CN105279396B - The Drought-resistant gene of plant module method of excavation - Google Patents
The Drought-resistant gene of plant module method of excavation Download PDFInfo
- Publication number
- CN105279396B CN105279396B CN201510697716.6A CN201510697716A CN105279396B CN 105279396 B CN105279396 B CN 105279396B CN 201510697716 A CN201510697716 A CN 201510697716A CN 105279396 B CN105279396 B CN 105279396B
- Authority
- CN
- China
- Prior art keywords
- gene
- drought
- gene module
- module
- plant
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Breeding Of Plants And Reproduction By Means Of Culturing (AREA)
Abstract
A kind of Drought-resistant gene of plant module method of excavation, this method is based on gene interaction network, Modularity analysis is carried out to interaction of genes network using Markov clustering, and the drought resistance function size of netic module assessed with full-length genome associated data using genetic chip.According to the drought resisting contribution of each netic module, crucial anti-drought gene module is screened.Compared with the conventional method, this method lays particular emphasis on the discovery of brand-new anti-drought gene group, drastically increases the efficiency and accuracy of Drought-resistant gene of plant excavation.
Description
Technical Field
The invention relates to biotechnology, in particular to a method for excavating a plant drought-resistant gene module.
Background
Plants can encounter various natural environments in the growing process, wherein a lot of natural disasters often cause the mass production reduction of crops, such as drought, waterlogging, plant diseases and insect pests and the like. The harm brought to agricultural production by the environmental stress is worldwide, and particularly, the harm caused by drought is very serious, so that the yield of crops can be reduced by 50 to 80 percent. Therefore, improving the drought and water-shortage tolerance of crops is one of the important targets of agricultural science and technology research. An important means at present is to use the drought-resistant related gene to carry out gene engineering transformation on crops so as to obtain a new variety of drought-resistant crops.
The mechanism of the plant adapting to the drought environment is very complex, and is the result of complex network comprehensive regulation formed by interaction of a plurality of genes/approaches. After the plants suffer from drought stress, the damage to the plants caused by water shortage is reduced or eliminated by signal perception, a series of complex signal transduction and activation of specific transcription regulation factors, further the expression of downstream functional genes is regulated and controlled, and corresponding adaptive reactions such as physiology, biochemistry and the like are started. The gene regulation networks are independent and closely connected with each other, and a complex gene network for responding to drought stress of plants is formed together. Therefore, the functional analysis of single or a few drought-resistant related genes can not systematically understand the molecular mechanism of plant drought resistance, and moreover, the genetic engineering modification of crops by using the single drought-resistant related gene is proved to have limited effect.
Disclosure of Invention
The invention aims to solve the problems and provides a method for discovering a plant drought-resistant gene module.
In order to achieve the purpose, the invention adopts the following technical scheme: a method for discovering a drought-resistant gene module of a plant comprises the following steps:
step 1, collecting gene interaction data from a BioGRID, intAct, DIP, MINT and STRING database, separating the interaction data of a target plant, and establishing a corresponding database.
And 2, performing modular analysis on the Gene network by adopting a clustering algorithm MCL of a Markov model, evaluating the functional similarity of the module genes under different inactivation parameter conditions of the MCL algorithm by utilizing Gene Ontology data, and determining the inactivation parameter when the functional similarity score of the module genes is the highest.
And 3, downloading and obtaining the gene chip data of the target plant under drought stress from a GEO database of NCBI (http:// www.ncbi.nlm.nih.gov /) and an Arrayexpress database of EBI (http:// www.ebi.ac.uk /), homogenizing the chip data by RMAExpress software, and calculating the drought stress response gene of the target plant by using an SAM software package (http:// statweb.stanford.edu/. Tibs/SAM /). And (3) carrying out significance analysis on drought stress response gene richness of the gene module by adopting accumulation super-geometric distribution. The specific calculation formula is as follows:
wherein, N is the base factor contained in the whole interaction network, M is the base factor containing drought stress response contained in the interaction network, K is the base factor contained in the target gene module, and x is the base factor containing drought stress response contained in the target gene module.
And 4, downloading whole genome associated data related to drought resistance of the target plant from a Gramene (http:// www. Gramene. Org /) database, then randomly replacing drought resistance phenotype data of each material, calculating a p value of each SNP by using an MLM (maximum likelihood model) model of tassel software, then counting the SNP ratio which is obviously associated with the drought resistance (p is less than 0.05) in each gene module, carrying out 9999 times of replacement tests in total, and analyzing the association degree between the gene module and the drought resistance.
And 5, sequencing the probabilities that the gene modules are rich in the drought stress response genes from small to large. Similarly, the degrees of association between the gene modules and drought resistance are also ranked from small to large. And (4) calculating the drought resistance of the gene module according to the geometric mean value of the two types of rankings of the gene module, and screening the key drought resistance gene module according to the drought resistance. The specific calculation formula is as follows:
wherein S is DEG Probabilistic ranking for drought stress response gene enrichment, S GWAS Rank the degree of association with drought resistance.
The invention adopts Markov clustering algorithm to carry out modularized analysis on the gene interaction network, and utilizes the gene chip and the whole genome associated data to evaluate the drought resistance of the gene module. And screening the key drought-resistant gene modules according to the drought-resistant contribution of each gene module. Compared with the prior art, the invention focuses on the discovery of a brand-new drought-resistant gene group, and greatly improves the efficiency and the accuracy of the plant drought-resistant gene discovery.
Drawings
FIG. 1 is a flow chart of the method for constructing the plant protein interaction network according to the present invention.
FIG. 2 shows the degree of correlation between gene modules and drought resistance.
FIG. 3 is the network distribution of key drought-resistant gene modules of rice.
Detailed Description
The following takes the discovery of a drought-resistant module of rice as an example, and refers to fig. 1, to specifically describe the specific implementation steps of the method for constructing a plant protein interaction network according to the present invention.
Step 1, collecting gene interaction data from a BioGRID, intAct, DIP, MINT and STRING database, separating rice gene interaction data and establishing a corresponding database.
And 2, analyzing the gene network module. The gene network is subjected to module analysis by adopting a clustering algorithm MCL of a Markov model, and the selection of an inflation parameter of the algorithm is very critical to the functional module analysis of the gene network. And (3) re-evaluating the functional similarity of the genes under different inflation parameters by using the Gene Ontology data, and determining that the biofunctionality of the module can be optimally embodied when the inflation parameter is 1.8.
Step 3, from NCBI (http: v/www.ncbi.nlm.nih.gov /) and EBI (http: the method comprises the steps of loading and obtaining gene chip data of a target plant under drought stress from an Arrayexpress database of// www.ebi.ac.uk /), 12 groups of experiments, wherein the specific database accession numbers are GSE6901, GSE24048, GSE26280, GSE23211, E-MEXP-2401 and E-MESAM-XP-2401, RMAExpress software is used for carrying out homogenization on the chip data, rice drought stress response genes are respectively calculated by adopting an HTTP (http:// statweb. Stanford.edu/. About. Tibs/SAM /), and the expression level of the 11,605 gene is identified to have obvious change under the drought condition.
And (3) carrying out drought stress response gene enrichment analysis on the 28 gene modules (the modules comprise at least 30 node genes) obtained in the step (2) by adopting cumulative hyper-geometric distribution. The cumulative hyper-geometric distribution formula is as follows:
wherein, N is the base factor contained in the whole interaction network, M is the drought stress response base factor contained in the interaction network, K is the base factor contained in the target gene module, and x is the drought stress response base factor contained in the target gene module.
And 4, downloading whole genome associated data related to drought resistance of rice from a Gramene (http:// www.gramene.org /) database, then randomly replacing the drought resistance phenotype data of 374 rice materials, calculating the p value of each SNP by using an MLM (maximum likelihood) model of tassel software, then counting the SNP ratio which is significantly associated with the drought resistance (p is less than 0.05) in each gene module, carrying out 9999 times of replacement tests, and analyzing the association degree between 28 gene modules (the modules comprise at least 30 node genes) obtained in the step 2 and the drought resistance, wherein the specific result is shown in figure 2.
And 5, sequencing the probabilities of the gene modules rich in the drought stress response genes from small to large. Similarly, the degrees of association between the gene modules and drought resistance were ranked from small to large. And (4) calculating the drought resistance of the gene module according to the geometric mean value of the two types of rankings of the gene module, and screening the key drought resistance gene module according to the drought resistance. The calculation formula is as follows:S DEG probabilistic ranking for drought stress response gene enrichment, S GWAS Rank the degree of association with drought resistance. Through calculation, the gene module with the number of 7 is the discovered rice drought-resistant gene module, and the distribution of the gene module in the interaction network is shown in fig. 3.
The analysis steps are also suitable for the drought resisting module development of other plants based on the gene network. The above description is not intended to limit the present invention, and other embodiments based on the idea of the present invention are within the scope of the present invention.
Claims (5)
1. A method for discovering a drought-resistant gene module of a plant is characterized by comprising the following steps: the method comprises the following steps:
1. separating and establishing a gene interaction database of the target plant;
2. performing modularized analysis on the gene interaction network by using a Markov clustering algorithm, and evaluating the biological significance of the gene module;
3. identifying drought stress response genes of target plants by using gene chip data, and performing significance analysis on the drought stress response genes enriched in the gene modules by adopting accumulated super-geometric distribution;
4. analyzing the association degree between the gene module and the drought resistance by using the whole genome association data;
5. calculating the drought resistance of the gene module according to the geometric mean value of the two types of rankings of the gene module, and screening the key drought resistance gene module according to the drought resistance;
performing modular analysis on the Gene network by adopting a clustering algorithm MCL of a Markov model, evaluating the functional similarity of the module genes under different inflation parameter conditions of the MCL algorithm by utilizing Gene Ontology data, and determining the inflation parameter when the functional similarity score of the module genes is highest;
the fifth step is specifically to sort the probability of the gene module being rich in the drought stress response genes from small to large, similarly sort the association degree between the gene module and the drought resistance from small to large, calculate the drought resistance of the gene module according to the geometric mean of the two types of ranks of the gene module, wherein the two types of ranks of the gene module refer to the probability rank of the gene module being rich in the drought stress response genes and the association degree rank between the gene module and the drought resistance, and screen the key drought resistance gene module according to the above, and the specific calculation formula is as follows:
wherein the content of the first and second substances,ranking the probability of being rich in drought stress response genes,rank the degree of association with drought resistance.
2. The method for discovering plant drought-resistant gene module according to claim 1, wherein the method comprises the following steps: specifically, the first step is to collect gene interaction data from BioGRID, intAct, DIP, MINT and STRING databases, separate the interaction data of the target plant and establish a corresponding database.
3. The method for discovering plant drought-resistant gene module according to claim 1, wherein the method comprises the following steps: downloading and obtaining the drought-stressed gene chip data of the target plant from a GEO database of NCBI and an Arrayexpress database of EBI, homogenizing the chip data by RMAExpress software, and calculating the drought stress response gene of the target plant by using an SAM software package; carrying out significance analysis on drought stress response gene richness of the gene module by adopting accumulation super-geometric distribution; the specific calculation formula is as follows:
wherein, N is the base factor contained in the whole interaction network, M is the drought stress response base factor contained in the interaction network, K is the base factor contained in the target gene module, and x is the drought stress response base factor contained in the target gene module.
4. The method for discovering the plant drought-resistant gene module according to claim 1, wherein the method comprises the following steps: downloading whole genome associated data related to drought resistance of the target plant from a Gramene database, then carrying out random replacement on drought resistance phenotype data of each material, calculating a p value of each SNP by using an MLM (Multi-level polymorphism) model of tassel software, then counting the SNP proportion obviously associated with the drought resistance in each gene module, carrying out 9999 times of replacement tests in total, and analyzing the association degree between the gene module and the drought resistance.
5. The method for discovering the plant drought-resistant gene module according to claim 4, wherein the method comprises the following steps: the significant association with drought resistance means p <0.05.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510697716.6A CN105279396B (en) | 2015-10-23 | 2015-10-23 | The Drought-resistant gene of plant module method of excavation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510697716.6A CN105279396B (en) | 2015-10-23 | 2015-10-23 | The Drought-resistant gene of plant module method of excavation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105279396A CN105279396A (en) | 2016-01-27 |
| CN105279396B true CN105279396B (en) | 2018-02-02 |
Family
ID=55148399
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510697716.6A Active CN105279396B (en) | 2015-10-23 | 2015-10-23 | The Drought-resistant gene of plant module method of excavation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105279396B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599607B (en) * | 2016-12-13 | 2018-12-04 | 北京林业大学 | A kind of construction method of photosynthetic pathway gene regulated and control network |
| CN109686405B (en) * | 2018-11-07 | 2022-06-28 | 中国科学院昆明植物研究所海盐工程技术中心 | Method for screening potential important genes of group genome based on MK-test |
| CN111781150B (en) * | 2020-06-10 | 2021-12-10 | 华中农业大学 | Method for identifying corn drought-resistant gene by hyperspectral technology |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104830967A (en) * | 2015-03-10 | 2015-08-12 | 中国农业科学院作物科学研究所 | Positioning method of rice selected introgression lines QTL |
-
2015
- 2015-10-23 CN CN201510697716.6A patent/CN105279396B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104830967A (en) * | 2015-03-10 | 2015-08-12 | 中国农业科学院作物科学研究所 | Positioning method of rice selected introgression lines QTL |
Non-Patent Citations (2)
| Title |
|---|
| Functional characterization of drought-responsive modules and genes in Oryza sativa:a network-based approach;Sanchari Sircar等;《Frontier in Genetics》;20150730;第6卷;1-15 * |
| Identification of gene modules associated with drought response in rice by network-based analysis;Lida Zhang等;《PLOS One》;20120525;第7卷(第5期);1-12 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105279396A (en) | 2016-01-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Varshney et al. | Designing future crops: genomics-assisted breeding comes of age | |
| Ramírez-González et al. | The transcriptional landscape of polyploid wheat | |
| Ma et al. | Machine learning–based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis | |
| Verma et al. | High-density linkage map construction and mapping of seed trait QTLs in chickpea (Cicer arietinum L.) using Genotyping-by-Sequencing (GBS) | |
| Borrill et al. | expVIP: a customizable RNA-seq data analysis and visualization platform | |
| Esposito et al. | Applications and trends of machine learning in genomics and phenomics for next-generation breeding | |
| Johnson et al. | Genome‐wide population structure analyses of three minor millets: Kodo millet, little millet, and proso millet | |
| Siggia | Computational methods for transcriptional regulation | |
| CN105279396B (en) | The Drought-resistant gene of plant module method of excavation | |
| De Carvalho et al. | Bioinformatics applied to biotechnology: A review towards bioenergy research | |
| Weighill et al. | Data integration in poplar:‘omics layers and integration strategies | |
| Wang et al. | Genome-wide analysis of multiple organellar RNA editing factor family in poplar reveals evolution and roles in drought stress | |
| He et al. | The history and diversity of rice domestication as resolved from 1464 complete plastid genomes | |
| Lian et al. | Exploration of ABA responsive miRNAs reveals a new hormone signaling crosstalk pathway regulating root growth of Populus euphratica | |
| Hong et al. | Infrastructures of systems biology that facilitate functional genomic study in rice | |
| Julca et al. | Toward kingdom-wide analyses of gene expression | |
| Gao et al. | Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputation | |
| Thant et al. | Genetic diversity and population structure of Myanmar rice (Oryza sativa L.) varieties using DArTseq-based SNP and silicoDArT markers | |
| Gu et al. | Novel sequencing and genomic technologies revolutionized rice genomic study and breeding | |
| Brown et al. | Genome-wide association mapping of transcriptome variation in Mimulus guttatus indicates differing patterns of selection on cis-versus trans-acting mutations | |
| Li et al. | MDSi: multi-omics database for setaria italica | |
| Sahito et al. | Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize | |
| Dolatabadian et al. | Copy number variation among resistance genes analogues in Brassica napus | |
| Wang et al. | Detecting QTL and candidate genes for plant height in soybean via linkage analysis and GWAS | |
| Abdullah-Zawawi et al. | Multi-omics approaches and resources for systems-level gene function prediction in the plant kingdom |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |