Intelligent prediction method of rice salt tolerance phenotype based on machine learning model

Abstract

The invention provides an intelligent prediction method for a rice salt tolerance phenotype based on a machine learning model, and relates to the technical field of bioinformatics. The rice salt-tolerant phenotype prediction method provided by the invention comprises the following steps: acquiring transcriptome data and phenotype data of rice; screening out differential genes related to salt stress resistance according to transcriptome data; and training an XGBoost model by using data of the differential genes, then processing transcriptome data of rice to be tested by using the trained XGBoost model, and predicting the salt tolerance phenotype of the rice to be tested. The prediction method can efficiently and accurately realize the prediction of the salt-tolerant phenotype of the rice. The rice salt-tolerant phenotype prediction device provided by the invention can be used for predicting the rice salt-tolerant phenotype, and the prediction result is accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of bioinformatics, and in particular to an intelligent prediction method for rice salt-tolerance phenotype based on a machine learning model. Background Art

[0002] Identification of rice salt tolerance is a key and basic link in the exploration of excellent germplasm resources and the cultivation of salt-tolerant varieties. Traditional methods for identifying rice salt tolerance mainly include salt tolerance evaluation during the germination period, tillering period and the entire growth period, such as the calculation of indicators such as survival rate, dead leaf rate, salt tolerance grade, and relative salt damage rate. In the specific implementation process, this method system obtains early salt tolerance data through seed germination experiments, combines the determination of physiological and biochemical indicators under salt stress treatment in the seedling stage, and the investigation of agronomic traits during the entire growth period in the field, to achieve a comprehensive evaluation of the salt tolerance of rice materials. However, the above identification system has significant limitations: first, there is inconsistency between the salt tolerance phenotype of rice seedlings and the salt tolerance performance at maturity; second, field identification based on the entire growth period requires the completion of the crop's entire growth cycle in a saline-alkali environment. This process requires high land resources, time costs and manpower investment, and it is difficult to meet the needs of rapid screening of large-scale germplasm resources.

[0003] In view of this, the present invention is proposed. Summary of the Invention

[0004] The first object of the present invention is to provide a method for predicting the salt-tolerance phenotype of rice to solve the above technical problems.

[0005] The second object of the present invention is to provide a device for predicting the salt-tolerance phenotype of rice.

[0006] The third object of the present invention is to provide an application of differential genes in the prediction of rice salt tolerance phenotype.

[0007] In order to achieve the above objectives, the following technical solutions are adopted:

[0008] In a first aspect, the present invention provides a method for predicting the salt-tolerance phenotype of rice, comprising the following steps:

[0009] a. Obtaining transcriptome data and phenotypic data of rice; the transcriptome data includes gene expression data of rice seedlings under normal treatment and gene expression data of rice seedlings under salt stress treatment; the phenotypic data includes plant height of mature rice under normal field management and plant height of mature rice under field management cultivated in saline-alkali land and irrigated with salt water;

[0010] b. Screening differentially expressed genes related to salt stress tolerance based on the transcriptome data;

[0011] c. Using the data of the differentially expressed genes to train an XGBoost model, and then using the trained XGBoost model to process the transcriptome data of the rice to be tested to predict the salt-tolerance phenotype of the rice to be tested; the transcriptome data of the rice to be tested includes the gene expression data of the normally treated rice seedlings to be tested and the gene expression data of the salt-stress treated rice seedlings to be tested.

[0012] As a further technical solution, the transcriptome data of rice is obtained by sequencing the above-ground parts of rice seedlings.

[0013] As a further technical solution, edegR was used to screen differentially expressed genes.

[0014] As a further technical solution, the differential genes are:

[0015] LOC_Os01g16530, LOC_Os04g46980, LOC_Os03g1284, LOC_Os01g14410, LOC_Os04g08740, LOC_Os01g46030, LOC_Os02g40450, LOC_Os03g15780, LOC_Os07g44430, LOC_Os03g58790, LOC_Os03g31150, LOC_Os07g12910, LOC_Os02g47200, LOC_Os10g28360, LOC_Os03g24930, LOC_Os04g57850, LOC_Os06 g41770, LOC_Os09g28310, LOC_Os09g23560, LOC_Os12g44310, LOC_Os02g05470, LOC_Os02g34560, LOC_Os07g09190, LOC_Os02g33430, LOC_Os04g34460, LOC_Os08g42850, LOC_Os02g44780, LOC_Os01g14630, LOC_Os03g06740, LOC_Os03g46770, LOC_Os10g30850, LOC_Os08g39840, LOC_Os02g01150, LOC_Os01g08320, LOC_Os05g38420, LOC_Os11g26570, LOC_Os08g06280, LOC_Os01g45830, LOC_Os06g27760, LOC_Os01g60020, LOC_Os09g02214, LOC_Os05g38150, LOC_Os08g45190, LOC_Os05g31670, LOC_Os02g12580, LOC_Os12g19470, LOC_Os03g16740, LOC_Os02g03040, LOC_Os03g61990, LOC_Os0 4g49210, LOC_Os01g45750, LOC_Os02g56500, LOC_Os11g26160, LOC_Os11g34270, LOC_Os05g50550, LOC_Os06g20320, LOC_Os01g01280, LOC_Os01g68480, LOC_Os06g51290, LOC_Os12g43130, LOC_Os04g02110, LOC_Os03g46650, LOC_Os07g36570, LOC_Os01g12420, LOC_Os12g43720, LOC_Os09g11480,LOC_Os09g11460, LOC_Os03g05806, LOC_Os05g28740, LOC_Os03g55930, LOC_Os05g02130, LOC_Os03g63480, LOC_Os01g07700, LOC_Os01g10610, LOC_Os01g11230, LOC_Os01g11520, LOC_Os01g11946, LOC_Os01g15490, LOC_Os01g21420, LOC_Os01g28540, LOC_Os01g33110, LOC_Os01g42800, LOC_Os0 1g43870, LOC_Os01g47350, LOC_Os01g47450, LOC_Os01g47490, LOC_Os01g50930, LOC_Os01g51040, LOC_Os01g51530, LOC_Os01g52170, LOC_Os01g55820, LOC_Os01g55880, LOC_Os01g56100, LOC_Os01g60830, LOC_Os01g61700, LOC_Os01g62440, LOC_Os01g62810, LOC_Os01g64630, LOC_Os01g65200, LOC_Os01g66170, LOC_Os01g66600, LOC_Os01g66850, LOC_Os01g69060, LOC_Os01g70080, LOC_Os01g70570, LOC_Os01g74650, LOC_Os02g02930, LOC_Os02g03050, LOC_Os02g10520, LOC_Os02g13170, LOC_Os02g15660, LOC_Os02g17304, LOC_Os02g20490, LOC_Os02g30310, LOC_Os02g32490, LOC_Os0 2g37880, LOC_Os02g39870, LOC_Os02g43090, LOC_Os02g43340, LOC_Os02g44102, LOC_Os02g45670, LOC_Os02g46320, LOC_Os02g47310, LOC_Os02g48770, LOC_Os02g52730, LOC_Os02g52770, LOC_Os02g54720, LOC_Os02g55120, LOC_Os03g01570, LOC_Os03g03020, LOC_Os03g05320, LOC_Os03g07180,LOC_Os03g07960, LOC_Os03g10060, LOC_Os03g12510, LOC_Os03g14040, LOC_Os03g16350, LOC_Os03g19709, LOC_Os03g19760, LOC_Os03g20330, LOC_Os03g26080, LOC_Os03g27760, LOC_Os03g28400, LOC_Os03g29770, LOC_Os03g31290, LOC_Os03g34280, LOC_Os03g37640, LOC_Os03g39830, LOC_Os0 3g42320, LOC_Os03g45930, LOC_Os03g47540, LOC_Os03g49630, LOC_Os03g50350, LOC_Os03g51160, LOC_Os03g55640, LOC_Os03g55970, LOC_Os03g56280, LOC_Os03g56370, LOC_Os03g56580, LOC_Os03g56869, LOC_Os03g57910, LOC_Os03g58750, LOC_Os03g61030, LOC_Os03g61490, LOC_Os03g61540, LOC_Os03g63850, LOC_Os04g02050, LOC_Os04g02970, LOC_Os04g04030, LOC_Os04g16080, LOC_Os04g18884, LOC_Os04g20200, LOC_Os04g32090, LOC_Os04g33920, LOC_Os04g37820, LOC_Os04g41870, LOC_Os04g43030, LOC_Os04g45490, LOC_Os04g46170, LOC_Os04g49350, LOC_Os04g51140, LOC_Os0 4g52110, LOC_Os04g52860, LOC_Os04g55600, LOC_Os04g55610, LOC_Os04g56320, LOC_Os04g57210, LOC_Os04g58734, LOC_Os04g58900, LOC_Os04g59150, LOC_Os04g59190, LOC_Os04g59200, LOC_Os04g59630, LOC_Os05g01110, LOC_Os05g05480, LOC_Os05g10690, LOC_Os05g11260, LOC_Os05g27660,LOC_Os05g28500、LOC_Os05g30250、LOC_Os05g31020、LOC_Os05g32220、LOC_Os05g33010、LOC_Os05g33500、LOC_Os05g33520、LOC_Os05g35460、LOC_Os05g39580、LOC_Os05g44020、LOC_Os05g46500、LOC_Os05g48855、LOC_Os05g50280、LOC_Os05g50980、LOC_Os05g51850、LOC_Os06g01460、LOC_Os0 6g05950, LOC_Os06g06170, LOC_Os06g12030, LOC_Os06g13660, LOC_Os06g19070, LOC_Os06g22340, LOC_Os06g23350, LOC_Os06g25010, LOC_Os06g28240, LOC_Os06g39344, LOC_Os06g43304, LOC_Os06g45100, LOC_Os06g47770, LOC_Os06g47800, LOC_Os06g49760, LOC_Os06g50070, LOC_Os06g50130, LOC_Os07g05870, LOC_Os07g06800, LOC_Os07g07320, LOC_Os07g18750, LOC_Os07g28040, LOC_Os07g33660, LOC_Os07g34260, LOC_Os07g38110, LOC_Os07g43050, LOC_Os07g46350, LOC_Os07g46780, LOC_Os07g46920, LOC_Os08g02230, LOC_Os08g06190, LOC_Os08g07540, LOC_Os08g08850, LOC_Os0 8g14440, LOC_Os08g14880, LOC_Os08g15460, LOC_Os08g20020, LOC_Os08g25700, LOC_Os08g29770, LOC_Os08g30210, LOC_Os08g30550, LOC_Os08g32630, LOC_Os08g36440, LOC_Os08g37345, LOC_Os08g44470, LOC_Os09g02400, LOC_Os09g04100, LOC_Os09g07570, LOC_Os09g08390, LOC_Os09g10980,LOC_Os09g30250, LOC_Os09g32290, LOC_Os10g01110, LOC_Os10g10170, LOC_Os10g19300, LOC_Os10g231 80. LOC_Os10g31810, LOC_Os10g32300, LOC_Os10g33104, LOC_Os10g38890, LOC_Os10g41040, LOC_Os10g 41749, LOC_Os11g02450, LOC_Os11g02964, LOC_Os11g11210, LOC_Os11g13620, LOC_Os11g13750, LOC_Os 11g16580, LOC_Os11g17610, LOC_Os11g26190, LOC_Os11g32890, LOC_Os11g34020, LOC_Os11g34720, LOC_ Os11g34840, LOC_Os11g34870, LOC_Os11g37300, LOC_Os11g37390, LOC_Os11g39020, LOC_Os11g39030, L OC_Os11g47960, LOC_Os11g48020, LOC_Os12g01210, LOC_Os12g02060, LOC_Os12g03860, LOC_Os12g0426 0, LOC_Os12g05230, LOC_Os12g07810, LOC_Os12g12580, LOC_Os12g17320, LOC_Os12g19530, LOC_Os12g21789, LOC_Os12g33130, LOC_Os12g34890, LOC_Os12g36170, and LOC_Os12g40790 (the reference gene is the Nipponbare reference genome (MSU7.0)).

[0016] As a further technical solution, the parameters of the XGBoost model are set as follows:

[0017] r (number of trees) = 400, colsample (proportion of features used to build each tree) = 0.5, eta (shrinkage of feature weights) = 0.04, subsample (fraction of the training data sample used to train each additional tree) = 0.3.

[0018] In a second aspect, the present invention provides a prediction device for salt-tolerance phenotype of rice, comprising an acquisition module and a prediction module;

[0019] The acquisition module is used to acquire transcriptome data of the rice to be tested; the transcriptome data of the rice to be tested includes gene expression data of the rice seedlings to be tested under normal treatment and gene expression data of the rice seedlings to be tested under salt stress treatment;

[0020] The prediction module is used to input the transcriptome data of the rice to be tested into a pre-trained prediction model, process the transcriptome data of the rice to be tested by the prediction model, and predict the salt tolerance phenotype of the rice to be tested;

[0021] The prediction model is trained by the following method:

[0022] a. Obtaining transcriptome data and phenotypic data of rice; the transcriptome data includes gene expression data of the normally treated rice seedlings and gene expression data of the salt stress treated rice seedlings; the phenotypic data includes plant height of mature rice under normal field management and plant height of mature rice under field management grown in saline-alkali land and irrigated with salt water;

[0023] b. Screening differentially expressed genes related to salt stress tolerance based on the transcriptome data;

[0024] c. Using the data of the differentially expressed genes to train the XGBoost model to obtain a pre-trained prediction model.

[0025] As a further technical solution, the transcriptome data of rice is obtained by sequencing the above-ground parts of rice seedlings.

[0026] As a further technical solution, the differential genes are:

[0027] LOC_Os01g16530, LOC_Os04g46980, LOC_Os03g1284, LOC_Os01g14410, LOC_Os04g08740, LOC_Os01g46030, LOC_Os02g40450, LOC_Os03g15780, LOC_Os07g44430, LOC_Os03g58790, LOC_Os03g31150, LOC_Os07g12910, LOC_Os02g47200, LOC_Os10g28360, LOC_Os03g24930, LOC_Os04g57850, LOC_Os06 g41770, LOC_Os09g28310, LOC_Os09g23560, LOC_Os12g44310, LOC_Os02g05470, LOC_Os02g34560, LOC_Os07g09190, LOC_Os02g33430, LOC_Os04g34460, LOC_Os08g42850, LOC_Os02g44780, LOC_Os01g14630, LOC_Os03g06740, LOC_Os03g46770, LOC_Os10g30850, LOC_Os08g39840, LOC_Os02g01150, LOC_Os01g08320, LOC_Os05g38420, LOC_Os11g26570, LOC_Os08g06280, LOC_Os01g45830, LOC_Os06g27760, LOC_Os01g60020, LOC_Os09g02214, LOC_Os05g38150, LOC_Os08g45190, LOC_Os05g31670, LOC_Os02g12580, LOC_Os12g19470, LOC_Os03g16740, LOC_Os02g03040, LOC_Os03g61990, LOC_Os0 4g49210, LOC_Os01g45750, LOC_Os02g56500, LOC_Os11g26160, LOC_Os11g34270, LOC_Os05g50550, LOC_Os06g20320, LOC_Os01g01280, LOC_Os01g68480, LOC_Os06g51290, LOC_Os12g43130, LOC_Os04g02110, LOC_Os03g46650, LOC_Os07g36570, LOC_Os01g12420, LOC_Os12g43720, LOC_Os09g11480,LOC_Os09g11460, LOC_Os03g05806, LOC_Os05g28740, LOC_Os03g55930, LOC_Os05g02130, LOC_Os03g63480, LOC_Os01g07700, LOC_Os01g10610, LOC_Os01g11230, LOC_Os01g11520, LOC_Os01g11946, LOC_Os01g15490, LOC_Os01g21420, LOC_Os01g28540, LOC_Os01g33110, LOC_Os01g42800, LOC_Os0 1g43870, LOC_Os01g47350, LOC_Os01g47450, LOC_Os01g47490, LOC_Os01g50930, LOC_Os01g51040, LOC_Os01g51530, LOC_Os01g52170, LOC_Os01g55820, LOC_Os01g55880, LOC_Os01g56100, LOC_Os01g60830, LOC_Os01g61700, LOC_Os01g62440, LOC_Os01g62810, LOC_Os01g64630, LOC_Os01g65200, LOC_Os01g66170, LOC_Os01g66600, LOC_Os01g66850, LOC_Os01g69060, LOC_Os01g70080, LOC_Os01g70570, LOC_Os01g74650, LOC_Os02g02930, LOC_Os02g03050, LOC_Os02g10520, LOC_Os02g13170, LOC_Os02g15660, LOC_Os02g17304, LOC_Os02g20490, LOC_Os02g30310, LOC_Os02g32490, LOC_Os0 2g37880, LOC_Os02g39870, LOC_Os02g43090, LOC_Os02g43340, LOC_Os02g44102, LOC_Os02g45670, LOC_Os02g46320, LOC_Os02g47310, LOC_Os02g48770, LOC_Os02g52730, LOC_Os02g52770, LOC_Os02g54720, LOC_Os02g55120, LOC_Os03g01570, LOC_Os03g03020, LOC_Os03g05320, LOC_Os03g07180,LOC_Os03g07960, LOC_Os03g10060, LOC_Os03g12510, LOC_Os03g14040, LOC_Os03g16350, LOC_Os03g19709, LOC_Os03g19760, LOC_Os03g20330, LOC_Os03g26080, LOC_Os03g27760, LOC_Os03g28400, LOC_Os03g29770, LOC_Os03g31290, LOC_Os03g34280, LOC_Os03g37640, LOC_Os03g39830, LOC_Os0 3g42320, LOC_Os03g45930, LOC_Os03g47540, LOC_Os03g49630, LOC_Os03g50350, LOC_Os03g51160, LOC_Os03g55640, LOC_Os03g55970, LOC_Os03g56280, LOC_Os03g56370, LOC_Os03g56580, LOC_Os03g56869, LOC_Os03g57910, LOC_Os03g58750, LOC_Os03g61030, LOC_Os03g61490, LOC_Os03g61540, LOC_Os03g63850, LOC_Os04g02050, LOC_Os04g02970, LOC_Os04g04030, LOC_Os04g16080, LOC_Os04g18884, LOC_Os04g20200, LOC_Os04g32090, LOC_Os04g33920, LOC_Os04g37820, LOC_Os04g41870, LOC_Os04g43030, LOC_Os04g45490, LOC_Os04g46170, LOC_Os04g49350, LOC_Os04g51140, LOC_Os0 4g52110, LOC_Os04g52860, LOC_Os04g55600, LOC_Os04g55610, LOC_Os04g56320, LOC_Os04g57210, LOC_Os04g58734, LOC_Os04g58900, LOC_Os04g59150, LOC_Os04g59190, LOC_Os04g59200, LOC_Os04g59630, LOC_Os05g01110, LOC_Os05g05480, LOC_Os05g10690, LOC_Os05g11260, LOC_Os05g27660,LOC_Os05g28500、LOC_Os05g30250、LOC_Os05g31020、LOC_Os05g32220、LOC_Os05g33010、LOC_Os05g33500、LOC_Os05g33520、LOC_Os05g35460、LOC_Os05g39580、LOC_Os05g44020、LOC_Os05g46500、LOC_Os05g48855、LOC_Os05g50280、LOC_Os05g50980、LOC_Os05g51850、LOC_Os06g01460、LOC_Os0 6g05950, LOC_Os06g06170, LOC_Os06g12030, LOC_Os06g13660, LOC_Os06g19070, LOC_Os06g22340, LOC_Os06g23350, LOC_Os06g25010, LOC_Os06g28240, LOC_Os06g39344, LOC_Os06g43304, LOC_Os06g45100, LOC_Os06g47770, LOC_Os06g47800, LOC_Os06g49760, LOC_Os06g50070, LOC_Os06g50130, LOC_Os07g05870, LOC_Os07g06800, LOC_Os07g07320, LOC_Os07g18750, LOC_Os07g28040, LOC_Os07g33660, LOC_Os07g34260, LOC_Os07g38110, LOC_Os07g43050, LOC_Os07g46350, LOC_Os07g46780, LOC_Os07g46920, LOC_Os08g02230, LOC_Os08g06190, LOC_Os08g07540, LOC_Os08g08850, LOC_Os0 8g14440, LOC_Os08g14880, LOC_Os08g15460, LOC_Os08g20020, LOC_Os08g25700, LOC_Os08g29770, LOC_Os08g30210, LOC_Os08g30550, LOC_Os08g32630, LOC_Os08g36440, LOC_Os08g37345, LOC_Os08g44470, LOC_Os09g02400, LOC_Os09g04100, LOC_Os09g07570, LOC_Os09g08390, LOC_Os09g10980,LOC_Os09g30250, LOC_Os09g32290, LOC_Os10g01110, LOC_Os10g10170, LOC_Os10g19300, LOC_Os10g 23180, LOC_Os10g31810, LOC_Os10g32300, LOC_Os10g33104, LOC_Os10g38890, LOC_Os10g41040, LOC _Os10g41749, LOC_Os11g02450, LOC_Os11g02964, LOC_Os11g11210, LOC_Os11g13620, LOC_Os11g137 50. LOC_Os11g16580, LOC_Os11g17610, LOC_Os11g26190, LOC_Os11g32890, LOC_Os11g34020, LOC_Os1 1g34720, LOC_Os11g34840, LOC_Os11g34870, LOC_Os11g37300, LOC_Os11g37390, LOC_Os11g39020, L OC_Os11g39030, LOC_Os11g47960, LOC_Os11g48020, LOC_Os12g01210, LOC_Os12g02060, LOC_Os12g03 860, LOC_Os12g04260, LOC_Os12g05230, LOC_Os12g07810, LOC_Os12g12580, LOC_Os12g17320, LOC_O s12g19530, LOC_Os12g21789, LOC_Os12g33130, LOC_Os12g34890, LOC_Os12g36170 and LOC_Os12g40790. ,

[0028] As a further technical solution, the parameters of the XGBoost model are set as follows:

[0029] r=400, colsample=0.5, eta=0.04, subsample=0.3.

[0030] In a third aspect, the present invention provides an application of differential genes in predicting rice salt tolerance phenotypes, wherein the differential genes are:

[0031] LOC_Os01g16530, LOC_Os04g46980, LOC_Os03g1284, LOC_Os01g14410, LOC_Os04g08740, LOC_Os01g46030, LOC_Os02g40450, LOC_Os03g15780, LOC_Os07g44430, LOC_Os03g58790, LOC_Os03g31150, LOC_Os07g12910, LOC_Os02g47200, LOC_Os10g28360, LOC_Os03g24930, LOC_Os04g57850, LOC_Os06 g41770, LOC_Os09g28310, LOC_Os09g23560, LOC_Os12g44310, LOC_Os02g05470, LOC_Os02g34560, LOC_Os07g09190, LOC_Os02g33430, LOC_Os04g34460, LOC_Os08g42850, LOC_Os02g44780, LOC_Os01g14630, LOC_Os03g06740, LOC_Os03g46770, LOC_Os10g30850, LOC_Os08g39840, LOC_Os02g01150, LOC_Os01g08320, LOC_Os05g38420, LOC_Os11g26570, LOC_Os08g06280, LOC_Os01g45830, LOC_Os06g27760, LOC_Os01g60020, LOC_Os09g02214, LOC_Os05g38150, LOC_Os08g45190, LOC_Os05g31670, LOC_Os02g12580, LOC_Os12g19470, LOC_Os03g16740, LOC_Os02g03040, LOC_Os03g61990, LOC_Os0 4g49210, LOC_Os01g45750, LOC_Os02g56500, LOC_Os11g26160, LOC_Os11g34270, LOC_Os05g50550, LOC_Os06g20320, LOC_Os01g01280, LOC_Os01g68480, LOC_Os06g51290, LOC_Os12g43130, LOC_Os04g02110, LOC_Os03g46650, LOC_Os07g36570, LOC_Os01g12420, LOC_Os12g43720, LOC_Os09g11480,LOC_Os09g11460, LOC_Os03g05806, LOC_Os05g28740, LOC_Os03g55930, LOC_Os05g02130, LOC_Os03g63480, LOC_Os01g07700, LOC_Os01g10610, LOC_Os01g11230, LOC_Os01g11520, LOC_Os01g11946, LOC_Os01g15490, LOC_Os01g21420, LOC_Os01g28540, LOC_Os01g33110, LOC_Os01g42800, LOC_Os0 1g43870, LOC_Os01g47350, LOC_Os01g47450, LOC_Os01g47490, LOC_Os01g50930, LOC_Os01g51040, LOC_Os01g51530, LOC_Os01g52170, LOC_Os01g55820, LOC_Os01g55880, LOC_Os01g56100, LOC_Os01g60830, LOC_Os01g61700, LOC_Os01g62440, LOC_Os01g62810, LOC_Os01g64630, LOC_Os01g65200, LOC_Os01g66170, LOC_Os01g66600, LOC_Os01g66850, LOC_Os01g69060, LOC_Os01g70080, LOC_Os01g70570, LOC_Os01g74650, LOC_Os02g02930, LOC_Os02g03050, LOC_Os02g10520, LOC_Os02g13170, LOC_Os02g15660, LOC_Os02g17304, LOC_Os02g20490, LOC_Os02g30310, LOC_Os02g32490, LOC_Os0 2g37880, LOC_Os02g39870, LOC_Os02g43090, LOC_Os02g43340, LOC_Os02g44102, LOC_Os02g45670, LOC_Os02g46320, LOC_Os02g47310, LOC_Os02g48770, LOC_Os02g52730, LOC_Os02g52770, LOC_Os02g54720, LOC_Os02g55120, LOC_Os03g01570, LOC_Os03g03020, LOC_Os03g05320, LOC_Os03g07180,LOC_Os03g07960, LOC_Os03g10060, LOC_Os03g12510, LOC_Os03g14040, LOC_Os03g16350, LOC_Os03g19709, LOC_Os03g19760, LOC_Os03g20330, LOC_Os03g26080, LOC_Os03g27760, LOC_Os03g28400, LOC_Os03g29770, LOC_Os03g31290, LOC_Os03g34280, LOC_Os03g37640, LOC_Os03g39830, LOC_Os0 3g42320, LOC_Os03g45930, LOC_Os03g47540, LOC_Os03g49630, LOC_Os03g50350, LOC_Os03g51160, LOC_Os03g55640, LOC_Os03g55970, LOC_Os03g56280, LOC_Os03g56370, LOC_Os03g56580, LOC_Os03g56869, LOC_Os03g57910, LOC_Os03g58750, LOC_Os03g61030, LOC_Os03g61490, LOC_Os03g61540, LOC_Os03g63850, LOC_Os04g02050, LOC_Os04g02970, LOC_Os04g04030, LOC_Os04g16080, LOC_Os04g18884, LOC_Os04g20200, LOC_Os04g32090, LOC_Os04g33920, LOC_Os04g37820, LOC_Os04g41870, LOC_Os04g43030, LOC_Os04g45490, LOC_Os04g46170, LOC_Os04g49350, LOC_Os04g51140, LOC_Os0 4g52110, LOC_Os04g52860, LOC_Os04g55600, LOC_Os04g55610, LOC_Os04g56320, LOC_Os04g57210, LOC_Os04g58734, LOC_Os04g58900, LOC_Os04g59150, LOC_Os04g59190, LOC_Os04g59200, LOC_Os04g59630, LOC_Os05g01110, LOC_Os05g05480, LOC_Os05g10690, LOC_Os05g11260, LOC_Os05g27660,LOC_Os05g28500、LOC_Os05g30250、LOC_Os05g31020、LOC_Os05g32220、LOC_Os05g33010、LOC_Os05g33500、LOC_Os05g33520、LOC_Os05g35460、LOC_Os05g39580、LOC_Os05g44020、LOC_Os05g46500、LOC_Os05g48855、LOC_Os05g50280、LOC_Os05g50980、LOC_Os05g51850、LOC_Os06g01460、LOC_Os0 6g05950, LOC_Os06g06170, LOC_Os06g12030, LOC_Os06g13660, LOC_Os06g19070, LOC_Os06g22340, LOC_Os06g23350, LOC_Os06g25010, LOC_Os06g28240, LOC_Os06g39344, LOC_Os06g43304, LOC_Os06g45100, LOC_Os06g47770, LOC_Os06g47800, LOC_Os06g49760, LOC_Os06g50070, LOC_Os06g50130, LOC_Os07g05870, LOC_Os07g06800, LOC_Os07g07320, LOC_Os07g18750, LOC_Os07g28040, LOC_Os07g33660, LOC_Os07g34260, LOC_Os07g38110, LOC_Os07g43050, LOC_Os07g46350, LOC_Os07g46780, LOC_Os07g46920, LOC_Os08g02230, LOC_Os08g06190, LOC_Os08g07540, LOC_Os08g08850, LOC_Os0 8g14440, LOC_Os08g14880, LOC_Os08g15460, LOC_Os08g20020, LOC_Os08g25700, LOC_Os08g29770, LOC_Os08g30210, LOC_Os08g30550, LOC_Os08g32630, LOC_Os08g36440, LOC_Os08g37345, LOC_Os08g44470, LOC_Os09g02400, LOC_Os09g04100, LOC_Os09g07570, LOC_Os09g08390, LOC_Os09g10980,LOC_Os09g30250, LOC_Os09g32290, LOC_Os10g01110, LOC_Os10g10170, LOC_Os10g19300, LOC_Os10g 23180, LOC_Os10g31810, LOC_Os10g32300, LOC_Os10g33104, LOC_Os10g38890, LOC_Os10g41040, LOC _Os10g41749, LOC_Os11g02450, LOC_Os11g02964, LOC_Os11g11210, LOC_Os11g13620, LOC_Os11g137 50. LOC_Os11g16580, LOC_Os11g17610, LOC_Os11g26190, LOC_Os11g32890, LOC_Os11g34020, LOC_Os1 1g34720, LOC_Os11g34840, LOC_Os11g34870, LOC_Os11g37300, LOC_Os11g37390, LOC_Os11g39020, L OC_Os11g39030, LOC_Os11g47960, LOC_Os11g48020, LOC_Os12g01210, LOC_Os12g02060, LOC_Os12g03 860, LOC_Os12g04260, LOC_Os12g05230, LOC_Os12g07810, LOC_Os12g12580, LOC_Os12g17320, LOC_O s12g19530, LOC_Os12g21789, LOC_Os12g33130, LOC_Os12g34890, LOC_Os12g36170 and LOC_Os12g40790. ,

[0032] Based on the above differential genes, machine learning models can be used to predict the salt tolerance phenotype of rice.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention provides a method for predicting the salt-tolerance phenotype of rice. By analyzing rice transcriptome and phenotypic data, differentially expressed genes associated with salt stress tolerance are screened. The screened differentially expressed genes are then used to construct an XGBoost model to predict the salt-tolerance phenotype of rice. This method can efficiently and accurately predict the salt-tolerance phenotype of rice.

[0035] The device for predicting the salt-tolerance phenotype of rice provided by the present invention can be used for predicting the salt-tolerance phenotype of rice, and the prediction result has good accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 The phenotypic prediction accuracy for 43 samples;

[0038] Figure 2 Correlation between observed and predicted plant heights of 16 samples. DETAILED DESCRIPTION

[0039] Below in conjunction with embodiment and example, embodiment of the present invention is described in detail, but those skilled in the art will appreciate that the following embodiment and example are only used to illustrate the present invention, and should not be considered as limiting the scope of the present invention. Based on the embodiment in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative work premise all fall within the scope of protection of the present invention. Unspecified conditions are carried out according to the conditions of normal conditions or manufacturer's recommendations. Reagents used or instruments not specified by the manufacturer are conventional products that can be purchased commercially.

[0040] In a first aspect, the present invention provides a method for predicting the salt-tolerance phenotype of rice, comprising the following steps:

[0041] a. Obtaining transcriptome data and phenotypic data of rice; the transcriptome data includes gene expression data of rice seedlings under normal treatment and gene expression data of rice seedlings under salt stress treatment; the phenotypic data includes plant height of mature rice under normal field management and plant height of mature rice under field management cultivated in saline-alkali land and irrigated with salt water;

[0042] b. Screening differentially expressed genes related to salt stress tolerance based on the transcriptome data;

[0043] c. Using the data of the differentially expressed genes to train an XGBoost model, and then using the trained XGBoost model to process the transcriptome data of the rice to be tested to predict the salt-tolerance phenotype of the rice to be tested; the transcriptome data of the rice to be tested includes the gene expression data of the normally treated rice seedlings to be tested and the gene expression data of the salt-stress treated rice seedlings to be tested.

[0044] It should be noted that, in the present invention, normal treatment refers to culturing rice using conventional methods, such as culturing using Yoshida nutrient solution; salt stress treatment refers to adding salt on the basis of conventional culture, such as culturing using Yoshida nutrient solution added with 150mM NaCl; normal field management refers to conventional field management during rice cultivation; the difference between field management with saline irrigation and normal field management is that, when planting in saline-alkali land, saline is used for irrigation, and the concentration of the saline can be, for example, 5‰.

[0045] The present invention provides a method for predicting the salt-tolerance phenotype of rice. By analyzing rice transcriptome and phenotypic data, differentially expressed genes associated with salt stress tolerance are screened. The screened differentially expressed genes are then used to construct an XGBoost model to predict the salt-tolerance phenotype of rice. This method can efficiently and accurately predict the salt-tolerance phenotype of rice.

[0046] In some optional embodiments, the transcriptome data of rice is obtained by sequencing the above-ground parts of rice seedlings.

[0047] In some optional embodiments, the transcriptome data analysis method includes: using Trimmomatic to perform quality control on all raw sequencing data, then aligning the quality-controlled data to the Nipponbare reference genome (MSU7.0) using Hisat2. The aligned sam files are then converted to bam format using Samtools and indexed. Finally, all genes are quantified using featureCounts software according to the annotation file provided with the Nipponbare reference genome.

[0048] The present invention does not impose any specific restrictions on normal treatment and salt stress treatment. In some optional embodiments, the gene expression data of the rice seedlings under normal treatment are the gene expression data of the rice seedlings 24 hours after normal treatment; the gene expression data of the rice seedlings under salt stress treatment are the gene expression data of the rice seedlings 24 hours after salt stress treatment.

[0049] In some optional embodiments, edegR is used to screen differential genes.

[0050] In some optional implementations, the parameters of the XGBoost model are set as follows:

[0051] r=400, colsample=0.5, eta=0.04, subsample=0.3.

[0052] In a second aspect, the present invention provides a prediction device for salt-tolerance phenotype of rice, comprising an acquisition module and a prediction module;

[0053] The acquisition module is used to acquire transcriptome data of the rice to be tested; the transcriptome data of the rice to be tested includes gene expression data of the rice seedlings to be tested under normal treatment and gene expression data of the rice seedlings to be tested under salt stress treatment;

[0054] The prediction module is used to input the transcriptome data of the rice to be tested into a pre-trained prediction model, process the transcriptome data of the rice to be tested by the prediction model, and predict the salt tolerance phenotype of the rice to be tested;

[0055] The prediction model is trained by the following method:

[0056] a. Obtaining transcriptome data and phenotypic data of rice; the transcriptome data includes gene expression data of the normally treated rice seedlings and gene expression data of the salt stress treated rice seedlings; the phenotypic data includes plant height of mature rice under normal field management and plant height of mature rice under field management cultivated in saline-alkali land and irrigated with salt water;

[0057] b. Screening differentially expressed genes related to salt stress tolerance based on the transcriptome data;

[0058] c. Using the data of the differentially expressed genes to train the XGBoost model to obtain a pre-trained prediction model.

[0059] The device for predicting the salt-tolerance phenotype of rice provided by the present invention is based on the above prediction method. The device can be used for predicting the salt-tolerance phenotype of rice, and the prediction result has good accuracy.

[0060] In some optional embodiments, the transcriptome data of rice is obtained by sequencing the above-ground parts of rice seedlings.

[0061] In some optional embodiments, edegR is used to screen differential genes.

[0062] In some optional implementations, the parameters of the XGBoost model are set as follows:

[0063] r=400, colsample=0.5, eta=0.04, subsample=0.3.

[0064] The present invention is further described below by way of specific examples. However, it should be understood that these examples are merely provided for more detailed description and are not to be construed as limiting the present invention in any form.

[0065] Example 1

[0066] Part I: Transcriptome data analysis and gene quantification:

[0067] In this study, a total of 43 rice germplasm resources were sequenced for normal transcriptomes and salt stress transcriptomes (sequencing data from: Uncovering key salt-tolerant regulators through a combined eQTL and GWAS analysis using the super pan-genome in rice [J]. National Science Review (English Edition), 2024, 11 (4): 49-63. DOI: 10.1093 / nsr / nwae043.). The specific sample processing process is as follows: seeds were soaked in 37°C water for two days. The germinated seeds were sown in a 96-well plate with the bottom removed. Then they were cultured in complete Yoshida nutrient solution for 16 days. The culture conditions were 12 hours of light / 12 hours of darkness, and the temperature was 30±2°C. The nutrient solution was changed every 3 days during the entire culture process. The seedlings were then divided into two parts, one was cultured in complete Yoshida nutrient solution, and the other was cultured in Yoshida nutrient solution supplemented with 150mM NaCl. After 24 hours of culture, the entire aboveground part was taken for transcriptome sequencing.

[0068] Phenotypic data are collected from a field experiment in Dongying, Shandong Province. All samples were grown under normal field management and under saline-alkali soil irrigated with 5‰ saline water. Plant height was measured at maturity under both field management practices as a phenotype.

[0069] All raw sequencing data were quality-controlled using Trimmomatic and then aligned to the Nipponbare reference genome (MSU7.0) using Hisat2. The aligned sam files were then converted to bam format and indexed using Samtools. Finally, featureCounts software was used to quantify all genes according to the annotation file provided with the Nipponbare reference genome.

[0070] Part II: Differential expression analysis between normal and salt-treated expression levels:

[0071] First, we filtered out low-expression genotypes from the expression matrix (retaining those with a CPM > 1 in at least 10 samples), normalized them, and used the R package (RUVSeq) to calculate batch effects for subsequent analysis. We then used the R package (edegR) for differential expression analysis. We divided the samples into five groups, reserving one sample each time and performing differential analysis on the remaining four samples for 43 replicates. We then selected genes with significant differential expression (FDR < 0.05) across all 43 replicates as core salt stress differentially expressed genes. Ultimately, we identified 309 core differentially expressed genes for subsequent candidate machine learning.

[0072] The differentially expressed genes are as follows:

[0073] LOC_Os01g16530, LOC_Os04g46980, LOC_Os03g1284, LOC_Os01g14410, LOC_Os04g08740, LOC_Os01g46030, LOC_Os02g40450, LOC_Os03g15780, LOC_Os07g44430, LOC_Os03g58790, LOC_Os03g31150, LOC_Os07g12910, LOC_Os02g47200, LOC_Os10g28360, LOC_Os03g24930, LOC_Os04g57850, LOC_Os06 g41770, LOC_Os09g28310, LOC_Os09g23560, LOC_Os12g44310, LOC_Os02g05470, LOC_Os02g34560, LOC_Os07g09190, LOC_Os02g33430, LOC_Os04g34460, LOC_Os08g42850, LOC_Os02g44780, LOC_Os01g14630, LOC_Os03g06740, LOC_Os03g46770, LOC_Os10g30850, LOC_Os08g39840, LOC_Os02g01150, LOC_Os01g08320, LOC_Os05g38420, LOC_Os11g26570, LOC_Os08g06280, LOC_Os01g45830, LOC_Os06g27760, LOC_Os01g60020, LOC_Os09g02214, LOC_Os05g38150, LOC_Os08g45190, LOC_Os05g31670, LOC_Os02g12580, LOC_Os12g19470, LOC_Os03g16740, LOC_Os02g03040, LOC_Os03g61990, LOC_Os0 4g49210, LOC_Os01g45750, LOC_Os02g56500, LOC_Os11g26160, LOC_Os11g34270, LOC_Os05g50550, LOC_Os06g20320, LOC_Os01g01280, LOC_Os01g68480, LOC_Os06g51290, LOC_Os12g43130, LOC_Os04g02110, LOC_Os03g46650, LOC_Os07g36570, LOC_Os01g12420, LOC_Os12g43720, LOC_Os09g11480,LOC_Os09g11460, LOC_Os03g05806, LOC_Os05g28740, LOC_Os03g55930, LOC_Os05g02130, LOC_Os03g63480, LOC_Os01g07700, LOC_Os01g10610, LOC_Os01g11230, LOC_Os01g11520, LOC_Os01g11946, LOC_Os01g15490, LOC_Os01g21420, LOC_Os01g28540, LOC_Os01g33110, LOC_Os01g42800, LOC_Os0 1g43870, LOC_Os01g47350, LOC_Os01g47450, LOC_Os01g47490, LOC_Os01g50930, LOC_Os01g51040, LOC_Os01g51530, LOC_Os01g52170, LOC_Os01g55820, LOC_Os01g55880, LOC_Os01g56100, LOC_Os01g60830, LOC_Os01g61700, LOC_Os01g62440, LOC_Os01g62810, LOC_Os01g64630, LOC_Os01g65200, LOC_Os01g66170, LOC_Os01g66600, LOC_Os01g66850, LOC_Os01g69060, LOC_Os01g70080, LOC_Os01g70570, LOC_Os01g74650, LOC_Os02g02930, LOC_Os02g03050, LOC_Os02g10520, LOC_Os02g13170, LOC_Os02g15660, LOC_Os02g17304, LOC_Os02g20490, LOC_Os02g30310, LOC_Os02g32490, LOC_Os0 2g37880, LOC_Os02g39870, LOC_Os02g43090, LOC_Os02g43340, LOC_Os02g44102, LOC_Os02g45670, LOC_Os02g46320, LOC_Os02g47310, LOC_Os02g48770, LOC_Os02g52730, LOC_Os02g52770, LOC_Os02g54720, LOC_Os02g55120, LOC_Os03g01570, LOC_Os03g03020, LOC_Os03g05320, LOC_Os03g07180,LOC_Os03g07960, LOC_Os03g10060, LOC_Os03g12510, LOC_Os03g14040, LOC_Os03g16350, LOC_Os03g19709, LOC_Os03g19760, LOC_Os03g20330, LOC_Os03g26080, LOC_Os03g27760, LOC_Os03g28400, LOC_Os03g29770, LOC_Os03g31290, LOC_Os03g34280, LOC_Os03g37640, LOC_Os03g39830, LOC_Os0 3g42320, LOC_Os03g45930, LOC_Os03g47540, LOC_Os03g49630, LOC_Os03g50350, LOC_Os03g51160, LOC_Os03g55640, LOC_Os03g55970, LOC_Os03g56280, LOC_Os03g56370, LOC_Os03g56580, LOC_Os03g56869, LOC_Os03g57910, LOC_Os03g58750, LOC_Os03g61030, LOC_Os03g61490, LOC_Os03g61540, LOC_Os03g63850, LOC_Os04g02050, LOC_Os04g02970, LOC_Os04g04030, LOC_Os04g16080, LOC_Os04g18884, LOC_Os04g20200, LOC_Os04g32090, LOC_Os04g33920, LOC_Os04g37820, LOC_Os04g41870, LOC_Os04g43030, LOC_Os04g45490, LOC_Os04g46170, LOC_Os04g49350, LOC_Os04g51140, LOC_Os0 4g52110, LOC_Os04g52860, LOC_Os04g55600, LOC_Os04g55610, LOC_Os04g56320, LOC_Os04g57210, LOC_Os04g58734, LOC_Os04g58900, LOC_Os04g59150, LOC_Os04g59190, LOC_Os04g59200, LOC_Os04g59630, LOC_Os05g01110, LOC_Os05g05480, LOC_Os05g10690, LOC_Os05g11260, LOC_Os05g27660,LOC_Os05g28500、LOC_Os05g30250、LOC_Os05g31020、LOC_Os05g32220、LOC_Os05g33010、LOC_Os05g33500、LOC_Os05g33520、LOC_Os05g35460、LOC_Os05g39580、LOC_Os05g44020、LOC_Os05g46500、LOC_Os05g48855、LOC_Os05g50280、LOC_Os05g50980、LOC_Os05g51850、LOC_Os06g01460、LOC_Os0 6g05950, LOC_Os06g06170, LOC_Os06g12030, LOC_Os06g13660, LOC_Os06g19070, LOC_Os06g22340, LOC_Os06g23350, LOC_Os06g25010, LOC_Os06g28240, LOC_Os06g39344, LOC_Os06g43304, LOC_Os06g45100, LOC_Os06g47770, LOC_Os06g47800, LOC_Os06g49760, LOC_Os06g50070, LOC_Os06g50130, LOC_Os07g05870, LOC_Os07g06800, LOC_Os07g07320, LOC_Os07g18750, LOC_Os07g28040, LOC_Os07g33660, LOC_Os07g34260, LOC_Os07g38110, LOC_Os07g43050, LOC_Os07g46350, LOC_Os07g46780, LOC_Os07g46920, LOC_Os08g02230, LOC_Os08g06190, LOC_Os08g07540, LOC_Os08g08850, LOC_Os0 8g14440, LOC_Os08g14880, LOC_Os08g15460, LOC_Os08g20020, LOC_Os08g25700, LOC_Os08g29770, LOC_Os08g30210, LOC_Os08g30550, LOC_Os08g32630, LOC_Os08g36440, LOC_Os08g37345, LOC_Os08g44470, LOC_Os09g02400, LOC_Os09g04100, LOC_Os09g07570, LOC_Os09g08390, LOC_Os09g10980,LOC_Os09g30250, LOC_Os09g32290, LOC_Os10g01110, LOC_Os10g10170, LOC_Os10g19300, LOC_Os10g 23180, LOC_Os10g31810, LOC_Os10g32300, LOC_Os10g33104, LOC_Os10g38890, LOC_Os10g41040, LOC _Os10g41749, LOC_Os11g02450, LOC_Os11g02964, LOC_Os11g11210, LOC_Os11g13620, LOC_Os11g137 50. LOC_Os11g16580, LOC_Os11g17610, LOC_Os11g26190, LOC_Os11g32890, LOC_Os11g34020, LOC_Os1 1g34720, LOC_Os11g34840, LOC_Os11g34870, LOC_Os11g37300, LOC_Os11g37390, LOC_Os11g39020, L OC_Os11g39030, LOC_Os11g47960, LOC_Os11g48020, LOC_Os12g01210, LOC_Os12g02060, LOC_Os12g03 860, LOC_Os12g04260, LOC_Os12g05230, LOC_Os12g07810, LOC_Os12g12580, LOC_Os12g17320, LOC_O s12g19530, LOC_Os12g21789, LOC_Os12g33130, LOC_Os12g34890, LOC_Os12g36170 and LOC_Os12g40790. ,

[0074] The third part is to use XGBoost to predict phenotypes based on differentially expressed genes:

[0075] Based on the 309 core differentially expressed genes, the cpm function of the edeg package was first used to convert the original counts matrix into a CMP expression matrix and perform a log transformation. 43 samples were trained and tested based on a 5-fold cross-validation strategy. Specifically, the XGBoost model (R version) was used with the following parameters: r (number of trees) = 400, colsample (feature ratio for building each tree) = 0.5, eta (shrinkage of feature weights) = 0.04, and subsample (the portion of the training data sample used to train each additional tree) = 0.3. The 43 samples were divided into 5 parts, 4 of which were used as training sets, and the remaining 1 was used as a test set for model training. This method was used to predict all samples, and the Pearson coefficient was used to calculate the correlation coefficient between the true phenotype and the predicted phenotype to evaluate the model prediction accuracy ( Figure 1 ), and calculate the average prediction accuracy of all samples (0.7121) as the final accuracy of the model.

[0076] Example 2

[0077] The trained model was used to predict plant heights of 16 new samples under normal and salt treatment. The expression levels of 309 core differentially expressed genes in these 16 samples under normal and salt treatment (treatment method is the same as Example 1) were used as input, and the correlation coefficient between the predicted value and the observed value was calculated as the accuracy of the model prediction ( Figure 2 ), and finally the predicted phenotypic values ​​were obtained (Table 1), and the model accuracy was calculated to be 0.6982.

[0078] Table 1 Observed and predicted phenotypic values ​​of 16 samples

[0079]

[0080]

[0081] Note: “CK” in the table stands for normal treatment, and “SALT” stands for salt stress treatment.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting salt tolerance phenotype of rice, characterized in that: The following steps are involved: a. Obtaining transcriptome data and phenotypic data of rice; the transcriptome data includes gene expression data of rice seedlings treated normally and gene expression data of rice seedlings treated with salt stress; the phenotypic data includes plant height of mature rice under normal field management and plant height of mature rice under field management grown in saline-alkali land and irrigated with salt water; b. Screening differentially expressed genes related to salt stress tolerance based on the transcriptome data; c. Using the data of the differentially expressed genes to train an XGBoost model, and then using the trained XGBoost model to process the transcriptome data of the rice to be tested to predict the salt-tolerance phenotype of the rice to be tested; the transcriptome data of the rice to be tested includes the gene expression data of the normally treated rice seedlings to be tested and the gene expression data of the salt-stress treated rice seedlings to be tested.

2. The prediction method according to claim 1, characterized in that Rice transcriptome data was obtained by sequencing the aboveground parts of rice seedlings.

3. The prediction method according to claim 1, wherein: edegR was used to screen differentially expressed genes.

4. The prediction method according to claim 1, wherein: The differential genes are: LOC_Os01g16530, LOC_Os04g46980, LOC_Os03g1284, LOC_Os01g14410, LOC_Os04g08740, LOC_Os01g46030, LOC_Os02g40450, LOC_Os03g15780, LOC_Os07g44430, LOC_Os03g58790, LOC_Os03g31150, LOC_Os07g12910, LOC_Os02g47200, LOC_Os10g28360, LOC_Os03g24930, LOC_Os04g57850, LOC_Os06 g41770, LOC_Os09g28310, LOC_Os09g23560, LOC_Os12g44310, LOC_Os02g05470, LOC_Os02g34560, LOC_Os07g09190, LOC_Os02g33430, LOC_Os04g34460, LOC_Os08g42850, LOC_Os02g44780, LOC_Os01g14630, LOC_Os03g06740, LOC_Os03g46770, LOC_Os10g30850, LOC_Os08g39840, LOC_Os02g01150, LOC_Os01g08320, LOC_Os05g38420, LOC_Os11g26570, LOC_Os08g06280, LOC_Os01g45830, LOC_Os06g27760, LOC_Os01g60020, LOC_Os09g02214, LOC_Os05g38150, LOC_Os08g45190, LOC_Os05g31670, LOC_Os02g12580, LOC_Os12g19470, LOC_Os03g16740, LOC_Os02g03040, LOC_Os03g61990, LOC_Os0 4g49210, LOC_Os01g45750, LOC_Os02g56500, LOC_Os11g26160, LOC_Os11g34270, LOC_Os05g50550, LOC_Os06g20320, LOC_Os01g01280, LOC_Os01g68480, LOC_Os06g51290, LOC_Os12g43130, LOC_Os04g02110, LOC_Os03g46650, LOC_Os07g36570, LOC_Os01g12420, LOC_Os12g43720, LOC_Os09g11480,LOC_Os09g11460, LOC_Os03g05806, LOC_Os05g28740, LOC_Os03g55930, LOC_Os05g02130, LOC_Os03g63480, LOC_Os01g07700, LOC_Os01g10610, LOC_Os01g11230, LOC_Os01g11520, LOC_Os01g11946, LOC_Os01g15490, LOC_Os01g21420, LOC_Os01g28540, LOC_Os01g33110, LOC_Os01g42800, LOC_Os0 1g43870, LOC_Os01g47350, LOC_Os01g47450, LOC_Os01g47490, LOC_Os01g50930, LOC_Os01g51040, LOC_Os01g51530, LOC_Os01g52170, LOC_Os01g55820, LOC_Os01g55880, LOC_Os01g56100, LOC_Os01g60830, LOC_Os01g61700, LOC_Os01g62440, LOC_Os01g62810, LOC_Os01g64630, LOC_Os01g65200, LOC_Os01g66170, LOC_Os01g66600, LOC_Os01g66850, LOC_Os01g69060, LOC_Os01g70080, LOC_Os01g70570, LOC_Os01g74650, LOC_Os02g02930, LOC_Os02g03050, LOC_Os02g10520, LOC_Os02g13170, LOC_Os02g15660, LOC_Os02g17304, LOC_Os02g20490, LOC_Os02g30310, LOC_Os02g32490, LOC_Os0 2g37880, LOC_Os02g39870, LOC_Os02g43090, LOC_Os02g43340, LOC_Os02g44102, LOC_Os02g45670, LOC_Os02g46320, LOC_Os02g47310, LOC_Os02g48770, LOC_Os02g52730, LOC_Os02g52770, LOC_Os02g54720, LOC_Os02g55120, LOC_Os03g01570, LOC_Os03g03020, LOC_Os03g05320, LOC_Os03g07180,LOC_Os03g07960, LOC_Os03g10060, LOC_Os03g12510, LOC_Os03g14040, LOC_Os03g16350, LOC_Os03g19709, LOC_Os03g19760, LOC_Os03g20330, LOC_Os03g26080, LOC_Os03g27760, LOC_Os03g28400, LOC_Os03g29770, LOC_Os03g31290, LOC_Os03g34280, LOC_Os03g37640, LOC_Os03g39830, LOC_Os0 3g42320, LOC_Os03g45930, LOC_Os03g47540, LOC_Os03g49630, LOC_Os03g50350, LOC_Os03g51160, LOC_Os03g55640, LOC_Os03g55970, LOC_Os03g56280, LOC_Os03g56370, LOC_Os03g56580, LOC_Os03g56869, LOC_Os03g57910, LOC_Os03g58750, LOC_Os03g61030, LOC_Os03g61490, LOC_Os03g61540, LOC_Os03g63850, LOC_Os04g02050, LOC_Os04g02970, LOC_Os04g04030, LOC_Os04g16080, LOC_Os04g18884, LOC_Os04g20200, LOC_Os04g32090, LOC_Os04g33920, LOC_Os04g37820, LOC_Os04g41870, LOC_Os04g43030, LOC_Os04g45490, LOC_Os04g46170, LOC_Os04g49350, LOC_Os04g51140, LOC_Os0 4g52110, LOC_Os04g52860, LOC_Os04g55600, LOC_Os04g55610, LOC_Os04g56320, LOC_Os04g57210, LOC_Os04g58734, LOC_Os04g58900, LOC_Os04g59150, LOC_Os04g59190, LOC_Os04g59200, LOC_Os04g59630, LOC_Os05g01110, LOC_Os05g05480, LOC_Os05g10690, LOC_Os05g11260, LOC_Os05g27660,LOC_Os05g28500、LOC_Os05g30250、LOC_Os05g31020、LOC_Os05g32220、LOC_Os05g33010、LOC_Os05g33500、LOC_Os05g33520、LOC_Os05g35460、LOC_Os05g39580、LOC_Os05g44020、LOC_Os05g46500、LOC_Os05g48855、LOC_Os05g50280、LOC_Os05g50980、LOC_Os05g51850、LOC_Os06g01460、LOC_Os0 6g05950, LOC_Os06g06170, LOC_Os06g12030, LOC_Os06g13660, LOC_Os06g19070, LOC_Os06g22340, LOC_Os06g23350, LOC_Os06g25010, LOC_Os06g28240, LOC_Os06g39344, LOC_Os06g43304, LOC_Os06g45100, LOC_Os06g47770, LOC_Os06g47800, LOC_Os06g49760, LOC_Os06g50070, LOC_Os06g50130, LOC_Os07g05870, LOC_Os07g06800, LOC_Os07g07320, LOC_Os07g18750, LOC_Os07g28040, LOC_Os07g33660, LOC_Os07g34260, LOC_Os07g38110, LOC_Os07g43050, LOC_Os07g46350, LOC_Os07g46780, LOC_Os07g46920, LOC_Os08g02230, LOC_Os08g06190, LOC_Os08g07540, LOC_Os08g08850, LOC_Os0 8g14440, LOC_Os08g14880, LOC_Os08g15460, LOC_Os08g20020, LOC_Os08g25700, LOC_Os08g29770, LOC_Os08g30210, LOC_Os08g30550, LOC_Os08g32630, LOC_Os08g36440, LOC_Os08g37345, LOC_Os08g44470, LOC_Os09g02400, LOC_Os09g04100, LOC_Os09g07570, LOC_Os09g08390, LOC_Os09g10980,LOC_Os09g30250、LOC_Os09g32290、LOC_Os10g01110、LOC_Os10g10170、LOC_Os10g19300、LOC_Os10g23180、LOC_Os10g31810、LOC_Os10g32300、LOC_Os10g33104、LOC_Os10g38890、LOC_Os10g41040、LOC_ Os10g41749、LOC_Os11g02450、LOC_Os11g02964、LOC_Os11g11210、LOC_Os11g13620、LOC_Os11g1375 0、LOC_Os11g16580、LOC_Os11g17610、LOC_Os11g26190、LOC_Os11g32890、LOC_Os11g34020、LOC_Os11 g34720、LOC_Os11g34840、LOC_Os11g34870、LOC_Os11g37300、LOC_Os11g37390、LOC_Os11g39020、LO C_Os11g39030、LOC_Os11g47960、LOC_Os11g48020、LOC_Os12g01210、LOC_Os12g02060、LOC_Os12g038 60、LOC_Os12g04260、LOC_Os12g05230、LOC_Os12g07810、LOC_Os12g12580、LOC_Os12g17320、LOC_Os12g19530、LOC_Os12g21789、LOC_Os12g33130、LOC_Os12g34890、LOC_Os12g36170 and LOC_Os12g40790。、 5. The prediction method according to claim 1, wherein: The parameters of the XGBoost model are set as follows: r=400, colsample=0.5, eta=0.04, subsample=0.

3.

6. A device for predicting salt tolerance phenotype of rice, characterized in that: Includes acquisition module and prediction module; The acquisition module is used to acquire transcriptome data of the rice to be tested; the transcriptome data of the rice to be tested includes gene expression data of the rice seedlings to be tested under normal treatment and gene expression data of the rice seedlings to be tested under salt stress treatment; The prediction module is used to input the transcriptome data of the rice to be tested into a pre-trained prediction model, process the transcriptome data of the rice to be tested by the prediction model, and predict the salt tolerance phenotype of the rice to be tested; The prediction model is trained by the following method: a. Obtaining transcriptome data and phenotypic data of rice; the transcriptome data includes gene expression data of the normally treated rice seedlings and gene expression data of the salt stress treated rice seedlings; the phenotypic data includes plant height of mature rice under normal field management and plant height of mature rice under field management cultivated in saline-alkali land and irrigated with salt water; b. Screening differentially expressed genes related to salt stress tolerance based on the transcriptome data; c. Using the data of the differentially expressed genes to train the XGBoost model to obtain a pre-trained prediction model.

7. The prediction device according to claim 6, characterized in that Rice transcriptome data was obtained by sequencing the aboveground parts of rice seedlings.

8. The prediction device according to claim 6, characterized in that The differential genes are: LOC_Os01g16530, LOC_Os04g46980, LOC_Os03g1284, LOC_Os01g14410, LOC_Os04g08740, LOC_Os01g46030, LOC_Os02g40450, LOC_Os03g15780, LOC_Os07g44430, LOC_Os03g58790, LOC_Os03g31150, LOC_Os07g12910, LOC_Os02g47200, LOC_Os10g28360, LOC_Os03g24930, LOC_Os04g57850, LOC_Os06 g41770, LOC_Os09g28310, LOC_Os09g23560, LOC_Os12g44310, LOC_Os02g05470, LOC_Os02g34560, LOC_Os07g09190, LOC_Os02g33430, LOC_Os04g34460, LOC_Os08g42850, LOC_Os02g44780, LOC_Os01g14630, LOC_Os03g06740, LOC_Os03g46770, LOC_Os10g30850, LOC_Os08g39840, LOC_Os02g01150, LOC_Os01g08320, LOC_Os05g38420, LOC_Os11g26570, LOC_Os08g06280, LOC_Os01g45830, LOC_Os06g27760, LOC_Os01g60020, LOC_Os09g02214, LOC_Os05g38150, LOC_Os08g45190, LOC_Os05g31670, LOC_Os02g12580, LOC_Os12g19470, LOC_Os03g16740, LOC_Os02g03040, LOC_Os03g61990, LOC_Os0 4g49210, LOC_Os01g45750, LOC_Os02g56500, LOC_Os11g26160, LOC_Os11g34270, LOC_Os05g50550, LOC_Os06g20320, LOC_Os01g01280, LOC_Os01g68480, LOC_Os06g51290, LOC_Os12g43130, LOC_Os04g02110, LOC_Os03g46650, LOC_Os07g36570, LOC_Os01g12420, LOC_Os12g43720, LOC_Os09g11480,LOC_Os09g11460, LOC_Os03g05806, LOC_Os05g28740, LOC_Os03g55930, LOC_Os05g02130, LOC_Os03g63480, LOC_Os01g07700, LOC_Os01g10610, LOC_Os01g11230, LOC_Os01g11520, LOC_Os01g11946, LOC_Os01g15490, LOC_Os01g21420, LOC_Os01g28540, LOC_Os01g33110, LOC_Os01g42800, LOC_Os0 1g43870, LOC_Os01g47350, LOC_Os01g47450, LOC_Os01g47490, LOC_Os01g50930, LOC_Os01g51040, LOC_Os01g51530, LOC_Os01g52170, LOC_Os01g55820, LOC_Os01g55880, LOC_Os01g56100, LOC_Os01g60830, LOC_Os01g61700, LOC_Os01g62440, LOC_Os01g62810, LOC_Os01g64630, LOC_Os01g65200, LOC_Os01g66170, LOC_Os01g66600, LOC_Os01g66850, LOC_Os01g69060, LOC_Os01g70080, LOC_Os01g70570, LOC_Os01g74650, LOC_Os02g02930, LOC_Os02g03050, LOC_Os02g10520, LOC_Os02g13170, LOC_Os02g15660, LOC_Os02g17304, LOC_Os02g20490, LOC_Os02g30310, LOC_Os02g32490, LOC_Os0 2g37880, LOC_Os02g39870, LOC_Os02g43090, LOC_Os02g43340, LOC_Os02g44102, LOC_Os02g45670, LOC_Os02g46320, LOC_Os02g47310, LOC_Os02g48770, LOC_Os02g52730, LOC_Os02g52770, LOC_Os02g54720, LOC_Os02g55120, LOC_Os03g01570, LOC_Os03g03020, LOC_Os03g05320, LOC_Os03g07180,LOC_Os03g07960, LOC_Os03g10060, LOC_Os03g12510, LOC_Os03g14040, LOC_Os03g16350, LOC_Os03g19709, LOC_Os03g19760, LOC_Os03g20330, LOC_Os03g26080, LOC_Os03g27760, LOC_Os03g28400, LOC_Os03g29770, LOC_Os03g31290, LOC_Os03g34280, LOC_Os03g37640, LOC_Os03g39830, LOC_Os0 3g42320, LOC_Os03g45930, LOC_Os03g47540, LOC_Os03g49630, LOC_Os03g50350, LOC_Os03g51160, LOC_Os03g55640, LOC_Os03g55970, LOC_Os03g56280, LOC_Os03g56370, LOC_Os03g56580, LOC_Os03g56869, LOC_Os03g57910, LOC_Os03g58750, LOC_Os03g61030, LOC_Os03g61490, LOC_Os03g61540, LOC_Os03g63850, LOC_Os04g02050, LOC_Os04g02970, LOC_Os04g04030, LOC_Os04g16080, LOC_Os04g18884, LOC_Os04g20200, LOC_Os04g32090, LOC_Os04g33920, LOC_Os04g37820, LOC_Os04g41870, LOC_Os04g43030, LOC_Os04g45490, LOC_Os04g46170, LOC_Os04g49350, LOC_Os04g51140, LOC_Os0 4g52110, LOC_Os04g52860, LOC_Os04g55600, LOC_Os04g55610, LOC_Os04g56320, LOC_Os04g57210, LOC_Os04g58734, LOC_Os04g58900, LOC_Os04g59150, LOC_Os04g59190, LOC_Os04g59200, LOC_Os04g59630, LOC_Os05g01110, LOC_Os05g05480, LOC_Os05g10690, LOC_Os05g11260, LOC_Os05g27660,LOC_Os05g28500、LOC_Os05g30250、LOC_Os05g31020、LOC_Os05g32220、LOC_Os05g33010、LOC_Os05g33500、LOC_Os05g33520、LOC_Os05g35460、LOC_Os05g39580、LOC_Os05g44020、LOC_Os05g46500、LOC_Os05g48855、LOC_Os05g50280、LOC_Os05g50980、LOC_Os05g51850、LOC_Os06g01460、LOC_Os0 6g05950, LOC_Os06g06170, LOC_Os06g12030, LOC_Os06g13660, LOC_Os06g19070, LOC_Os06g22340, LOC_Os06g23350, LOC_Os06g25010, LOC_Os06g28240, LOC_Os06g39344, LOC_Os06g43304, LOC_Os06g45100, LOC_Os06g47770, LOC_Os06g47800, LOC_Os06g49760, LOC_Os06g50070, LOC_Os06g50130, LOC_Os07g05870, LOC_Os07g06800, LOC_Os07g07320, LOC_Os07g18750, LOC_Os07g28040, LOC_Os07g33660, LOC_Os07g34260, LOC_Os07g38110, LOC_Os07g43050, LOC_Os07g46350, LOC_Os07g46780, LOC_Os07g46920, LOC_Os08g02230, LOC_Os08g06190, LOC_Os08g07540, LOC_Os08g08850, LOC_Os0 8g14440, LOC_Os08g14880, LOC_Os08g15460, LOC_Os08g20020, LOC_Os08g25700, LOC_Os08g29770, LOC_Os08g30210, LOC_Os08g30550, LOC_Os08g32630, LOC_Os08g36440, LOC_Os08g37345, LOC_Os08g44470, LOC_Os09g02400, LOC_Os09g04100, LOC_Os09g07570, LOC_Os09g08390, LOC_Os09g10980,LOC_Os09g30250、LOC_Os09g32290、LOC_Os10g01110、LOC_Os10g10170、LOC_Os10g19300、LOC_Os10g23180、LOC_Os10g31810、LOC_Os10g32300、LOC_Os10g33104、LOC_Os10g38890、LOC_Os10g41040、LOC_ Os10g41749、LOC_Os11g02450、LOC_Os11g02964、LOC_Os11g11210、LOC_Os11g13620、LOC_Os11g1375 0、LOC_Os11g16580、LOC_Os11g17610、LOC_Os11g26190、LOC_Os11g32890、LOC_Os11g34020、LOC_Os11 g34720、LOC_Os11g34840、LOC_Os11g34870、LOC_Os11g37300、LOC_Os11g37390、LOC_Os11g39020、LO C_Os11g39030、LOC_Os11g47960、LOC_Os11g48020、LOC_Os12g01210、LOC_Os12g02060、LOC_Os12g038 60、LOC_Os12g04260、LOC_Os12g05230、LOC_Os12g07810、LOC_Os12g12580、LOC_Os12g17320、LOC_Os12g19530、LOC_Os12g21789、LOC_Os12g33130、LOC_Os12g34890、LOC_Os12g36170 and LOC_Os12g40790。、 9. The prediction device according to claim 6, characterized in that The parameters of the XGBoost model are set as follows: r=400, colsample=0.5, eta=0.04, subsample=0.

3.

10. Application of differentially expressed genes in the prediction of salt tolerance phenotype in rice, characterized in that: The differential genes are: LOC_Os01g16530, LOC_Os04g46980, LOC_Os03g1284, LOC_Os01g14410, LOC_Os04g08740, LOC_Os01g46030, LOC_Os02g40450, LOC_Os03g15780, LOC_Os07g44430, LOC_Os03g58790, LOC_Os03g31150, LOC_Os07g12910, LOC_Os02g47200, LOC_Os10g28360, LOC_Os03g24930, LOC_Os04g57850, LOC_Os06 g41770, LOC_Os09g28310, LOC_Os09g23560, LOC_Os12g44310, LOC_Os02g05470, LOC_Os02g34560, LOC_Os07g09190, LOC_Os02g33430, LOC_Os04g34460, LOC_Os08g42850, LOC_Os02g44780, LOC_Os01g14630, LOC_Os03g06740, LOC_Os03g46770, LOC_Os10g30850, LOC_Os08g39840, LOC_Os02g01150, LOC_Os01g08320, LOC_Os05g38420, LOC_Os11g26570, LOC_Os08g06280, LOC_Os01g45830, LOC_Os06g27760, LOC_Os01g60020, LOC_Os09g02214, LOC_Os05g38150, LOC_Os08g45190, LOC_Os05g31670, LOC_Os02g12580, LOC_Os12g19470, LOC_Os03g16740, LOC_Os02g03040, LOC_Os03g61990, LOC_Os0 4g49210, LOC_Os01g45750, LOC_Os02g56500, LOC_Os11g26160, LOC_Os11g34270, LOC_Os05g50550, LOC_Os06g20320, LOC_Os01g01280, LOC_Os01g68480, LOC_Os06g51290, LOC_Os12g43130, LOC_Os04g02110, LOC_Os03g46650, LOC_Os07g36570, LOC_Os01g12420, LOC_Os12g43720, LOC_Os09g11480,LOC_Os09g11460, LOC_Os03g05806, LOC_Os05g28740, LOC_Os03g55930, LOC_Os05g02130, LOC_Os03g63480, LOC_Os01g07700, LOC_Os01g10610, LOC_Os01g11230, LOC_Os01g11520, LOC_Os01g11946, LOC_Os01g15490, LOC_Os01g21420, LOC_Os01g28540, LOC_Os01g33110, LOC_Os01g42800, LOC_Os0 1g43870, LOC_Os01g47350, LOC_Os01g47450, LOC_Os01g47490, LOC_Os01g50930, LOC_Os01g51040, LOC_Os01g51530, LOC_Os01g52170, LOC_Os01g55820, LOC_Os01g55880, LOC_Os01g56100, LOC_Os01g60830, LOC_Os01g61700, LOC_Os01g62440, LOC_Os01g62810, LOC_Os01g64630, LOC_Os01g65200, LOC_Os01g66170, LOC_Os01g66600, LOC_Os01g66850, LOC_Os01g69060, LOC_Os01g70080, LOC_Os01g70570, LOC_Os01g74650, LOC_Os02g02930, LOC_Os02g03050, LOC_Os02g10520, LOC_Os02g13170, LOC_Os02g15660, LOC_Os02g17304, LOC_Os02g20490, LOC_Os02g30310, LOC_Os02g32490, LOC_Os0 2g37880, LOC_Os02g39870, LOC_Os02g43090, LOC_Os02g43340, LOC_Os02g44102, LOC_Os02g45670, LOC_Os02g46320, LOC_Os02g47310, LOC_Os02g48770, LOC_Os02g52730, LOC_Os02g52770, LOC_Os02g54720, LOC_Os02g55120, LOC_Os03g01570, LOC_Os03g03020, LOC_Os03g05320, LOC_Os03g07180,LOC_Os03g07960, LOC_Os03g10060, LOC_Os03g12510, LOC_Os03g14040, LOC_Os03g16350, LOC_Os03g19709, LOC_Os03g19760, LOC_Os03g20330, LOC_Os03g26080, LOC_Os03g27760, LOC_Os03g28400, LOC_Os03g29770, LOC_Os03g31290, LOC_Os03g34280, LOC_Os03g37640, LOC_Os03g39830, LOC_Os0 3g42320, LOC_Os03g45930, LOC_Os03g47540, LOC_Os03g49630, LOC_Os03g50350, LOC_Os03g51160, LOC_Os03g55640, LOC_Os03g55970, LOC_Os03g56280, LOC_Os03g56370, LOC_Os03g56580, LOC_Os03g56869, LOC_Os03g57910, LOC_Os03g58750, LOC_Os03g61030, LOC_Os03g61490, LOC_Os03g61540, LOC_Os03g63850, LOC_Os04g02050, LOC_Os04g02970, LOC_Os04g04030, LOC_Os04g16080, LOC_Os04g18884, LOC_Os04g20200, LOC_Os04g32090, LOC_Os04g33920, LOC_Os04g37820, LOC_Os04g41870, LOC_Os04g43030, LOC_Os04g45490, LOC_Os04g46170, LOC_Os04g49350, LOC_Os04g51140, LOC_Os0 4g52110, LOC_Os04g52860, LOC_Os04g55600, LOC_Os04g55610, LOC_Os04g56320, LOC_Os04g57210, LOC_Os04g58734, LOC_Os04g58900, LOC_Os04g59150, LOC_Os04g59190, LOC_Os04g59200, LOC_Os04g59630, LOC_Os05g01110, LOC_Os05g05480, LOC_Os05g10690, LOC_Os05g11260, LOC_Os05g27660,LOC_Os05g28500、LOC_Os05g30250、LOC_Os05g31020、LOC_Os05g32220、LOC_Os05g33010、LOC_Os05g33500、LOC_Os05g33520、LOC_Os05g35460、LOC_Os05g39580、LOC_Os05g44020、LOC_Os05g46500、LOC_Os05g48855、LOC_Os05g50280、LOC_Os05g50980、LOC_Os05g51850、LOC_Os06g01460、LOC_Os0 6g05950, LOC_Os06g06170, LOC_Os06g12030, LOC_Os06g13660, LOC_Os06g19070, LOC_Os06g22340, LOC_Os06g23350, LOC_Os06g25010, LOC_Os06g28240, LOC_Os06g39344, LOC_Os06g43304, LOC_Os06g45100, LOC_Os06g47770, LOC_Os06g47800, LOC_Os06g49760, LOC_Os06g50070, LOC_Os06g50130, LOC_Os07g05870, LOC_Os07g06800, LOC_Os07g07320, LOC_Os07g18750, LOC_Os07g28040, LOC_Os07g33660, LOC_Os07g34260, LOC_Os07g38110, LOC_Os07g43050, LOC_Os07g46350, LOC_Os07g46780, LOC_Os07g46920, LOC_Os08g02230, LOC_Os08g06190, LOC_Os08g07540, LOC_Os08g08850, LOC_Os0 8g14440, LOC_Os08g14880, LOC_Os08g15460, LOC_Os08g20020, LOC_Os08g25700, LOC_Os08g29770, LOC_Os08g30210, LOC_Os08g30550, LOC_Os08g32630, LOC_Os08g36440, LOC_Os08g37345, LOC_Os08g44470, LOC_Os09g02400, LOC_Os09g04100, LOC_Os09g07570, LOC_Os09g08390, LOC_Os09g10980,LOC_Os09g30250、LOC_Os09g32290、LOC_Os10g01110、LOC_Os10g10170、LOC_Os10g19300、LOC_Os10g23180、LOC_Os10g31810、LOC_Os10g32300、LOC_Os10g33104、LOC_Os10g38890、LOC_Os10g41040、LOC_ Os10g41749、LOC_Os11g02450、LOC_Os11g02964、LOC_Os11g11210、LOC_Os11g13620、LOC_Os11g1375 0、LOC_Os11g16580、LOC_Os11g17610、LOC_Os11g26190、LOC_Os11g32890、LOC_Os11g34020、LOC_Os11 g34720、LOC_Os11g34840、LOC_Os11g34870、LOC_Os11g37300、LOC_Os11g37390、LOC_Os11g39020、LO C_Os11g39030、LOC_Os11g47960、LOC_Os11g48020、LOC_Os12g01210、LOC_Os12g02060、LOC_Os12g038 60、LOC_Os12g04260、LOC_Os12g05230、LOC_Os12g07810、LOC_Os12g12580、LOC_Os12g17320、LOC_Os12g19530、LOC_Os12g21789、LOC_Os12g33130、LOC_Os12g34890、LOC_Os12g36170 and LOC_Os12g40790。、

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