Construction method and application of database for identifying germplasm resources of Chinese fir

Abstract

The invention discloses a database construction method for identifying cunninghamia lanceolata germplasm resources and application, and belongs to the technical field of biological breeding. The invention discloses a database construction method for identifying cunninghamia lanceolata germplasm resources. The method comprises the following steps: 1) collecting data; 2) data standardization processing; 3) designing a database; 4) creating a database; 5) algorithm integration; 6) establishing a database website; and 7) inputting, updating and maintaining data. The database for identifying the cedarwood germplasm resources comprises a cedarwood germplasm resource data management module, a picture management module, a data analysis module and a user management module. The result of the embodiment shows that the database is suitable for genetic diversity analysis, principal component analysis and comprehensive evaluation of the cunninghamia lanceolata germplasm resources.

Description

Technical Field

[0001] The invention belongs to the technical field of biological breeding, and more specifically, relates to a method for constructing a database for identifying Chinese fir germplasm resources and its application. Background Art

[0002] Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is a plant of the genus Cunninghamia in the Cupressaceae family. It is one of the evergreen coniferous tree species unique to my country and an important commercial timber and afforestation tree species in southern my country. It has the characteristics of fast growth, good yield, high material quality and wide range of uses. The identification and management of its germplasm resources are of great significance to forestry production and Chinese fir genetic breeding.

[0003] my country has been engaged in forest genetic improvement since the 1950s, and it has been more than 60 years since then. It has now entered the fourth generation of genetic improvement research and has accumulated a large number of Chinese fir breeding resources. Years of Chinese fir breeding work have generated complex information data, including basic archive information and trait information of Chinese fir germplasm resources. Trait information includes phenotypic traits such as Chinese fir morphology and physiological characteristics, characteristic traits such as wood quality characteristics and resistance. At present, the above-mentioned Chinese fir breeding resource information data still have problems such as inconsistent resource description and scattered data, which makes it low in efficiency and insufficient in accuracy when used in the identification of Chinese fir germplasm resources. Therefore, it is urgent to build a database for the identification of Chinese fir germplasm resources to comprehensively and systematically preserve and manage Chinese fir germplasm information data, and use computer programming to achieve rapid and accurate identification of Chinese fir germplasm resources.

[0004] The significance of building a database for identifying Chinese fir germplasm resources is that it can centrally manage and integrate various Chinese fir breeding resource information, including data on morphology, physiology, etc. Through the database, systematic management, evaluation and identification of Chinese fir germplasm resources can better understand its advantages and potential, and provide a scientific basis for Chinese fir breeding. In addition, the database can also promote resource sharing and communication, promote cooperation and development in the field of Chinese fir breeding, and provide assistance for future high-generation improvement breeding of Chinese fir. Summary of the invention

[0005] In view of the deficiencies in the prior art, the object of the present invention is to provide a method for constructing a database for identifying Chinese fir germplasm resources. Another object of the present invention is to provide a database for identifying Chinese fir germplasm resources, which is used to quickly and accurately identify and evaluate Chinese fir germplasm resources. Another object of the present invention is to provide an application of a database for identifying Chinese fir germplasm resources in the genetic diversity analysis of Chinese fir germplasm resources. Another object of the present invention is to provide an application of a database for identifying Chinese fir germplasm resources in the principal component analysis of Chinese fir germplasm resources. Another object of the present invention is to provide an application of a database for identifying Chinese fir germplasm resources in the comprehensive evaluation of Chinese fir germplasm resources.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] A method for constructing a database for identifying Chinese fir germplasm resources,

[0008] 1) Data collection: Collect and organize the data information of Chinese fir germplasm resources, divide them into four categories: basic information, morphological characteristics and biological characteristics, quality characteristics, and resistance characteristics, and save them as electronic files;

[0009] 2) Data standardization: The collected data are cleaned, formatted and standardized according to the description specifications and data standards of Chinese fir germplasm resources;

[0010] 3) Database design: Design the database structure, database relationship model and data table according to the requirements of each functional module and the characteristics of Chinese fir germplasm resources information;

[0011] Database creation: Create database and data model based on Django framework and MySQL database software;

[0012] 5) Algorithm integration: Integrate all analysis function codes used in the data analysis module into the database;

[0013] 6) Database website construction: configure the connection between the website and the database, and implement the interaction logic between the page and the database in the back-end code of the website;

[0014] 7) Data entry, update and maintenance: Enter the standardized data from step 2) into the database, and regularly check, update and maintain the data.

[0015] The functional modules include a Chinese fir germplasm resource data management module, a picture management module, a data analysis module, and a user management module.

[0016] The Chinese fir germplasm resource data management module is used to store data on basic information, morphological characteristics, biological characteristics, quality characteristics and resistance characteristics of Chinese fir germplasm resources, as well as operations of adding, deleting, modifying and viewing data;

[0017] The picture management module is used to store picture files of Chinese fir germplasm resources;

[0018] The data analysis module is used to perform visual statistical analysis and comprehensive evaluation of excellent traits of Chinese fir germplasm resources;

[0019] The user management module is used to store user login information and manage user permissions.

[0020] The data analysis module includes two parts: statistical analysis visualization and comprehensive evaluation of excellent traits.

[0021] Step 2) The description specifications and data standards of Chinese fir germplasm resources include basic information, morphological characteristics and biological characteristics, quality characteristics and adaptability characteristics.

[0022] The basic information includes germplasm number, germplasm name, seed source, resource type, breeding method, breeding unit, breeding year, germplasm picture, preservation time, preservation material type, preservation method, and physical status.

[0023] The morphological characteristics and biological characteristics include color, white powder coverage of new and old leaves, size, needle density, leaf shape index, serration density, branch angle, crown shape, branch height, tree height, breast diameter, internode distance, color, smoothness, thickness, cone size, fruit shape coefficient, bract opening condition, bract shape, and bract number;

[0024] The quality characteristics include seedling height, ground diameter, crown roundness, straightness, taper and scarring, tree height growth, breast diameter growth and single tree volume growth in a specific period of time, wood hardness, density, bending strength, compressive strength, fiber content shrinkage coefficient;

[0025] The adaptability includes the disease and insect resistance of Chinese fir and the site conditions; the disease resistance includes anthracnose, yellowing, red blight, damping-off of Chinese fir seedlings, and sudden wilting of Chinese fir seedlings; the insect resistance includes the small Chinese fir leaf roller, termites, and two-striped Chinese fir longhorn beetle; the disease and insect resistance of Chinese fir is divided into five levels, namely high resistance, resistance, medium resistance, susceptible, and high susceptible; the disease and insect resistance calculation formula is: DI = ∑ [number of diseased plants (organs) at each level × representative value of each disease level] * 100 / [total number of plants (organs) surveyed × maximum [Representative value of high disease level] Calculate the disease index DI; the disease index less than 5 is highly resistant; the disease index greater than or equal to 5 and less than 10 is resistant; the disease index greater than or equal to 10 and less than 20 is moderately resistant; the disease index greater than or equal to 20 and less than 40 is susceptible; the disease index greater than or equal to 40 is highly susceptible; the site conditions include the longitude and latitude of the provenance, the average annual temperature, the average annual precipitation, the average annual sunshine hours, the frost-free period, relative humidity, soil type, soil texture, and soil pH.

[0026] The database constructed by any of the methods is used to identify Chinese fir germplasm resources.

[0027] The database is used for genetic diversity analysis of Chinese fir germplasm resources.

[0028] The database is used for principal component analysis of Chinese fir germplasm resources.

[0029] The database is used in the comprehensive evaluation of Chinese fir germplasm resources.

[0030] Beneficial effects of the present invention:

[0031] 1) The database for identifying Chinese fir germplasm resources constructed by the present invention performs phenotypic identification and evaluation and gene sequence comparison on germplasm resources during identification and management of Chinese fir germplasm, so as to identify Chinese fir germplasm quickly and accurately. The process is simple, efficient and low in cost, which provides strong technical support for Chinese fir breeding and is of great significance to the preservation, utilization and improvement of Chinese fir germplasm resources.

[0032] 2) The results of the embodiments of the present invention show that: the constructed database is applied to the identification of Chinese fir germplasm resources, and the existing Chinese fir germplasm resource information in the database can be directly checked for comparison, or the relevant information of the Chinese fir germplasm resources to be identified can be input and the database data analysis module can be used for rapid identification. The database is suitable for genetic diversity analysis, principal component analysis and comprehensive evaluation of Chinese fir germplasm resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a diagram of the database structure used to identify Chinese fir germplasm resources;

[0034] Figure 2 It is the database ER model diagram;

[0035] Figure 3 Management page map for germplasm lists;

[0036] Figure 4 Added new page diagram for germplasm list management;

[0037] Figure 5 Editing page maps for germplasm inventory management;

[0038] Figure 6 View page map for germplasm inventory management;

[0039] Figure 7 This is a list of breeding resource information;

[0040] Figure 8 Added new pages and diagrams for breeding resource information management;

[0041] Fig. 9Editing page diagrams for breeding resource information management;

[0042] Fig.10 View page diagram for breeding resource information management;

[0043] Fig.11 It is the picture list diagram in the picture file management;

[0044] Fig.12 Edit page images for image file management;

[0045] Fig.13 In the picture file management, the user clicks "Browse" to open the local folder of the computer and select the image file;

[0046] Fig.14 This is a visualization diagram of the statistical analysis results of germplasm resource traits;

[0047] Fig.15 This is a visualization diagram of the principal component analysis results of germplasm resources;

[0048] Fig.16 It is the algorithm flow chart of genetic diversity index (H');

[0049] Fig.17 It is the flowchart of principal component analysis algorithm;

[0050] Fig.18 The heat map of correlation for principal component analysis;

[0051] Fig.19 Heat map of the component matrix for principal component analysis;

[0052] Fig. 20 Scree plot for principal component analysis. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described below in conjunction with specific embodiments. Unless otherwise specified in the following embodiments, the technical means used are conventional means well known to those skilled in the art.

[0054] The Chinese fir breeding materials used in this application are from Yangkou State-owned Forest Farm in Fujian Province.

[0055] Example 1 Database Construction

[0056] 1. Data Collection

[0057] The information records of 10 randomly selected Chinese fir breeding materials from the Chinese fir germplasm resource bank of Yangkou Forest Farm in Fujian were used as the experimental objects. The data were divided into four categories, namely basic information, environmental characteristics, morphological characteristics and biological characteristics, and quality characteristics, and saved as electronic files.

[0058] 2. Data standardization

[0059] The steps for processing the collected Chinese fir germplasm resource data are as follows:

[0060] 1) Remove duplicate data. Check the collected data information and remove duplicate records.

[0061] 2) Correct erroneous data. For obviously erroneous data information, correct it by re-investigating and obtaining accurate data.

[0062] 3) Unify the data format. According to the “Specifications and Data Standards for Description of Chinese Fir Germplasm Resources” (Tables 1-4), correct the data that do not meet the format requirements to ensure that the data format of each corresponding item is unified.

[0063] The above-mentioned description specifications of Chinese fir germplasm resources total 72 items, including:

[0064] 1) Basic information: germplasm number, germplasm name, seed source, resource type, propagation method, breeding unit, breeding year, germplasm picture, preservation time, preservation material type, preservation method, and physical status.

[0065] 2) Morphological characteristics and biological characteristics: color, white powder coverage of new and old leaves, size, needle density, leaf shape index, serration density, branch angle, crown shape, branch height, tree height, breast diameter, internode distance, color, smoothness, thickness, cone size, fruit shape coefficient, bract opening condition, bract shape, and bract number.

[0066] 3) Quality characteristics: seedling height, ground diameter, crown roundness, straightness, taper, tree height growth in a specific period of time, breast diameter growth and single tree volume growth, wood hardness, wood density, wood water absorption rate, bending strength, compressive strength, fiber content, shrinkage coefficient.

[0067] 4) Adaptability: including the disease and insect resistance of Chinese fir and site conditions. In terms of disease and insect resistance, nine diseases and pests that may affect its health are listed: diseases include anthracnose, yellowing disease, red blight, damping-off disease of Chinese fir seedlings, and sudden wilting disease of Chinese fir seedlings; pests include Chinese fir leaf roller, termites, and two-striped Chinese fir longhorn beetles. The disease and insect resistance of Chinese fir is divided into five levels, namely high resistance (HR), resistance (R), medium resistance (MR), susceptible (S), and highly susceptible (HS). The disease and insect resistance of Chinese fir is divided into five levels, namely high resistance (HR), resistance (R), medium resistance (MR), susceptible (S), and highly susceptible (HS). The disease index DI is calculated according to the formula DI = ∑ [number of diseased plants (organs) at each level × representative value of each disease level] * 100 / [total number of plants (organs) surveyed × representative value of the highest disease level]. Disease index less than 5 is highly resistant (HR); disease index greater than or equal to 5 and less than 10 is resistant (R); disease index greater than or equal to 10 and less than 20 is moderately resistant (MR); disease index greater than or equal to 20 and less than 40 is susceptible (S); disease index greater than or equal to 40 is highly susceptible (S). Site conditions include longitude and latitude of provenance, average annual temperature, average annual precipitation, average annual sunshine hours, frost-free period, relative humidity, soil type, soil texture, and soil pH. Site conditions include longitude and latitude of provenance, average annual temperature, average annual precipitation, average annual sunshine hours, frost-free period, relative humidity, soil type, soil texture, and soil pH.

[0068] Table 1 Basic information

[0069] Information Items content Germplasm number Tree species code (5 digits) + administrative code of origin (6 digits) + sequence number (4 digits) Germplasm name Germplasm name Resource Type Provenance / family / clone / old tree / bred variety / new variety Reproduction method Seed propagation / cutting / grafting / root propagation / tillering propagation / tissue culture Breeding Unit Collectors or individuals of wild resources Year of breeding Year of germplasm development Save time The time when the collection unit collected or saved it, expressed in year, month, and day, in the format of YYYYMMDD Save material type Seeds / DNA materials / DNA extracts / active materials Storage method On-site storage / off-site storage / facility (low temperature storage) storage Physical state Good / Average / Poor / Missing Germplasm pictures Image attachment, in JPG format.

[0070] Table 2 Morphological characteristics and biological characteristics

[0071]

[0072]

[0073] Table 3 Quality characteristics

[0074]

[0075]

[0076] Table 4 Adaptability characteristics

[0077]

[0078]

[0079] 3. Database structure design

[0080] According to the requirements of the four functional modules of the database, the database structure and relational model are designed, and according to the information recording situation of Chinese fir germplasm resources and the actual application scenarios of the data, the data table and index are designed. The steps are as follows:

[0081] 1) Design database structure: The database structure is as follows Figure 1 The database is divided into four functional modules, namely germplasm resource data management module, image management module, data analysis module, and user management module. The composition and functions of the four modules are as follows:

[0082] (1) Germplasm resource data management module, which is used to store basic information, environmental characteristics, morphological characteristics, biological characteristics, quality characteristics and other data information of Chinese fir germplasm resources. It is divided into two small modules: germplasm list management and breeding resource information management, which is convenient for users to retrieve and view germplasm information.

[0083] (2) Image management module, used to store Chinese fir germplasm resource image files. Image files correspond to germplasm resource information records one by one, that is, all images are managed separately as a separate module in the form of a gallery.

[0084] (3) Data analysis module, which integrates a variety of Python-based data analysis functions, including genetic diversity analysis, principal component analysis and comprehensive evaluation, visual statistical analysis of Chinese fir germplasm resources, and comprehensive evaluation of excellent traits.

[0085] ①Genetic diversity analysis based on Python is achieved by using Python language to write codes to calculate frequency, basic parameters (including mean, median, maximum, minimum, range, standard deviation), coefficient of variation CV and Shannon-Wiener's genetic diversity index (H').

[0086] ② Principal component analysis and comprehensive evaluation based on Python use Python extension libraries such as scikit-learn, StandardScaler, and matplotlib to write code to gradually complete Bartlett's sphericity test, KMO test, standardize data, calculate covariance matrix, eigenvalues ​​and eigenvectors, draw scatter plots and line graphs, calculate the contribution rate and cumulative contribution rate of eigenvalues, select principal components and corresponding eigenvector matrices, calculate principal component scores, draw heat maps, construct comprehensive score function equations, and calculate comprehensive scores for comprehensive evaluation and ranking.

[0087] ③ Visual statistical analysis is based on all the information on Chinese fir germplasm resources included in the database, and statistics are made from multiple dimensions such as provenance, morphological characteristics, biological characteristics, and quality characteristics, and displayed in the form of various statistical graphs;

[0088] ④ Comprehensive evaluation of excellent traits: taking all Chinese fir germplasm information included in the database as the object, genetic diversity analysis, principal component analysis and comprehensive evaluation ranking of each phenotypic trait were carried out;

[0089] (4) User management module, used to store user login information and manage user permissions.

[0090] 2) Establish database ER model: Establish database ER model according to database functional requirements ( Figure 2 ) is used to describe the relationship between entities and the attributes of entities. Users manage germplasm resource information through operations such as uploading and editing. There is a many-to-many relationship (m:n) between users and the information they manage, and multiple users can manage multiple pieces of information data.

[0091] 3) Design data tables: According to the record status of Chinese fir germplasm resources information and the actual application scenarios of data, the Chinese fir germplasm resources information is divided into four categories: basic information, environmental characteristics, morphological characteristics and biological characteristics, and quality characteristics. Data tables (Tables 5-8) are established respectively, and data attributes are specified in accordance with the Django data model specifications.

[0092] Table 5 Basic information

[0093] Field Name Data Types Field Description Remark id AutoField Serial number Primary Key germ_id CharField(max_length=10) serial number Germplasm number

[0094] Table 6 Environmental characteristics

[0095]

[0096]

[0097] Table 7 Morphological characteristics and biological characteristics

[0098]

[0099]

[0100] Table 8 Quality characteristics

[0101]

[0102] 4. Database creation

[0103] Create a Django project, connect and configure the MySQL database, and complete the creation of the database and data model. The steps are as follows:

[0104] 1) Create a Django project: Create a blank Pycharm project folder, open Pycharm, enter the newly created folder, and enter the command "Django-admin startprojectgms" to create a Django project through the Windows command line to create a new project gms.

[0105] 2) Create a database: Enter the bin subfolder directory of the MySQL installation folder, enter the command "mysql-uroot-p" and password, and after successful login, enter the command "CREATE DATABASE gms" to create a database gms.

[0106] 3) Connect to the database: Fill in the name, address, port, user name and password of the created database in the DATABASES configuration item of the settings.py file, and use the pymysql module to connect to the database.

[0107] 4) Create data model: Create a data table through Django model, that is, create a class in the models.py file of Django project, and define the class of Chinese fir germplasm resources, the class of users, and the fields and attributes they contain according to the data table in step 3.

[0108] 5. Algorithm Integration

[0109] All the codes for genetic diversity analysis and principal component analysis in the data analysis module are integrated into the database to realize the function of germplasm identification and evaluation of Chinese fir germplasm resources. The key codes are as follows:

[0110] 1) Genetic diversity analysis

[0111] #Calculate the average average = values.mean()

[0112] #Calculate the median median = values.median()

[0113] #Calculate the maximum value max_value = values.max()

[0114] #Calculate the minimum value min_value=values.min()

[0115] #Calculate the range range_value = max_value - min_value

[0116] # Calculate the standard deviation std_dev = values.std()

[0117] #Calculate coefficient of variation CV cv = (std_dev / average)*100 if average! = 0 else None

[0118] #Calculate Shannon-Wiener's genetic diversity index H'

[0119] def shannon_entropy(x):

[0120] counts = np.bincount(x)

[0121] probs = counts / len(x)

[0122] probs = probs[probs! = 0] # exclude the case where the probability is zero to avoid warnings

[0123] return-np.sum(probs*np.log(probs))

[0124] 2) Principal component analysis

[0125] #Bartlett's sphericity test

[0126] chi_square_value,p_value=calculate_bartlett_sphericity(df)

[0127] #KMO test

[0128] kmo_all,kmo_model=calculate_kmo(df)

[0129] #standardization

[0130] scaler = StandardScaler()

[0131] df_scaled=scaler.fit_transform(df)

[0132] df_scaled=pd.DataFrame(df_scaled,columns=df.columns)#Convert to DataFrame

[0133] # Calculate the covariance matrix

[0134] cov_matrix=np.cov(df_scaled,rowvar=False)

[0135] # Solve for eigenvalues ​​and eigenvectors

[0136] featValue,featVec=np.linalg.eig(cov_matrix)

[0137] #Draw scatter plot and line chart

[0138] plt.scatter(range(1,df_scaled.shape[1]+1),featValue)

[0139] plt.plot(range(1,df_scaled.shape[1]+1),featValue)

[0140] plt.xticks(np.arange(1,22,1),fontsize=12)

[0141] #Calculate the contribution of eigenvalues

[0142] gx=featValue / np.sum(featValue)

[0143] #Calculate the cumulative contribution of eigenvalues

[0144] lg = np.cumsum(gx)

[0145] #Select the main component

[0146] k=[i for iin range(len(lg))if lg[i]<0.85]

[0147] k = list(k)

[0148] #Select the eigenvector matrix corresponding to the principal component

[0149] selectVec=np.matrix(featVec.T[k]).T

[0150] selectVe=selectVec*(-1)

[0151] #Principal component score

[0152] finalData=np.dot(df_scaled,selectVec)

[0153] #Draw a heat map

[0154] plt.figure(figsize=(14,14))

[0155] ax=sns.heatmap(selectVec,annot=True,cmap="BuPu",xticklabels=np.arange(1,selectVec.shape[1]+1),yticklabels=np.arange(1,selectVec.shape[0]+1))

[0156] 6. Database website construction

[0157] A database system platform with B / S architecture built based on Django. The website pages are designed and written using HTML, CSS, and JavaScript, and the interface layout is divided into the title bar, menu bar, and content bar. The title bar and menu bar are fixed parts of all pages, and the menu bar is set to indicate which functional module page is currently in. According to the scenario requirements of different functional modules, different list styles are set in the content bar, and operation buttons are embedded. Configure the data transmission route in the Django project urls.py file, and implement the interaction logic with the database in views.py, including the operations of adding, deleting, checking, and modifying data and data analysis functions, create html files for each functional page, and write the front-end page.

[0158] The following is a description of each functional page of the website.

[0159] 1) Germplasm list management

[0160] Germplasm List Management Page ( Figure 3 ) includes germplasm resource information list and information adding, querying, editing, viewing, and deleting functions. The germplasm resource information list selects the most important data in the data table for display, which is divided into sequence number, number, time, provenance, longitude, latitude, tree height, diameter at breast height, cone type, color, volume, wood water absorption rate, wood hardness, thousand-grain weight, seedling rate, and operation bar. Users can edit, view, delete, and other operations in the operation bar.

[0161] Search: The user enters the field to be queried in the Search box and clicks the Enter key, and the list displays the information records that meet the search criteria.

[0162] Delete: When the user clicks "Delete", the germplasm resource data record in the corresponding row will be deleted.

[0163] New: After the user clicks "New", it will jump to the new page ( Figure 4 ). Users can enter the newly added germplasm resource data information in the input field of the new page according to the required format.

[0164] Edit: After the user clicks "Edit", it will jump to the edit page ( Figure 5 ). Users can edit data information and image files on the editing page, that is, modify the original data in the input box, change the image, and fill in the original blank data information or image file.

[0165] View: After the user clicks "View", the page will be redirected ( Figure 6). Germplasm resource information is divided into four parts, namely common characteristics, environmental characteristics, morphological characteristics and biological characteristics, and quality characteristics. Users can browse all data and image files of the corresponding germplasm resources on the viewing page.

[0166] 2) Breeding resource management

[0167] Breeding resource information management includes breeding resource information list ( Figure 7 ) and information adding, querying, editing, viewing, and deleting functions. The implementation of each function and the page effect are basically the same as those of germplasm resource information management. Breeding resource information includes data on offspring, which is stored in an independent form and is distinguished from the germplasm resource information module.

[0168] Search: The user enters the field to be queried in the Search box and clicks the Enter key, and the list displays the information records that meet the search criteria.

[0169] Delete: When the user clicks "Delete", the breeding resource data record of the corresponding row will be deleted.

[0170] New: After the user clicks "New", it will jump to the new page ( Figure 8 ). This page is basically the same as the germplasm resource addition page.

[0171] Edit: After the user clicks "Edit", it will jump to the edit page ( Fig. 9 ). This page is basically the same as the germplasm resource editing page.

[0172] View: After the user clicks "View", the page will be redirected ( Fig.10 ). The page displays detailed breeding resource information in a table format, similar to the germplasm resource viewing page.

[0173] 3) Image management

[0174] Considering the particularity that image information is stored in the form of files, in order to improve the convenience of viewing and editing germplasm images in practical applications, an image file management module is set up to supplement the management of image files. Image file management includes image list and editing and viewing functions. Image list ( Fig.11 ) Arrange and display the serial number, germplasm number and germplasm image. Users can edit or view operations in the operation bar.

[0175] Search: The user enters the field to be queried in the Search box and clicks the Enter key, and the list will display the image information records that meet the search criteria.

[0176] Edit: After the user clicks "Edit", it will jump to the edit page ( Fig.12 ). Users can modify the ID, germplasm image file, delete or replace the image. Click "Browse" to open the local folder on the computer and select the image file ( Fig.13 ).

[0177] View: After the user clicks "View", he / she will be redirected to the detailed information viewing page of the corresponding germplasm resource, which is the same as the viewing page in 1) and 2).

[0178] 4) Data analysis

[0179] The statistical analysis results of germplasm resource traits are presented in the form of visual charts ( Fig.14 ), such as line graphs, radar graphs and bar graphs. The results of comprehensive evaluation and analysis of germplasm resources, including principal component analysis, genetic diversity analysis and comprehensive scores, are also displayed in charts on the system page ( Fig.15 ).

[0180] 7. Data entry, update and maintenance

[0181] After the data collected in step 1 are standardized in step 2, Tables 9-12 are obtained, and the data are imported into the database in Excel format through the admin background of the Django project. According to the actual construction progress of the Chinese fir germplasm resource bank, the data information recorded in the database is regularly checked to see if it is consistent with the actual situation of the breeding materials, and the data is updated and maintained.

[0182] Table 9 Basic information of 10 germplasm resources

[0183] Serial number serial number 1 JXGZ-1 2 JXGZ-2 3 JXGZ-7 4 JXGZ-8 5 JXGZ-9 6 JXGZ-11 7 JXGZ-13 8 JXGZ-14 9 JXGZ-15 10 JXGZ-25-2

[0184] Table 10 Environmental characteristics of 10 germplasm resources

[0185] Serial number time Seed Source longitude latitude Average altitude Average annual temperature Annual precipitation Average annual sunshine hours Frost-free period Relative humidity 1 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75 2 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75 3 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75 4 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75 5 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75 6 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75 7 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75 8 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75 9 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75 10 2018 Ganzhou, Jiangxi 114.03 25.87 949.3 17.9 1606.0 1488.0 300 75

[0186] Table 11 Morphological characteristics and biological properties of 10 germplasm resources

[0187]

[0188]

[0189] Table 12 Quality characteristics of 10 germplasm resources

[0190]

[0191] Example 2 Database Application

[0192] 70 Chinese fir breeding materials from the Chinese fir germplasm resource bank of Fujian Yangkou Forest Farm were randomly selected as experimental objects, and their trait data were processed using Python scripts. Genetic diversity analysis, principal component analysis and comprehensive evaluation were carried out to screen out excellent germplasm resources with good growth traits and wood properties.

[0193] 1. Genetic diversity analysis

[0194] 26 traits of 70 Chinese fir germplasm resources were selected for analysis, which were divided into 21 quantitative traits and 5 threshold traits. Python language was used to write codes to calculate the frequency, basic parameters (including mean, median, maximum, minimum, range, standard deviation), coefficient of variation CV and Shannon-Wiener's genetic diversity index (H'). The algorithm flow chart is shown in the figure below. Fig.16 shown.

[0195] The results of genetic diversity analysis of threshold traits are shown in Table 13. The coefficients of variation of the five threshold traits range from 19.89% to 46.13%, and the mean coefficient of variation is 34.81%. The lowest coefficient of variation is the presence or absence of white powder on new leaves, which is 19.89%. It can be inferred that this trait is relatively stable; in comparison, the coefficient of variation of the presence or absence of white powder on old leaves is close to the average value, indicating that the presence or absence of white powder on old leaves is relatively uniform. The highest coefficient of variation is needle color, which is 46.13%. For the Shannon-Wiener's genetic diversity index (H'), its range is 0.43 to 2.01, and the mean value of the diversity index is 1.04. The diversity indexes of the presence or absence of white powder on new leaves and the presence or absence of white powder on old leaves are both lower than the mean, at 0.43 and 0.69, respectively. The diversity indexes of the remaining three traits: bract shape (1.02), degree of opening (1.03) and needle color (2.01) were all greater than 1, among which the diversity indexes of bract shape and degree of opening were close to the mean; the highest diversity index was needle color and its diversity index was much greater than the mean.

[0196] Table 13 Calculation results of threshold trait frequency, coefficient of variation and diversity index

[0197]

[0198] The results of genetic diversity analysis of quantitative traits are shown in Table 14. The coefficients of variation of the 21 quantitative traits ranged from 10.36% to 68.11%, and the average coefficient of variation was 23.69%. The trait with the largest coefficient of variation was volume (68.11%), and the smallest was fruit shape coefficient (10.36%), the latter of which was relatively stable. The traits with coefficients of variation greater than the mean were bract length (24.23%), tree height (m), wood wet weight (28.36%), wood dry weight (28.8%), diameter at breast height (31.09%), needle hardness (31.48%), cone volume (34.27%), seedling emergence rate (66.09%), and volume (68.11%), indicating that the above 9 traits had a high degree of variation and were relatively unstable.

[0199] For Shannon-Wiener's genetic diversity index (H'), the diversity index of 21 quantitative traits ranged from 0.55 to 1.77, and the mean of the diversity index was 0.89. There were three traits with the highest diversity index, namely bract length, serration density and needle length, which were 1.17, and there were also three traits with the lowest diversity index, namely volume, wood dry weight and wood density, which were 0.55, indicating that these three traits were relatively simple. There were a total of 10 traits with diversity indexes exceeding the mean. Excluding the above three traits with the highest diversity index, the other seven traits were fruit volume (1.08), leaf shape index (1.08), 1000-grain weight (1.04), needle width (1.04), tree height (0.99), cone length (0.96), and bract width (0.95), indicating that the diversity of these 10 traits was relatively rich.

[0200] There are 5 traits related to needles. Except for the diversity index of needle hardness (0.8) which is lower than the mean, the other 4 traits: serration density (1.17), needle length (1.17), leaf shape index (1.08) and needle width (1.04) are all greater than the mean of the diversity index, indicating that needle traits are relatively rich and have great utilization potential. Similarly, there are 7 traits related to cones, including bract length (1.17), fruit volume (1.08), cone length (0.96), bract width (0. 95) and cone width (0.89) were greater than or equal to the mean of the diversity index. Only two traits were lower than the mean, namely fruit shape coefficient (0.8) and bract length-width ratio (0.73). This shows that the cone traits are rich and diverse, which can provide more possibilities for Chinese fir breeding. There are 4 traits related to wood, namely wood wet weight (0.8), wood water absorption rate (0.73), wood dry weight (0.55) and wood density (0.55), which are all lower than the mean of the diversity index. It can be inferred that the wood traits are relatively simple.

[0201] Observing the minimum value and range, the range of 15 traits was greater than the minimum value, indicating that these traits have great potential for genetic improvement.

[0202] Table 14 Calculation results of basic parameters, coefficient of variation and diversity index of quantitative traits

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[0205] 2. Principal Component Analysis

[0206] The Python script of the data analysis module was used to perform principal component analysis on 21 quantitative traits of 70 Chinese fir germplasm resources. The principal component analysis algorithm flow is as follows: Fig.17 shown.

[0207] Correlation heat map Fig.18 As shown, it can be seen that the five quantitative traits of Chinese fir, namely tree height, diameter at breast height, timber volume, wood wet weight and wood dry weight, are significantly positively correlated, wood density is significantly negatively correlated with wood water absorption rate, and Chinese fir cone width, cone length and cone volume are significantly positively correlated.

[0208] The results of principal component eigenvalues, principal component scores, contribution rates and cumulative contribution rates are shown in Table 15, and the component matrix heat map is shown in Fig.19 shown.

[0209] The eigenvalue of the first principal component is 5.207, and the contribution rate is 24.797%, which can represent 24.797% of all trait information. The traits with higher loads in the eigenvector are tree height (0.345), diameter at breast height (0.385), volume (0.364), wood wet weight (0.379), wood dry weight (0.362), cone length (0.246), cone width (0.298) and fruit volume (0.288);

[0210] The eigenvalue of the second principal component is 3.102, and the contribution rate is 14.769%, which can represent 14.769% of all property information. The traits with higher loads in the eigenvector are wood water absorption rate (0.462), needle length (0.238) and leaf shape index (0.305);

[0211] The eigenvalue of the third principal component is 2.492, and the contribution rate is 11.868%, which can represent 11.868% of all trait information. The traits with higher loads in the eigenvector are tree height (0.270) and timber volume (0.249);

[0212] The eigenvalue of the fourth principal component is 1.950, and the contribution rate is 9.287%, which can represent 9.287% of all trait information. The traits with higher loads in the eigenvector are bract length (0.324), bract width (0.391), needle length (0.527) and leaf shape index (0.527).

[0213] The eigenvalue of the fifth principal component is 1.683, and the contribution rate is 8.013%, which can represent 8.013% of all trait information. The traits with higher loads in the eigenvector are needle length (0.304) and needle width (0.547);

[0214] The eigenvalue of the sixth principal component is 1.330, and the contribution rate is 6.331%, which can represent 6.331% of all trait information, and there is no trait with a high load in the eigenvector;

[0215] The eigenvalue of the seventh principal component is 1.136, and the contribution rate is 5.412%, which can represent 5.412% of all trait information. The traits with higher loads in the eigenvector are bract length-width ratio (0.311), serration density (0.348) and leaf shape index (0.263).

[0216] Table 15 Principal component analysis results

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[0218]

[0219] Draw a scree plot based on the eigenvalues ​​( Fig. 20 ), the eigenvalues ​​of the first seven items were all greater than 1, and the cumulative contribution rate reached 80.478%, which means that the first seven principal components can basically represent most of the genetic information of 21 quantitative traits of 70 Chinese fir germplasm resources.

[0220] 3. Comprehensive evaluation

[0221] The above principal component analysis extracts the first 7 principal components and obtains the eigenvalues, component matrix and standardized matrix (i.e. the two matrices mentioned in 3). The specific steps of comprehensive evaluation are as follows:

[0222] 1) According to the characteristic vectors of 21 quantitative traits, the weights of each principal component were determined, and the comprehensive score function equation was constructed;

[0223] 2) Add the first 7 principal component eigenvalues ​​obtained in principal component analysis and calculate the principal components;

[0224] 3) Perform dot multiplication of the component matrix [21,7] and the standardized data matrix [70,7] (where 7 in the matrix refers to the 7 principal components obtained by principal component analysis, 21 refers to the 21 quantitative traits, and 70 refers to the 70 data). Then, apply the comprehensive evaluation function to calculate the comprehensive score (F) of the 70 Chinese fir germplasm resources and sort them in descending order.

[0225] The calculation formula of the comprehensive score F is as follows:

[0226] F1=0.345X1+0.385X2+0.364X3+0.379X4+0.362X5+0.028X6-0.046X7+0.246X8+0.298X9+0.288X10-0.059X1 1+0.112X12+0.043X13+0.105X14+0.112X15+0.107X16-0.162X17-0.037X18+0.080X19+0.017X20+0.052X21

[0227] F2=-0.002X1+0.020X2+0.003X3+0.030X4-0.125X5+0.462X6-0.466X7-0.123X8+0.061X9-0.004X10-0.298X11-0.290X12-0.188X13-0.182X14-0.035X15+0.173X16+0.054X17-0.268X18+0.239X19-0.189X20+0.305X21

[0228] F3=0.271X1+0.184X2+0.249X3+0.159X4+0.170X5-0.048X6+0.039X7-0.463X8-0.408X9-0.452X10-0.115X11-0.022X12+0.106X13-0.142X14-0.307X15+0.110X16-0.165X17+0.055X18-0.025X19+0.052X20-0.057X21

[0229] F4=-0.045X1-0.059X2-0.048X3-0.131X4-0.082X5-0.136X6+0.153X7+0.018X8-0.041X9-0.033X10+0.106X11+0.324X12+0.391X13-0.014X14-0.041X15+0.060X16-0.271X17-0.113X18+0.527X19-0.085X20+0.527X21

[0230] F5=-0.027X1-0.023X2-0.030X3-0.089X4-0.043X5-0.174X6+0.171X7-0.076X8+0.120X9+0.037X10-0.321X11-0.231X12-0.405X13+0.159X14-0.057X15-0.166X16-0.370X17-0.039X18+0.303X19+0.547X20-0.006X21

[0231] F6=0.061X1-0.044X2+0.062X3+0.041X4+0.112X5-0.229X6+0.211X7+0.116X8+0.046X9+0.045X10+0.117X1 1-0.492X12-0.015X13-0.651X14+0.144X15-0.250X16-0.123X17+0.122X18+0.024X19-0.224X20+0.131X21

[0232] F7=-0.053X1+0.067X2+0.001X3+0.118X4+0.194X5-0.228X6+0.196X7+0.034X8-0.050X9+0.0148X10+0.126X 11-0.097X12-0.396X13+0.310X14-0.470X15-0.125X16+0.348X17-0.133X18+0.087X19-0.341X20+0.264X21

[0233] Substitute the comprehensive evaluation function F=[λ1 / (λ1+λ2+λ3+λ4+λ5+λ6+λ7)]F1+[(λ2 / λ1+λ2+λ3+λ4+λ5+λ6+λ7)]F2+[(λ3 / λ1+λ2+λ3+λ4+λ5+λ6+λ7)]F3+[(λ4 / λ1+λ 2+λ3+λ4+λ5+λ6+λ7)]F4+[(λ5 / λ1+λ2+λ3+λ4+λ5+λ6+λ7)]F5+[(λ6 / λ1+λ2+λ3+λ4+λ5+λ6+λ7)]F6+[(λ7 / λ1+λ2+λ3+λ4+λ5+λ6+λ7)]F7

[0234] Among them, λ1, λ2, λ3, λ4, λ5, λ6 and λ7 are the eigenvalues ​​corresponding to each principal component respectively.

[0235] That is, F = 0.308F1 + 0.184F2 + 0.147F3 + 0.115F4 + 0.0995F5 + 0.0787F6 + 0.0672F7

[0236] Finally, the comprehensive evaluation function was applied to calculate the comprehensive scores (F) of the 70 Chinese fir germplasm resources, and they were sorted in descending order (Table 16). The germplasm with higher scores was superior.

[0237] Table 16 Ranking of superior germplasm

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[0241] The top three germplasm resources are JXGZ-20, JXGZ-31, and JXGZ-26, with comprehensive scores (F) of 1.705, 1.556, and 1.396, respectively.

[0242] The above description is only illustrative rather than restrictive of the present invention. Those skilled in the art will understand that many modifications, changes or equivalents may be made without departing from the spirit and scope defined by the appended claims, but all will fall within the scope of protection of the present invention.

Claims

1. A method for constructing a database for identifying Chinese fir germplasm resources, characterized in that: 1) Data collection: Collect and organize the data information of Chinese fir germplasm resources, divide them into four categories: basic information, morphological characteristics and biological characteristics, quality characteristics, and resistance characteristics, and save them as electronic files; 2) Data standardization: The collected data are cleaned, formatted and standardized according to the description specifications and data standards of Chinese fir germplasm resources; 3) Database design: Design the database structure, database relationship model and data table according to the requirements of each functional module and the characteristics of Chinese fir germplasm resources information; 4) Database creation: Create database and data model based on Django framework and MySQL database software; 5) Algorithm integration: Integrate all analysis function codes used in the data analysis module into the database; 6) Database website construction: configure the connection between the website and the database, and implement the interaction logic between the page and the database in the back-end code of the website; 7) Data entry, update and maintenance: Enter the standardized data from step 2) into the database, and regularly check, update and maintain the data.

2. The construction method according to claim 1, characterized in that: The functional modules include a Chinese fir germplasm resource data management module, a picture management module, a data analysis module, and a user management module.

3. The construction method according to claim 2, characterized in that: The Chinese fir germplasm resource basic information module is used to store data information on the Chinese fir germplasm resource basic information, morphological characteristics and biological characteristics, quality characteristics and resistance characteristics; The picture management module is used to store picture files of Chinese fir germplasm resources; The data analysis module is used to perform visual statistical analysis and comprehensive evaluation of excellent traits of Chinese fir germplasm resources; The user management module is used to store user login information and manage user permissions.

4. The construction method according to claim 3, characterized in that: The data analysis module includes two parts: statistical analysis visualization and comprehensive evaluation of excellent traits.

5. The construction method according to claim 1, characterized in that: Step 2) The description specifications and data standards of Chinese fir germplasm resources include basic information, morphological characteristics and biological characteristics, quality characteristics and adaptability characteristics.

6. The construction method according to claim 5, characterized in that: The basic information includes germplasm number, germplasm name, seed source, resource type, propagation method, breeding unit, breeding year, germplasm picture, preservation time, preservation material type, preservation method, and physical status; The morphological characteristics and biological characteristics include color, white powder coverage of new and old leaves, size, needle density, leaf shape index, serration density, branch angle, crown shape, branch height, tree height, breast diameter, internode distance, color, smoothness, thickness, cone size, fruit shape coefficient, bract opening condition, bract shape, and bract number; The quality characteristics include seedling height, ground diameter, crown roundness, straightness, taper, tree height growth, breast diameter growth and single tree volume growth in a specific period of time, wood hardness, wood density, wood moisture content, bending strength, compressive strength, fiber content, shrinkage coefficient; The adaptability includes the disease and insect resistance of Chinese fir and the site conditions; the disease resistance includes anthracnose, yellowing, red blight, damping-off of Chinese fir seedlings, and sudden wilting of Chinese fir seedlings; the insect resistance includes the small Chinese fir leaf roller, termites, and two-striped Chinese fir longhorn beetle; the disease and insect resistance of Chinese fir is divided into five levels, namely high resistance, resistance, medium resistance, susceptible, and high susceptible; the disease and insect resistance calculation formula is: DI = ∑ [number of diseased plants (organs) at each level × representative value of each disease level] * 100 / [total number of plants (organs) surveyed × maximum [Representative value of high disease level] Calculate the disease index DI; the disease index less than 5 is highly resistant; the disease index greater than or equal to 5 and less than 10 is resistant; the disease index greater than or equal to 10 and less than 20 is moderately resistant; the disease index greater than or equal to 20 and less than 40 is susceptible; the disease index greater than or equal to 40 is highly susceptible; the site conditions include the longitude and latitude of the provenance, the average annual temperature, the average annual precipitation, the average annual sunshine hours, the frost-free period, relative humidity, soil type, soil texture, and soil pH.

7. A database for identifying Chinese fir germplasm resources constructed according to any one of the methods described in claims 1-6.

8. Application of the database described in claim 7 in genetic diversity analysis of Chinese fir germplasm resources.

9. Application of the database described in claim 7 in principal component analysis of Chinese fir germplasm resources.

10. Application of the database described in claim 7 in comprehensive evaluation of Chinese fir germplasm resources.

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