HIV-1 drug resistance gene detection method based on ngs technology

Abstract

The application discloses a HIV-1 drug-resistant gene detection method based on NGS technology, belongs to the technical field of gene detection, adopts a series of processes such as NGS data quality control and filtration, genome alignment and extraction of effective information, construction of consistent sequences, construction of a pseudo-reference genome, HIVdb drug resistance detection, visualization of NGS sequencing results and HIVdb drug resistance detection results, a traceability evolution tree and final output results to complete HIV-1 drug-resistant gene detection. Compared with traditional first-generation Sanger sequencing technology, the system and method adopt the application, can detect drug-resistant sites with a mutation frequency of less than 10%, have higher detection sensitivity, and have important significance for early screening and detection of HIV-1 virus and doctors to formulate more effective drug use schemes after the onset.

Description

Technical Field

[0001] This invention relates to the field of gene detection technology, and in particular to a method for detecting HIV-1 drug resistance genes based on NGS technology. Background Technology

[0002] Currently, there are three main methods for HIV drug resistance testing: genotypic resistance testing (GART), phenotypic resistance testing (PART), and virtual phenotype (vPT). Traditional genotypic resistance testing is based on nucleic acid sequencing. First, the nucleic acid sequences of HIV protease and reverse transcriptase genes are amplified using RT-PCR, and then compared with the nucleic acid sequences of a reference strain to determine if resistance sites have mutated. Sanger sequencing-based testing methods offer high accuracy and comprehensive information, and are considered the gold standard for clinical HIV drug resistance testing.

[0003] However, the Sanger sequencing system currently in use has low sensitivity and is not very effective at detecting mutations below 20%. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides an HIV-1 drug resistance gene detection method based on NGS technology. This method offers high accuracy and sensitivity, enabling the detection of low-frequency drug resistance mutations. It is of great significance for early screening and detection of HIV-1 virus and for doctors to develop more effective medication plans after the onset of the disease.

[0005] The technical solution of the present invention is as follows:

[0006] A method for detecting HIV-1 drug resistance genes based on NGS technology includes the following steps:

[0007] S1. Use FastQC software to perform quality control analysis on NGS data and generate an initial quality control report. After filtering NGS data using FastP software, use FastQC software again to perform quality control analysis on the filtered NGS data and generate a secondary quality control report. At the same time, determine whether the quality control analysis data meets the set requirements. If yes, proceed with subsequent testing. If not, indicate that the NGS data is abnormal and its relationship with the NGS experiment.

[0008] S2. The mem algorithm in BWA software is used to align the NGS data with the reference sequence to generate an alignment file SAM. The SAM file is converted into a BAM file using samtools software. The alignment results with an accuracy of over 99.9% are then extracted from the BAM file as valid information and sorted.

[0009] S3, a consistent genome sequence is constructed by using the converted BAM file and the reference genome sequence by using samtools and ivar software;

[0010] S4, a pseudo-reference genome sequence construction submodule is configured to construct a pseudo-reference genome by using the converted BAM file and the reference genome sequence by using samtools, bcftools and vcfutils software;

[0011] S5, HIVdb information is input into the consistent genome sequence and the pseudo-reference genome sequence for drug resistance detection, and a drug resistance result is obtained;

[0012] S6, NGS sequencing result data containing sequencing depth information, sequencing coverage information and sequencing quality control report are generated, and a consistent HIVdb drug resistance detection result visualization report is output according to the report of the original Stanford HIVdb system;

[0013] S7, the constructed genome sequence is subjected to multiple sequence alignment with the sequences of the HIVdb database (or a local database), and an evolutionary tree is established;

[0014] S8, NGS sequencing result visualization report, HIVdb drug resistance detection visualization report, genome sequence or consistent sequence or pseudo-reference genome FASTA file, alignment file BAM and evolutionary tree picture are generated.

[0015] The beneficial effects of the present application are:

[0016] Compared with the traditional first-generation Sanger sequencing technology, the system and method of the present application can detect drug resistance sites with a mutation frequency of less than 10%, and has higher detection sensitivity, which is of great significance for early screening and detection of HIV-1 virus and doctors to develop more effective drug regimens after the onset. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of the HIV-1 drug resistance gene detection system based on NGS technology according to an embodiment of the present application;

[0018] Figure 2 is a sequencing data diagram according to an embodiment of the present application;

[0019] Figure 3 is a sequencing depth and coverage data diagram according to an embodiment of the present application;

[0020] Figure 4 is a GC content distribution diagram according to an embodiment of the present application;

[0021] Figure 5is a process diagram of the HIV-1 drug resistance gene detection method based on the NGS technology according to the embodiment of the present application;

[0022] Figure 6 is a target gene fragment diagram according to the embodiment of the present application. DETAILED DESCRIPTION

[0023] The embodiments of the present application are further described below with reference to the accompanying drawings.

[0024] A HIV-1 drug resistance gene detection method based on NGS technology, comprising the following steps:

[0025] S1, fastqc software is used for quality control analysis of NGS data and a preliminary quality control report is issued, and after filtering NGS data by fastp software, fastqc software is used again for quality control analysis of the filtered NGS data and a secondary quality control report is issued, at the same time, it is judged whether the quality control analysis data meets the set requirements, if yes, subsequent detection is carried out, if not, it is prompted that the NGS data is abnormal and related to the NGS experiment part;

[0026] S2, the mem algorithm in BWA software is used to align the NGS data with the reference sequence to generate the alignment file SAM, samtools software is used to convert the SAM file into a BAM file, and Q30 is extracted from the BAM file as effective information and sorted, and the specific sequencing data is as shown in Figure 2 The quality score 30 (Q30) indicates that the error rate is 1 / 1000 (which means that each 1000bp sequencing read may contain one error), which indicates that the reliability of the base is 99.9%, and Q30 data usually accounts for about 80% of the total data, but this proportion fluctuates greatly depending on the sample quality, operation level, reagent quality and instrument state, in the present embodiment, the average Q30 obtained by sequencing is greater than or equal to 91%, which meets the sequencing requirements;

[0027] S3, samtools and ivar software are used to construct a consistent genome sequence by using the converted BAM file and the reference genome sequence;

[0028] S4, a pseudo-reference genome sequence construction submodule is used to construct a pseudo-reference genome by using the converted BAM file and the reference genome sequence by using samtools, bcftools and vcfutils software;

[0029] S5, HIVdb information is input into the consistent genome sequence and the pseudo-reference genome sequence for drug resistance detection, and the drug resistance result is obtained;

[0030] S6, generating NGS sequencing result data containing sequencing depth information, sequencing coverage information and sequencing quality control report, the specific sequencing depth and coverage data are as shown in the following table Figure 3 As shown in the table, the sequencing depth (also known as read depth) describes the number of times a given nucleotide in the genome is read in the experiment, the coverage describes the average number of reads aligned or "covered" with the known reference base, the sequencing coverage level usually determines whether a certain degree of confidence can be achieved for variant discovery at a specific base position, in this embodiment, the average sequencing depth of the pol region (2260-5214) is 53813X, and the sequencing coverage reaches 99.93%;

[0031] In addition, the HIVdb drug resistance detection result visualization report of the consistency output according to the original Stanford HIVdb system is also output;

[0032] S7, performing multi-sequence alignment on the constructed genome sequence and the sequence of the HIVdb database (or local database) to establish a phylogenetic tree;

[0033] S8, generating NGS sequencing result visualization report, HIVdb drug resistance detection visualization report, genome sequence or consistent sequence or pseudo-reference genome FASTA file, alignment file BAM, phylogenetic tree picture.

[0034] In addition, the embodiment also performs GC content inspection, and the distribution is as shown in the following table Figure 4 The GC content distribution inspection is to detect the GC content of each sequence, compare the GC content of the sample sequence with the theoretical GC content distribution graph, and detect whether the sample data has pollution and other problems. In theory, the GC content is approximately normally distributed, and the peak value of the normal distribution curve corresponds to the GC content of the genome. If the GC content distribution graph of the sample is not normally distributed, or two or more peaks appear, it indicates that there may be other source DNA sequence pollution or adapter sequence dimer pollution in the sequencing data. In this embodiment, the GC content and the theoretical GC content distribution graph are similar and normally distributed, indicating that there is no other source DNA pollution in this embodiment.

[0035] The present application utilizes high-throughput sequencing technology (NGS) to detect the drug resistance of the pol region of the HIV in the clinical sample on the Illumina Miseq / Miniseq sequencing platform, which can assist the clinician in analyzing the cause of treatment failure and formulating a remedial treatment plan, and the specific process is as shown in the following figure Figure 5

[0036] The sample collected in this embodiment is a plasma sample.

[0037] The target gene fragment detected in this embodiment is as shown in the following table Figure 6 ​As shown, the gene region including the amino acids 4-99 of the protease region, the gene region including the amino acids 38-248 of the reverse transcriptase region and the gene region including the amino acids 50-288 of the integrase strand transfer inhibitor.

[0038] The detection results of the present example are shown in the following table:

[0039]

[0040] The above-described examples only express the specific implementation of the present application, which is described in more detail and in more detail, but it cannot be understood as a limitation on the scope of the patent of the present application. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application.

Claims

1. A method for detecting HIV-1 drug resistance genes based on NGS technology, characterized in that, Includes the following steps: S1. Use FastQC software to perform quality control analysis on NGS data and generate an initial quality control report. After filtering NGS data using FastP software, use FastQC software again to perform quality control analysis on the filtered NGS data and generate a secondary quality control report. At the same time, determine whether the quality control analysis data meets the set requirements. If yes, proceed with subsequent testing. If not, indicate that the NGS data is abnormal and its relationship with the NGS experiment. S2. The mem algorithm in BWA software is used to align the NGS data with the reference sequence to generate an alignment file SAM. The SAM file is converted into a BAM file using samtools software. The alignment results with an accuracy of over 99.9% are then extracted from the BAM file as valid information and sorted. S3. Using samtools and ivar software, a consistent genome sequence was constructed by combining the converted BAM file with the reference genome sequence. S4, the pseudo-reference genome sequence construction submodule, is used to construct a pseudo-reference genome using the converted BAM file and the reference genome sequence with the samtools, bcftools and vcfutils software; S5. Input HIVdb information into the consistent genome sequence and pseudo-reference genome sequence to perform drug resistance detection and obtain drug resistance results; S6. Generate NGS sequencing results data containing sequencing depth information, sequencing coverage information, and sequencing quality control reports, and output a consistent visualization report of HIVdb drug resistance test results. S7. Perform multiple sequence alignment between the constructed genome sequence and the sequence in the HIVdb database to build a phylogenetic tree; S8 generates NGS sequencing result visualization reports, HIVdb drug resistance detection visualization reports, FASTA files of genome sequence or consistency sequence or pseudo-reference genome, BAM alignment files, and phylogenetic tree images.

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