A method for detecting antibiotic resistance genes based on short nucleotide fragments

Abstract

The present invention relates to the field of antibiotic resistance gene detection, and discloses a method for detecting antibiotic resistance genes based on short nucleotide fragments, comprising the following steps: obtaining the gene sequence of the antibiotic resistance gene and the whole genome sequencing data of the bacterial strain, performing data preprocessing to identify the nucleotide fragments, and obtaining a nucleotide sequence index reference data set C1 and a comparison data set C2; obtaining a short nucleotide fragment reference data set S1 and a comparison data set S2 of a specified length from the reference data set C1 and the comparison data set C2 respectively; dynamically matching the reference data set S1 and the comparison data set S2; calculating the comparison rate based on the dynamic matching result; and outputting the antibiotic resistance gene detection result. The method of the present invention can solve the impact of the low accuracy of the whole genome sequencing results and the limitations of the antibiotic resistance gene database on the detection of antibiotic resistance genes, and improve the accuracy and detection rate of antibiotic resistance gene detection.

Description

Technical Field

[0001] The present invention relates to the field of antibiotic resistance gene detection, and in particular to a method for detecting antibiotic resistance genes based on short nucleotide fragments. Background Art

[0002] Bacterial resistance, especially the increase in resistant strains of bacteria related to human health, will bring severe challenges to clinical treatment. Bacterial resistance refers to the phenomenon that bacteria are insensitive to antimicrobial drugs and is a special form of bacterial survival. With the development of molecular biology technology, studies have found that bacterial resistance is related to the expression of its antibiotic resistance genes.

[0003] Antibiotic Resistance Genes (ARGs) are nucleotide sequences that encode drug resistance traits. They can not only make bacteria themselves resistant, but also be transferred to other bacteria through mobile elements such as plasmids, transposons, and integrons in the environment or clinics, making other bacteria resistant to antibiotics. Therefore, identifying antibiotic resistance genes is of great significance for studying the transmission mechanism of bacterial resistance.

[0004] Polymerase Chain Reaction (PCR) is a commonly used method for detecting antibiotic resistance genes. It is a molecular biological technology used to amplify specific DNA fragments. It is more suitable for detecting a single antibiotic resistance gene. If multiple antibiotic resistance genes are detected at the same time, multiple or multiple PCR tests are required, which is cumbersome.

[0005] Whole Genome Sequencing (WGS) can perform individual analysis on bacteria with unknown genome sequences to determine complete gene sequence information. The sequencing idea is to extract genomic DNA and randomly break it into short nucleotide fragments, and finally sequence the short nucleotide fragments. The obtained sequence is assembled and spliced ​​through bioinformatics methods to obtain the complete genome sequence of the bacteria. By sequencing the whole genome of bacteria and using sequence comparison methods, all antibiotic resistance genes carried by bacteria can be detected.

[0006] Currently, the main methods for detecting antibiotic resistance genes based on whole genome data are: (1) comparing sequencing data with sequences in the antibiotic resistance gene database through a global alignment algorithm; (2) using the characteristic information of protein homology and protein variation to predict the antibiotic resistance genes carried in the bacterial whole genome.

[0007] However, the current methods for detecting antibiotic resistance genes based on whole genome data have the following problems: (1) Most antibiotic resistance gene databases lack effective and sustainable management processes, resulting in the inability to timely update and manage newly emerging antibiotic resistance genes in the database; (2) The naming of antibiotic resistance gene libraries is not unified, resulting in the same antibiotic resistance gene being mistaken for different antibiotic resistance genes; (3) Whole genome sequencing technology may reduce the accuracy of sequencing results during the sequencing and splicing process, and detect incorrect antibiotic resistance genes. Summary of the invention

[0008] In order to solve the above technical problems, the present invention provides a method for detecting antibiotic resistance genes based on short nucleotide fragments. The method can solve the problem of low accuracy of whole genome sequencing results, untimely updating of antibiotic resistance gene databases and inconsistent naming, which affects the detection of antibiotic resistance genes, and improve the accuracy and detection rate of antibiotic resistance gene detection.

[0009] The specific technical scheme of the present invention is:

[0010] A method for detecting antibiotic resistance genes based on short nucleotide fragments comprises the following steps:

[0011] Step S1: Obtain the gene sequence of the antibiotic resistance gene and the whole genome sequencing data of the bacterial strain, perform data preprocessing to identify nucleotide fragments, and obtain a nucleotide sequence index reference data set C1 and a comparison data set C2;

[0012] Step S2: obtaining a short nucleotide fragment reference dataset S1 and a comparison dataset S2 of a specified length from the reference dataset C1 and the comparison dataset C2 respectively;

[0013] Step S3: dynamically matching the reference data set S1 and the comparison data set S2;

[0014] Step S4: Calculate the comparison rate based on the result of step S3;

[0015] Step S5: Output the antibiotic resistance gene detection results.

[0016] In step (1), the gene sequence of the antibiotic resistance gene and the whole genome sequencing data of the bacterial strain can be obtained from databases such as NCBI.

[0017] The present invention uses the SNM algorithm to detect antibiotic resistance genes. Although it is also based on the gene sequence of antibiotic resistance genes and the whole genome sequencing data of bacterial strains, it is different from the prior art in that:

[0018] ① The method of the present invention does not use an antibiotic resistance gene database, but obtains the gene sequence of a specific antibiotic resistance gene from a database such as NCBI and then inputs it automatically, thereby avoiding the impact of the antibiotic resistance gene database's inability to update in time and inconsistent naming on the test results, and preventing the inability to detect all antibiotic resistance genes due to problems with the antibiotic resistance gene database, or the same antibiotic resistance gene being mistaken for different antibiotic resistance genes; 2○ The present invention takes into account that whole genome sequencing technology may reduce the accuracy of sequencing results during the sequencing and splicing process, resulting in the detection of erroneous antibiotic resistance genes, and combines the principles of whole genome sequencing technology to adopt a method of dynamic matching after obtaining short nucleotide fragments, which can reduce the impact of the accuracy of whole genome sequencing results on antibiotic resistance gene detection and improve the accuracy and detection rate of antibiotic resistance genes.

[0019] In addition, the short nucleotide fragments generated in step S2 of the present invention have high specificity, and the short nucleotide fragment reference data set S1 theoretically represents the characteristic information of the drug-resistant gene. In step S3, the short nucleotide fragments are dynamically matched to accurately detect the antibiotic resistance gene in the sample.

[0020] Preferably, in step S1, the data preprocessing comprises the following steps:

[0021] Step S11: inputting the gene sequence of the antibiotic resistance gene and the whole genome sequencing data of the bacterial strain respectively;

[0022] Step S12: Read the nucleotide fragments in the data file row by row and segment them using identifiers;

[0023] Step S13: The first row of information in the identified nucleotide fragment is stored in name, and the nucleotide information from the second row to the last row is connected and stored in reads, to obtain a nucleotide sequence index reference data set C1 and a comparison data set C2.

[0024] The preprocessing in step S1 is to identify nucleotide fragments. The specific process is as follows: the input data is read row by row and segmented by an identifier. The identifier can be '>'. The nucleotide fragments are obtained and stored in the nucleotide sequence index table. The storage format is<name,reads> , where name is the nucleotide annotation and reads is the identified nucleotide fragment.

[0025] Preferably, in step S2, the designated length is 30 to 35 bp (base pairs).

[0026] Furthermore, in step S2, the specified length is 31 bp.

[0027] The length of the short nucleotide fragment is set to 31bp because the setting of the nucleotide fragment length needs to take into account the sequence specificity (i.e. a nucleotide sequence unique to a gene) and the comparison efficiency. If the length is too small, the specificity of the short nucleotide fragment will be reduced, and if the length is too large, the comparison efficiency will be reduced.

[0028] Preferably, the specific process of step S2 includes the following steps:

[0029] Step S21: inputting the reference data set C1 and the comparison data set C2 respectively;

[0030] Step S22: setting the sliding window length of the short nucleotide fragments, taking 1 bp as the moving step, and cutting the reference data set C1 and the comparison data set C2;

[0031] Step S23: According to the result obtained in step S22, the frequency of occurrence of each short nucleotide fragment is counted and stored in the short nucleotide fragment reference data set S1 and the comparison data set S2.

[0032] In step S23, the short nucleotide fragment reference dataset S1 and the comparison dataset S2 can be used<index,value> format, where index is the short nucleotide fragment and value is the frequency of occurrence of the short nucleotide fragment.

[0033] Preferably, the specific process of step S3 includes the following steps:

[0034] Step S31: extracting a subset from the reference data set S1;

[0035] Step S32: traverse the comparison data set S2;

[0036] Step S33: Compare the subset in the reference dataset S1 with the comparison dataset S2 in sequence to determine whether the subset in the reference dataset S1 appears in the comparison dataset S2; if so, the comparison is successful, and step S34 is performed; if not, the comparison fails, and step S35 is performed;

[0037] Step S34: Return 1, the result is temporarily stored in the result array, and then proceed to step S36;

[0038] Step S35: determine whether the comparison data set S2 is empty; if it is empty, return 0, and the result is temporarily stored in the result array, and then proceed to step S36; if it is not empty, return to step S32;

[0039] Step S36: determine whether the reference data set S1 is empty; if it is empty, generate a result file from the result array; if it is not empty, return to step S31.

[0040] Furthermore, the format of the result array is<index,result> , where index is the short nucleotide fragment in the reference dataset S1, and result is the comparison result.

[0041] Preferably, the specific process of step S4 includes the following steps:

[0042] Step S41: performing statistics on the result array, the statistical objects include the total number of aligned short nucleotide fragments and the total number of successfully aligned short nucleotide fragments, or the total number of failed aligned short nucleotide fragments;

[0043] Step S42: Calculate the comparison rate based on the statistical results.

[0044] Preferably, in step S4, the comparison ratio is calculated according to the following formula:

[0045]

[0046] or

[0047] Preferably, in step S5, the antibiotic resistance gene detection result includes: strain number, name of the detected antibiotic resistance gene, total number of compared short nucleotide fragments, total number of successfully compared short nucleotide fragments and comparison rate.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] (1) The present invention effectively solves the impact of the limitations of the antibiotic resistance gene database on antibiotic resistance gene detection by automatically inputting the antibiotic resistance gene sequence, thereby improving the accuracy of the detection results;

[0050] (2) The present invention takes into account the principle of whole genome sequencing technology, reduces the impact of inaccurate whole genome sequencing results on antibiotic resistance gene detection, and improves the accuracy and detection rate of antibiotic resistance gene detection;

[0051] (3) The short nucleotide fragments generated by the present invention have high specificity, and the short nucleotide fragment reference data set S1 theoretically represents the characteristic information of drug-resistant genes;

[0052] (4) The present invention dynamically matches short nucleotide fragments and can accurately detect antibiotic resistance genes in samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of detecting antibiotic resistance genes in Example 1 of the present invention.

[0054] Figure 2It is a flowchart of obtaining the short nucleotide fragment reference data set S1 and the comparison data set S2 (step S2) in Example 1 of the present invention.

[0055] Figure 3 It is a flowchart of dynamically comparing the reference data set S1 and the comparison data set S2 (step S3) in embodiment 1 of the present invention. DETAILED DESCRIPTION

[0056] The present invention will be further described below in conjunction with the embodiments.

[0057] Example 1

[0058] A method for detecting antibiotic resistance genes based on short nucleotide fragments, such as Figure 1 As shown, the following steps are included:

[0059] Step S1: Obtain antibiotic resistance gene bla from NCBI database KPC-2 The gene sequence of , and the whole genome sequencing data of Klebsiella pneumoniae, are respectively preprocessed to identify nucleotide fragments. The data preprocessing specifically includes the following steps:

[0060] Step S11: Input antibiotic resistance gene bla KPC-2 's gene sequence and whole genome sequencing data of Klebsiella pneumoniae;

[0061] Step S12: using the readSequence function, reading the nucleotide fragments in the data file line by line, and segmenting them using '>' as an identifier;

[0062] Step S13: The first row of information in the identified nucleotide fragment is stored in name, and the nucleotide information from the second row to the last row is connected and stored in reads to obtain a nucleotide sequence index table, i.e., a nucleotide sequence index reference data set C1 and a comparison data set C2.<name,reads> format, where name is the nucleotide annotation and reads is the identified nucleotide fragment.

[0063] Step S2: Obtain 31 bp short nucleotide fragment reference dataset S1 and comparison dataset S2 from reference dataset C1 and comparison dataset C2 respectively. The specific process is as follows: Figure 2 As shown, the following steps are included:

[0064] Step S21: inputting the reference data set C1 and the comparison data set C2 respectively;

[0065] Step S22: setting the short nucleotide segment sliding window length to 31 bp and the moving step length to 1 bp, and using the creatSNT function to cut the reference data set C1 and the comparison data set C2;

[0066] Step S23: According to the results obtained in step S22, the frequency of occurrence of each short nucleotide fragment is counted, and the short nucleotide fragment reference data set S1 and the comparison data set S2 are output.<index,value> format, where index is the short nucleotide fragment and value is the frequency of occurrence of the short nucleotide fragment.

[0067] The statistical results of the occurrence frequency of short nucleotide fragments obtained in step S2 (reference data set S1) are shown in Table 1.

[0068] Table 1 Statistical results of short nucleotide fragment data

[0069] Index(short nucleotide fragments) Value(Frequency) CCGCTGGGAGCTGGAGCTGAACTCCGCCATC 1 TGTTGATTGGCTAAAGGGAAACACGACCGGC 1 …… …… GCAGCGGCAGCAGTTTGTTGATTGGCTAAAG 1

[0070] Step S3: Dynamically match the reference dataset S1 and the comparison dataset S2. The specific process is as follows: Figure 3 As shown, the following steps are included:

[0071] Step S31: extracting a subset from the reference data set S1;

[0072] Step S32: traverse the comparison data set S2;

[0073] Step S33: using the matchSNT function, the subset in the reference dataset S1 is compared with the comparison dataset S2 in sequence to determine whether the subset in the reference dataset S1 appears in the comparison dataset S2; if so, proceed to step S34; if not, proceed to step S35;

[0074] Step S34: Return 1, the result is temporarily stored in the result array, and then proceed to step S36;

[0075] Step S35: Use the matchSNT function to determine whether the comparison data set S2 is empty; if it is empty, return 0, and temporarily store the result in the result array, and then proceed to step S36; if it is not empty, return to step S32;

[0076] Step S36: Use the matchSNT function to determine whether the reference dataset S1 is empty; if it is empty, generate the result file AMR_result.csv from the result array in the format<index,result> , where index is the short nucleotide fragment in the reference data set S1, and result is the comparison result; if it is not empty, return to step S31.

[0077] Step S4: Calculate the comparison rate based on the result of step S3. The specific process includes the following steps:

[0078] Step S41: reading the result file outputted in step S36, and counting the total number of short nucleotide fragments in the reference data set S1 (i.e., the total number of aligned short nucleotide fragments), and the total number of short nucleotides detected in the aligned data set S2 (i.e., the total number of successfully aligned short nucleotide fragments);

[0079] Step S42: Calculate the comparison rate according to the following formula based on the statistical results:

[0080]

[0081] Step S5: Output the antibiotic resistance gene detection results, as shown in Table 2. Including strain number, name of the antibiotic resistance gene detected, total number of short nucleotide fragments, number of short nucleotide fragments detected and comparison rate.

[0082] Table 2

[0083]

[0084] Although the present invention uses more terms such as short nucleotide fragments and antibiotic resistance genes, it does not exclude the possibility of using other terms. These terms are used only to more conveniently describe and explain the essence of the present invention; interpreting them as any additional restrictions is contrary to the spirit of the present invention. The raw materials and equipment used in the present invention, unless otherwise specified, are all commonly used raw materials and equipment in the art; the methods used in the present invention, unless otherwise specified, are all conventional methods in the art.

[0085] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent transformation made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for detecting antibiotic resistance genes based on short nucleotide fragments, characterized in that: The following steps are involved: Step S1: Obtain the gene sequence of the antibiotic resistance gene and the whole genome sequencing data of the bacterial strain, perform data preprocessing to identify nucleotide fragments, and obtain a nucleotide sequence index reference data set C1 and a comparison data set C2; Step S2: obtaining a short nucleotide fragment reference dataset S1 and a comparison dataset S2 of a specified length from the reference dataset C1 and the comparison dataset C2 respectively; Step S3: Dynamically match the reference dataset S1 and the comparison dataset S2. The specific process includes the following steps: Step S31: extracting a subset from the reference data set S1; Step S32: traverse the comparison data set S2; Step S33: Compare the subset in the reference dataset S1 with the comparison dataset S2 in sequence to determine whether the subset in the reference dataset S1 appears in the comparison dataset S2; if so, the comparison is successful, and step S34 is performed; if not, the comparison fails, and step S35 is performed; Step S34: Return 1, the result is temporarily stored in the result array, and then proceed to step S36; Step S35: determine whether the comparison data set S2 is empty; if it is empty, return 0, and the result is temporarily stored in the result array, and then proceed to step S36; if it is not empty, return to step S32; Step S36: determine whether the reference data set S1 is empty; if it is empty, generate a result file from the result array; if it is not empty, return to step S31; Step S4: Calculate the comparison rate based on the result of step S3; Step S5: Output the antibiotic resistance gene detection results.

2. The method according to claim 1, characterized in that In step S1, the data preprocessing includes the following steps: Step S11: inputting the gene sequence of the antibiotic resistance gene and the whole genome sequencing data of the bacterial strain respectively; Step S12: Read the nucleotide fragments in the data file row by row and segment them using identifiers; Step S13: The first row of information in the identified nucleotide fragment is stored in name, and the nucleotide information from the second row to the last row is connected and stored in reads, to obtain a nucleotide sequence index reference data set C1 and a comparison data set C2.

3. The method according to claim 1, characterized in that In step S2, the designated length is 30 to 35 bp.

4. The method according to claim 3, characterized in that In step S2, the specified length is 31 bp.

5. The method according to claim 1 or 3, characterized in that: The specific process of step S2 includes the following steps: Step S21: inputting the reference data set C1 and the comparison data set C2 respectively; Step S22: setting the sliding window length of the short nucleotide fragments, taking 1 bp as the moving step, and cutting the reference data set C1 and the comparison data set C2; Step S23: According to the result obtained in step S22, the frequency of occurrence of each short nucleotide fragment is counted and stored in the short nucleotide fragment reference data set S1 and the comparison data set S2.

6. The method according to claim 1, characterized in that The format of the result array is<index,result> , where index is the short nucleotide fragment in the reference dataset S1, and result is the comparison result.

7. The method according to claim 1, characterized in that The specific process of step S4 includes the following steps: Step S41: performing statistics on the result array, the statistical objects include the total number of aligned short nucleotide fragments and the total number of successfully aligned short nucleotide fragments, or the total number of failed aligned short nucleotide fragments; Step S42: Calculate the comparison rate based on the statistical results.

8. The method according to claim 7, characterized in that In step S4, the comparison rate is calculated according to the following formula: or 9. The method according to claim 1, characterized in that In step S5, the antibiotic resistance gene detection result includes: strain number, name of the detected antibiotic resistance gene, total number of compared short nucleotide fragments, total number of successfully compared short nucleotide fragments and comparison rate.

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