Drug resistance group analysis method based on 16S rRNA amplicon sequencing

Abstract

The invention belongs to the field of bioinformatics analysis, and relates to an analysis method for predicting drug resistance gene composition, abundance and drug resistance rate of a bacterial community in a sample based on a 16S rRNA amplicon sequencing result. According to the method disclosed by the invention, the application range based on 16S rRNA amplicon sequencing is greatly expanded, and a data re-mining method which is simple, convenient, feasible and low in cost is provided for research on drug-resistant gene transmission in the environment.

Description

Technical Field

[0001] The present invention belongs to the field of bioinformatics analysis, and in particular relates to an analysis method for inferring the composition and abundance of drug-resistant genes in a sample based on functional genes predicted by 16S rRNA amplicons. Background Art

[0002] Protein function prediction methods rely on databases that are manually annotated by experts; among them, a widely used database is the KEGG (Kyoto Encyclopedia of Genes and Genomes) database; this database consists of a comprehensive and integrated database of molecular pathways, networks, and genes involved in various cellular processes such as metabolism, signal transduction, and disease. The KO (KEGG Orthology) database is a database that represents molecular functions in terms of functional homologs; functional homologs are manually defined in the KEGG molecular network; KO numbers are obtained based on experimental results of genes and proteins in specific organisms, and these numbers are subsequently used to assign homologous genes in other organisms.

[0003] Antibiotic resistance genes (ARGs) have attracted widespread attention as emerging contaminants. Plasmids, integrons, and transposons carrying ARGs in bacteria can undergo horizontal gene transfer between strains of the same species and between strains of different species. Even after the resistant strain dies, the naked DNA carrying ARGs will exist for a long time under the protection of deoxynucleotidases. At present, the analysis of ARGs mainly relies on methods such as high-throughput quantitative polymerase chain reaction (qPCR) and metagenomics. Although these methods can generate accurate information about the abundance and distribution of ARGs, they are complex and time-consuming. Amplicon sequencing based on 16s rRNA is an important method for studying the structure of microbial communities. It is simple to operate and low-cost, and is widely used in the field of microbial ecology. Amplicon sequencing based on 16s rRNA is mainly used to study the structure and composition of microbial communities, while functional prediction based on PICRUSt2 realizes the analysis of microbial functions based on amplicon sequencing. If the analysis of drug-resistant genes can be achieved based on functional prediction, on the one hand, the application scope of 16s rRNA amplicon sequencing can be expanded, and on the other hand, the analysis cost of ARGs in the environment can be greatly reduced. Summary of the invention

[0004] The technical problem to be solved by the invention is: how to analyze the composition and abundance of ARGs in samples based on 16s rRNA amplicon sequencing, thereby expanding the scope of application of the method and reducing the cost of ARGs analysis in samples. The 16s rRNA analysis method for predicting the composition and abundance of drug resistance genes established by the invention can realize the abundance and composition of ARGs in samples based on functional prediction, and calculate the drug resistance rate of samples.

[0005] The purpose of the present invention is to provide an analytical method for inferring the abundance and composition of drug-resistant genes in samples based on the functional genes predicted by 16S rRNA. Specifically, the sequence is first obtained by amplicon determination of 16S rRNA specific primers, and the ASV sequence information is obtained by QIIME2 analysis; then the KEGG functional genes present in the sample are predicted by PICRUSt2, and the quantitative relationship of KEGG in ASV is established. Through the relationship between KO and ARG in the KEGG database, KO belonging to ARG is screened, and a quantitative relationship list of ARG contained in ASV is established to obtain the abundance of ARG in each sample, and further infer the abundance and composition information of drug-resistant bacteria and drug-resistant genes in the sample.

[0006] The present invention adopts the following technical solutions:

[0007] 1. An analytical method for predicting the composition and abundance of drug resistance genes of bacterial communities in samples based on 16S rRNA amplicon sequencing sequences, characterized in that it comprises the following steps:

[0008] 1) Perform 16S rRNA amplicon sequencing on all bacteria in the sample to obtain the 16S rRNA sequences of all bacteria in the sample;

[0009] 2) Obtaining the types and abundance of bacteria contained in the sample, including analyzing the 16SrRNA sequence using PICRUSt2 software and outputting the types and abundance of bacteria with ASV numbers contained in the sample;

[0010] 3) Obtain the drug-resistant genes and their abundance contained in various bacteria, including using PICRUSt2 software to analyze 16S rRNA sequences, output the functional genes and their numbers contained in bacteria with various ASV numbers, and screen and retain the drug-resistant genes and their numbers contained in bacteria with various ASV numbers through the drug-resistant gene list;

[0011] 4) Based on the types and abundances of bacteria with ASV numbers obtained in steps 2) and 3) and the number and quantity of drug-resistant genes contained in bacteria with various ASV numbers, the types and abundances of drug-resistant genes in the sample are obtained by calculation.

[0012] 2. The method described in item 1 is characterized in that it also includes the step of using QIIME2 software to analyze and obtain the ASV of bacteria, and obtaining the annotation of bacteria by comparing with the Greengene2 database.

[0013] 3. The method described in item 1 or 2 is characterized in that the 16S rRNA sequence is a full-length 16S rRNA sequence or a 16S rRNA sequence in the V3-V4 region or the V4 region.

[0014] 4. The method described in item 1 is characterized in that step 2) and step 3) also include designating the bacterial species with ASV number as ASV sequence.

[0015] 5. The method described in item 1 is characterized in that the list of drug-resistant genes in step 3) is obtained from the KEGG database, preferably from https: / / www.kegg.jp / kegg-bin / show_brite?ko01504.keg.

[0016] 6. The method described in item 1 is characterized in that the calculation described in step 4) is: multiplying the abundance of the bacteria with the ASV number obtained in step 2) by the number of resistance genes contained in the bacteria with the ASV number obtained in step 3) to obtain the abundance of the resistance gene contained in the bacteria with the ASV number in the sample; and adding up the abundance of the resistance gene contained in the bacteria with all ASV numbers in the sample to obtain the abundance of the resistance gene in the sample, and using the same method to further obtain the abundance of all resistance genes in the sample.

[0017] 7. A method for predicting the drug resistance rate of a bacterial community in a sample, characterized in that the steps of the method described in any one of items 1-6 are used to obtain the types and abundances of bacteria with ASV numbers containing drug resistance genes in the sample, and the abundances of all bacteria with ASV numbers containing drug resistance genes in the sample are added together and multiplied by 100% to obtain the drug resistance rate of the sample.

[0018] definition

[0019] KO (KEGG Orthology): KO is a part of KEGG, which provides functional classification of genes and proteins; KO helps understand the function and regulation of biological systems by linking genes with biological processes, metabolic pathways, etc. In the KEGG protein or enzyme classification system, proteins with similar sequences and similar functions in the same pathway are grouped together and defined as a type of KO, written as "K+number", such as K03585, K18138, K12340, etc.

[0020] OTU: operational taxonomic unit.

[0021] ASV: Amplicon sequence variant, refers to the ASV number of each microorganism obtained by measuring and analyzing the DNA sequence in the microbial community through high-throughput sequencing technology; compared with traditional OTU, ASV can more accurately identify microbial species, distinguish between closely related species and subspecies levels of microorganisms, and increase the accuracy and sensitivity of microbial diversity analysis.

[0022] ARB: Antibiotic-resistant bacteria.

[0023] VGT: vertical gene transfer.

[0024] HGT: horizontal gene transfer.

[0025] The present invention adopts the above technical solution, which has the following advantages:

[0026] 1. Compared with qPCR and metagenomic methods, the present invention provides predictions for the composition of ARGs in samples based on 16S rRNA, which is much simpler and easier to implement and can significantly reduce costs.

[0027] 2. Based on the 16S rRNA amplicon sequencing sequence, resistant bacteria and resistance groups can be output, and resistance genes can be reversely calculated and predicted through bacterial communities, which provides a new analysis method for the occurrence of ARGs in the environment.

[0028] 3. The present invention can accurately identify the relationship between ARG and bacterial community, clearly calculate the drug resistance rate of bacterial community in the sample, realize the comprehensive analysis of drug resistance, and provide rich data for the study of drug resistance. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 These are the VGT and HGT network diagrams of each sample. The upper row of diagrams is the VGT network diagram, in which the small dots are ARGs, the large dots are ARBs, and the lines indicate the existence of relationships; the lower row of diagrams is the HGT network diagram, in which the dots are ARBs, and the lines indicate the existence of relationships. DETAILED DESCRIPTION

[0030] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can

[0031] The embodiments are provided to help the present invention be better understood and implemented, but they are not intended to limit the present invention.

[0032] Example 1 Method for obtaining ARG abundance and drug resistance rate of bacterial communities in a sample

[0033] The method for obtaining ARG composition, abundance and drug resistance rate based on 16S rRNA amplicon sequencing sequence information includes the following steps:

[0034] 1. Sequencing step: Based on amplicon sequencing, the 16S rRNA hypervariable region sequence of the bacterial genome in the sample is obtained. In this embodiment, sludge and water samples collected from a sewage treatment plant in Beijing are used as examples for illustration.

[0035] The V3-V4 region of the 16S gene sequence is a highly variable region, which is usually used for bacterial diversity analysis because the sequence differences in this region can well reflect the classification information of microorganisms; this part of the region can provide sufficient variability to distinguish different microbial communities, facilitating comparison and annotation; in this embodiment, the V4 region can be used to distinguish different types of bacteria.

[0036] 2. Analyze the 16S rRNA sequence through PICRUSt2 (http: / / github.com / picrust / picrust2) and output the bacterial species with the ASV number contained in the sample to predict the functional characteristics of the bacterial community contained in the sample. A1, A2, A3, B1 and B2 are different samples for example. The specific steps are as follows (or refer to the instructions for use of PICRUSt2 software):

[0037] Create table A_Bac_manifest.tsv according to the data name, as shown in Table 1.

[0038] Table 1

[0039]

[0040] cd / 01X_amplicon

[0041] conda activate base

[0042] conda activate qiime2_amplicon_2023.9

[0043] Use QIIME2 tool (www.qiime2.org) to transform and import sequence data

[0044] time qiime tools import \

[0045] --type 'SampleData[PairedEndSequencesWithQuality]' \

[0046] --input-path A_Bac_manifest.tsv \

[0047] --output-path A_Bac_manifest.qza \

[0048] --input-format PairedEndFastqManifestPhred33V2

[0049] Display the basic information of the imported sequence and determine the parameters of DADA2 according to the Q value of the sequence

[0050] time qiime demux summarize \

[0051] --i-data A_Bac_manifest.qza \

[0052] --o-visualization 01A_Bacsample_demux_PEinfor.qzv

[0053] Sequence quality control and denoising: Sequence quality control and denoising using DADA2

[0054] time qiime dada2 denoise-paired \

[0055] --i-demultiplexed-seqs A_Bac_manifest.qza \

[0056] --p-trim-left-f 19 \

[0057] --p-trim-left-r 20 \

[0058] --p-trunc-len-f 220 \

[0059] --p-trunc-len-r 220 \

[0060] --pn-threads 50 \

[0061] --o-table A_Bac_table.qza \

[0062] --o-representative-sequences A_Bac_rep-seqs.qza \

[0063] Display the obtained feature table information

[0064] time qiime feature-table summarize \

[0065] --i-table A_Bac_table.qza \

[0066] --o-visualization 03A_Bac_table.qzv \

[0067] Display basic information of the obtained rep sequences

[0068] time qiime feature-table tabulate-seqs \

[0069] --i-data A_Bac_rep-seqs.qza \

[0070] --o-visualization 04A_Bac_rep-seqs.qzv

[0071] Find the minimum number of valid reads of the sample from 03A_Bac_table.qzv as `MIN_READS`, and perform leveling:

[0072] time qiime feature-table rarefy \

[0073] --i-table A_Bac_table.qza \

[0074] --p-sampling-depth MIN_READS \

[0075] --o-rarefied-table A_Bac_rarefied-table.qza

[0076] Species Annotation: Classifier Training - Greengene2 Database

[0077] Change min-length and max-length from 04A_Bac_rep-seqs.qzv

[0078] time qiime feature-classifier extract-reads \

[0079] --i-sequences 03Greengene2 / Greengene2.qza

[0080] --p-f-primer GTGYCAGCMGCCGCGGTAA

[0081] --p-r-primer GGACTACNVGGGTWTCTAAT

[0082] --p-min-length 215

[0083] --p-max-length 290

[0084] --p-n-jobs 50

[0085] --o-reads 03Greengene2 / Greengene2-ref-seqs.qza

[0086] time qiime feature-classifier fit-classifier-naive-bayes

[0087] --i-reference-reads 03Greengene2 / Greengene2-ref-seqs.qza

[0088] --i-reference-taxonomy 03Greengene2 / Greengene2_tax.qza

[0089] --o-classifier 03Greengene2 / Greengene2-classifier.qza

[0090] Species annotation: Species annotation is performed according to the Greengene2 database

[0091] time qiime feature-classifier classify-sklearn

[0092] --i-classifier 03Greengene2 / Greengene2-classifier.qza

[0093] --i-reads A_Bac_rep-seqs.qza

[0094] --pn-jobs 50 \

[0095] --o-classification 03Greengene2 / A_Bac_rep-seqs.qza

[0096] Species annotation: Greengene2 output

[0097] time qiime metadata tabulate \

[0098] --m-input-file 03Greengene2 / A_Bac_rep-seqs.qza \

[0099] --o-visualization 03Greengene2 / 01A_Bac_rep-seqs.qzv

[0100] time qiime taxa barplot \

[0101] --i-tableA_Bac_rarefied-table.qza \

[0102] --i-taxonomy 03Greengene2 / A_Bac_rep-seqs.qza \

[0103] --m-metadata-file A_Bac_manifest.tsv \

[0104] --o-visualization 03Greengene2 / 02A_Bac_table.qzv

[0105] Species annotation: Output biom format file and table of ASVs

[0106] time qiime tools export \

[0107] --input-path A_Bac_rarefied-table.qza \

[0108] --output-path 03A_Bac_table

[0109] biom convert -i 03A_Bac_table / feature-table.biom \

[0110] -o 03A_Bac_table / feature-table.txt \

[0111] --to-tsv

[0112] qiime tools export \

[0113] --input-path A_Bac_rep-seqs.qza \

[0114] --output-path 06A_Bac_rep-seqs

[0115] conda deactivate

[0116] conda activate picrust2

[0117] picrust2_pipeline.py -s 06A_Bac_rep-seqs / dna-sequences.fasta -i 03A_Bac_table / feature-table.biom -o 06Picrust2_stra -p 50 --stratified

[0118] 3. Analyze the 16S rRNA sequence using PICRUSt2 (http: / / github.com / picrust / picrust2) and output the abundance of bacteria with the ASV number contained in the sample

[0119] The "feature-table.txt" obtained in step 2 is the number of OTU reads in all collected samples; add an ASV column and sort the OTU IDs by ASV; list and count the abundance of ASV contained in each sample, as shown in Table 2.

[0120] Table 2

[0121]

[0122] 4. Obtain the correspondence between KO and ARG, for example, refer to KEGG Antimicrobial Resistance Genes (https: / / www.kegg.jp / kegg-bin / show_brite?ko01504.keg).

[0123] Obtaining the drug resistance genes contained in various bacteria and their abundance, which includes the following steps:

[0124] 5. Relationship between bacteria with ASV numbers and the drug resistance genes they contain

[0125] The file "KO_predicted" in the folder 06Picrust2_stra obtained in step 2 shows the quantitative relationship of KO in ASV; locate the ASV in the sample, screen or locate the KO belonging to ARG according to the corresponding relationship between KO and ARG obtained in step 4, delete other KO, delete ASV with a sum of 0 (that is, delete the column with a sum of 0 and the row with a sum of 0), and obtain the quantitative relationship between ARB with ASV number and ARG with KO number in all samples, as shown in Table 3. For example, K03585, K18138, K12340, K18299, K17836, K18139 and K08641 are examples of KO number ARG.

[0126] Table 3

[0127]

[0128] 6. Abundance of ARB (antibiotic-resistant bacteria, i.e. bacteria containing the drug-resistant gene ARG) in each sample

[0129] In Table 2 of step 3, select the ARB with the ASV number in step 5, re-tabulate, and obtain the abundance of the ARB with the ASV number contained in each sample, as shown in Table 4. Specifically, for example, since the bacteria with the ASV number in Table 3 in step 5 all contain ARG, the bacteria with the ASV number listed in Table 3 are all ARBs, such as ASV11, ASV9370, ASV10030, etc., and then select the rows of these ARBs in Table 2, or delete the rows that are not these ARBs, and obtain Table 4.

[0130] Table 4

[0131]

[0132] 7. Calculation of drug resistance rate of bacterial community

[0133] Table 4 shows the abundance of ARB in each sample. By adding up the abundance of all ARBs in the samples in Table 4, the resistance rate of the bacterial community in the sample can be calculated (the percentage of the total ARB abundance in the sample is equal to the resistance rate). Specifically, in the A1 sample, the A1 column of Table 4 (the abundance of ARBs of each ASV number in A1) is added, for example, 0.5484+2.1521+0.0567+1.3836...=36.5872, indicating that the resistance rate of the bacterial community in the A1 sample is 36.59%.

[0134] 8. Calculation of the abundance of drug resistance gene ARG in ARB (antibiotic-resistant bacteria, i.e. bacteria containing drug resistance gene ARG)

[0135] For sample A1, the ARB abundances of the ASV numbers in the A1 column of Table 4 in step 6 were multiplied by the corresponding ARG numbers in Table 3 in step 5 to obtain the abundances of ARGs in different ARBs in sample A1 (as shown in Table 5). Specifically, for example, the ARB abundances of the ASV numbers in the A1 column in step 6 were 0.5484 (ASV11), 0, 0, 2.1521 (ASV7259), 0.0567 (ASV1704), etc., and the ARGs (K03585, K18138, K12340, K18299, K17836, K18139, K08641) in ASV11 in step 5 were 3, 3, 1, 1, 0, 2, 3, etc., respectively. 84 was multiplied by 3, 3, 1, 1, 0, 2, 3, etc., respectively, and the abundance of ARB in ASV11 was 1.6452, 1.6452, 0.5484, 0.5484, 0, 1.0968, etc., respectively; similarly, the abundance of ARB in ASV7259 was calculated to be 8.6084, 6.4563, 2.1521, 2.1521, 0, 2.1521, etc., respectively; the abundance of each drug-resistant gene ARG in other ARBs was calculated in turn, which is Table 5.

[0136] For other samples, the same method can be used to analyze and calculate the abundance of drug-resistant gene ARG in each sample.

[0137] Table 5

[0138]

[0139] The method commonly used in the prior art to detect the drug resistance rate of bacteria in a sample is the culture medium sensitivity method, which is to inoculate the sample to be tested on a culture medium and grow it on a plate containing different antibiotics to evaluate the sensitivity to antibiotics. The drug resistance rate detected by this method is not significantly different from the drug resistance rate predicted by the method of the present invention. Therefore, the method of the present invention can be used to predict the drug resistance rate of bacteria in a sample.

[0140] 9. Calculation of the abundance of drug resistance gene ARG in each sample

[0141] The abundance of drug-resistant gene ARG in the sample is summed and calculated, thereby the abundance of ARG in the sample can be calculated. Specifically, for example, according to Table 5, the abundance of ARG in the A1 sample in the ARB numbered by ASV, the sum of K03585 (a kind of ARG) in the A1 sample can be obtained by adding the values ​​of the K03585 column. In this embodiment, it is equal to 17.4551, that is, the abundance of K03585 (a kind of ARG) in the A1 sample is 17.4551; the abundance of other ARGs can be calculated by the same method. Using the same method, the abundance of ARGs in other samples can be calculated separately, and the abundance of each ARG in each sample can be obtained, as shown in Table 6.

[0142] Table 6

[0143]

[0144] The composition and abundance of drug-resistant genes in samples commonly detected in the prior art are performed by metagenomic analysis and high-throughput quantitative polymerase chain reaction (qPCR). The composition and abundance of drug-resistant genes detected by this method are not significantly different from the composition and abundance of drug-resistant genes predicted by the method of the present invention. Therefore, the method of the present invention can be used to predict the composition and abundance of drug-resistant genes in samples.

[0145] Example 2 Association analysis between antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARG)

[0146] 1. The method described in Example 1 was used to obtain the abundance information of ARGs in different samples.

[0147] 2. Using the data in Table 5, create edge files (as shown in Table 7) and point files (as shown in Table 8), and use Gephi to draw the VGT network diagram of the sample, as shown in Figure 1 Figure 4. VGT network of each sample (to distinguish the contribution of VGT and HGT to a specific ARG, we assumed that if the microorganism carrying the most abundant ARG was also the most abundant host of that ARG, then we considered that host to be the main host of the ARG. The contribution of VGT was the proportion of the main host among all hosts, and the rest was attributed to the contribution of HGT).

[0148] Table 7

[0149]

[0150] Table 8

[0151]

[0152] 3. Locate the ARB in step 2 to the genus, as shown in Table 9, and create edge files (as shown in Table 10) and point files (as shown in Table 11). Further, the VGT and HGT network diagrams of each sample were obtained by drawing the Gephi software (http: / / gehi.org), as shown in Figure 1 shown.

[0153] In the study of antibiotic resistance, VGT and HGT network diagrams play an important role in helping to understand the transmission of resistance genes and their impact on microbial populations. Specifically, VGT refers to the process of gene transmission from parent to offspring, that is, the transmission of genetic information to offspring through reproduction. VGT network diagrams show the transmission pattern of resistance genes between bacterial generations, which is crucial for understanding the accumulation of resistance over time within the same species, and helps to track the evolution of strains and evaluate how resistance gradually increases in offspring. HGT refers to the direct exchange of genes between different species or individuals without relying on reproduction. In bacterial populations, HGT is the main way for resistance genes to spread rapidly, especially through media such as plasmids, transposons or bacteriophages. HGT network diagrams can help clarify the rapid transmission paths of resistance genes between different bacterial species and explain how resistance spreads rapidly between different species and even different ecological niches, leading to difficult-to-control resistant infections.

[0154] Table 9

[0155]

[0156] Table 10

[0157]

[0158] Table 11

[0159]

[0160] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An analytical method for predicting the composition and abundance of drug resistance genes of bacterial communities in samples based on 16S rRNA amplicon sequencing sequences, characterized in that: The following steps are involved: 1) Perform 16S rRNA amplicon sequencing on all bacteria in the sample to obtain the 16S rRNA sequences of all bacteria in the sample; 2) Obtaining the types and abundance of bacteria contained in the sample, including using PICRUSt2 software to analyze the 16S rRNA sequence and output the types and abundance of bacteria with ASV numbers contained in the sample; 3) Obtain the drug-resistant genes and their abundance contained in various bacteria, including using PICRUSt2 software to analyze the 16SrRNA sequence, output the functional genes and their numbers contained in bacteria with various ASV numbers, and screen and retain the drug-resistant genes and their numbers contained in bacteria with various ASV numbers through the drug-resistant gene list; 4) Based on the types and abundances of bacteria with ASV numbers obtained in steps 2) and 3) and the number and quantity of drug-resistant genes contained in bacteria with various ASV numbers, the types and abundances of drug-resistant genes in the sample are obtained by calculation.

2. The method according to claim 1, characterized in that It also includes the steps of using QIIME2 software to analyze and obtain bacterial ASVs, and obtaining bacterial annotations by comparing with the Greengene2 database.

3. The method according to claim 1 or 2, characterized in that The 16S rRNA sequence is a full-length 16S rRNA sequence or a 16S rRNA sequence in the V3-V4 region or the V4 region.

4. The method according to claim 1, characterized in that Step 2) and step 3) also include assigning the bacterial species of the ASV number to the ASV sequence.

5. The method according to claim 1, characterized in that The drug resistance gene list in step 3) is obtained from the KEGG database, preferably from https: / / www.kegg.jp / kegg-bin / show_brite?ko01504.keg.

6. The method according to claim 1, characterized in that The calculation described in step 4) is: multiply the abundance of the bacteria with the ASV number obtained in step 2) by the number of resistance genes contained in the bacteria with the ASV number obtained in step 3) to obtain the abundance of the resistance gene contained in the bacteria with the ASV number in the sample; and add up the abundance of the resistance gene contained in the bacteria with all ASV numbers in the sample to obtain the abundance of the resistance gene in the sample, and use the same method to further obtain the abundance of all resistance genes in the sample.

7. A method for predicting the drug resistance rate of bacterial communities in a sample, characterized in that: Using the steps of the method described in any one of claims 1 to 6, the types and abundances of bacteria with ASV numbers containing drug-resistant genes in the sample are obtained, and the abundances of all bacteria with ASV numbers containing drug-resistant genes in the sample are added up and multiplied by 100% to obtain the drug resistance rate of the sample.

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