Marine factor microbial diversity database construction method and related device
By constructing a database of marine factor microbial diversity, the problem of inefficient marine microbial research in the existing technology has been solved, and the convenience and research efficiency of rapid acquisition of marine microbial diversity data has been achieved.
Patent Information
- Application Number
- CN202411289048.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-14
- Publication Date
- 2025-01-07
AI Technical Summary
Existing marine microbial research is mainly limited to a certain region or a single factor, and the metagenomic database is only at the sequencing stage. The lack of a comprehensive marine factor microbial diversity database, resulting in inefficiency in research and difficulty in obtaining data.
By obtaining metagenomic samples of marine microorganisms, processing them to obtain species abundance tables and functional abundance tables, and combining marine environmental variable data, a marine factor microbial diversity database was constructed to reduce subsequent analysis work.
It has achieved rapid acquisition of marine microbial diversity data, reduced the need for field sampling and metagenomic analysis, reduced dependence on historical documents, and improved research efficiency and convenience of data acquisition.
Smart Images
Figure CN119274665A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of database construction, and in particular to a method and related device for constructing a marine factor microbial diversity database based on metagenomic assembly analysis. Background Art
[0002] It is well known that microorganisms are ubiquitous in the marine environment. Marine microorganisms account for 98% of the main productivity of the ocean and play a key role in biogeochemical processes such as carbon and nutrient cycles. These marine microorganisms and the communities they form drive and respond to environmental changes, including changes in temperature, carbon chemistry, nutrients and oxygen content associated with climate change, as well as changes in ocean stratification and ocean currents. However, since more than 99% of marine microorganisms cannot be cultured using current technologies, the study of microbial genomics is extremely limited. Metagenomic studies can be conducted using high-throughput sequencing technology, which does not rely on the cultivation of marine microorganisms, allowing people to obtain the genomes of marine microbial communities. Through some genomic analysis methods, the structural patterns, diversity and functional potential of marine microorganisms can be further studied, which greatly promotes the progress of marine microbial research. Genomic studies of marine microorganisms not only help to understand the origin, evolution, diversity and adaptability of life to complex and extreme environments, but also have important significance for the application of synthetic biology in energy, pharmaceuticals and environmental management.
[0003] The existing literature is limited to the study of marine microorganisms in a certain region or under a single factor. The database of marine metagenomes is only at the sequencing stage and needs to be processed by a series of biological analysis tools to obtain data on marine microbial diversity. In addition to field sampling and sequencing for data analysis, the only source of data for scholars is a large amount of historical documents. However, for scholars, field sampling is time-consuming and laborious, and metagenome analysis is often time-consuming. If you want to quickly obtain such statistical knowledge, you need to search historical documents and judge their authority. The knowledge obtained is also limited by the region, year, etc. that the writers of historical documents want to show.
[0004] In recent years, the rapid development of machine learning methods has provided new ideas for research in the field of ecological environment, which provides a new idea for studying the diversity and composition of microorganisms in any sea area. If we want to apply machine learning to explore the relationship between environmental factors and the diversity and composition of marine microorganisms, we must build a complete and comprehensive database of marine factor microorganisms, which can reduce the analysis work of later scholars and is of great significance to the study of marine microorganisms. Summary of the invention
[0005] The purpose of this application is to provide a method and related devices for constructing a marine factor microbial diversity database, which can construct a complete and comprehensive marine factor microbial diversity database, reduce the analysis work of later scholars, and is of great significance to the research on marine microorganisms.
[0006] To achieve the above objectives, this application provides the following solutions:
[0007] In a first aspect, the present application provides a method for constructing a marine factor microbial diversity database, the method comprising:
[0008] Obtain multiple metagenomic samples of marine microorganisms;
[0009] For each of the metagenomic samples, the metagenomic sample is processed to obtain a species abundance table and a function abundance table corresponding to the metagenomic sample; the species abundance table includes the content of each species included in the metagenomic sample; the function abundance table includes the content of each function included in the metagenomic sample;
[0010] Obtaining raw data information and marine environmental variable data information corresponding to each of the metagenomic samples; the raw data information includes longitude, latitude, depth and sampling time; the marine environmental variable data information includes at least one of dissolved oxygen, temperature, pH, chlorophyll a, salinity, phosphate, silicate and nitrate;
[0011] The species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each of the metagenome samples are stored in a database to obtain a marine factor microbial diversity database.
[0012] Optionally, multiple metagenomic samples of marine microorganisms are obtained, including:
[0013] Using the marine metagenome as input, searching in NCBI to obtain a sequence file; the sequence file includes relevant information of each genome in the multiple genomes, and the relevant information includes the genome ID number, processing strategy, source, selection method, sequencing method and sequencing depth;
[0014] The sequence file is screened according to a preset data screening principle to obtain several genome ID numbers; the preset data screening principle is: the genome processing strategy is whole genome sequencing, the source of the genome is metagenomics, the genome selection method is random, the genome sequencing method is double-end, and the genome sequencing depth is less than or equal to the preset depth;
[0015] Using several genome ID numbers as input, we searched and downloaded them in NCBI to obtain multiple metagenomic samples of marine microorganisms; one genome ID number corresponds to one metagenomic sample, and the genome ID number is the ID number of the metagenomic sample.
[0016] Optionally, the metagenome sample is processed to obtain a species abundance table and a function abundance table corresponding to the metagenome sample, specifically including:
[0017] The metagenomic samples are sequentially subjected to quality control, gene assembly, gene prediction, clustering and redundancy removal, gene quantification, species annotation and functional annotation to obtain a species abundance table and a functional abundance table corresponding to the metagenomic samples.
[0018] Optionally, obtaining raw data information and marine environmental variable data information corresponding to each of the metagenome samples specifically includes:
[0019] For each of the metagenomic samples, the ID number of the metagenomic sample is used as input to search in NCBI to obtain the raw data information corresponding to the metagenomic sample;
[0020] Obtain observation data on marine environmental variables obtained by observing multiple marine observation sites; for each of the metagenome samples, respectively match the longitude, latitude and depth corresponding to the metagenome sample with the longitude, latitude and depth of multiple marine observation sites to obtain the marine observation site that matches the metagenome sample, and use the observation data of the marine observation site that matches the metagenome sample as the marine environmental variable data information corresponding to the metagenome sample.
[0021] Optionally, the species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each of the metagenome samples are stored in a database to obtain a marine factor microbial diversity database, specifically including:
[0022] The ID number, species abundance table, functional abundance table, original data information and marine environmental variable data information corresponding to each of the metagenomic samples are stored in an Excel table, and the Excel table is imported into an Access database to obtain a marine factor microbial diversity database; the marine factor microbial diversity database uses the ID number as the primary key.
[0023] Optionally, after obtaining the marine factor microbial diversity database, the method for constructing the marine factor microbial diversity database further includes: updating, visualizing and performing data analysis on the marine factor microbial diversity database; the data analysis includes query analysis, distribution analysis and data map analysis.
[0024] In a second aspect, the present application provides a device for constructing a marine factor microbial diversity database, the device comprising:
[0025] A sample acquisition module, used to obtain multiple metagenomic samples of marine microorganisms;
[0026] A sample processing module, for processing each of the metagenome samples to obtain a species abundance table and a function abundance table corresponding to the metagenome sample; the species abundance table includes the content of each species included in the metagenome sample; the function abundance table includes the content of each function included in the metagenome sample;
[0027] A data information acquisition module, used to acquire raw data information and marine environmental variable data information corresponding to each of the metagenomic samples; the raw data information includes longitude, latitude, depth and sampling time; the marine environmental variable data information includes at least one of dissolved oxygen, temperature, pH, chlorophyll a, salinity, phosphate, silicate and nitrate;
[0028] The database construction module is used to store the species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each of the metagenome samples in the database to obtain a marine factor microbial diversity database.
[0029] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for constructing a marine factor microbial diversity database.
[0030] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for constructing a marine factor microbial diversity database.
[0031] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for constructing a marine factor microbial diversity database.
[0032] According to the specific embodiments provided in this application, this application discloses the following technical effects:
[0033] The present application provides a method for constructing a marine factor microbial diversity database and related devices. For each metagenome sample of marine microorganisms, the metagenome sample is processed to obtain a species abundance table and a function abundance table corresponding to the metagenome sample, and the original data information and marine environmental variable data information corresponding to each metagenome sample are obtained. The species abundance table, function abundance table, original data information and marine environmental variable data information corresponding to each metagenome sample are stored in a database to obtain a marine factor microbial diversity database, thereby being able to construct a complete and comprehensive marine factor microbial diversity database. By querying the marine factor microbial diversity database, relevant data of marine microorganisms can be obtained, and there is no need for field sampling, metagenome analysis, or self-searching of historical documents and judging authority, which reduces the analysis work of later scholars and is of great significance to the study of marine microorganisms. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0035] Figure 1 This is an application environment diagram of a method for constructing a marine factor microbial diversity database provided in Example 1 of the present application.
[0036] Figure 2 A schematic flow chart of a method for constructing a marine factor microbial diversity database provided in Example 1 of the present application.
[0037] Figure 3 A schematic diagram of the technical route of a method for constructing a marine factor microbial diversity database provided in Example 1 of the present application.
[0038] Figure 4 A schematic diagram of the functional modules of a device for constructing a marine factor microbial diversity database provided in Example 2 of the present application.
[0039] Figure 5 A schematic diagram of the structure of a computer device provided in Example 3 of the present application. DETAILED DESCRIPTION
[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0041] Example 1
[0042] The method for constructing a marine factor microbial diversity database provided in the embodiments of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal can send the metagenome sample to be processed to the server. After the server receives the metagenome sample to be processed, the server obtains multiple metagenome samples of marine microorganisms for the metagenome sample to be processed; for each metagenome sample, the metagenome sample is processed to obtain the species abundance table and function abundance table corresponding to the metagenome sample; the original data information and marine environmental variable data information corresponding to each metagenome sample are obtained; the species abundance table, function abundance table, original data information and marine environmental variable data information corresponding to each metagenome sample are stored in the database to obtain a marine factor microbial diversity database. The server can feedback the obtained marine factor microbial diversity database to the terminal.
[0043] In addition, in some embodiments, the method for constructing a marine factor microbial diversity database can also be implemented independently by a server or a terminal. For example, the terminal can directly process the metagenomic samples to be processed, or the server can obtain the metagenomic samples to be processed from the data storage system and process the metagenomic samples to be processed.
[0044] The terminal may be, but is not limited to, various desktop computers, laptop computers, and IoT devices, and the server may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.
[0045] like Figure 2 and Figure 3 As shown, a method for constructing a marine factor microbial diversity database is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 Taking the server in as an example, the method for constructing a marine factor microbial diversity database includes the following steps:
[0046] Step S1, obtaining multiple metagenomic samples of marine microorganisms.
[0047] Step S2, for each of the metagenomic samples, the metagenomic sample is processed to obtain a species abundance table and a function abundance table corresponding to the metagenomic sample; the species abundance table includes the content of each species included in the metagenomic sample; the function abundance table includes the content of each function included in the metagenomic sample.
[0048] Step S3, obtaining the original data information and marine environmental variable data information corresponding to each of the metagenomic samples; the original data information includes longitude, latitude, depth and sampling time; the marine environmental variable data information includes at least one of dissolved oxygen, temperature, pH, chlorophyll a, salinity, phosphate, silicate and nitrate.
[0049] Step S4, storing the species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each of the metagenome samples into a database to obtain a marine factor microbial diversity database.
[0050] By implementing the above-mentioned steps S1 to S4, this embodiment can construct a complete and comprehensive marine factor microbial diversity database. By querying the marine factor microbial diversity database, relevant data of marine microorganisms can be obtained. There is no need for field sampling, metagenome analysis, or searching for historical documents and judging authority, which reduces the analysis work of later scholars and is of great significance to the study of marine microorganisms.
[0051] In S1, multiple metagenomic samples of marine microorganisms were obtained, including:
[0052] (1) Using the marine metagenome as input, a search was performed in NCBI to obtain a sequence file. The sequence file includes relevant information of each genome in multiple genomes, including the genome ID number, processing strategy, source, selection method, sequencing method, and sequencing depth.
[0053] This embodiment uses "marine metagenome" for retrieval, specifically using "marinemetagenome" as input, and searches in the National Center for Biotechnology Information (NCBI) to collect and obtain the original sequence file, that is, download the sequence file with the suffix ".fastq", which is a text format that stores biological sequences and corresponding quality evaluations. The specific operation method is: log in to the NCBI website, select the "SRA" database in the search bar, and use "marine metagenome" as a keyword to find relevant information of a total of 160,447 genomes to obtain the sequence file.
[0054] (2) The sequence file is screened according to a preset data screening principle to obtain several genome ID numbers. The preset data screening principle is: the genome processing strategy is whole genome sequencing, the source of the genome is metagenomics, the genome selection method is random, the genome sequencing method is double-end, and the genome sequencing depth is less than or equal to the preset depth.
[0055] In this embodiment, the preset data screening principles are: ① The processing strategy (Strategy) of the genome should be "WGS" (Whole Genome Sequencing); ② The source (Source) of the genome should be "METAGENOMIC" (metagenomic); ③ The selection method (Selection) of the genome should be "RANDOM" (random); ④ The sequencing method (Layout) of the genome should be "PAIRED" (double-end); ⑤ The sequencing depth (depth) of the genome should be less than or equal to the preset depth, and the preset depth can be 200m. This embodiment is screened according to the above standards to obtain the ID numbers of several genomes that meet the preset screening principles. The ID number corresponds to the genome one by one, and the ID number of each genome is different.
[0056] (3) Using several genome ID numbers as input, we searched and downloaded them from NCBI to obtain multiple metagenomic samples of marine microorganisms. One genome ID number corresponds to one metagenomic sample, and the genome ID number is the ID number of the metagenomic sample.
[0057] Using several genome ID numbers as input, use Aspera software in the Linux system to use the Shell command line to download metagenomic samples from NCBI. In this way, representative marine genome data can be determined, and metagenomic samples of marine microorganisms can be downloaded from NCBI to obtain multiple metagenomic samples of marine microorganisms.
[0058] In S2, for each metagenomic sample, the metagenomic sample is processed to obtain the species abundance table and function abundance table corresponding to the metagenomic sample, specifically including: performing quality control, gene assembly, gene prediction, clustering and redundancy removal, gene quantification, species annotation and function annotation on the metagenomic sample in turn to obtain the species abundance table and function abundance table corresponding to the metagenomic sample.
[0059] Metagenomic samples are gene sequence files that can be analyzed and processed using bioinformatics methods in the Linux operating system. 16 / 18.04) operating system, and the Bioconda biological software library (URL: https: / / bioconda.github.io / ) was used to configure the software for analyzing metagenomic samples, including quality control and evaluation report summary software: FastQC (v0.11.9), MultiQC (v1.9), fastp (v0.21.0); gene assembly software: Megahit (v1.2.9); gene prediction software: prodigal (v2.6.3); gene clustering and redundancy removal software: cd-hit (v4.8.1); gene quantification software: Salmon (v1.8.0); species annotation and function annotation software: Kraken2 (v2.1.1), emapper (v2.1.6); of course, other quality control and evaluation report summary software, gene assembly software, gene prediction software, gene clustering and redundancy removal software, gene quantification software, and species annotation and function annotation software can also be selected as needed.
[0060] The specific implementation methods of various processing methods are as follows:
[0061] (1) Quality Control
[0062] This example uses FastQC, MultiQC, fastp and other software to perform quality control on metagenomic samples, as follows:
[0063] 1) Perform consistency check on the raw data information and sources corresponding to the metagenomic samples to ensure the integrity and accuracy of the raw data information and sources.
[0064] 2) Use fastp software to perform quality control on the metagenomic samples (which are sequencing sequence data), remove sequencing adapters and low-quality sequences generated during the sequencing process. Low-quality sequences refer to sequences with a positive sequence length of 20-150bp, a reverse sequence length of 20-150bp, and an average quality of >30, etc., to obtain pure sequences to ensure the accuracy of subsequent analysis.
[0065] The present embodiment can also use FastQC software to evaluate the quality of the metagenomic sample, generate multiple output reports, and use MultiQC software to integrate multiple output reports generated by FastQC software into one to obtain a total output report. FastQC software is then used to evaluate the quality of the pure sequence, generate multiple output reports, and use MultiQC software to integrate multiple output reports generated by FastQC software into one to obtain a total output report. The total output report of the metagenomic sample and the total output report of the pure sequence are compared to determine the effect of quality control. Among them, multiple output reports include the distribution of sequencing reads, GC content (the ratio of guanine and cytosine in the four bases of DNA) and sequencing error rate, etc., to identify whether there are problems such as sequencing quality decline or pollution.
[0066] (2) Gene assembly
[0067] For the pure sequence obtained after processing by fastp software, Megahit software is used for gene assembly. The basic process is as follows: Megahit software splices the overlap regions between reads in the pure sequence to obtain contigs (overlapping groups), which include multiple contigs (also called assembly sequences).
[0068] (3) Gene prediction
[0069] For multiple assembled sequences obtained after assembly with Megahit software, gene prediction was performed using prodigal software. The basic process is as follows: using the assembled sequence obtained by gene assembly (greater than or equal to 1000bp) as input, open reading frame (ORF, i.e., potential coding region that may encode polypeptide chains or proteins) was predicted to obtain prediction results, which included nucleic acid sequences and protein sequences. Based on the prediction results, nucleic acid sequences with a length of less than 100nt were filtered out, and finally the gene sequence gene.fa predicted by prodigal software was obtained. The gene sequence included the filtered nucleic acid sequence and protein sequence.
[0070] (4) Clustering and redundancy removal
[0071] For the gene sequence gene.fa obtained after prediction by the prodigal software, cd-hit software was used for clustering and de-redundancy. The basic process is as follows: when clustering, the identity (identity) is 95% and the coverage (coverage) is 90% by default, and the longest sequence is selected as the representative sequence; when de-redundancy, the number of reads of the gene aligned in each sample is calculated, and the genes with the number of reads aligned in each sample less than or equal to 2 are filtered out to obtain the de-redundant nucleic acid sequence nucleotide.fa and protein sequence protein.fa, so as to realize the quantification and comparison of all metagenomic samples based on a unified reference sequence.
[0072] (5) Gene quantification
[0073] For the nucleic acid sequence nucleotide.fa and protein sequence protein.fa obtained after clustering and de-redundancy by cd-hit software, gene quantification was performed using Salmon software to obtain the gene quantification file gene.TPM.
[0074] (6) Species annotation and functional annotation
[0075] The obtained nucleic acid sequence nucleotide.fa was combined with the gene quantification file gene.TPM, and species annotation was performed using Kraken2 software to obtain the abundance of microorganisms at different phylogenetic levels of kingdom, phylum, class, order, family, genus, and species, and a species abundance table was obtained.
[0076] The obtained protein sequence protein.fa is combined with the gene quantification file gene.TPM, and aligned to the KEGG and CAZymes functional databases through the emapper software for functional annotation to obtain a functional abundance table, thereby completing the functional annotation process by aligning with the reference database, wherein the annotated functional type can be determined according to user needs.
[0077] In S3, the original data information and marine environmental variable data information corresponding to each metagenome sample are obtained, including:
[0078] (1) For each metagenomic sample, the ID number of the metagenomic sample is used as input and searched in NCBI to obtain the raw data information corresponding to the metagenomic sample.
[0079] (2) Obtain observation data on marine environmental variables obtained by observing multiple marine observation stations; for each metagenome sample, respectively match the longitude, latitude, and depth corresponding to the metagenome sample with the longitude, latitude, and depth of multiple marine observation stations to obtain the marine observation station that matches the metagenome sample, and use the observation data of the marine observation station that matches the metagenome sample as the marine environmental variable data information corresponding to the metagenome sample.
[0080] In this embodiment, marine environmental variable data is downloaded from the marine environmental data website, and the marine environmental variable data information of each metagenome sample is obtained by sorting. Specifically, the observation data of marine environmental variables of multiple marine observation sites in .nc format are downloaded from the official website of the Global Ocean Database WOA (website: https: / / www.nodc.noaa.gov / OC5 / woa18) and CMEMS (website: https: / / marine.copernicus.eu / ), and the netCDF4 library and pandas library are used in python3.10 software to convert the .nc file into a .csv file, and then the R package (RANN_2.6.1) is used in RStudio software to match the metagenome sample according to "longitude", "latitude" and "depth", so as to obtain the marine environmental variable data information of each metagenome sample. The marine environmental variables are shown in Table 1 below.
[0081] Table 1 Units and sources of marine environmental variables
[0082] Environment variables English name unit source Dissolved oxygen dissolvedoxygen(DO) <![CDATA[μmol·kg -1 ]]> WorldOceanAtlas2018 temperature temperature ℃ WorldOceanAtlas2018 pH pH / CMEMS001029 Chlorophyll a Chlorophyla <![CDATA[mg·m -3 ]]> CMEMS001029 salinity Salinity <![CDATA[g·kg -1 ]]> WorldOceanAtlas2018 Phosphate Phosphate <![CDATA[μmol·kg -1 ]]> WorldOceanAtlas2018 Silicate silicate <![CDATA[μmol·kg -1 ]]> WorldOceanAtlas2018 Nitrates nitrate <![CDATA[μmol·kg -1 ]]> WorldOceanAtlas2018
[0083] In this embodiment, metagenomic samples of marine microorganisms worldwide are collected, each metagenomic sample is processed to obtain a species abundance table and a function abundance table corresponding to each metagenomic sample, the original data information corresponding to each metagenomic sample is extracted, and the marine environmental variable data information corresponding to each metagenomic sample is sorted out, and the ID number of each metagenomic sample is extracted into an Excel table. For each metagenomic sample, the species abundance table, the function abundance table, the original data information and the marine environmental variable data information are stored together in an Excel table, corresponding to the ID number of the metagenomic sample, that is, the ID number, the species abundance table, the function abundance table, the original data information and the marine environmental variable data information of each metagenomic sample are extracted into an Excel table, and an Access database is further constructed and arranged according to certain ID numbers to facilitate subsequent query processes in the Access database.
[0084] In S4, the species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each metagenomic sample are stored in the database to obtain a marine factor microbial diversity database, which specifically includes: storing the ID number, species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each metagenomic sample in an Excel table, and importing the Excel table into an Access database to obtain a marine factor microbial diversity database, which uses the ID number as the primary key.
[0085] Specifically, after processing each metagenomic sample, the species abundance table and function abundance table (i.e., marine microbial species and functional diversity data) obtained by analysis are sorted, and the species abundance table and function abundance table corresponding to multiple metagenomic samples are merged into a total table (i.e., Excel table) using python, and the total table is imported into the Access database to form a marine microbial diversity database based on metagenomic assembly analysis. Further, an Excel table consisting of the ID number, original data information, and marine environmental variable data information corresponding to each metagenomic sample is imported into the Access database to form a marine factor microbial diversity database based on metagenomic assembly analysis. The specific method of importing the Excel table into the Access database is as follows: create a new Access database, select the Excel table option in the import data, select the Excel table, retain the first row of information containing the title, select the sample ID number as the primary key to facilitate subsequent retrieval, and determine the name of the newly created database.
[0086] This embodiment can also use Access software to update, visualize and analyze the marine factor microbial diversity database. That is, after obtaining the marine factor microbial diversity database, the marine factor microbial diversity database construction method of this embodiment also includes: updating, visualizing and analyzing the marine factor microbial diversity database, and the data analysis includes query analysis, distribution analysis and data map analysis.
[0087] Among them, database update refers to: using the Access cross-tab query wizard to summarize and supplement or directly merge or import tables. Later, in the form of an online website, a crowdsourcing platform can be used to allow users to input literature data, text statistics, and upload images, and process the ID of the metagenomic samples of marine microorganisms, species abundance tables, functional abundance tables, original data information, and marine environmental variable data information to achieve data amplification, providing a real-time, comprehensive, and scalable global marine factor microbial diversity database, which can become a knowledge acquisition platform.
[0088] Database visualization refers to: combining SQL, VBA, and controls to quickly develop dynamic charts. Specifically, create a query first, insert a chart in the design mode of the form, set the data source to the query just created, and then create a static chart. Then, in the click event VBA of the query button, set the chart ROWSOUCE = "SQL statement", and then replace part of the SQL statement with variables. In this way, you can create dynamic graphics and develop dynamic charts, so that the Access database integrates data storage, data filtering, and data visualization.
[0089] Among them, data analysis refers to: using SQL language to perform query analysis, distribution analysis or data map analysis in Access database. Query analysis is to create a new "Query Design" in the "Create" tab, cancel the display table and enter the SQL dialog box, and use the query function SELECT for analysis. Distribution analysis is to first establish an interval in Access to mark, create a new query, and use the Switch function in SQL to determine the distribution analysis within the interval. Data map analysis first summarizes the data in Access according to a certain requirement, pastes the data into Excel, selects to open PowerMAP to generate a data map.
[0090] This embodiment discloses a method for constructing a marine factor microbial diversity database based on metagenomic assembly analysis, first downloading a metagenomic sample of marine microorganisms; then processing each metagenomic sample by quality control, gene assembly, gene prediction, clustering and de-redundancy, gene quantification, species annotation and function annotation methods to obtain a species abundance table and a function abundance table; then merging the species abundance table and the function abundance table of a single metagenomic sample into a total table, together with the original data information and the marine environmental variable data information, storing them into an Access database, and updating, visualizing and analyzing the Access database. This embodiment can convert the metagenomic data of marine microorganisms into a microbial diversity feature table (i.e., a species abundance table and a function abundance table) that can be directly analyzed and drawn, collects a large amount of marine microbial diversity data scattered in a genome database, and stores them into an Access database, i.e., constructing a marine factor microbial diversity database based on metagenomic assembly analysis, which can enable users to conveniently query marine microbial data, especially to facilitate other research that may be conducted using machine learning in the future.
[0091] This embodiment specifies the screening principle, downloads the metagenomic samples of marine microorganisms from the microbial genome database website, processes the metagenomic samples into species abundance tables and function abundance tables with the help of a series of bioinformatics analysis tools, records the species abundance tables and function abundance tables corresponding to each metagenomic sample in an Excel table, and records the longitude, latitude, depth, sampling time and marine environmental variable data information corresponding to each metagenomic sample at the time of sampling in the longitude, latitude, depth, sampling time and marine environmental variable data information corresponding to each metagenomic sample, imports the Access software to generate an Access database, and sets the ID number of the metagenomic sample as the primary key field in the Access software to facilitate subsequent supplementary searches, and new metagenomic samples can be processed in the same way later. Compared with the prior art, this embodiment constructs a marine factor microbial diversity database based on metagenomic assembly analysis, which enables users to conveniently query marine sample microbial diversity data, especially for other studies that may be conducted using machine learning in the future. This marine factor microbial diversity database has the advantages of large amount, simplicity, global coverage of data, multiple factor analysis and timeliness, etc. In addition, it also uses the functions of the Access database itself to provide additional services such as updates, visualization, and data analysis to attract users to expand the database and apply the database.
[0092] The present application embodiment also provides an application scenario, which applies the above-mentioned method for constructing a database of marine factor microorganism diversity. Specifically, the method for constructing a database of marine factor microorganism diversity provided by the present embodiment can be applied in a marine microorganism query scenario, and the marine microorganism query scenario includes a data acquisition link, a data processing link, and a human-computer interaction link. The data acquisition link is used to obtain multiple metagenome samples of marine microorganisms and the original data information and marine environmental variable data information corresponding to each metagenome sample. The data processing link is used to process multiple metagenome samples, and the species abundance table, function abundance table, original data information, and marine environmental variable data information corresponding to each metagenome sample are stored in the database to obtain a marine factor microorganism diversity database. The human-computer interaction link is used to enable users to query in the marine factor microorganism diversity database through human-computer interaction to complete the query of marine microorganisms. The method for constructing a database of marine factor microorganism diversity provided by the present embodiment belongs to the data processing link.
[0093] Example 2
[0094] Based on the same inventive concept, the embodiment of the present application also provides a marine factor microbial diversity database construction device for implementing the marine factor microbial diversity database construction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiment of the marine factor microbial diversity database construction device provided below can refer to the limitations of the marine factor microbial diversity database construction method above, and will not be repeated here.
[0095] like Figure 4 As shown, a device for constructing a marine factor microbial diversity database is provided, and the marine factor microbial diversity database construction device comprises:
[0096] The sample acquisition module M1 is used to obtain multiple metagenomic samples of marine microorganisms.
[0097] The sample processing module M2 is used to process each of the metagenomic samples to obtain a species abundance table and a function abundance table corresponding to the metagenomic sample; the species abundance table includes the content of each species included in the metagenomic sample; the function abundance table includes the content of each function included in the metagenomic sample.
[0098] The data information acquisition module M3 is used to obtain the original data information and marine environmental variable data information corresponding to each of the metagenome samples; the original data information includes longitude, latitude, depth and sampling time; the marine environmental variable data information includes at least one of dissolved oxygen, temperature, pH, chlorophyll a, salinity, phosphate, silicate and nitrate.
[0099] The database construction module M4 is used to store the species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each of the metagenome samples into a database to obtain a marine factor microbial diversity database.
[0100] Example 3
[0101] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for constructing a marine factor microbial diversity database is implemented.
[0102] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0103] In an exemplary embodiment, a computer device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for constructing a marine factor microbial diversity database as described in Example 1.
[0104] Example 4
[0105] An embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for constructing a marine factor microbial diversity database described in Example 1 is implemented.
[0106] Example 5
[0107] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for constructing a marine factor microbial diversity database described in Example 1.
[0108] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0109] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0110] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0111] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.
Claims
1. A method for constructing a marine factor microbial diversity database, characterized in that: The method for constructing a marine factor microbial diversity database comprises: Obtain multiple metagenomic samples of marine microorganisms; For each of the metagenomic samples, the metagenomic sample is processed to obtain a species abundance table and a function abundance table corresponding to the metagenomic sample; the species abundance table includes the content of each species included in the metagenomic sample; the function abundance table includes the content of each function included in the metagenomic sample; Obtaining raw data information and marine environmental variable data information corresponding to each of the metagenomic samples; the raw data information includes longitude, latitude, depth and sampling time; the marine environmental variable data information includes at least one of dissolved oxygen, temperature, pH, chlorophyll a, salinity, phosphate, silicate and nitrate; The species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each of the metagenome samples are stored in a database to obtain a marine factor microbial diversity database.
2. The method for constructing a marine factor microbial diversity database according to claim 1, characterized in that: Obtain multiple metagenomic samples of marine microorganisms, including: Using the marine metagenome as input, searching in NCBI to obtain a sequence file; the sequence file includes relevant information of each genome in the multiple genomes, and the relevant information includes the genome ID number, processing strategy, source, selection method, sequencing method and sequencing depth; The sequence file is screened according to a preset data screening principle to obtain several genome ID numbers; the preset data screening principle is: the genome processing strategy is whole genome sequencing, the source of the genome is metagenomics, the genome selection method is random, the genome sequencing method is double-end, and the genome sequencing depth is less than or equal to the preset depth; Using several genome ID numbers as input, we searched and downloaded them in NCBI to obtain multiple metagenomic samples of marine microorganisms; one genome ID number corresponds to one metagenomic sample, and the genome ID number is the ID number of the metagenomic sample.
3. The method for constructing a marine factor microbial diversity database according to claim 1, characterized in that: The metagenome sample is processed to obtain a species abundance table and a function abundance table corresponding to the metagenome sample, specifically including: The metagenomic samples are sequentially subjected to quality control, gene assembly, gene prediction, clustering and redundancy removal, gene quantification, species annotation and functional annotation to obtain a species abundance table and a functional abundance table corresponding to the metagenomic samples.
4. The method for constructing a marine factor microbial diversity database according to claim 2, characterized in that: Obtaining the original data information and marine environmental variable data information corresponding to each of the metagenome samples, specifically including: For each of the metagenomic samples, the ID number of the metagenomic sample is used as input to search in NCBI to obtain the raw data information corresponding to the metagenomic sample; Obtain observation data on marine environmental variables obtained by observing multiple marine observation sites; for each of the metagenome samples, respectively match the longitude, latitude and depth corresponding to the metagenome sample with the longitude, latitude and depth of multiple marine observation sites to obtain the marine observation site that matches the metagenome sample, and use the observation data of the marine observation site that matches the metagenome sample as the marine environmental variable data information corresponding to the metagenome sample.
5. The method for constructing a marine factor microbial diversity database according to claim 2, characterized in that: The species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each of the metagenome samples are stored in a database to obtain a marine factor microbial diversity database, which specifically includes: The ID number, species abundance table, functional abundance table, original data information and marine environmental variable data information corresponding to each of the metagenomic samples are stored in an Excel table, and the Excel table is imported into an Access database to obtain a marine factor microbial diversity database; the marine factor microbial diversity database uses the ID number as the primary key.
6. The method for constructing a marine factor microbial diversity database according to claim 1, characterized in that: After obtaining the marine factor microbial diversity database, the marine factor microbial diversity database construction method further includes: updating, visualizing and data analyzing the marine factor microbial diversity database; the data analysis includes query analysis, distribution analysis and data map analysis.
7. A device for constructing a marine factor microbial diversity database, characterized in that: The marine factor microbial diversity database construction device comprises: A sample acquisition module, used to obtain multiple metagenomic samples of marine microorganisms; A sample processing module, for processing each of the metagenome samples to obtain a species abundance table and a function abundance table corresponding to the metagenome sample; the species abundance table includes the content of each species included in the metagenome sample; the function abundance table includes the content of each function included in the metagenome sample; A data information acquisition module, used to acquire raw data information and marine environmental variable data information corresponding to each of the metagenomic samples; the raw data information includes longitude, latitude, depth and sampling time; the marine environmental variable data information includes at least one of dissolved oxygen, temperature, pH, chlorophyll a, salinity, phosphate, silicate and nitrate; The database construction module is used to store the species abundance table, functional abundance table, raw data information and marine environmental variable data information corresponding to each of the metagenome samples in the database to obtain a marine factor microbial diversity database.
8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for constructing a marine factor microbial diversity database according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for constructing a marine factor microbial diversity database according to any one of claims 1 to 6 is implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for constructing a marine factor microbial diversity database according to any one of claims 1 to 6 is implemented.