IncC plasmid fragment analysis method, device, electronic equipment and storage medium

Abstract

The application provides an IncC plasmid fragment analysis method and device, electronic equipment and storage medium, and relates to the field of microbial genome informatics analysis. The method comprises the following steps: screening a candidate strain containing an IncC plasmid replicon from a second-generation sequencing sequence of a strain to be analyzed; using at least two plasmid fragment identification tools to identify the plasmid fragments of the candidate strain respectively, and determining potential fragments of the IncC plasmid; comparing the potential fragments with core genes of the IncC plasmid to determine whether they are contig fragments of the IncC plasmid; dividing the core gene interval based on the distribution of the core genes on the determined contig fragments of the IncC plasmid, identifying drug-resistant genes and associating them with the core gene interval, and determining the distribution hotspots of the drug-resistant genes by statistics. The application can accurately identify the IncC plasmid and its contig fragments in the second-generation sequencing sequence, and analyze the distribution hotspots of the drug-resistant genes of the IncC plasmid.

Description

Technical Field

[0001] This invention relates to the field of microbial genome information analysis, and in particular to an IncC plasmid fragment analysis method, apparatus, electronic device, and storage medium. Background Technology

[0002] Plasmids are small, circular, double-stranded DNA molecules independent of bacterial chromosomes. As important mobile genetic elements, they play a central role in bacterial genetic variation, adaptive evolution, and modern bioengineering. Incompatibility Groups C (IncC) have a wide host range, including Vibrio cholerae, Salmonella enterica, Yersinia pestis, and Escherichia coli. IncC plasmids possess important functional modules such as replication, conjugation transfer, and plasmid maintenance systems. Through their conjugation transfer system, they can achieve conjugation transfer between bacterial cells.

[0003] Accurate identification of IncC plasmid fragments in bacterial genomes is a crucial prerequisite for studying their evolutionary patterns, transmission mechanisms, and the distribution characteristics of drug resistance genes. Next-generation sequencing (NGS) of bacterial genomes offers advantages such as low cost, high throughput, and high accuracy, making it particularly suitable for scenarios requiring rapid, accurate, and large-scale analysis of bacterial strain genomes, and it has been widely applied in the field of pathogenic microorganisms. However, the sequences output by NGS are not continuous nucleic acid fragments. Therefore, accurately identifying contiguous sequences belonging to plasmids, especially those belonging to specific types of plasmids, presents significant technical limitations.

[0004] Currently, identifying plasmid contig fragments from bacterial next-generation sequencing sequences mainly relies on various tools and algorithms. The key processes of these tools are either based on sequence similarity comparison with a reference library or depend on machine learning models. Their identification effectiveness is limited by the size and range of the database, the setting of homology comparison parameters, or the depth and breadth of the training set data. When applied to the identification of specific types of plasmids (such as IncC plasmids), a high false positive rate is easily generated because homologous sequences may exist between different plasmid types or between plasmids and chromosomes. Existing literature reports that IncC plasmids have a specific core genomic structure, but IncC plasmids and SXT / R391 family integrative conjugating elements present on chromosomes share many functional modules, such as the conjugation transfer system. Many shared genes between the two are homologous, and their sequence similarity is high. Therefore, relying solely on IncC plasmid core gene similarity comparison is also insufficient to accurately distinguish IncC plasmids and SXT / R391 family integrative conjugating elements in bacterial next-generation sequencing sequences. Furthermore, the IncC plasmid, as an important mobile genetic element, carries several drug resistance genes of significant clinical concern, such as those against sulfonamides, β-lactams, and tetracyclines. Accurately identifying drug resistance genes and rapidly determining their distribution hotspots based on next-generation sequencing of the IncC plasmid can accelerate our understanding of the distribution characteristics of adaptive genes in newly acquired strains, thus contributing to better addressing the safety risks posed by the widespread dissemination of drug resistance genes in public health practice. However, current techniques do not further reveal how to analyze the distribution hotspots of drug resistance genes on plasmids.

[0005] Therefore, based on obtaining the bacterial next-generation sequencing sequence, how to accurately identify whether the IncC plasmid and its contig fragment exist, and analyze the distribution hotspots of the IncC plasmid drug resistance gene, is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] This invention provides an IncC plasmid fragment analysis method, apparatus, electronic device, and storage medium, which can accurately identify the presence of IncC plasmids and their contig fragments based on bacterial next-generation sequencing sequences, and analyze the distribution hotspots of drug resistance genes in IncC plasmids.

[0007] This invention provides a method for analyzing IncC plasmid fragments, comprising: Obtain the next-generation sequencing sequence of the strain to be analyzed; Candidate strains containing IncC plasmid replicon replicons were screened from the second-generation sequencing sequences using a plasmid replicon type identification tool. Using at least two plasmid fragment identification tools, the second-generation sequencing sequences of the candidate strains were used to identify plasmid fragments, and the potential fragments of the IncC plasmid were determined based on the identification results. The potential fragment is compared with the core gene of the IncC plasmid, and the comparison results are used to determine whether the potential fragment is a contig fragment of the IncC plasmid. Based on the distribution of core genes on the contig fragment of the IncC plasmid, the sequence regions between adjacent core genes are divided as core gene intervals. Identify the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid, and associate the drug resistance gene with the core gene region based on the locus coordinates; The frequency of drug resistance genes detected in each core gene region of the IncC plasmid of each candidate strain was statistically analyzed. Based on the frequency, the distribution hotspots of drug resistance genes in the IncC plasmid are determined.

[0008] According to the IncC plasmid fragment analysis method provided by the present invention, the step of dividing the sequence regions between adjacent core genes as core gene intervals based on the distribution of core genes on the determined contig fragments of the IncC plasmid includes: When the core genes are all concentrated in a single contig fragment of the IncC plasmid, the arrangement of the core genes in the representative plasmid of the IncC plasmid is used as a reference. The arrangement of the core genes on the single contig fragment is adjusted to be the same as the starting point and the same as the direction of the core genes in the representative plasmid. The sequence region between the adjacent core genes after adjustment is taken as the core gene interval. When the core genes are distributed across multiple contig fragments of the IncC plasmid, and a target contig fragment contains a predetermined number of core genes, it is determined whether the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid. If they are inconsistent, the core genes on the target contig fragment are arranged in descending order according to their locus coordinates to adjust them to be consistent with the arrangement direction of the core genes in the representative plasmid, and the sequence regions between adjacent core genes after sorting are taken as the core gene intervals.

[0009] According to the IncC plasmid fragment analysis method provided by the present invention, the step of identifying the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid, and associating the drug resistance gene with the core gene region based on the locus coordinates, includes: When the core genes are all concentrated in a single contig fragment of the IncC plasmid, the first target drug resistance gene and its first target gene locus coordinates located on the single contig fragment are screened based on the locus coordinates of the drug resistance gene. The first target drug resistance gene is integrated with the core gene on the single contig fragment to obtain the first functional gene; Based on the coordinates of the first target locus and the coordinates of the core gene on the single contig fragment, the first functional genes are arranged in ascending order according to the locus coordinates to obtain the sorted first functional genes. Using the arrangement direction of the core genes in the representative plasmid of the IncC plasmid as a reference, the arrangement of the sorted first functional genes is adjusted to be the same as the starting point and the same as the direction of the core genes in the representative plasmid. When the core gene is distributed across multiple contig fragments of the IncC plasmid, and a target contig fragment contains a predetermined number of core genes, a second target drug resistance gene and its second target contig fragment are screened based on the locus coordinates of the drug resistance gene. The second target drug resistance gene is integrated with the core gene on the target contig fragment to obtain the second functional gene; If the arrangement direction of the core genes on the target contig fragment is inconsistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid, then based on the coordinates of the second target gene locus and the coordinate information of the core genes on the target contig fragment, the second functional genes are arranged in descending order of the gene locus coordinates to adjust them to be consistent with the arrangement direction of the core genes in the representative plasmid. If the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid, then based on the coordinates of the second target gene locus and the coordinate information of the core genes on the target contig fragment, the second functional genes are arranged in ascending order according to the gene locus coordinates.

[0010] According to the IncC plasmid fragment analysis method provided by the present invention, the core genes of the IncC plasmid include the following 43 genes: mobI, repA, sppA, dsbA, parA, parB, dcm2, topB, traI, traD, traJ, traL, traE, traK, traB, traV, traA, dsbC, traC, trhF, traW, traU, traN, ssb, bet, exo, nuc, pri, rhs, int, yacC, ter, kfrA, uvrD, traF, traH, traG, eex, acr1, acaD, acaC, acr2, stbA. The core genes are arranged in a direction starting from the mobI gene and pointing towards the repA gene.

[0011] According to the present invention, an IncC plasmid fragment analysis method is provided, wherein comparing the potential fragment with the core gene of the IncC plasmid, and determining whether the potential fragment is a contig fragment of the IncC plasmid based on the comparison result, includes: Using the protein sequence of the core gene of the IncC plasmid as the query sequence and the nucleic acid database constructed from the potential fragments of the candidate strain as the target database, a tBLASTn search was performed to obtain the alignment results; Based on the alignment results and preset screening conditions, potential fragments containing at least one of the core genes are identified as contig fragments of the IncC plasmid; The preset screening conditions include: sequence consistency not less than 85%, sequence coverage not less than 85%, and expected value less than 0.001.

[0012] According to the present invention, an IncC plasmid fragment analysis method is provided, wherein at least two plasmid fragment identification tools are used to identify plasmid fragments in the next-generation sequencing sequences of the candidate strains, and potential fragments of the IncC plasmid are determined based on the identification results, comprising: Using a first plasmid fragment identification tool and a second plasmid fragment identification tool, the second-generation sequencing sequences of the candidate strains were identified to obtain a first identification result and a second identification result. The intersection of the first identification result and the second identification result is taken as the potential fragment of the IncC plasmid.

[0013] According to the IncC plasmid fragment analysis method provided by the present invention, the identification of the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid includes: Using a drug resistance gene identification tool, the contig fragment of the candidate strain is compared with a preset drug resistance gene database to obtain the types of drug resistance genes and their locus coordinates on the contig fragment of the determined IncC plasmid. The screening criteria for the alignment are: sequence consistency ≥ 97% and coverage ≥ 97%.

[0014] The present invention also provides an IncC plasmid fragment analysis device, comprising: The sequencing sequence acquisition module is used to acquire the second-generation sequencing sequence of the strain to be analyzed; The candidate strain screening module is used to screen candidate strains containing IncC plasmid replicons from the second-generation sequencing sequence using a plasmid replicon type identification tool. The potential fragment determination module is used to identify plasmid fragments in the second-generation sequencing sequence of the candidate strain using at least two plasmid fragment identification tools, and to determine the potential fragments of the IncC plasmid based on the identification results. The contig fragment determination module is used to compare the potential fragment with the core gene of the IncC plasmid, and determine whether the potential fragment is a contig fragment of the IncC plasmid based on the comparison result; The interval partitioning module is used to partition the sequence regions between adjacent core genes based on the distribution of core genes on the contig fragment of the determined IncC plasmid, and to define them as core gene intervals. The drug resistance gene identification module is used to identify the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid, and to associate the drug resistance gene with the core gene region based on the locus coordinates; The frequency statistics module is used to count the frequency of drug resistance genes detected in each core gene region of the IncC plasmid of each candidate strain. The hotspot determination module is used to determine the distribution hotspots of drug resistance genes in the IncC plasmid based on the frequency.

[0015] 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 computer program to implement the IncC plasmid fragment analysis method as described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the IncC plasmid fragment analysis method as described in any of the preceding claims.

[0017] This invention provides a method, apparatus, electronic device, and storage medium for IncC plasmid fragment analysis. First, the next-generation sequencing (NGS) sequence of the strain to be analyzed is obtained. Then, using a plasmid replicon type identification tool, candidate strains containing IncC plasmid replicons are screened from the NGS sequence. This initial screening quickly narrows down the range of strains potentially carrying the IncC plasmid, eliminating a large number of negative samples without IncC replicons, reducing unnecessary subsequent computational overhead, and improving overall analysis efficiency. Next, at least two plasmid fragment identification tools are used to identify plasmid fragments in the NGS sequences of the candidate strains. Based on the identification results, potential IncC plasmid fragments are determined. By utilizing the complementarity between multiple plasmid fragment identification tools, false positive results caused by database bias or model limitations of a single tool are effectively filtered out, thereby obtaining more reliable potential IncC plasmid fragments. Finally, the potential fragment is compared with the core gene of the IncC plasmid. Based on the comparison results, it is determined whether the potential fragment is a contig fragment of the IncC plasmid. Homology comparison verification is performed using the core gene unique to the IncC plasmid. This further refines the screening process. Only sequence fragments that simultaneously contain the IncC replicon, possess plasmid common characteristics, and encode the IncC core gene can be ultimately confirmed as contig fragments of the IncC plasmid. In summary, this invention, through the above three-step progressive screening, verifies the second-generation sequencing sequences of the analyzed strains from three different dimensions: replicon specificity, plasmid general characteristics, and plasmid type-specific core gene. This significantly improves the accuracy and reliability of identifying IncC plasmid contig fragments from second-generation sequencing sequences and effectively reduces the false positive risk of misidentifying homologous chromosomal elements or other types of plasmids as IncC plasmids. Furthermore, based on the accurate identification of contig fragments in the IncC plasmid, the analysis of drug resistance genes is linked to the structural characteristics (core gene regions) of the plasmid. Given that the plasmid is a closed circular double-stranded DNA molecule, the starting point and direction of the core gene arrangement are unified. By dividing the core gene regions and statistically analyzing the distribution frequency of drug resistance genes within these core gene regions in a large number of strains, the distribution hotspots of drug resistance genes are determined. Through the above methods, this invention provides a method for identifying IncC plasmid fragments in next-generation sequencing sequences and quantitatively assessing the distribution patterns of drug resistance genes on IncC plasmids, providing a high-resolution tool for understanding the spread, evolution, and monitoring of drug resistance genes. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is one of the flowcharts of the IncC plasmid fragment analysis method provided by the present invention.

[0020] Figure 2 This is the second schematic diagram of the IncC plasmid fragment analysis method provided by the present invention.

[0021] Figure 3 This is the third flowchart of the IncC plasmid fragment analysis method provided by the present invention.

[0022] Figure 4 This is the fourth flowchart of the IncC plasmid fragment analysis method provided by this invention.

[0023] Figure 5 This is a schematic diagram illustrating the process of adjusting the arrangement of the core gene of the IncC plasmid in the VIB_AA2995AA_AS strain provided by this invention.

[0024] Figure 6 This is a schematic diagram of the IncC plasmid fragment analysis device provided by the present invention.

[0025] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0027] Plasmids are small, circular, double-stranded DNA molecules independent of bacterial chromosomes. As important mobile genetic elements, they play a central role in bacterial genetic variation, adaptive evolution, and modern bioengineering. Incompatibility Groups C (IncC) have a broad host range, including Vibrio cholerae, Salmonella enterica, Yersinia pestis, and Escherichia coli. IncC plasmids possess important functional modules such as replication, conjugation transfer, and plasmid maintenance systems. Through their conjugation transfer system, they can achieve conjugation transfer between bacterial cells. Furthermore, IncC plasmids often carry various drug resistance genes, including those for sulfonamides, β-lactams, and tetracyclines, thus attracting widespread clinical attention.

[0028] Accurate identification of IncC plasmid fragments in bacterial genomes is a crucial prerequisite for studying their evolutionary patterns, transmission mechanisms, and the distribution characteristics of drug resistance genes. Next-generation sequencing (NGS) of bacterial genomes offers advantages such as low cost, high throughput, and high accuracy, making it particularly suitable for scenarios requiring rapid, accurate, and large-scale analysis of bacterial strain genomes, and it has been widely applied in the field of pathogenic microorganisms. However, the sequences output by NGS are not continuous nucleic acid fragments. Therefore, accurately identifying contiguous sequences belonging to plasmids, especially those belonging to specific types of plasmids, presents significant technical limitations.

[0029] Currently, identifying plasmid contig fragments from bacterial next-generation sequencing sequences mainly relies on various tools and algorithms: The first category consists of sequence alignment-based tools, such as PlasmidFinder. These tools identify plasmid types by comparing the sequence to be tested with a database of known plasmid replicons. However, the effectiveness of these tools is limited by the coverage of the reference database and the alignment parameters, and they have limited ability to identify novel plasmids not included in the database or plasmids with significant variations.

[0030] The second category comprises tools based on machine learning or deep learning, such as plasmidVerify and PLASMe. PlasmidVerify first annotates the sequence with genes, then runs the hmmsearch tool to predict the protein families (HMMs) contained in the sequence, and classifies the DNA fragments into plasmid or chromosome origins based on a Naive Bayes classifier. PLASMe, on the other hand, integrates sequence alignment components and a Transformer model, enabling it to identify distantly related plasmid fragments with low similarity to known plasmids. However, the recognition performance of these tools is limited by the depth and breadth of the training dataset and is not optimized for specific types of plasmids (such as IncC plasmids).

[0031] Based on the above analysis, when these tools are applied to the identification of specific types of plasmids (such as IncC plasmids), they are prone to producing high false positive rates due to the potential for homologous sequences between different plasmid types or between plasmids and chromosomes. Existing literature reports that IncC plasmids possess a specific core genomic structure, but IncC plasmids and SXT / R391 family integrative conjugating elements located on chromosomes share many functional modules, such as the conjugation transfer system. Several shared genes between the two exhibit homology and high sequence similarity. Therefore, relying solely on the similarity comparison of IncC plasmid core genes is insufficient to accurately distinguish between IncC plasmids and SXT / R391 family integrative conjugating elements in bacterial next-generation sequencing sequences. Furthermore, IncC plasmids, as important mobile genetic elements, carry several clinically significant drug resistance genes, such as those for sulfonamides, β-lactams, and tetracyclines. Based on the obtained IncC plasmid next-generation sequencing sequence, accurately identifying drug resistance genes within the sequence and rapidly determining their distribution hotspots can help accelerate the understanding of the distribution characteristics of adaptive genes in newly acquired strains. This will contribute to better addressing the safety risks posed by the widespread dissemination of drug resistance genes in public health practice. However, current technologies have not further revealed how to analyze the distribution hotspots of drug resistance genes on plasmids.

[0032] Therefore, based on obtaining the bacterial next-generation sequencing sequence, how to accurately identify whether IncC plasmids and their fragments exist, and analyze the distribution hotspots of IncC plasmid drug resistance genes, is a technical problem that urgently needs to be solved in this field.

[0033] Based on the above problems, this invention proposes an IncC plasmid fragment analysis method, apparatus, electronic device, and storage medium, which are described below in conjunction with... Figures 1-7 Describe it.

[0034] Figure 1 This is one of the flowcharts illustrating the IncC plasmid fragment analysis method provided by the present invention, as shown below. Figure 1 As shown, the IncC plasmid fragment analysis method includes steps S110, S120, S130, S140, S150, S160, S170 and S180.

[0035] Step S110: Obtain the second-generation sequencing sequence of the strain to be analyzed.

[0036] Next-generation sequencing (NGS) sequences refer to raw sequencing data consisting of a large number of short reads obtained through high-throughput sequencing platforms. These short reads are then assembled using bioinformatics to form longer, continuous nucleic acid fragments.

[0037] Specifically, genomic DNA is first extracted from the strain to be analyzed, and paired-end sequencing is performed using a high-throughput sequencing platform (e.g., Illumina NovaSeq 6000) to obtain raw sequencing data. The raw sequencing data is then subjected to quality control (e.g., removal of adapters and low-quality sequences) to obtain high-quality next-generation sequencing sequences.

[0038] Step S120: Using a plasmid replicon type identification tool, candidate strains containing IncC plasmid replicons are screened from the second-generation sequencing sequences.

[0039] Using a plasmid replicon type identification tool, we can identify whether the next-generation sequencing sequence of the strain to be analyzed contains IncC (Incompatibility Groups C) plasmid replicons. Strains containing IncC plasmid replicons are selected and recorded as candidate strains.

[0040] In one embodiment, the plasmid replicon type identification tool is PlasmidFinder. Specifically, a reference database is first determined based on the type of the strain to be analyzed. For example, if the strain to be analyzed is *Escherichia coli*, then the reference database corresponding to the Enterobacteriaceae family is selected as the reference database. Then, the next-generation sequencing sequence is compared and analyzed with the reference database.

[0041] In one specific implementation, after obtaining the next-generation sequencing sequence of the strain to be analyzed, the next-generation sequencing sequence is first assembled into a contig fragment set using assembly software (such as SPAdes assembly software), and saved as a FASTA (a nucleic acid or protein sequence storage format) file named "xxx.fasta". PlasmidFinder is run with default parameters, specifically identity ≥ 95% and coverage ≥ 60%. The PlasmidFinder execution command is as follows: python3 plasmidfinder.py -i xxx.fasta -o xxx -p plasmidfinder_db -x -t 95 -l 60.

[0042] In this configuration, `python3 plasmidfinder.py` indicates that the PlasmidFinder program is run using Python 3 (a programming language); `-i` specifies the contig sequence file assembled from the second-generation sequencing sequence of the strain to be analyzed, where `xxx.fasta` is the FASTA format file of the contig fragment; `-o xxx` specifies the output directory, i.e., the path to save the results file; `-p` specifies the plasmid replicon reference database, where `plasmidfinder_db` represents the reference database; `-x` indicates additional parameters, such as outputting more detailed information or enabling a specific mode; `-t 95` sets the sequence consistency threshold to 95%; and `-l 60` sets the coverage threshold to 60%.

[0043] The above method can quickly screen out strains that may carry the IncC plasmid, saving subsequent computing resources.

[0044] Step S130: Using at least two plasmid fragment identification tools, the second-generation sequencing sequence of the candidate strain is used to identify plasmid fragments, and the potential fragments of the IncC plasmid are determined based on the identification results.

[0045] After initially screening candidate strains, at least two plasmid fragment identification tools were used to identify plasmid fragments in the second-generation sequencing sequences of the candidate strains, resulting in at least two identification results. Based on the obtained identification results, potential fragments of the IncC plasmid were determined.

[0046] When selecting plasmid fragment identification tools, it is preferable to use a variety of plasmid fragment identification tools with different principles in order to improve the accuracy of potential fragment identification results.

[0047] In one embodiment, a first plasmid fragment identification tool and a second plasmid fragment identification tool are used to identify plasmid fragments in the next-generation sequencing sequence of the candidate strain, respectively, to obtain a first identification result and a second identification result; the intersection of the first identification result and the second identification result is taken as a potential fragment of the IncC plasmid.

[0048] In another embodiment, a first plasmid fragment identification tool, a second plasmid fragment identification tool, and a third plasmid fragment identification tool are used to identify plasmid fragments in the next-generation sequencing sequence of the candidate strain, respectively, to obtain a first identification result, a second identification result, and a third identification result; the intersection of the first identification result, the second identification result, and the third identification result is taken as a potential fragment of the IncC plasmid.

[0049] In another embodiment, a first plasmid fragment identification tool and a second plasmid fragment identification tool are used to identify plasmid fragments in the next-generation sequencing sequences of candidate strains, respectively, to obtain a first identification result and a second identification result; the union of the first identification result and the second identification result is taken as a potential fragment of the IncC plasmid.

[0050] The specific execution process can be found in the following examples, which will not be elaborated here.

[0051] Using the above method, fragments that may be IncC plasmids can be preliminarily screened from all contig fragments among candidate strains identified as containing IncC plasmid replicons.

[0052] Step S140: Compare the potential fragment with the core gene of the IncC plasmid, and determine whether the potential fragment is a contig fragment of the IncC plasmid based on the comparison result.

[0053] Core genes are conserved genes shared by all members of the IncC plasmid family. They are responsible for core functions such as plasmid replication, stability maintenance, and conjugation transfer.

[0054] Core genes include, but are not limited to: replication system genes (repA, ter), plasmid maintenance system genes (parA, parB, stbA), DNA metabolism system genes (dcm2, topB, ssb, bet, exo, nuc, pri, int, yacC, kfrA, uvrD), conjugation transfer system genes (mobI, traI, traD, traJ, traL, traE, traK, traB, traV, traA, traC, trhF, traW, traU, traN, traF, traH, traG), regulatory system genes (acr1, acaD, acaC, acr2) and other system genes (sppA, dsbA, dsbC, rhs, eex).

[0055] In one embodiment, the core gene of the IncC plasmid is the core gene of the representative plasmid of the IncC plasmid, which is the pAR060302 plasmid. The pAR060302 plasmid has the number NC_012692 at the National Center for Biotechnology Information (NCBI). The 43 core genes of this plasmid are ordered as follows: mobI, repA, sppA, dsbA, parA, parB, dcm2, topB, traI, traD, traJ, traL, traE, traK, traB, traV, traA, dsbC, traC, trhF, traW, traU, traN, ssb, bet, exo, nuc, pri, rhs, int, yacC, ter, kfrA, uvrD, traF, traH, traG, eex, acr1, acaD, acaC, acr2, stbA. In this embodiment of the invention, this example will be used for illustration.

[0056] The protein numbers of the core genes are shown in Table 1 below.

[0057] Table 1

[0058] To definitively confirm whether the aforementioned potential fragment truly originates from the IncC plasmid, and not from other types of plasmids or integrative conjugating elements on chromosomes (such as SXT / R391 family integrative conjugating elements), core gene verification is required. Specifically, the potential fragment is compared with the core gene of the IncC plasmid, and the comparison results are used to determine whether the potential fragment is a contig fragment of the IncC plasmid.

[0059] In one embodiment, the protein sequence of the core gene of the IncC plasmid is used as the query sequence, and a nucleic acid database constructed from potential fragments of candidate strains is used as the target database. A tBLASTn search is performed to obtain the alignment results. Based on the alignment results, the potential fragment containing at least one core gene is identified as the contig fragment of the IncC plasmid. The specific execution process can be referred to in the following embodiments, which will not be elaborated here.

[0060] Step S150: Based on the distribution of core genes on the contig fragment of the determined IncC plasmid, the sequence regions between adjacent core genes are divided as core gene intervals.

[0061] It should be noted that due to differences in sequencing quality and assembly, to ensure the accuracy of the analysis results, subsequent analysis can only be performed when the distribution meets specific conditions: When the core gene of the IncC plasmid is concentrated in a single contig fragment, the core gene region can be divided relatively accurately, and the distribution hotspot analysis of drug resistance genes can be carried out accordingly; when the core gene of the IncC plasmid is distributed across multiple contig fragments, if one of the contig fragments contains a predetermined number of core genes, then that contig fragment can also be divided relatively accurately into some core gene regions, and the subsequent distribution hotspot analysis of drug resistance genes can be carried out accordingly.

[0062] After obtaining the contig fragments of the IncC plasmids identified in each candidate strain, the distribution of the core genes on the contig fragments of the identified IncC plasmids was analyzed for each candidate strain.

[0063] Specifically, the genome structure of the contig fragments of the identified IncC plasmids is analyzed, and the position coordinates of all core genes on these contig fragments are extracted to determine whether these core genes are concentrated on one contig fragment of the identified IncC plasmids or scattered on multiple contig fragments.

[0064] Then, based on the distribution of core genes on the contig fragment of the determined IncC plasmid, and taking into account the characteristic that the plasmid is a closed circular double-stranded DNA molecule, the starting point and direction of the core gene arrangement were unified, and the sequence regions between adjacent core genes were divided as core gene intervals.

[0065] Specifically, when the core genes are all concentrated in a single contig fragment of the IncC plasmid, the arrangement of the core genes in the representative plasmid of the IncC plasmid is used as a reference. The arrangement of the core genes on the single contig fragment is adjusted to be the same as the starting point and the same as the direction of the core genes in the representative plasmid. The sequence region between the adjacent core genes after adjustment is taken as the core gene interval.

[0066] When the core genes are distributed across multiple contig fragments of the IncC plasmid, and a target contig fragment contains a predetermined number of core genes, it is determined whether the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid. If they are inconsistent, the core genes on the target contig fragment are arranged in descending order according to the locus coordinates to adjust them to be consistent with the arrangement direction of the core genes in the representative plasmid, and the sequence regions between adjacent core genes after sorting are taken as core gene intervals.

[0067] Step S160: Identify the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid, and associate the drug resistance gene with the core gene region based on the locus coordinates.

[0068] Analysis of all contig fragments of candidate strains identified drug resistance genes and their locus coordinates on the contig fragments of the determined IncC plasmid in the candidate strains.

[0069] In one embodiment, a drug resistance gene identification tool is used to compare all contig fragments of candidate strains with a preset drug resistance gene database to obtain the types of drug resistance genes and their locus coordinates on the contig fragments of the identified IncC plasmid in the candidate strains. The screening criteria for comparison are: sequence identity ≥97% and coverage ≥97%. The specific execution process can be referred to the following embodiments, which will not be elaborated here.

[0070] Then, based on locus coordinates, the drug resistance gene is associated with the core gene region. The specific process is as follows: When all core genes are concentrated in a single contig fragment of the IncC plasmid, the first target drug resistance gene and its first target gene locus coordinates are screened based on the locus coordinates of the drug resistance gene. The first target drug resistance gene is integrated with the core gene on the single contig fragment to obtain the first functional gene. Based on the coordinates of the first target gene locus and the coordinates of the core gene on the single contig fragment, the first functional genes are arranged in ascending order according to their locus coordinates to obtain the sorted first functional genes. Using the arrangement direction of the core gene in the representative plasmid of the IncC plasmid as a reference, the arrangement of the sorted first functional genes is adjusted to be the same as the starting point and the same direction as the arrangement of the core gene in the representative plasmid.

[0071] When the core gene is distributed across multiple contig fragments of the IncC plasmid, and a target contig fragment contains a predetermined number of core genes, a second target drug resistance gene located on the target contig fragment and its second target gene locus coordinates are selected based on the locus coordinates of the drug resistance gene. The second target drug resistance gene is then integrated with the core gene on the target contig fragment to obtain a second functional gene. If the arrangement direction of the core gene on the target contig fragment is inconsistent with the arrangement direction of the core gene in the representative plasmid of the IncC plasmid, the second functional genes are arranged in descending order of locus coordinates based on the second target gene locus coordinates and the coordinate information of the core gene on the target contig fragment to adjust them to be consistent with the arrangement direction of the core gene in the representative plasmid. If the arrangement direction of the core gene on the target contig fragment is consistent with the arrangement direction of the core gene in the representative plasmid of the IncC plasmid, the second functional genes are arranged in ascending order of locus coordinates based on the second target gene locus coordinates and the coordinate information of the core gene on the target contig fragment.

[0072] The specific execution process can be found in the following examples, which will not be elaborated here.

[0073] Step S170: Calculate the frequency of drug resistance genes detected in each core gene region of the IncC plasmid of each candidate strain.

[0074] Next, the frequency of drug resistance genes detected in each core gene region of the IncC plasmid of each candidate strain was counted.

[0075] When a drug resistance gene is located between two core genes, the region where the drug resistance gene is located is defined as the distribution site between the two core genes.

[0076] When analyzing a batch (e.g., 50) of candidate strains all containing the IncC plasmid, for each core gene region, count how many strains detected at least one drug resistance gene within that region, and calculate the frequency of drug resistance gene detection in that region. Frequency = Number of IncC plasmids detecting drug resistance genes in that region / Total number of IncC plasmids in the total number of candidate strains.

[0077] Step S180: Based on the frequency, determine the distribution hotspots of drug resistance genes in the IncC plasmid.

[0078] Finally, based on the frequencies obtained from this statistical analysis, the distribution hotspots of drug resistance genes in the IncC plasmid were identified.

[0079] In one embodiment, if the frequency is greater than a preset frequency threshold (e.g., 50%), the corresponding core gene region is determined to be the hotspot for the distribution of drug resistance genes in the IncC plasmid of the batch of candidate strains.

[0080] The distribution hotspots of drug resistance genes revealed which regions in the IncC plasmid genome are the most frequently inserted and retained plastic regions of exogenous drug resistance genes.

[0081] The IncC plasmid fragment analysis method provided in this invention first obtains the next-generation sequencing (NGS) sequence of the strain to be analyzed. Then, using a plasmid replicon type identification tool, candidate strains containing IncC plasmid replicons are screened from the NGS sequence. This initial screening quickly narrows down the range of strains potentially carrying the IncC plasmid, eliminating a large number of negative samples without IncC replicons, reducing unnecessary subsequent computational overhead, and improving overall analysis efficiency. Next, at least two plasmid fragment identification tools are used to identify plasmid fragments in the NGS sequences of the candidate strains, and potential IncC plasmid fragments are determined based on the identification results. By utilizing the complementarity between multiple plasmid fragment identification tools, false positive results caused by database bias or model limitations of a single tool are effectively filtered out, thereby obtaining more reliable potential IncC plasmid fragments. Finally, the potential fragment is compared with the core gene of the IncC plasmid. Based on the comparison results, it is determined whether the potential fragment is a contig fragment of the IncC plasmid. Homology comparison verification is performed using the core gene unique to the IncC plasmid. This further refines the screening process. Only sequence fragments that simultaneously contain the IncC replicon, possess plasmid common characteristics, and encode the IncC core gene can be ultimately confirmed as contig fragments of the IncC plasmid. In summary, this embodiment of the invention, through the above three-step progressive screening, verifies the second-generation sequencing sequences of the strains to be analyzed from three different dimensions: replicon specificity, plasmid general characteristics, and plasmid type-specific core gene. This significantly improves the accuracy and reliability of identifying IncC plasmid contig fragments from second-generation sequencing sequences and effectively reduces the false positive risk of misidentifying homologous chromosomal elements or other types of plasmids as IncC plasmids. Furthermore, based on the accurate identification of contig fragments in the IncC plasmid, the analysis of drug resistance genes is linked to the structural characteristics (core gene regions) of the plasmid. Given that the plasmid is a closed circular double-stranded DNA molecule, the starting point and direction of the core gene arrangement are unified. By dividing the core gene regions and statistically analyzing the distribution frequency of drug resistance genes within these core gene regions in a large number of strains, the distribution hotspots of drug resistance genes are determined. Through the above methods, this invention provides a method for identifying IncC plasmid fragments in next-generation sequencing sequences and quantitatively assessing the distribution patterns of drug resistance genes on IncC plasmids, providing a high-resolution tool for understanding the spread, evolution, and monitoring of drug resistance genes.

[0082] Based on any of the above embodiments Figure 2 This is the second schematic diagram of the IncC plasmid fragment analysis method provided by the present invention, as shown below. Figure 2As shown, step S150 includes step S151 and step S152.

[0083] It should be noted that the execution order of steps S151 and S152 is not important.

[0084] Step S151: When the core genes are all concentrated in a single contig fragment of the IncC plasmid, with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid as a reference, the arrangement of the core genes on the single contig fragment is adjusted to be the same as the starting point and the same as the direction of the core genes in the representative plasmid, and the sequence region between the adjusted adjacent core genes is taken as the core gene interval.

[0085] The orientation of the core genes in the IncC plasmid is the same as the orientation of the core genes in the representative plasmid of IncC (pAR060302 plasmid).

[0086] When all core genes are concentrated on one of the contig fragments of the aforementioned IncC plasmid (i.e., concentrated on a single contig fragment), all 43 core genes and their corresponding coordinate information within that single contig fragment are first obtained. Given that the plasmid is a circular DNA molecule, to facilitate subsequent analysis, the arrangement of these 43 core genes on the single contig fragment is adjusted to match the starting point and direction of the core genes in the representative plasmid of the IncC plasmid, based on the arrangement direction of the core genes in the representative plasmid. Specifically, they are arranged in ascending order according to the locus coordinates. Then, starting with the mobI gene, they are uniformly arranged in the direction pointing towards the repA gene, without changing the original relative order between the core genes. Because this operation is based on the circular nature of the plasmid, it only normalizes the starting point and direction, without affecting the actual arrangement relationship between the core genes, changing the nucleotide composition and gene content, or affecting the presence or absence of genes. After completing the above adjustments, the sequence region between adjacent core genes after adjustment is defined as the core gene region of the IncC plasmid.

[0087] The above processing ensures that the most complete and accurate interval map is obtained under ideal assembly.

[0088] Step S152: When the core genes are distributed across multiple contig fragments of the IncC plasmid and a target contig fragment contains a preset number of core genes, determine whether the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid; if not, arrange the core genes on the target contig fragment in descending order according to the locus coordinates to adjust them to be consistent with the arrangement direction of the core genes in the representative plasmid, and take the sequence region between adjacent core genes after sorting as the core gene interval.

[0089] The preset number can be set to more than half of the total number of core genes. For example, when there are 43 core genes, the preset number can be set to no less than 22.

[0090] When core genes are distributed across multiple contig fragments of the IncC plasmid, and one contig fragment contains a predetermined number of core genes, this contig fragment containing the predetermined number of core genes is designated as the target contig fragment. All core genes and their corresponding coordinates within this target contig fragment are then obtained. To facilitate comparative analysis, it is further determined whether the overall arrangement direction of the core genes in the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid. If consistent, they are arranged in ascending order according to locus coordinates, and the sequence region between any two adjacent core genes is defined as the core gene region of the IncC plasmid. If inconsistent, the core genes on the target contig fragment are arranged in descending order according to locus coordinates to align with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid. The sequence region between any two adjacent core genes after sorting is defined as the core gene region of the IncC plasmid. Given the reversible orientation of contig fragments and the double-stranded structure of plasmid DNA, the above processing does not alter the nucleotide composition or gene content of the sequence, nor does it affect the presence or absence of genes. After completing the above sorting, the sequence region between any two adjacent core genes is defined as the core gene region of the IncC plasmid.

[0091] The above processing provides a robust workflow for processing large amounts of common sequencing data where plasmids are not fully assembled. By selecting the longest fragment containing most core genes as the analysis object and performing necessary directional normalization, meaningful interval analysis can still be performed under imperfect data conditions.

[0092] The IncC plasmid fragment analysis method provided in this invention offers clear and operable core gene region partitioning schemes for two different sequencing quality and assembly differences that may occur in actual sequencing data. This ensures that regardless of the assembly integrity of the IncC plasmid, as long as there is a contig fragment containing a sufficient number of core genes, the contig fragment can be standardized and meaningful core gene regions can be partitioned from it. This avoids the predicament of being unable to perform subsequent analysis due to plasmid sequence breaks, maximizes the use of existing sequencing data, and improves the robustness and applicability of the method.

[0093] Based on any of the above embodiments, the step "associating the locus coordinates of the drug resistance gene with the core gene region based on the locus coordinates" includes: steps S161, S162, S163, S164, S165, S166, S167, and S168.

[0094] It should be noted that the execution order of steps S161-S164 and steps S165-S168 is not important and they can be executed in parallel.

[0095] Step S161: When all the core genes are concentrated in a single contig fragment of the IncC plasmid, the first target drug resistance gene and its first target gene locus coordinates located on the single contig fragment are screened based on the gene locus coordinates of the drug resistance gene.

[0096] When the core genes are all concentrated on one of the contig fragments of the aforementioned identified IncC plasmid, that is, concentrated on a single contig fragment, the drug resistance gene located on that single contig fragment (denoted as the first target drug resistance gene) and its locus coordinates (denoted as the first target locus coordinates) are screened based on the locus coordinates of the drug resistance gene.

[0097] Step S162: Integrate the first target drug resistance gene with the core gene on the single contig fragment to obtain the first functional gene.

[0098] Step S163: Based on the coordinates of the first target locus and the coordinates of the core gene on the single contig fragment, the first functional genes are arranged in ascending order according to the locus coordinates to obtain the sorted first functional genes.

[0099] Step S164: Using the arrangement direction of the core genes in the representative plasmid of the IncC plasmid as a reference, adjust the arrangement of the sorted first functional genes to be the same as the starting point and the same as the direction of the core genes in the representative plasmid.

[0100] The drug resistance gene and core gene in this single contig fragment were integrated and uniformly sorted according to their locus positions: First, the first target drug resistance gene is integrated with the core gene on a single contig fragment, resulting in a set of the first target drug resistance gene and the core gene on that single contig fragment, denoted as the first functional gene. Based on the coordinates of the first target gene locus and the coordinates of the core gene on the single contig fragment, the first functional genes are arranged in ascending order of locus coordinates. Then, for ease of comparison and analysis, the arrangement of the first functional genes is further adjusted to match the starting point and direction of the core genes in the representative plasmid of the IncC plasmid, i.e., starting from the mobI gene and arranging in the direction pointing towards the repA gene. Since the plasmid is a circular DNA molecule, the above adjustment only normalizes the starting point and does not change the relative positional relationships between core genes or between core genes and drug resistance genes.

[0101] Step S165: When the core gene is distributed in multiple contig fragments of the IncC plasmid and a target contig fragment contains a preset number of core genes, a second target drug resistance gene and its second target contig coordinates located on the target contig fragment are screened based on the gene locus coordinates of the drug resistance gene.

[0102] When the core gene is distributed across multiple contig fragments of the IncC plasmid, and there exists a contig fragment containing a predetermined number of core genes, the contig fragment containing the predetermined number of core genes is designated as the target contig fragment.

[0103] Based on the locus coordinates of the drug resistance gene, the drug resistance gene located on the target contig fragment (denoted as the second target drug resistance gene) and its locus coordinates (denoted as the second target locus coordinates) are screened.

[0104] Step S166: Integrate the second target drug resistance gene with the core gene on the target contig fragment to obtain the second functional gene.

[0105] Step S167: If the arrangement direction of the core genes on the target contig fragment is inconsistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid, then based on the coordinates of the second target gene locus and the coordinate information of the core genes on the target contig fragment, the second functional genes are arranged in descending order according to the gene locus coordinates to adjust them to be consistent with the arrangement direction of the core genes in the representative plasmid.

[0106] Step S168: If the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid, then based on the coordinates of the second target gene locus and the coordinate information of the core genes on the target contig fragment, the second functional genes are arranged in ascending order according to the gene locus coordinates.

[0107] The second target drug resistance gene is integrated with the core gene on the target contig fragment to obtain a set of the second target drug resistance gene and the core gene on the target contig fragment, denoted as the second functional gene. If the arrangement direction of the core gene on the target contig fragment is inconsistent with the arrangement direction of the core gene in the representative plasmid of the IncC plasmid, the second functional gene is arranged in descending order of the locus coordinates based on the coordinate information of the second target gene and the core gene on the target contig fragment to unify its arrangement direction compared with the core gene in the representative plasmid of the IncC plasmid.

[0108] If the orientation of the core genes on the target contig fragment is consistent with the orientation of the core genes in the representative plasmid of IncC plasmid, then the second functional genes are arranged in ascending order of the locus coordinates based on the coordinates of the second target gene locus and the coordinate information of the core genes on the target contig fragment.

[0109] Given that the contig fragment is directionally reversible and the plasmid DNA is a double-stranded structure, the above treatment will not change the nucleotide composition of the sequence or the gene content, nor will it affect the presence or absence of the gene.

[0110] The IncC plasmid fragment analysis method provided in this invention integrates drug resistance genes and core genes based on the different distributions of core genes on the contig fragments of a determined IncC plasmid. Then, based on coordinate sorting and orientation normalization, the drug resistance genes and core genes in the contig fragments are integrated into a structured dataset with a linear arrangement consistent with the orientation in the representative IncC plasmid. This process eliminates data heterogeneity caused by incomplete sequencing assembly or DNA double-strand orientation, ensuring that each drug resistance gene can be accurately and standardizedly located within a specific interval between adjacent core genes. This provides a structurally unified and batch-comparable analytical basis for subsequent cross-strain IncC plasmid drug resistance gene distribution frequency statistics and hotspot interval identification.

[0111] Based on any of the above embodiments Figure 3 This is the third flowchart of the IncC plasmid fragment analysis method provided by the present invention, as shown below. Figure 3 As shown, step S140 includes steps S141 and S142.

[0112] Step S141: Using the protein sequence of the core gene of the IncC plasmid as the query sequence and the nucleic acid database constructed from the potential fragments of the candidate strain as the target database, perform a tBLASTn search to obtain the alignment result.

[0113] A nucleic acid database is constructed based on potential fragments from candidate strains, serving as the target database.

[0114] In one implementation, the database creation command is as follows: makeblastdb -in xxx.fasta -dbtype nucl -out xxx_database.

[0115] In this configuration, `makeblastdb` indicates the creation of a BLAST (Basic Local Alignment Search Tool) database; `-in xxx.fasta` indicates the input of a FASTA format file containing potential fragments from candidate strains; `-dbtype nucl` specifies the database type as a nucleic acid database; and `-out xxx_database` indicates the output database name.

[0116] The pAR060302 plasmid was used as the representative plasmid of IncC plasmid, and the protein sequences of 43 core genes in it were used as the query sequences.

[0117] Then, perform a tBLASTn search (a local alignment tool that uses protein sequences to search nucleotide databases) to obtain the alignment results.

[0118] In one implementation, the tBLASTn search command is: tblastn -query pAR060302_core.fasta -db xxx_database -outfmt "6qseqid sseqid pident length qcovs qcovhsp qcovus mismatch gapopen qstart qendsstart send evalue bitscore" -out tblastn.txt.

[0119] Here, `tblastn` indicates that a tBLASTn search will be performed; `-query pAR060302_core.fasta` indicates the query sequence file, i.e., the protein sequence file of the 43 core genes of plasmid pAR060302; `-db xxx_database` indicates the target database; `-outfmt “6 qseqid sseqid pident length qcovs qcovhsp qcovus mismatchgapopen qstart qend sstart send evalue bitscore”` indicates that the results will be output in tabular format 6, and specifies the output format parameters including: query sequence ID (Identifier), target sequence ID, consistency percentage, alignment length, coverage of the query sequence to the target sequence, and a single HSP (High-scoring Segment). The query coverage of the pair (high-scoring fragment pair), the query coverage of each unique target sequence, the number of mismatches, the number of empty pairs, the start coordinates of the query sequence, the end coordinates of the query sequence, the start coordinates of the target sequence, the end coordinates of the target sequence, the expected value, and the standardized alignment score. -out tblastn.txt indicates the output file.

[0120] Step S142: Based on the alignment results and preset screening conditions, potential fragments containing at least one of the core genes are identified as contig fragments of the IncC plasmid. The preset screening conditions include: sequence consistency not less than 85%, sequence coverage not less than 85%, and expected value less than 0.001.

[0121] After obtaining the comparison results, i.e. the output file of tBLASTn, the output file is parsed and filtered using preset filtering conditions.

[0122] In one implementation, the preset screening criteria are: identity (referring to the pident of the aforementioned output) ≥ 85%, coverage (referring to the qcovs of the aforementioned output) ≥ 85%, and e-value (expected value) < 0.001. These screening criteria ensure that potential fragments are only confirmed when they have a clear homology with the core gene, thus improving the reliability of contig fragment identification in the IncC plasmid.

[0123] Then, based on the sseqid (target sequence ID, i.e., the ID of the potential fragment) in the alignment results that have passed the screening, the corresponding potential fragment is recorded. If a potential fragment of a candidate strain has at least one alignment record in the alignment results that meets the preset screening criteria, that is, it proves that the fragment encodes a protein of at least one core gene of the IncC plasmid, then it is finally identified as a contig fragment of a real IncC plasmid in the candidate strain.

[0124] It should be noted that setting the criterion of containing at least one core gene is based on the special nature of the IncC plasmid genome structure (the core gene is an essential component of its backbone). This provides a final specific identity label for a potential fragment that has been screened by the plasmid fragment identification tool, ensuring high accuracy while avoiding the omission of incomplete contig fragments due to the need to match multiple genes.

[0125] The IncC plasmid fragment analysis method provided in this invention uses tBLASTn search to compare and verify the protein sequence representing the core gene of the IncC plasmid with potential fragments. It can effectively cross synonymous mutations and sensitively detect nucleotide sequence variations caused by codon differences. Compared with nucleic acid-nucleic acid alignment (BLASTn), it can better ensure that the core gene of distantly related or highly variable IncC plasmids is not missed.

[0126] Based on any of the above embodiments Figure 4 This is the fourth flowchart of the IncC plasmid fragment analysis method provided by the present invention, as shown below. Figure 4 As shown, step S130 includes step S131 and step S132.

[0127] Step S131: Using the first plasmid fragment identification tool and the second plasmid fragment identification tool, the second-generation sequencing sequence of the candidate strain is used to identify plasmid fragments, respectively, to obtain the first identification result and the second identification result.

[0128] In one embodiment, the first plasmid fragment identification tool is plasmidVerify (a bioinformatics tool for plasmid sequence verification), and the second plasmid fragment identification tool is PLASMe (a bioinformatics tool for plasmid fragment identification).

[0129] The plasmidVerify tool was used to identify plasmid fragments in the second-generation sequencing sequences of candidate strains. The identification results are recorded as the first identification result. This tool first annotates the contig fragments corresponding to the second-generation sequencing sequences, then runs the hmmsearch (Hidden Markov Model search) tool to predict protein families. Based on the predicted protein family information and known HMM (Hidden Markov Model) data, a Naive Bayes classifier is applied to classify DNA (Deoxyribonucleic acid) fragments into plasmids or chromosome fragments. The first identification result is the set of contig fragments identified as plasmids by plasmidVerify.

[0130] In one implementation, the command to execute plasmidVerify is: python3 plasmidverify.py -f xxx.fasta -o xxx --hmm Pfam-A.hmm.

[0131] Here, `python3 plasmidverify.py` means calling Python 3 to run the plasmidverify program; `-f` specifies the contig sequence file assembled from the second-generation sequencing sequences of the input candidate strains, and `xxx.fasta` is the FASTA format file of the contig fragment; `-o xxx` specifies the output directory, i.e., the path to save the result files; `--hmm Pfam-A.hmm` specifies the Pfam database file (Pfam-A.hmm), which contains HMM data for all known protein families and is used to search for protein family characteristics in sequences.

[0132] Simultaneously, PLASMe was used to identify plasmid fragments from the second-generation sequencing sequences of candidate strains, yielding the identification results, denoted as the second identification result. PLASMe integrates a sequence alignment component and a Transformer (a deep learning model based on a self-attention mechanism) model, enabling the identification of distantly related plasmid fragments. The second identification result is the set of contig fragments identified as plasmids by PLASMe.

[0133] In one implementation, the PLASMe execution command is: python3 PLASMe.py xxx.fasta xxx_output -c 0.99 -i 0.99 -p 0.99 -t 50 -d plasme / DB.

[0134] Here, `python3 PLASMe.py` indicates that the PLASMe program is run using Python 3; `xxx.fasta` is the FASTA format file of the contig fragment assembled from the second-generation sequencing sequence of the candidate strain; `xxx_output` indicates the output directory, i.e., the path to save the results file; `-c 0.99` sets the coverage threshold to 99%; `-i 0.99` sets the consistency threshold to 99%; `-p 0.99` sets the probability threshold to 99%; `-t 50` sets the number of threads to 50; and `-d plasme / DB` specifies the path to the reference database that PLASMe depends on.

[0135] Step S132: Take the intersection of the first identification result and the second identification result as the potential fragment of the IncC plasmid.

[0136] For each candidate strain containing an IncC plasmid replicon, the intersection of the plasmidVerify and PLASMe identification results is taken, and the contig fragment in the intersection is identified as a potential fragment of the IncC plasmid in the candidate strain.

[0137] The IncC plasmid fragment analysis method provided in this invention uses two plasmid fragment identification tools based on different principles and takes the intersection of the identification results as potential fragments of the IncC plasmid. This invention leverages the complementarity of the two tools in their judgment logic to filter out false positive fragments that are misidentified by only one tool, thus improving the accuracy of plasmid fragment identification.

[0138] Based on any of the above embodiments, step S160 includes: step S169.

[0139] Step S169: Using a drug resistance gene identification tool, the contig fragment of the candidate strain is compared with a preset drug resistance gene database to obtain the types of drug resistance genes and their locus coordinates on the contig fragment of the determined IncC plasmid.

[0140] The screening criteria for the alignment are: sequence consistency ≥ 97% and coverage ≥ 97%.

[0141] Drug resistance gene identification tools were used to identify drug resistance genes in candidate strains.

[0142] Specifically, a drug resistance gene identification tool is used to compare all contig fragments of candidate strains with a pre-set drug resistance gene database to obtain the types of drug resistance genes and their locus coordinates on the contig fragments of the identified IncC plasmid in the candidate strains.

[0143] In one embodiment, the drug resistance gene identification tool is the ResFinder program (a bioinformatics tool for detecting antimicrobial drug resistance genes). Based on sequence alignment principles, this program compares the genomic sequence of candidate bacteria with a pre-defined drug resistance gene database (a database of known drug resistance genes in ResFinder) to predict and determine the types of drug resistance genes on the contig fragments of the identified IncC plasmids in the candidate strains and their corresponding locus coordinates.

[0144] Furthermore, the screening criteria for the comparison are: sequence identity ≥97% and coverage ≥97%. Preferably, sequence identity ≥99% and coverage ≥99%. By adopting strict screening criteria, drug resistance genes in the genome of candidate strains can be identified with high confidence, effectively eliminating false positive identification results caused by sequencing errors, assembly errors, or homologous pseudogenes.

[0145] In one specific implementation, the ResFinder execution command is as follows: python3 run_resfinder.py -ifa xxx.fasta -k kma -acq -t 0.99 -l 0.99 -o xxx -db_res db_resfinder.

[0146] In this configuration, `python3 run_resfinder.py` indicates that the resfinder program is run using Python 3; `-ifa` specifies the input contig sequence set file, where `xxx.fasta` is the FASTA format file of the contig fragments of the candidate strain; `-k kma` specifies that the KMA (K-mer Alignment) algorithm is used; `-acq` indicates that only acquired resistance genes are obtained (excluding intrinsic resistance caused by chromosomal point mutations); `-t 0.99` sets the sequence consistency threshold for alignment to 99%; `-l 0.99` sets the coverage threshold for alignment to 99%; `-o xxx` specifies the output directory, i.e., the path to save the results file; and `-db_res db_resfinder` specifies the path to the pre-defined resistance gene database in resfinder.

[0147] After the ResFinder program runs, it will output the name of each detected drug resistance gene, the coordinates of the gene locus (the contig ID, and the start and end coordinates on the contig), and information such as the percentage of sequence identity and coverage with the reference gene in the preset drug resistance gene database.

[0148] The IncC plasmid fragment analysis method provided in this invention, by using the ResFinder tool and setting strict consistency and coverage thresholds, can effectively eliminate false positive results caused by homologous genes or sequencing / assembly errors. Simultaneously, it accurately outputs the locus coordinates of drug resistance genes, providing necessary location information for subsequent mapping to the core gene region of the IncC plasmid, making it a crucial link in the entire analysis process.

[0149] Furthermore, the solutions of the embodiments of the present invention will be described further through the following examples.

[0150] (1) Download 12 bacterial next-generation sequencing genome sequences with assembly barcode numbers VIB_AA0362AA_AS, VIB_AA0365AA_AS, VIB_BA2101AA_AS, VIB_AA2951AA_AS, VIB_AA2963AA_AS, VIB_AA2995AA_AS, VIB_AA2996AA_AS, VIB_AA3005AA_AS, VIB_AA3006AA_AS, VIB_AA9299AA_AS, VIB_EA9102AA_AS, and VIB_EA9103AA_AS from EnteroBase (EnteroBacterial Genome Database). Use the plasmid replicon type identification tool PlasmidFinder to identify whether these 12 bacterial next-generation sequencing sequences contain replicons of IncC plasmid. Based on the type of strain, the Enterobacteriaceae reference library was selected as the reference database. The strain sequences were compared and analyzed with the reference database. The default parameters for Plasmidfinder were identity≥95% and coverage≥60%. The execution command for PlasmidFinder was: python3 plasmidfinder.py -i xxx.fasta -o xxx -pplasmidfinder_db -x -t 95 -l 60.

[0151] According to the PlasmidFinder identification results, a total of 10 strains contained replicons of the IncC plasmid, namely VIB_AA0362AA_AS, VIB_AA0365AA_AS, VIB_BA2101AA_AS, VIB_AA2951AA_AS, VIB_AA2963AA_AS, VIB_AA2995AA_AS, VIB_AA2996AA_AS, VIB_AA3005AA_AS, VIB_AA3006AA_AS, and VIB_AA9299AA_AS.

[0152] (2) For strains identified as containing IncC plasmid replicons, potential contig fragments of the IncC plasmid were predicted. Plasmid contig fragments in 10 strains containing IncC plasmid replicons were identified using plasmidVerify and PLASMe, respectively.

[0153] The command to execute plasmidVerify is: python3 plasmidverify.py -f xxx.fasta -o xxx --hmm Pfam-A.hmm.

[0154] The execution command for PLASMe is: python3 PLASMe.py xxx.fasta xxx_output -c 0.99 -i 0.99 -p 0.99 -t 50 -d plasme / DB.

[0155] The recognition results of plasmidVerify are shown in Table 2 below.

[0156] Table 2

[0157] The recognition results of PLASMe are shown in Table 3 below.

[0158] Table 3

[0159] For each strain containing an IncC plasmid replicon, the intersection of the plasmidVerify and PLASMe identification results is taken, and these contig fragments in the intersection are identified as potential fragments of the IncC plasmid in these 10 strains.

[0160] The intersection of the recognition results of plasmidVerify and PLASMe is shown in Table 4 below.

[0161] Table 4

[0162] (3) Confirmation of contig fragments of IncC plasmid. A library was constructed using the nucleic acid sequences of potential fragments from candidate strains. The pAR060302 plasmid was used as the representative plasmid of IncC plasmid. The 43 core gene protein sequences in the plasmid were used as query sequences for tBLASTn search. The output format parameters were set to -outfmt "6 qseqid sseqid pidentlength qcovs qcovhsp qcovus mismatch gapopen qstart qend sstart send evaluebitscore". The screening criteria for tBLASTn were identity≥85%, coverage≥85%, and evalue<0.001.

[0163] The tBLASTn database creation command is: makeblastdb -in xxx.fasta -dbtype nucl -out xxx_database, The tBLASTn search command is: tblastn -query pAR060302_core.fasta -db xxx_database -outfmt "6qseqid sseqid pident length qcovs qcovhsp qcovus mismatch gapopen qstart qendsstart send evalue bitscore" -out tblastn.txt.

[0164] If a potential contig fragment of the IncC plasmid predicted in (2) contains at least one core gene of the IncC plasmid, then the contig fragment is determined to be an IncC plasmid fragment.

[0165] Table 5 shows the contig fragments containing the IncC plasmid core gene in the intersection of plasmidVerify and PLASMe identification results.

[0166] Table 5

[0167] Due to variations in sequencing quality and assembly, to ensure the accuracy of the analysis results, subsequent analyses can only be performed when the core gene distribution meets specific conditions: When the core gene is concentrated in a single contig fragment, the core gene region can be divided relatively accurately, and the hotspot analysis of drug resistance gene distribution can be carried out accordingly; when the core gene of the IncC plasmid is distributed across multiple contig fragments, if one of the contig fragments contains no less than half of the core gene, then that contig fragment can also be divided relatively accurately into some core gene regions, and the subsequent hotspot analysis of drug resistance gene distribution can be carried out accordingly.

[0168] In plasmids VIB_AA0362AA_AS, VIB_AA0365AA_AS, VIB_AA2951AA_AS, VIB_AA2963AA_AS, VIB_AA2995AA_AS, VIB_AA2996AA_AS, VIB_AA3005AA_AS, and VIB_AA3006AA_AS, the core genes are all concentrated in the same contig fragment, therefore they are included in the subsequent hotspot analysis of drug resistance gene distribution. In strains VIB_AA9299AA_AS and VIB_BA2101AA_AS, there were two contig fragments containing the core gene of the IncC plasmid, and each contig fragment contained at least half of the core gene. Therefore, the NODE_20_length_89538_cov_65.858664 fragment of strain VIB_AA9299AA_AS and the NODE_14_length_83762_cov_10.963620 fragment of strain VIB_BA2101AA_AS were selected for subsequent analysis.

[0169] (4) Division of the core gene region of the IncC plasmid. Because plasmids are circular DNA molecules, the starting position of the core gene in the IncC plasmid within the linear contig may differ due to variations in sequencing and assembly. In strains VIB_AA0362AA_AS, VIB_AA0365AA_AS, VIB_AA2951AA_AS, VIB_AA2963AA_AS, VIB_AA2995AA_AS, VIB_AA2996AA_AS, VIB_AA3005AA_AS, and VIB_AA3006AA_AS, the core genes of the IncC plasmid are all concentrated in a single contig fragment. To facilitate analysis, referring to the arrangement direction of the core genes in the representative plasmid pAR060302, the arrangement of the core genes in the above contig fragments was adjusted to have the same starting point and direction as the core genes in the representative plasmid. Specifically, they were arranged in ascending order according to the locus coordinates. Based on this, the mobI gene was used as the starting point, and the arrangement proceeded in the direction pointing towards the repA gene. Since the plasmid itself is a circular DNA molecule, this treatment does not change the actual relative arrangement direction between the core genes; it only normalizes the start points of the arrangement and does not alter the nucleotide composition or gene content of the sequence, nor does it affect the presence or absence of genes. After completing the above normalization process, the sequence region between any two adjacent core genes is defined as the core gene region of the IncC plasmid.

[0170] Figure 5 This is a schematic diagram illustrating the process of adjusting the arrangement of the core gene of the IncC plasmid in the VIB_AA2995AA_AS strain provided by this invention. Taking VIB_AA2995AA_AS as an example, Figure 5 The process of adjusting the arrangement of the core genes in the IncC plasmid of strain VIB_AA2995AA_AS based on the arrangement of the core genes in the pAR060302 plasmid is illustrated.

[0171] Although the mobI gene was not detected in the contig fragments NODE_20_length_89538_cov_65.858664 of strain VIB_AA9299AA_AS and NODE_14_length_83762_cov_10.963620 of strain VIB_BA2101AA_AS, the overall arrangement of all core genes contained therein was reversed compared to the corresponding gene sequence in the representative plasmid. To facilitate subsequent comparative analysis, the core genes in these fragments were arranged in descending order according to their locus coordinates to ensure consistency with the core gene arrangement in the representative plasmid of the IncC plasmid. Given the reversible orientation of contig fragments and the double-stranded structure of plasmid DNA, the above treatment does not alter the nucleotide composition or gene content of the sequence, nor does it affect the presence or absence of genes. After completing the above standardization process, the sequence region between any two adjacent core genes was defined as the core gene region of the IncC plasmid.

[0172] The core gene regions after division are shown in Table 6 below.

[0173] Table 6

[0174] (5) Identification of drug resistance genes in the strain. The ResFinder program was used to identify drug resistance genes in the strain and obtain the types of acquired drug resistance genes and their corresponding locus coordinates in each contig fragment of the strain.

[0175] The command to execute ResFinder is: python3 run_resfinder.py -ifa xxx.fasta -k kma -acq -t 0.99 -l 0.99 -o xxx -db_res db_resfinder.

[0176] (6) Identify the drug resistance genes in the IncC plasmid and analyze their core gene regions. Based on the results of the drug resistance genes contained in all contig fragments of the strain, identify the drug resistance genes contained in the relevant contig fragments of the IncC plasmid.

[0177] Based on the ResFinder results, the drug resistance genes and their location coordinates contained in the contig fragments of the IncC plasmid in these 10 strains are shown in Table 7 below.

[0178] Table 7

[0179] The identification results of core genes within the IncC plasmid fragments identified in each strain were integrated with the identification results of drug resistance genes. In strains VIB_AA0362AA_AS, VIB_AA0365AA_AS, VIB_AA2951AA_AS, VIB_AA2963AA_AS, VIB_AA2995AA_AS, VIB_AA2996AA_AS, VIB_AA3005AA_AS, and VIB_AA3006AA_AS, the core genes of the IncC plasmids were all concentrated in a single contig fragment. For ease of analysis, the arrangement of core genes on the above contig fragments was adjusted to have the same starting point and direction as the core genes in the representative plasmids. Specifically, they were arranged in ascending order according to the locus coordinates, and then uniformly arranged with the mobI gene as the starting point, pointing in the direction towards the repA gene. Since the plasmid itself is a circular DNA molecule, this treatment does not change the actual relative positions between the core gene and the drug resistance gene. It only normalizes the start point and direction of the arrangement, and does not change the nucleotide composition of the sequence or the gene content, nor does it affect the presence or absence of the gene.

[0180] Although the mobI gene was not detected in the contig fragments NODE_20_length_89538_cov_65.858664 of strain VIB_AA9299AA_AS and NODE_14_length_83762_cov_10.963620 of strain VIB_BA2101AA_AS, the overall arrangement of all core genes contained therein was reversed compared to the corresponding genes in the representative plasmid. For ease of subsequent comparative analysis, the merged results from the above fragments were arranged in descending order according to locus coordinates to ensure consistency with the core gene arrangement in the representative plasmid. Given the reversible orientation of contig fragments and the double-stranded structure of plasmid DNA, the above treatment does not alter the nucleotide composition or gene content of the sequence, nor does it affect the presence or absence of genes.

[0181] The positional relationship between the core gene and the drug resistance gene in the contig fragment containing the core gene of the plasmid in 10 strains containing the IncC plasmid is shown in Table 8 below.

[0182] Table 8

[0183] In strains VIB_AA0362AA_AS, VIB_AA0365AA_AS, VIB_AA2951AA_AS, VIB_AA2963AA_AS, VIB_AA2995AA_AS, VIB_AA2996AA_AS, VIB_AA3005AA_AS, and VIB_AA3006AA_AS, the core genes of the IncC plasmids were all concentrated in a single contig fragment. Based on the positional relationship analysis between the core genes of the IncC plasmids and the drug resistance genes, drug resistance genes were detected in 7 out of the 8 IncC plasmids from the above 8 strains, with 7 plasmids showing a distribution between the dsbA and parA genes.

[0184] In the NODE_20_length_89538_cov_65.858664 fragment of strain VIB_AA9299AA_AS and the NODE_14_length_83762_cov_10.963620 fragment of strain VIB_BA2101AA_AS, although the dsbA gene was not detected, the parA gene was located at the end of the identified core gene in the contig fragment. Based on the relative positions of the core genes dsbA and parA in the representative plasmid, it can be determined that the drug resistance gene in the above fragments is distributed in the region between the dsbA and parA genes.

[0185] Of the 10 IncC plasmids from a total of 10 strains, 9 IncC plasmids showed drug resistance genes distributed in the dsbA and parA regions. Therefore, the dsbA and parA regions were identified as hotspots for drug resistance gene distribution in the IncC plasmids of this batch of data.

[0186] The IncC plasmid fragment analysis device provided by the present invention is described below. The IncC plasmid fragment analysis device described below can be referred to in correspondence with the IncC plasmid fragment analysis method described above.

[0187] Figure 6 This is a schematic diagram of the IncC plasmid fragment analysis device provided by the present invention, as shown below. Figure 6 As shown, the device includes a sequencing sequence acquisition module 610, a candidate strain screening module 620, a potential fragment identification module 630, a contig fragment judgment module 640, a region division module 650, a drug resistance gene identification module 660, a frequency statistics module 670, and a distribution hotspot identification module 680; wherein: The sequencing sequence acquisition module 610 is used to acquire the second-generation sequencing sequence of the strain to be analyzed; The candidate strain screening module 620 is used to screen candidate strains containing IncC plasmid replicons from the second-generation sequencing sequence using a plasmid replicon type identification tool. The potential fragment determination module 630 is used to identify plasmid fragments in the second-generation sequencing sequence of the candidate strain using at least two plasmid fragment identification tools, and to determine the potential fragments of the IncC plasmid based on the identification results. The contig fragment determination module 640 is used to compare the potential fragment with the core gene of the IncC plasmid, and determine whether the potential fragment is a contig fragment of the IncC plasmid based on the comparison result. The interval partitioning module 650 is used to partition the sequence regions between adjacent core genes based on the distribution of core genes on the contig fragment of the determined IncC plasmid, as core gene intervals. The drug resistance gene identification module 660 is used to identify the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid, and associate the drug resistance gene with the core gene region based on the locus coordinates; The frequency statistics module 670 is used to count the frequency of drug resistance genes detected in each core gene region of the IncC plasmid of each candidate strain. The hotspot determination module 680 is used to determine the distribution hotspots of drug resistance genes in the IncC plasmid based on the frequency.

[0188] The IncC plasmid fragment analysis device provided in this invention first obtains the next-generation sequencing (NGS) sequence of the strain to be analyzed. Using a plasmid replicon type identification tool, candidate strains containing IncC plasmid replicons are screened from the NGS sequence. This initial screening quickly narrows down the range of strains that may carry the IncC plasmid, eliminating a large number of negative samples that do not contain IncC replicons, reducing unnecessary subsequent computational overhead, and improving overall analysis efficiency. Then, at least two plasmid fragment identification tools are used to identify plasmid fragments in the NGS sequences of the candidate strains, and potential IncC plasmid fragments are determined based on the identification results. By utilizing the complementarity between multiple plasmid fragment identification tools, false positive identification results caused by database bias or model limitations of a single tool are effectively filtered out, thereby obtaining more reliable potential IncC plasmid fragments. Finally, the potential fragment is compared with the core gene of the IncC plasmid. Based on the comparison results, it is determined whether the potential fragment is a contig fragment of the IncC plasmid. Homology comparison verification is performed using the core gene unique to the IncC plasmid. This further refines the screening process. Only sequence fragments that simultaneously contain the IncC replicon, possess plasmid common characteristics, and encode the IncC core gene can be ultimately confirmed as contig fragments of the IncC plasmid. In summary, this embodiment of the invention, through the above three-step progressive screening, verifies the second-generation sequencing sequences of the strains to be analyzed from three different dimensions: replicon specificity, plasmid general characteristics, and plasmid type-specific core gene. This significantly improves the accuracy and reliability of identifying IncC plasmid contig fragments from second-generation sequencing sequences and effectively reduces the false positive risk of misidentifying homologous chromosomal elements or other types of plasmids as IncC plasmids. Furthermore, based on the accurate identification of contig fragments in the IncC plasmid, the analysis of drug resistance genes is linked to the structural characteristics (core gene regions) of the plasmid. Given that the plasmid is a closed circular double-stranded DNA molecule, the starting point and direction of the core gene arrangement are unified. By dividing the core gene regions and statistically analyzing the distribution frequency of drug resistance genes within these core gene regions in a large number of strains, the distribution hotspots of drug resistance genes are determined. Through the above methods, this invention provides a method for identifying IncC plasmid fragments in next-generation sequencing sequences and quantitatively assessing the distribution patterns of drug resistance genes on IncC plasmids, providing a high-resolution tool for understanding the spread, evolution, and monitoring of drug resistance genes.

[0189] According to the IncC plasmid fragment analysis device provided by the present invention, the interval division module 650 is specifically used for: When the core genes are all concentrated in a single contig fragment of the IncC plasmid, the arrangement of the core genes in the representative plasmid of the IncC plasmid is used as a reference. The arrangement of the core genes on the single contig fragment is adjusted to be the same as the starting point and the same as the direction of the core genes in the representative plasmid. The sequence region between the adjacent core genes after adjustment is taken as the core gene interval. When the core genes are distributed across multiple contig fragments of the IncC plasmid, and a target contig fragment contains a predetermined number of core genes, it is determined whether the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid. If they are inconsistent, the core genes on the target contig fragment are arranged in descending order according to their locus coordinates to adjust them to be consistent with the arrangement direction of the core genes in the representative plasmid, and the sequence regions between adjacent core genes after sorting are taken as the core gene intervals.

[0190] According to the IncC plasmid fragment analysis device provided by the present invention, the drug resistance gene recognition module 660 is specifically used for: When the core genes are all concentrated in a single contig fragment of the IncC plasmid, the first target drug resistance gene and its first target gene locus coordinates located on the single contig fragment are screened based on the locus coordinates of the drug resistance gene. The first target drug resistance gene is integrated with the core gene on the single contig fragment to obtain the first functional gene; Based on the coordinates of the first target locus and the coordinates of the core gene on the single contig fragment, the first functional genes are arranged in ascending order according to the locus coordinates to obtain the sorted first functional genes. Using the arrangement direction of the core genes in the representative plasmid of the IncC plasmid as a reference, the arrangement of the sorted first functional genes is adjusted to be the same as the starting point and the same as the direction of the core genes in the representative plasmid. When the core gene is distributed across multiple contig fragments of the IncC plasmid, and a target contig fragment contains a predetermined number of core genes, a second target drug resistance gene and its second target contig fragment are screened based on the locus coordinates of the drug resistance gene. The second target drug resistance gene is integrated with the core gene on the target contig fragment to obtain the second functional gene; If the arrangement direction of the core genes on the target contig fragment is inconsistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid, then based on the coordinates of the second target gene locus and the coordinate information of the core genes on the target contig fragment, the second functional genes are arranged in descending order of the gene locus coordinates to adjust them to be consistent with the arrangement direction of the core genes in the representative plasmid. If the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid, then based on the coordinates of the second target gene locus and the coordinate information of the core genes on the target contig fragment, the second functional genes are arranged in ascending order according to the gene locus coordinates.

[0191] According to the IncC plasmid fragment analysis device provided by the present invention, the core genes of the IncC plasmid include the following 43 genes: mobI, repA, sppA, dsbA, parA, parB, dcm2, topB, traI, traD, traJ, traL, traE, traK, traB, traV, traA, dsbC, traC, trhF, traW, traU, traN, ssb, bet, exo, nuc, pri, rhs, int, yacC, ter, kfrA, uvrD, traF, traH, traG, eex, acr1, acaD, acaC, acr2, stbA. The core genes are arranged in a direction starting from the mobI gene and pointing towards the repA gene.

[0192] According to the IncC plasmid fragment analysis device provided by the present invention, the contig fragment determination module 640 is specifically used for: Using the protein sequence of the core gene of the IncC plasmid as the query sequence and the nucleic acid database constructed from the potential fragments of the candidate strain as the target database, a tBLASTn search was performed to obtain the alignment results; Based on the alignment results and preset screening conditions, potential fragments containing at least one of the core genes are identified as contig fragments of the IncC plasmid; The preset screening conditions include: sequence consistency not less than 85%, sequence coverage not less than 85%, and expected value less than 0.001.

[0193] According to an IncC plasmid fragment analysis device provided by the present invention, the potential fragment determination module 630 is specifically used for: Using a first plasmid fragment identification tool and a second plasmid fragment identification tool, the second-generation sequencing sequences of the candidate strains were identified to obtain a first identification result and a second identification result. The intersection of the first identification result and the second identification result is taken as the potential fragment of the IncC plasmid.

[0194] According to the IncC plasmid fragment analysis device provided by the present invention, the drug resistance gene recognition module 660 is further used for: Using a drug resistance gene identification tool, the contig fragment of the candidate strain is compared with a preset drug resistance gene database to obtain the types of drug resistance genes and their locus coordinates on the contig fragment of the determined IncC plasmid. The screening criteria for the alignment are: sequence consistency ≥ 97% and coverage ≥ 97%.

[0195] It should be noted that the IncC plasmid fragment analysis device provided in this embodiment of the invention can implement all the method steps implemented in the IncC plasmid fragment analysis method embodiment and achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0196] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute an IncC plasmid fragment analysis method. This method includes: obtaining the second-generation sequencing sequence of the strain to be analyzed; using a plasmid replicon type identification tool to screen candidate strains containing IncC plasmid replicons from the second-generation sequencing sequence; using at least two plasmid fragment identification tools to perform plasmid fragment identification on the second-generation sequencing sequence of the candidate strains, and determining potential fragments of the IncC plasmid based on the identification results; comparing the potential fragments with the core gene of the IncC plasmid, and determining whether the potential fragments are... Whether it is a contig fragment of the IncC plasmid; based on the distribution of core genes on the determined contig fragment of the IncC plasmid, the sequence regions between adjacent core genes are divided as core gene intervals; the drug resistance genes and their locus coordinates on the determined contig fragment of the IncC plasmid are identified, and the drug resistance genes are associated with the core gene intervals based on the locus coordinates; the frequency of drug resistance genes detected in each core gene interval in the IncC plasmid of each candidate strain is counted; based on the frequency, the distribution hotspots of drug resistance genes in the IncC plasmid are determined.

[0197] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0198] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the IncC plasmid fragment analysis method provided by the above methods. This method includes: obtaining the second-generation sequencing sequence of the strain to be analyzed; using a plasmid replicon type identification tool to screen candidate strains containing IncC plasmid replicons from the second-generation sequencing sequence; using at least two plasmid fragment identification tools to identify plasmid fragments in the second-generation sequencing sequence of the candidate strains, and determining potential fragments of the IncC plasmid based on the identification results; and performing plasmid fragment identification on the potential fragments and the core gene of the IncC plasmid. The process involves: performing alignment; determining whether the potential fragment is a contig fragment of the IncC plasmid based on the alignment results; identifying the distribution of core genes on the determined contig fragments of the IncC plasmid, defining the sequence regions between adjacent core genes as core gene intervals; identifying drug-resistant genes and their locus coordinates on the determined contig fragments of the IncC plasmid, and associating the drug-resistant genes with the core gene intervals based on the locus coordinates; statistically analyzing the frequency of drug-resistant genes detected in each core gene interval of the IncC plasmids of each candidate strain; and determining the distribution hotspots of drug-resistant genes in the IncC plasmids based on the frequencies.

[0199] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0200] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing IncC plasmid fragments, characterized in that, include: Obtain the next-generation sequencing sequence of the strain to be analyzed; Candidate strains containing IncC plasmid replicon replicons were screened from the second-generation sequencing sequences using a plasmid replicon type identification tool. Using at least two plasmid fragment identification tools, the second-generation sequencing sequences of the candidate strains were used to identify plasmid fragments, and the intersection of the identification results was taken as the potential fragment of the IncC plasmid. Using the protein sequence of the core gene of the IncC plasmid as the query sequence and the nucleic acid database constructed from the potential fragments of the candidate strain as the target database, a tBLASTn search is performed to obtain the alignment results; based on the alignment results and preset screening conditions, the potential fragment containing at least one of the core genes is identified as the contig fragment of the IncC plasmid. Based on the distribution of core genes on the contig fragments of the IncC plasmid, the sequence regions between adjacent core genes are defined as core gene intervals. This includes: when the core genes are all concentrated on a single contig fragment of the IncC plasmid, using the arrangement direction of the core genes in the representative plasmid of the IncC plasmid as a reference, adjusting the arrangement of the core genes on the single contig fragment to match the starting point and direction of the core genes in the representative plasmid, and defining the adjusted sequence regions between adjacent core genes as the core gene intervals; when the core genes are distributed across multiple contig fragments of the IncC plasmid, and a target contig fragment contains a predetermined number of core genes, determining whether the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid; if not, arranging the core genes on the target contig fragment in descending order according to their locus coordinates to match the arrangement direction of the core genes in the representative plasmid, and defining the sequence regions between adjacent core genes after sorting as the core gene intervals. Identify the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid, and associate the drug resistance gene with the core gene region based on the locus coordinates; For each of the core gene regions, the ratio of the number of IncC plasmids containing drug resistance genes detected in each core gene region to the number of IncC plasmids in the candidate strain is calculated to obtain the frequency of drug resistance genes detected in each core gene region. The core gene intervals with frequencies greater than a preset frequency threshold are identified as hotspots for the distribution of drug resistance genes in the IncC plasmid.

2. The IncC plasmid fragment analysis method according to claim 1, characterized in that, The process of identifying the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid, and associating the drug resistance gene with the core gene region based on the locus coordinates, includes: When the core genes are all concentrated in a single contig fragment of the IncC plasmid, the first target drug resistance gene and its first target gene locus coordinates located on the single contig fragment are screened based on the locus coordinates of the drug resistance gene. The first target drug resistance gene is integrated with the core gene on the single contig fragment to obtain the first functional gene; Based on the coordinates of the first target locus and the coordinates of the core gene on the single contig fragment, the first functional genes are arranged in ascending order according to the locus coordinates to obtain the sorted first functional genes. Using the arrangement direction of the core genes in the representative plasmid of the IncC plasmid as a reference, the arrangement of the sorted first functional genes is adjusted to be the same as the starting point and the same as the direction of the core genes in the representative plasmid. When the core gene is distributed across multiple contig fragments of the IncC plasmid, and a target contig fragment contains a predetermined number of core genes, a second target drug resistance gene and its second target contig fragment are screened based on the locus coordinates of the drug resistance gene. The second target drug resistance gene is integrated with the core gene on the target contig fragment to obtain the second functional gene; If the arrangement direction of the core genes on the target contig fragment is inconsistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid, then based on the coordinates of the second target gene locus and the coordinate information of the core genes on the target contig fragment, the second functional genes are arranged in descending order of the gene locus coordinates to adjust them to be consistent with the arrangement direction of the core genes in the representative plasmid. If the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid, then based on the coordinates of the second target gene locus and the coordinate information of the core genes on the target contig fragment, the second functional genes are arranged in ascending order according to the gene locus coordinates.

3. The IncC plasmid fragment analysis method according to claim 1, characterized in that, The core genes of the IncC plasmid include the following 43 genes: mobI, repA, sppA, dsbA, parA, parB, dcm2, topB, traI, traD, traJ, traL, traE, traK, traB, traV, traA, dsbC, traC, trhF, traW, traU, traN, ssb, bet, exo, nuc, pri, rhs, int, yacC, ter, kfrA, uvrD, traF, traH, traG, eex, acr1, acaD, acaC, acr2, and stbA. The core genes are arranged with the mobI gene as the starting point, pointing towards the repA gene.

4. The method for analyzing IncC plasmid fragments according to any one of claims 1 to 3, characterized in that, The preset screening conditions include: sequence consistency not less than 85%, sequence coverage not less than 85%, and expected value less than 0.

001.

5. The method for analyzing IncC plasmid fragments according to any one of claims 1 to 3, characterized in that, The process of using at least two plasmid fragment identification tools to identify plasmid fragments in the next-generation sequencing sequences of the candidate strains, and using the intersection of the identification results as a potential fragment of the IncC plasmid, includes: The candidate strain's next-generation sequencing sequence was subjected to plasmid fragment identification using a first plasmid fragment identification tool and a second plasmid fragment identification tool, respectively, to obtain a first identification result and a second identification result; wherein, the first plasmid fragment identification tool and the second plasmid fragment identification tool are PLASMe and plasmidVerify, respectively; The intersection of the first identification result and the second identification result is taken as the potential fragment of the IncC plasmid.

6. The method for analyzing IncC plasmid fragments according to any one of claims 1 to 3, characterized in that, The identification of the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid includes: Using a drug resistance gene identification tool, the contig fragment of the candidate strain is compared with a preset drug resistance gene database to obtain the types of drug resistance genes and their locus coordinates on the contig fragment of the determined IncC plasmid. The screening criteria for the alignment are: sequence consistency ≥ 97% and coverage ≥ 97%.

7. An IncC plasmid fragment analysis device, characterized in that, include: The sequencing sequence acquisition module is used to acquire the second-generation sequencing sequence of the strain to be analyzed; The candidate strain screening module is used to screen candidate strains containing IncC plasmid replicons from the second-generation sequencing sequence using a plasmid replicon type identification tool. The potential fragment identification module is used to identify plasmid fragments in the second-generation sequencing sequence of the candidate strain using at least two plasmid fragment identification tools, and to take the intersection of the identification results as the potential fragments of the IncC plasmid. The contig fragment identification module is used to perform a tBLASTn search using the protein sequence of the core gene of the IncC plasmid as the query sequence and the nucleic acid database constructed from the potential fragments of the candidate strain as the target database to obtain the alignment result; based on the alignment result and preset screening conditions, the potential fragment containing at least one of the core genes is identified as the contig fragment of the IncC plasmid. The interval partitioning module is used to partition the sequence regions between adjacent core genes based on the distribution of core genes on the contig fragments of a determined IncC plasmid, as core gene intervals. This includes: when the core genes are all concentrated on a single contig fragment of the IncC plasmid, using the arrangement direction of core genes in a representative plasmid of the IncC plasmid as a reference, adjusting the arrangement of core genes on the single contig fragment to match the starting point and direction of the core genes in the representative plasmid, and using the adjusted sequence regions between adjacent core genes as the core gene intervals; when the core genes are distributed across multiple contig fragments of the IncC plasmid, and a target contig fragment contains a preset number of core genes, determining whether the arrangement direction of the core genes on the target contig fragment is consistent with the arrangement direction of the core genes in the representative plasmid of the IncC plasmid; if not, arranging the core genes on the target contig fragment in descending order according to locus coordinates to match the arrangement direction of the core genes in the representative plasmid, and using the sorted sequence regions between adjacent core genes as the core gene intervals. The drug resistance gene identification module is used to identify the drug resistance gene and its locus coordinates on the contig fragment of the determined IncC plasmid, and to associate the drug resistance gene with the core gene region based on the locus coordinates; The frequency statistics module is used to calculate the ratio of the number of IncC plasmids containing drug resistance genes detected in each of the core gene intervals to the number of IncC plasmids in the candidate strain, thereby obtaining the frequency of drug resistance genes detected in each of the core gene intervals. The hotspot determination module is used to identify the core gene intervals with frequencies greater than a preset frequency threshold as the distribution hotspots of drug resistance genes in the IncC plasmid.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the IncC plasmid fragment analysis method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the IncC plasmid fragment analysis method as described in any one of claims 1 to 6.

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