, the regulatory relationship matrix between transcription factors and KEGG pathways was obtained. The formula is as follows:

[0096]

[0097] Among them, t represents transcription factor; k represents KEGG pathway; Z tk It is represented as the correlation matrix between transcription factor t and KEGG pathway k; Sm tk It is expressed as the average regulatory relationship coefficient of transcription factor t on all genes in KEGG pathway k; μ t Expressed as the average regulatory coefficient of transcription factor t on all genes; δ t It is expressed as the standard deviation of the regulatory relationship coefficient of transcription factor t on all genes.

[0098] The regulatory relationship matrix between the transcription factor-KEGG pathway is represented by Z tk, where t is the transcription factor and k is the KEGG pathway;

[0099] The regulatory relationship matrix between the transcription factor-KEGG pathways includes a regulatory relationship matrix between normal rice transcription factors-KEGG pathways and a regulatory relationship matrix between rice transcription factors-KEGG pathways under stress conditions.

[0100] The screening regulates key transcription factors related to light reaction and carbon metabolism pathways under adverse conditions, and selects pathways related to photosynthesis, carbon metabolism, carbon fixation and chlorophyll synthesis according to the KEGG database; and screens transcription factors that have a positive regulatory relationship with the KEGG pathways of light reaction and carbon metabolism under normal conditions, and a negative regulatory relationship with the KEGG pathways of light reaction and carbon metabolism under stress conditions according to the regulatory relationship matrix between transcription factors and KEGG pathways.

[0101] like Figure 1-Figure 7 As shown, the present invention provides another technical solution, a method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network to screen key transcription factors;

[0102] According to the KEGG database, KEGG pathways related to photosynthesis, carbon metabolism, carbon fixation, and chlorophyll synthesis were selected;

[0103] Under normal conditions, transcription factors that were positively regulated by these KEGG pathways were screened, and 164 candidate transcription factors were finally obtained;

[0104] like Figure 5 As shown in the figure, red indicates a positive correlation, and blue indicates a negative correlation. Under normal conditions, the selected transcription factors show a consistent positive regulatory relationship with pathways related to photosynthesis, carbon metabolism, carbon fixation, and chlorophyll synthesis;

[0105] Under stress conditions, the relationship between these transcription factors and the selected pathways was analyzed, and 8 key transcription factors were screened out that showed positive regulatory relationships under normal conditions but turned into negative regulatory relationships under stress; Figure 6 As shown, under normal conditions, transcription factors have a positive regulatory relationship with pathways such as photosynthesis and carbon metabolism, but under stress conditions, they turn into a negative regulatory relationship;

[0106] Taking one of the transcription factors as an example, the results are as follows Figure 7 As shown in the figure, the X-axis represents the regulatory relationship under normal conditions, and the Y-axis represents the regulatory relationship under stress conditions; the X and Y coordinates of each point correspond to the relationship between pathways such as photosynthesis and carbon metabolism and transcription factors under normal and stress conditions, respectively; when the X and Y values ​​of a KEGG pathway differ greatly, it indicates that the regulatory relationship between the KEGG pathway and the transcription factor has changed significantly under stress;

[0107] Under normal conditions, these transcription factors show a positive regulatory relationship with the related pathways of photosynthesis, carbon metabolism, carbon fixation and chlorophyll synthesis. However, under stress conditions, their regulatory effects are significantly inhibited. It is speculated that when responding to environmental stress, transcription factors maintain a basic level of resistance by regulating photosynthesis and carbon metabolism.

[0108] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory networks, characterized in that: The method comprises the steps of: S1. Obtain rice RNA-seq data under normal and stress conditions; S2, standardize and quantify RNA-seq data to generate a gene expression matrix; S3, obtaining the gene-gene relationship matrix based on the gene expression matrix; S4. By predicting the transcription factors of the rice genome, the gene-gene relationship matrix is ​​converted into a transcription factor-gene correlation coefficient matrix; S5. By annotating rice genes with KEGG pathways, the transcription factor-gene correlation coefficient matrix is ​​converted into a regulatory relationship matrix between transcription factors and KEGG pathways; S6. Screen key transcription factors that regulate light response and carbon metabolism pathways under adverse conditions.

2. The method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network according to claim 1, characterized in that: Step S1 includes: The normal rice RNA-seq data includes raw data obtained by RNA-seq sequencing of rice samples collected from different developmental stages and different tissue parts; The rice RNA-seq data under stress include raw data obtained by RNA-seq sequencing of rice samples collected at different developmental stages and different tissue locations under different non-biological stresses.

3. The method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network according to claim 1, characterized in that: Step S2 includes: Download the rice reference genome sequence file and annotation file; According to the rice reference genome sequence file and annotation file, the RNA-seq data is compared with the rice reference genome, and gene quantitative analysis is performed; The comparison comprises comparing the read segments obtained by RNA-seq sequencing with the rice reference genome to obtain the position information of the read segments on the reference genome; The gene quantification estimates the expression level of each gene by counting the number of reads aligned to each gene according to the aligned reads. The formula is as follows: Among them, FPKM is expressed as the number of mapped fragments per million per kilobase of transcript; cDNA Fragments is expressed as the number of fragments mapped to a certain gene; Mapped Reads (Millions) is expressed as the total number of reads mapped to the rice reference genome, in units of millions; Transcript Length (kb) is expressed as the length of the gene transcript, in units of kilobases; According to the gene quantification results, the Pearson correlation coefficient between biological replicate samples was calculated using the following formula: Where r is the Pearson correlation coefficient; X i and Y i They are expressed as the number of aligned fragments per kilobase of transcript per million for the i-th gene in the two samples, respectively; and They are expressed as the average number of aligned fragments per kilobase of transcript per million for all genes in the two samples, respectively; Delete samples with a Pearson correlation coefficient less than 0.8 to obtain a gene expression matrix; the gene expression matrix is ​​represented by E = E gs , where g is the rice gene and s is the sample.

4. The method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network according to claim 1, characterized in that: Step S3 includes: Through Fisher-Z transformation, the Pearson correlation coefficient is converted into a standardized correlation coefficient so that the standardized correlation coefficient conforms to the standard normal distribution N(0,1). The formula is as follows: Wherein, z represents the standardized correlation coefficient after Fisher-Z transformation; r represents the Pearson correlation coefficient; Through Fisher-Z transformation, the gene-gene relationship matrix is ​​obtained; the gene-gene relationship matrix is ​​expressed as: C = C g1g2 , where g1 and g2 are rice genes.

5. The method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network according to claim 1, characterized in that: Step S4 includes: Use the iTAK database to predict transcription factors in the rice genome; According to the CLR algorithm, the inverse Gaussian method is used to introduce weighted information to calculate the specific correlation coefficients between all rice genes on the rice genome and each transcription factor, and obtain the transcription factor-gene relationship matrix; the transcription factor-gene relationship matrix is ​​expressed as S=S tg , where t is a transcription factor and g is a rice gene.

6. The method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network according to claim 5, characterized in that: Each transcription factor is divided into two groups according to the gene-gene relationship matrix C = C g1g2 , converted by weighted merging method, the standardized vector V of transcription factor t and rice gene is obtained t , the formula is as follows: V t =stouffer(S t ); Among them, S t It is expressed as the correlation score between transcription factor t and all rice genes; Each rice gene is classified according to the gene-gene relationship matrix C = C g1g2 , and the standardized vector V of rice gene g and transcription factor is obtained by weighted merging method. g , the formula is as follows: V g =stouffer(S g ); Among them, S g It is expressed as the correlation score between rice gene g and all transcription factors; According to the CLR algorithm, the specific correlation coefficient between each transcription factor and each rice gene was calculated. The formula is as follows: CLR(t,g)=S tg =V t T V g ; Wherein, CLR(t,g) represents the specific correlation coefficient between transcription factor t and rice gene g; S tg Represented as a transcription factor-gene relationship matrix.

7. The method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network according to claim 1, characterized in that: Step S5 includes: The KEGG pathway annotation is performed on all rice genes in the rice genome using the BlastKOALA tool according to the KEGG database, each rice gene is mapped to a KEGG pathway, and the relevant pathway information is returned; Using the Z-scores calculation method in PAGE, the regulatory relationship matrix between transcription factors and KEGG pathways was obtained. The formula is as follows: Among them, t represents transcription factor; k represents KEGG pathway; Z tk It is represented as the correlation matrix between transcription factor t and KEGG pathway k; Sm tk It is expressed as the average regulatory relationship coefficient of transcription factor t on all genes in KEGG pathway k; μ t Expressed as the average regulatory coefficient of transcription factor t on all genes; δ t It is expressed as the standard deviation of the regulatory relationship coefficient of transcription factor t on all genes.

8. The method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network according to claim 7, characterized in that: The regulatory relationship matrix between the transcription factor-KEGG pathway is represented by Z tk , where t is the transcription factor and k is the KEGG pathway; The regulatory relationship matrix between the transcription factor-KEGG pathways includes a regulatory relationship matrix between normal rice transcription factors-KEGG pathways and a regulatory relationship matrix between rice transcription factors-KEGG pathways under stress conditions.

9. The method for screening transcription factors with high light efficiency in adverse conditions based on gene regulatory network according to claim 7, characterized in that: According to the regulatory relationship matrix between the transcription factor-KEGG pathway, a transcription factor-function association network under normal and stress conditions is constructed; the transcription factor-function association network under normal and stress conditions represents the association between each transcription factor in the rice genome and a specific biological pathway under normal and stress conditions, respectively, and is used to predict the transcription factors that regulate specific biological pathways.

10. The method for screening transcription factors with high light efficiency under adverse conditions based on gene regulatory network according to claim 1, characterized in that: Step S6 includes: The screening regulates key transcription factors related to light reaction and carbon metabolism pathways under adverse conditions, and selects pathways related to photosynthesis, carbon metabolism, carbon fixation and chlorophyll synthesis according to the KEGG database; and screens transcription factors that have a positive regulatory relationship with the KEGG pathways of light reaction and carbon metabolism under normal conditions, and a negative regulatory relationship with the KEGG pathways of light reaction and carbon metabolism under stress conditions according to the regulatory relationship matrix between transcription factors and KEGG pathways.

Images