Establishment method of plasmodium genome database, database and tracing method
By performing whole-genome sequencing and database construction on Chinese Plasmodium falciparum samples, and combining this with machine learning algorithms to screen SNP combinations, the problem of the lack of Chinese information in international malaria databases has been solved. This enables rapid and accurate tracing of the geographical origin of Plasmodium falciparum, supporting the prevention and control of imported malaria.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-04-03
AI Technical Summary
The existing international malaria database lacks genomic information on Plasmodium falciparum in China, making it impossible to accurately assess local genetic diversity and difficult to precisely trace imported malaria cases.
By performing whole-genome sequencing on Chinese Plasmodium falciparum samples, a database containing Chinese genetic information was constructed. Machine learning algorithms were used to screen geographically source-tracing SNP combinations, and population genetic analysis was combined to develop a source-tracing method.
It enables rapid and accurate tracing of the geographical origin of Plasmodium falciparum, fills a gap in the database, and provides direct data support for the prevention and control of imported malaria.
Smart Images
Figure CN121789798A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of genome sequencing technology, and in particular to a method for establishing a Plasmodium genome database, the database itself, and a method for tracing its origin. Background Technology
[0002] Malaria is a significant global public health problem, with malaria caused by Plasmodium falciparum being the most severe. With the development of high-throughput sequencing technology, whole-genome sequencing of pathogens has become an important tool for studying their population structure, transmission patterns, and evolutionary dynamics.
[0003] China achieved zero local malaria cases for the first time in 2017 and received malaria elimination certification from the World Health Organization in 2021. However, thousands of imported cases still occur annually, and there are also suspected local cases with no travel history. Therefore, tracing the source at the genomic level requires first establishing a database containing genomic information of Plasmodium parasites from different regions. Through bioinformatics analysis, molecular markers can be identified, and visualization analysis software can be used to quickly and easily determine the origin of the parasite strain.
[0004] Currently, large-scale Plasmodium falciparum genome databases exist internationally. For example, the Pf7K project sequenced the whole genomes of over 20,000 Plasmodium falciparum strains from 33 countries worldwide (collected between 1984 and 2018). This database revealed significant genetic differentiation of Plasmodium falciparum across different continents (e.g., the genetic differentiation index Fst between African and Asian strains is as high as 0.15–0.25). However, this database, and similar international public databases, severely lack Plasmodium falciparum samples and genomic information from China. This makes it impossible to accurately assess the genetic diversity, population characteristics, and role in regional transmission of Plasmodium falciparum native to China, and also poses significant challenges to the precise tracing of imported malaria cases.
[0005] In summary, this paper proposes a method for establishing a Plasmodium genome database, including the database itself and a source tracing method. The goal is to create a genome database containing genetic information of Plasmodium falciparum in China and develop corresponding source tracing technologies to address the shortcomings of existing technologies. This is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0006] In view of this, the present invention provides a method for establishing a Plasmodium genome database, a database, and a source tracing method, aiming to solve the problems of the lack of Plasmodium falciparum genome information in my country and the insufficient research on related genetic diversity in my country in the existing global malaria database. By performing whole-genome sequencing and analysis on wild strains of Plasmodium falciparum in my country, a dedicated genetic information database is constructed, and population-specific molecular genetic markers are screened to establish source tracing SNP combinations, revealing the transmission risk of imported malaria and potential infection foci, providing a reference for the prevention and control of imported malaria in my country.
[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for establishing a Plasmodium genome database includes the following steps: S11 First Genome Data Acquisition Steps: Obtain Plasmodium falciparum samples from China, perform whole-genome sequencing on the samples, and obtain first genome data; S12 Second Genome Data Acquisition Steps: Obtain the international Plasmodium falciparum whole genome data from public databases to obtain the second genome data; S13 Data processing steps: Perform variant detection on the first and second genome data to obtain the single nucleotide polymorphism (SNP) dataset; S14 Database Construction Steps: Based on the SNP dataset, construct a database containing genetic information of Plasmodium falciparum in China.
[0008] The above method, optionally, includes the following specific steps in the S13 data processing procedure: S1301 performs quality control and filtering on sequencing data to obtain high-quality data; S1302 performs sequence alignment and variant detection on high-quality data, identifying SNPs across the entire genome; S1303 filters SNPs to obtain a high-quality SNP dataset.
[0009] Optionally, the above method may also include the S1304 analysis step: performing population genetic analysis on the obtained SNP dataset, which includes calculating one or more of nucleotide diversity π, Tajima's D, linkage disequilibrium LD, and integrated haplotype score iHS.
[0010] Optionally, in the above method, the database in the S14 database construction step is based on a B / S architecture, developed using ASP.NET technology, and uses SQL Server as the backend database management system.
[0011] A method for establishing a Plasmodium genome database, resulting in a database containing genetic information of Plasmodium falciparum in China.
[0012] A method for geographic tracing of Plasmodium falciparum, based on the aforementioned database containing genetic information of Plasmodium falciparum in China, includes the following steps: S21 Data Acquisition Steps: Obtain a high-quality SNP dataset from the database; S22 Data Preprocessing Steps: Preprocess and perform feature selection on the acquired high-quality SNP dataset to filter out SNPs located in the neutral-conservative region; S23 Model Construction Steps: Based on the selected SNPs, a geographic origin tracing and classification model is constructed using machine learning algorithms; S24 Prediction Step: Using the aforementioned geographic origin classification model, predict the geographic origin of the Plasmodium falciparum sample to be tested.
[0013] Optionally, the feature selection in S22 of the above method includes feature dimensionality reduction based on the population genetic differentiation index Fst.
[0014] Optionally, the machine learning algorithm in S23 of the above method includes one or more of XGBoost, Support Vector Machine (SVM), or Convolutional Neural Network (CNN).
[0015] As can be seen from the above technical solution, compared with the prior art, the present invention provides a method for establishing a Plasmodium genome database, a database, and a source tracing method, which has the following beneficial effects: (1) Data integrity: For the first time, the whole genome data of Plasmodium falciparum in China (such as Yunnan and Hainan) and international public data were systematically integrated to construct a Plasmodium genome database containing Chinese information, filling an important gap; (2) Accuracy of source tracing: By combining population genetics and machine learning algorithms (such as XGBoost and CNN), SNP molecular marker combinations with geographical distinguishability (SNP barcodes) are screened from the whole genome, realizing rapid and accurate source tracing of the geographical origin of Plasmodium falciparum; (3) High application value: The database and tracing method constructed by this invention can provide direct and powerful data support and technical tools for analyzing the transmission chain of imported malaria in my country, identifying potential epidemic sources, and formulating prevention and control strategies. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0017] Figure 1 This is a flowchart of a method for establishing a Plasmodium genome database disclosed in this invention; Figure 2 This is a flowchart of the method for geographic tracing of Plasmodium falciparum using a database disclosed in this invention; Figure 3 This is an overall flowchart of the method for establishing a malaria genome database and the geographical tracing method disclosed in the embodiments of the present invention; Figure 4 This is a graph showing the SNP principal component analysis results disclosed in an embodiment of the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0020] See Figure 1 As shown, this invention discloses a method for establishing a Plasmodium genome database, comprising the following steps: S11 First Genome Data Acquisition Steps: Obtain Plasmodium falciparum samples from China, perform whole-genome sequencing on the samples, and obtain first genome data; S12 Second Genome Data Acquisition Steps: Obtain the international Plasmodium falciparum whole genome data from public databases to obtain the second genome data; S13 Data processing steps: Perform variant detection on the first and second genome data to obtain the single nucleotide polymorphism (SNP) dataset; S14 Database Construction Steps: Based on the SNP dataset, construct a database containing genetic information of Plasmodium falciparum in China.
[0021] Furthermore, the Plasmodium falciparum samples from China in S11 came from the vicinity of Yingjiang County, Yunnan Province, and Dongfang City and Ledong Li Autonomous County, Hainan Province. The samples were collected between 2004 and 2010 and were determined to be locally transmitted cases based on epidemiological history.
[0022] Furthermore, the specific details of the S13 data processing steps are as follows: S1301 performs quality control and filtering on sequencing data to obtain high-quality data; The criteria for high-quality data are: (a) biased; (b) call rate > 95%; (c) quality scores of > 30; (d) base quality > 30; (e) sample average coverage > 10-fold or sample average coverage < 2000-fold. S1302 performs sequence alignment and variant detection on high-quality data, identifying SNPs across the entire genome; S1303 filters SNPs to obtain a high-quality SNP dataset.
[0023] Furthermore, the analysis includes the S1304 step: performing population genetic analysis on the obtained SNP dataset, which includes calculating one or more of the following: nucleotide diversity π, Tajima's D, linkage disequilibrium LD, and integrated haplotype score iHS.
[0024] Furthermore, the database in the S14 database construction step is based on a B / S architecture, developed using ASP.NET technology, and uses SQL Server as the backend database management system.
[0025] The present invention also discloses a database containing genetic information of Plasmodium falciparum from China, obtained by a method for establishing a Plasmodium genome database.
[0026] A method for geographical tracing of Plasmodium falciparum, based on the aforementioned database containing genetic information of Plasmodium falciparum in China, see [link to relevant documentation]. Figure 2 As shown, it includes the following steps: S21 Data Acquisition Steps: Obtain a high-quality SNP dataset from the database; S22 Data Preprocessing Steps: Preprocess and perform feature selection on the acquired high-quality SNP dataset to filter out SNPs located in the neutral-conservative region; S23 Model Construction Steps: Based on the selected SNPs, a geographic origin tracing and classification model is constructed using machine learning algorithms; S24 Prediction Step: Using the aforementioned geographic origin classification model, predict the geographic origin of the Plasmodium falciparum sample to be tested.
[0027] Furthermore, feature selection in S22 includes feature dimensionality reduction based on the population genetic differentiation index Fst.
[0028] Furthermore, the machine learning algorithms in S23 include one or more of XGBoost, Support Vector Machine (SVM), or Convolutional Neural Network (CNN).
[0029] In one specific embodiment, 33 SNP sites were extracted from the high-quality SNP set of 163 training samples, and then principal component analysis was performed using GCTA. The results are as follows: Figure 4 As shown, these 33 SNPs can effectively classify the 163 samples into four categories, with the classification results perfectly consistent with the geographical location information. To ensure the reliability of the discriminant function, we further tested its accuracy using two methods: back-substitution test and new sample (51 strains) test. The back-substitution test achieved 100% accuracy in classifying the four populations. Of the 51 new samples, 30 were from foreign databases (10 from West Africa, 10 from Thailand-Myanmar, and 10 from Thailand-Cambodia), and 20 were collected in our laboratory (11 from China-Myanmar, 8 from Thailand-Cambodia, and 2 from West Africa). In the new sample test, 51 samples were correctly classified, and 6 were incorrectly classified, resulting in a correct classification rate of 88.23%. The accuracy rates for the new sample tests of the China-Myanmar, Thailand-Myanmar, Thailand-Cambodia, and West Africa populations were 81.82%, 80%, 88.89%, and 100%, respectively.
[0030] See Figure 3 The diagram shows the overall flowchart of the methods for establishing the Plasmodium genome database and the geographic source tracing method, including the establishment of the Plasmodium genome database and geographic source tracing based on the database and machine learning; the specific content is as follows: I. Establishment of the Plasmodium genome database (1) Sample Acquisition and Sequencing: Wild-type Plasmodium falciparum strains were collected from Yunnan and Hainan provinces, China (2007–2010). For whole blood samples, in vitro resuscitation and culture were performed first (after resuscitating the wild-type Plasmodium falciparum strains preserved in liquid nitrogen, conventional 1640 culture medium was added and the samples were cultured in a three-gas incubator (gas conditions: 5% O2, 5% CO2, 90% N2), the culture medium was changed every 1–2 days, and fresh red blood cells (type O blood) were replaced every 4 days) to increase the number of parasites. Then, high-quality genomic DNA was extracted using a kit. For filter paper blood samples, DNA was extracted directly. Whole genome sequencing was performed using the Illumina NovaSeq platform, and structural variation analysis and genome assembly were performed. Before sequencing, mixed infection between samples was ruled out by methods such as PCR.
[0031] (2) Data acquisition: Global whole genome sequencing data of Plasmodium falciparum were downloaded from public databases such as Pf7K project and MalariaGEN as second genome data.
[0032] (3) Mutation detection: Data quality control: FastQC was used to assess the quality of the raw sequencing data, and Trimmomatic was used to remove low-quality bases and adapter sequences.
[0033] Sequence alignment: High-quality sequencing reads were aligned to the Plasmodium falciparum reference genome (e.g., version 3D7) using BWA-MEM software.
[0034] SNP Calling: SNP detection is performed using the best practice workflow of GATK (Genome Analysis Toolkit), including steps such as labeling repetitive sequences, base quality recalibration, and HaplotypeCaller.
[0035] SNP filtering: Use VCFtools or Bcftools to filter according to the following conditions: QUAL > 30, DP > 10, MQ > 40, missing rate < 10%, and finally obtain a high-quality SNP dataset (VCF file).
[0036] (4) Database construction: Backend development: The database table structure is designed using Microsoft SQL Server 2012. The main tables include: Sample_Info (sample information, such as number, origin, and collection year), SNP_Data (SNP locus genotype data), and Genetic_Analysis (stores the results of calculated genetic diversity indicators).
[0037] Front-end development: The user interface is developed using the ASP.NET (C#) framework, combined with HTML5, CSS3, and JavaScript.
[0038] Functionality: The implemented functional modules include: data uploading and storage, sample and SNP information query and retrieval, and visualization of population genetic analysis results (such as PCA plots and phylogenetic trees).
[0039] (5) Population genetic analysis (optional): nucleotide diversity (π) and Tajima's D value of Chinese strains were calculated using VCFtools; linkage disequilibrium (LD) decay was calculated using PLINK; and iHS value was calculated using selscan software to assess the genetic diversity level and historical selection signals of Chinese Plasmodium falciparum populations.
[0040] II. Geographic Origin Tracing Based on Databases and Machine Learning (see...) Figure 3 (As shown)
[0041] Data preparation and feature engineering: Genotypic data of Plasmodium falciparum samples from major import sources, including China, Southeast Asia, and West Africa, were exported from the constructed database.
[0042] SNP sites with minor allele frequency (MAF) < 0.01 were filtered out.
[0043] SNPs were functionally annotated using tools such as ANNOVAR, with priority given to SNPs located in intron regions and four-fold degenerate sites, to form the initial feature set.
[0044] Plink is used to calculate the Fst value of each SNP across different geographic groups, and the top K SNPs with the highest Fst values (e.g., K=1000) are selected as features for model training.
[0045] Model training and evaluation: The sample data was randomly divided into a training set and an independent test set in a 7:3 ratio, ensuring that the sample proportions from different geographical sources were consistent during the division (stratified sampling).
[0046] Classification models were trained on the training set using XGBoost, SVM, and CNN algorithms, respectively. Hyperparameter tuning was performed using grid search (for SVM) and Bayesian optimization (for XGBoost).
[0047] During training, 10-fold cross-validation was used to evaluate the stability of the model.
[0048] Finally, the performance metrics of the optimal model, such as accuracy, precision, recall, and F1 score, are evaluated on independent test sets.
[0049] Source tracing application: Save the best-trained model (e.g., a model built on XGBoost). When you obtain the SNP data of the Plasmodium falciparum genome from an imported malaria case, input it into the model to output the most likely geographical origin prediction and its probability.
[0050] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described database establishment method.
[0051] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described database establishment method.
[0052] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for establishing a Plasmodium genome database, characterized in that, Includes the following steps: S11 First Genome Data Acquisition Steps: Obtain Plasmodium falciparum samples from China, perform whole-genome sequencing on the samples, and obtain first genome data; S12 Second Genome Data Acquisition Steps: Obtain the international Plasmodium falciparum whole genome data from public databases to obtain the second genome data; S13 Data processing steps: Perform variant detection on the first and second genome data to obtain the single nucleotide polymorphism (SNP) dataset; S14 Database Construction Steps: Based on the SNP dataset, construct a database containing genetic information of Plasmodium falciparum in China.
2. The method for establishing a Plasmodium genome database according to claim 1, characterized in that, The specific details of the S13 data processing steps are as follows: S1301 performs quality control and filtering on sequencing data to obtain high-quality data; S1302 performs sequence alignment and variant detection on high-quality data, identifying SNPs across the entire genome; S1303 filters SNPs to obtain a high-quality SNP dataset.
3. The method for establishing a Plasmodium genome database according to claim 2, characterized in that, It also includes the S1304 analysis step: performing population genetic analysis on the obtained SNP dataset, which includes calculating one or more of the following: nucleotide diversity π, Tajima's D, linkage disequilibrium LD, and integrated haplotype score iHS.
4. The method for establishing a Plasmodium genome database according to claim 3, characterized in that, The database in the S14 database construction step is based on a B / S architecture, developed using ASP.NET technology, and uses SQL Server as the backend database management system.
5. A Plasmodium genome database, characterized in that, A database containing genetic information of Plasmodium falciparum from China, obtained by using the method for establishing a Plasmodium genome database according to any one of claims 1-4.
6. A method for geographical tracing of Plasmodium falciparum, characterized in that, The database containing genetic information of Plasmodium falciparum from China, obtained based on claim 5, includes the following steps: S21 Data Acquisition Steps: Obtain a high-quality SNP dataset from the database; S22 Data Preprocessing Steps: Preprocess and perform feature selection on the acquired high-quality SNP dataset to filter out SNPs located in the neutral-conservative region; S23 Model Construction Steps: Based on the selected SNPs, a geographic origin tracing and classification model is constructed using machine learning algorithms; S24 Prediction Step: Using the aforementioned geographic origin classification model, predict the geographic origin of the Plasmodium falciparum sample to be tested.
7. The method according to claim 6, characterized in that, Feature selection in S22 includes feature dimensionality reduction based on the population genetic differentiation index Fst.
8. The method according to claim 6, characterized in that, The machine learning algorithms in S23 include one or more of XGBoost, Support Vector Machine (SVM), or Convolutional Neural Network (CNN).