Unknown pathogenic microorganism identification and analysis system and method based on nanopore sequencing

Abstract

The invention discloses an unknown pathogenic microorganism identification and analysis system and method based on nanopore sequencing. Comprising the steps of microorganism database construction and updating, microorganism information standardization labeling, nanopore sequencing analysis, sequencing data quality control, host sequencing read segment removal, microorganism database comparison annotation, congeneric sequencing read segment assembly and microorganism database comparison calculation. For each representative sequencing read segment, calculating and comparing the representative sequencing read segment with any genome sequence in the microorganism database, when a first calculation and comparison condition is met, outputting that the representative sequencing read segment is consistent with the species to which the genome sequence meeting the first calculation and comparison condition belongs, and when a second calculation and comparison condition is met, outputting that the representative sequencing read segment is consistent with the species to which the genome sequence meeting the first calculation and comparison condition belongs. And outputting that the representative sequencing read and the species to which the sequence meeting the second calculation comparison condition belongs are in a homologous relationship, and when the representative sequencing read and each sequence in the microorganism database do not meet the two conditions, outputting that the representative sequencing read is not compared in the microorganism database.

Description

Technical Field

[0001] The present invention relates to the technical field of gene sequencing analysis, and in particular to a system and method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing. Background Art

[0002] With the acceleration of globalization, the flow of people and materials in various countries is frequent. The disease burden caused by many emerging and unconfirmed pathogens is increasing, bringing unprecedented challenges to human health. For example, a series of diseases such as SARS, avian influenza, influenza A (H1N1), influenza H7N9, Ebola virus and monkeypox have shown the potential for rapid spread and serious consequences. These diseases not only pose a threat to public health, but may also cause economic losses and social panic, prompting public health systems in various countries to continuously strengthen the monitoring, rapid identification and response capabilities of pathogens. In the face of the threat of infectious diseases, rapid and accurate pathogen detection is particularly important, which can provide an important basis for effective prevention and control measures.

[0003] At present, traditional pathogenic microorganism identification technologies mainly include morphological observation, cell physiological and biochemical characteristics, bacterial culture typing, gene chips and automated microbial analysis systems. These methods usually rely on the cultivation of microorganisms, have a long cycle, and are extremely sensitive to culture conditions, and are prone to false negative or false positive results. In addition, detection methods based on specific primers, probes or antibodies (such as antigen-antibody reactions and PCR detection) rely on prior knowledge of the sequences of known microorganisms when identifying microorganisms, which makes them limited in effectiveness when dealing with unknown or mutated pathogens. The limitations of these technologies make it extremely difficult to quickly and accurately identify pathogens during infectious disease outbreaks, which affects disease control and the timeliness of intervention measures.

[0004] The emergence of new generation sequencing technology, especially nanopore sequencing technology, provides a new solution for microbial identification. Nanopore sequencing technology has the following significant advantages: no culture is required, which shortens the detection time and is suitable for clinical scenarios that urgently need rapid response; no prior knowledge is required, nanopore sequencing can identify unknown and mutated pathogens, expand the detection range, and enhance the ability to respond to emerging pathogens; the sequencing reads are long, and the technology can obtain complete genomic information, making subsequent genomic analysis and functional research more accurate.

[0005] Metagenomic technology can reveal the genetic information of all microorganisms in the sample, including DNA viruses, bacteria, fungi, parasites, etc., by directly performing high-throughput sequencing on all nucleic acids in the sample. It does not rely on the prior culture of microorganisms or the known sequence information of specific pathogens, so it is particularly suitable for the rapid identification of unknown or rare pathogens.

[0006] Therefore, nanopore sequencing technology has shown important value in many fields such as metagenomic sequencing, pathogen monitoring, genome sequencing of new species, and epigenetic research, and has become a powerful tool for rapid identification of microorganisms.

[0007] Although nanopore sequencing technology has significant advantages in theory, it still faces a series of challenges in practical applications. First, the accuracy of nanopore sequencing is only about 95%, and the sequencing error rate is high, which poses a great challenge to species identification and homology analysis; second, it is necessary to quickly and accurately identify pathogens from complex biological samples, requiring data analysis algorithms and tools to be efficient to ensure that actionable results are generated in a short time; finally, for unknown pathogens that have never been seen in nature or pathogenic microorganisms that are not included in the database, especially overseas infectious diseases, how to identify them from the results is still a major problem encountered in pathogen data analysis. Summary of the invention

[0008] In view of the problems and shortcomings of the prior art, the present invention provides a system and method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing.

[0009] The present invention solves the above technical problems through the following technical solutions:

[0010] The present invention provides an unknown pathogenic microorganism identification and analysis system based on nanopore sequencing, which is characterized in that it includes a microorganism database construction and update module, a microorganism information standardization and annotation module, a nanopore sequencing analysis module, a sequencing data quality control module, a host sequencing read removal module, a microorganism database comparison and annotation module, a same-genus sequencing read assembly module and a microorganism database comparison and calculation module;

[0011] The microbial database construction and update module is used to obtain multiple microbial species information based on a public database, each microbial species information includes at least a genome sequence, perform integrity assessment on each genome sequence to remove genome sequences of poor quality, remove redundant genome sequences according to the similarity principle for each retained genome sequence, identify and merge multiple genome sequences with similarities greater than a set threshold, and retain only one genome sequence of the same species or similar genome sequence after redundancy removal, construct a microbial database based on the microbial species information to which the retained genome sequences belong, and regularly update the microbial database;

[0012] The microbial information standardization and annotation module is used to classify the information of each microbial species in the microbial database according to the level attributes of kingdom, phylum, class, order, family, genus and species in turn, and perform standardized classification and annotation of species, and after traversing each microbial species information, issue a manual correction reminder message to remind manual correction of the automatically annotated microbial database;

[0013] The nanopore sequencing analysis module is used to extract all nucleic acids in the sample to be tested, the nucleic acid is DNA or RNA, the DNA is terminally repaired or the RNA is reverse transcribed and terminally repaired to obtain products, a nanopore sequencing platform is used to build a library and sequence the obtained products, and the sequencing directory of the nanopore sequencing process is monitored in real time, and whenever a new sequencing sequence file is generated, it is used as a sequencing sequence file to be analyzed until the condition for the completion of nanopore sequencing is met or no new sequencing sequence file is generated within a set time, and the sequencing sequence file to be analyzed includes multiple sequencing reads;

[0014] The sequencing data quality control module is used to perform operations of removing read segment adapter contamination, removing low-quality read segments, and removing short read segments on each sequencing read segment in each sequencing sequence file to be analyzed, thereby obtaining a plurality of sequencing read segments retained in the sequencing sequence file to be analyzed;

[0015] The host sequencing read removal module is used to compare each sequencing read retained in each sequencing sequence file to be analyzed with the host genome sequence, and extract the sequencing reads that are not aligned to the host genome sequence;

[0016] The microbial database comparison and annotation module is used to compare each extracted sequencing read with the microbial database, and when the similarity between the sequencing read and the read in the genome sequence of a species reaches a set similarity, the sequencing read is annotated with species at the species level and genus level of the species, and when the similarity between the sequencing read and the read in the genome sequence of a species does not reach the set similarity, the sequencing read is removed, so that each retained sequencing read and its species annotation at the species level and genus level can be obtained;

[0017] The same-genus sequencing read assembly module is used to assemble all sequencing reads annotated to the same genus as a read set for the retained sequencing reads, and to perform consensus sequence assembly on all sequencing reads in the read set of the same genus to obtain a number of representative sequencing reads, thereby obtaining representative sequencing reads under each genus;

[0018] The microbial database comparison calculation module is used to compare the representative sequencing read with any genome sequence in the microbial database for each representative sequencing read. When a first calculation comparison condition is met, it is output that the species to which the representative sequencing read belongs is consistent with the genome sequence that meets the first calculation comparison condition. When a second calculation comparison condition is met, it is output that the species to which the representative sequencing read belongs is homologous to the genome sequence that meets the second calculation comparison condition. When the representative sequencing read and each genome sequence in the microbial database do not meet the first calculation comparison condition and the second calculation comparison condition, information that the representative sequencing read is not compared in the microbial database is output.

[0019] The present invention also provides a method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing, which is characterized in that it comprises the following steps:

[0020] S1. Based on a public database, multiple pieces of microbial species information are obtained, each piece of microbial species information includes at least a genome sequence, each genome sequence is evaluated for integrity to remove genome sequences of poor quality, and for each retained genome sequence, redundant genome sequences are removed according to the similarity principle, and multiple genome sequences with similarities greater than a set threshold are identified and merged. After redundancy removal, only one genome sequence of the same species or similar genome sequence is retained, and a microbial database is constructed based on the microbial species information to which the retained genome sequence belongs;

[0021] S2, classify in sequence according to the level attributes of kingdom, phylum, class, order, family, genus, and species, perform standardized classification annotation on each microbial species information in the microbial database, and after traversing each microbial species information, issue a manual correction reminder message to remind manual correction of the automatically annotated microbial database;

[0022] S3, extracting all nucleic acids in the sample to be tested, the nucleic acid is DNA or RNA, the DNA is terminal-repaired or the RNA is reverse transcribed and terminal-repaired to obtain products, using a nanopore sequencing platform to build a library and sequence the obtained products, and monitoring the sequencing directory of the nanopore sequencing process in real time, and whenever a new sequencing sequence file is generated, it is used as a sequencing sequence file to be analyzed until the condition for the completion of nanopore sequencing is met or no new sequencing sequence file is generated within a set time, and the sequencing sequence file to be analyzed includes multiple sequencing reads;

[0023] S4. For each sequencing sequence file to be analyzed, operations of removing read segment adapter contamination, removing low-quality read segments, and removing short read segments are performed on each sequencing read segment in the sequencing sequence file to be analyzed, thereby obtaining a plurality of sequencing read segments retained in the sequencing sequence file to be analyzed;

[0024] S5. For each sequencing read segment retained in each sequencing sequence file to be analyzed, each sequencing read segment is aligned with the host genome sequence, and the sequencing read segments that are not aligned to the host genome sequence are extracted;

[0025] S6. Compare each extracted sequencing read with the microbial database. When the similarity between the sequencing read and the read in the genome sequence of a species reaches a set similarity, use the species level and genus level of the species to annotate the sequencing read. When the similarity between the sequencing read and the read in the genome sequence of a species does not reach the set similarity, remove the sequencing read, so as to obtain each retained sequencing read and its species annotation at the species level and genus level;

[0026] S7. For the retained sequencing reads, all sequencing reads annotated to the same genus are taken as a read set, and consensus sequences are assembled for all sequencing reads in the read set of the same genus to obtain several representative sequencing reads, thereby obtaining representative sequencing reads under each genus;

[0027] S8. For each representative sequencing read, the representative sequencing read is compared with any genome sequence in the microbial database. When the first comparison condition is met, the representative sequencing read is output to be consistent with the species to which the genome sequence that meets the first comparison condition belongs. When the second comparison condition is met, the representative sequencing read is output to be homologous to the species to which the genome sequence that meets the second comparison condition belongs. When the representative sequencing read and each genome sequence in the microbial database do not meet the first comparison condition and the second comparison condition, the information that the representative sequencing read is not compared in the microbial database is output.

[0028] The positive and progressive effects of the present invention are:

[0029] The present invention provides an unknown pathogenic microorganism identification and analysis system and method based on nanopore sequencing, extracting all nucleic acids in the sample to be tested, then performing end repair on DNA, reverse transcription and end repair on RNA, and carrying out library construction and sequencing on the nanopore sequencing platform to identify the unknown pathogenic microorganism based on the sequencing data. The present invention is applicable to the characteristics of nanopore sequencing such as high sequencing error rate, can accurately identify microorganisms, and is helpful in discovering unknown pathogenic microorganisms.

[0030] The present invention has the following characteristics: 1. Nanopore sequencing data is compared with the microbial database twice and assembled at the genus level, thereby reducing the impact of the high error rate of nanopore sequencing and more accurately identifying homologous microorganisms; 2. The analysis cycle is short. After the entire sequencing is completed, only an additional 3-5 hours are required to complete the entire analysis process; 3. Since there are many unknown pathogens in nature or microorganisms not included in the microbial genome database, this system can prompt the unknown pathogenic microorganisms present in the sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a structural block diagram of an unknown pathogenic microorganism identification and analysis system based on nanopore sequencing according to a preferred embodiment of the present invention.

[0032] Figure 2 The present invention is a flowchart of a method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0034] like Figure 1 As shown, this embodiment provides an unknown pathogenic microorganism identification and analysis system based on nanopore sequencing, which includes a microorganism database construction and update module 1, a microorganism information standardization and annotation module 2, a nanopore sequencing analysis module 3, a sequencing data quality control module 4, a host sequencing read removal module 5, a microorganism database comparison and annotation module 6, a same-genus sequencing read assembly module 7, a microorganism database comparison and calculation module 8 and a microorganism abundance statistics module 9.

[0035] The microbial database construction and update module 1 is used to obtain multiple microbial species information based on public databases (including NCBI, BV-BRC, EMBL, DDB, etc.), each microbial species information includes at least a genome sequence, and each genome sequence is evaluated for integrity to remove genome sequences of poor quality. For each retained genome sequence, redundant genome sequences are removed according to the similarity principle, and multiple genome sequences with a similarity greater than a set threshold (such as 90%) are identified and merged. After redundancy removal, the result is that only one of the same species or similar genome sequence is retained, and a microbial database is constructed based on the microbial species information to which the retained genome sequence belongs; thereafter, after detecting that the public database has been updated, the microbial database is reconstructed using the updated public database, and the microbial information standardization and annotation module is called to perform standardized classification annotation of species on each microbial species information in the reconstructed microbial database.

[0036] In this example, QUAST software (a software used to evaluate the quality of genome assembly, analyzing the integrity, accuracy and consistency of genome assembly) was used to perform integrity assessment on each genome sequence to remove genome sequences of poor quality, such as removing genome sequences with an N base ratio greater than 5% or sequences shorter than 1000 bp.

[0037] In this embodiment, the genome sequence is de-redundanted using CD-HIT software (a tool for efficient clustering and de-redundant sequences, used to process large-scale nucleic acid or protein sequence data to identify and merge similar sequences, thereby reducing redundancy in the data set), and redundant sequences are removed according to a similarity threshold of 90%, ensuring that only one representative sequence is retained for the same species or similar genome sequences, so as to improve the uniqueness and efficiency of the microbial database. The constructed microbial database will be updated based on the update of the public database in the future to ensure that the downloaded genome sequence is consistent with the latest research results, and to promptly remove outdated sequences or repeated genome sequences with changes in the genome sequence in the microbial database.

[0038] The microbial information standardization and annotation module 2 is used to classify the information according to the level attributes of kingdom, phylum, class, order, family, genus and species in turn, and to perform standardized classification and annotation of the species for each microbial species information in the microbial database. After traversing the information of each microbial species, it issues a manual correction reminder message to remind the manual correction of the automatically annotated microbial database.

[0039] In this embodiment, since the microbial annotation information of different databases in the public database is inconsistent, such as the database only includes the name of the microbial species, lacking analytical information. Therefore, it is necessary to standardize the species classification information, and refer to the NCBITaxonomy classification system to standardize the microbial species information, including kingdom, phylum, class, order, family, genus, species, etc. For example, the standardized classification information of Escherichia coli is Bacteria (bacteria); Proteobacteria (Proteobacteria); Gammaproteobacteria (γ-Proteobacteria); Enterobacterales (Enterobacteriales); Enterobacteriaceae (Enterobacteriaceae); Escherichia (Escherichia coli); Escherichia coli (Escherichia coli).

[0040] In this embodiment, a script can be written for automatic processing, and corresponding annotations can be made according to the genome sequence of the species; if the species name is inconsistent with the database, manual processing is required; the microbial database is automatically annotated and manually corrected by referring to the NCBI database.

[0041] The nanopore sequencing analysis module 3 is used to extract all nucleic acids (DNA or RNA) in the sample to be tested, and products are obtained by end-repair of DNA or reverse transcription and end-repair of RNA. The obtained products are constructed using a nanopore sequencing platform (joints required for nanopore sequencing are added to both sides of the molecule) and sequenced, and the sequencing directory of the nanopore sequencing process is monitored in real time. Whenever a new sequencing sequence file is generated, it is used as a sequencing sequence file to be analyzed until the conditions for completing nanopore sequencing are met or no new sequencing sequence files are generated within a set time. Each sequencing sequence file to be analyzed includes multiple sequencing reads.

[0042] In this embodiment, the nanopore sequencing platform monitors the current changes during the sequencing process as the DNA or RNA molecules pass through the nanopore, and converts them into sequence information in real time and outputs them in the fastq file format. The watchdog package of python (Python is a widely used high-level programming language) is used to monitor the sequencing directory in real time (sequencing will generate files, and the sequencing directory is the path to save the sequencing files). Whenever a new sequencing sequence file is generated, the system will automatically call the subsequent analysis process (analysis is performed at any time during the sequencing process, and there are only sequences in the sequencing file-at this time, it will also contain many segments of sequences and unique numbers of sequencing sequences), and the sequence is annotated with microorganisms. Until the sequencing is completed (there will be special information when the normal sequencing is completed, which can be captured) or no new sequencing files are generated for a long time, the nanopore sequencing analysis module automatically exits.

[0043] The sequencing data quality control module 4 is used to remove read segment adapter contamination, remove low-quality read segments, and remove short read segments for each sequencing read segment in the sequencing sequence file to be analyzed, thereby obtaining a number of sequencing read segments retained in the sequencing sequence file to be analyzed, and performing a quality check on the sequencing read segments retained in the sequencing sequence file to be analyzed to confirm that adapter contamination, low-quality read segments, and short read segments have been effectively removed.

[0044] In this embodiment, during the quality control of nanopore sequencing data, Porechop (a software for processing nanopore sequencing data, used to remove adapter sequences from raw sequencing reads) was used to remove adapter contamination, and NanoFilt (a software for filtering and quality control of raw nanopore sequencing data) was used to remove low-quality reads and remove short reads. FastQC (a software for evaluating the quality of high-throughput sequencing data) was then used to perform a quality check on the sequencing reads retained after quality control to confirm that adapter contamination, low-quality reads, and short reads had been effectively removed.

[0045] The host sequencing read removal module 5 is used to align each sequencing read retained in each sequencing sequence file to be analyzed with the host genome sequence, and extract the sequencing reads that are not aligned to the host genome sequence.

[0046] Since sequencing reads contain sequencing reads from the host, and host reads usually account for a large proportion, if they are not removed, the microbial signals will be submerged, resulting in inaccurate analysis of the diversity and composition of the microbial community. At the same time, removing host reads can significantly reduce the amount of data, thereby simplifying subsequent analysis, reducing the computational burden, and improving analysis efficiency and accuracy.

[0047] In this example, minimap2 (a software for fast and efficient sequence alignment between long sequences and reference genomes or other sequences) and samtools (a software for processing alignment files such as SAM and BAM) are used in a forward and backward execution order to remove host sequencing reads. First, minimap2 is used to align sequencing reads to the host genome sequence (same species or homologous); then samtools is used to extract sequencing reads that are not aligned to the host genome sequence for subsequent microbial database alignment analysis.

[0048] The microbial database comparison and annotation module 6 is used to compare each extracted sequencing read with the microbial database. When the similarity between the sequencing read and the read in the genome sequence of a species reaches a set similarity, the sequencing read is annotated with species at the species level and genus level of the species. When the similarity between the sequencing read and the read in the genome sequence of a species does not reach the set similarity, the sequencing read is removed, so that each retained sequencing read and its species annotation at the species level and genus level can be obtained.

[0049] In this embodiment, kraken2 (a fast and accurate microbial classification tool) and bracken (a tool for microbial classification and abundance estimation, used in conjunction with kraken2) software are used for microbial database comparison and species annotation. The host-free sequencing reads are directly compared with the microbial database to perform species and genus level species annotation on bacteria, archaea, eukaryotes and viruses in the sample to be tested. The read-based metagenomic group species annotation method is more comprehensive and accurate, eliminating the resource consumption of the assembly and gene prediction process, and can annotate more low-abundance species. The accuracy of species annotation and species relative abundance is also greatly improved.

[0050] The same-genus sequencing read assembly module 7 is used to, for the retained sequencing reads, treat all sequencing reads annotated to the same genus as a read set, and perform consensus sequence assembly on all sequencing reads in the read set of the same genus to obtain several representative sequencing reads, thereby obtaining representative sequencing reads under each genus.

[0051] Since the error rate of nanopore sequencing is high, the average sequencing error rate can reach 5%. Therefore, when using sequencing reads directly for species annotation, there is a possibility of species annotation errors, such as annotating sequencing reads from a species to the genome of a homologous species with a similar genome, resulting in errors in subsequent species analysis. Therefore, in order to solve the problem of high error rate in nanopore sequencing, this system innovatively introduces the operation of assembling sequencing reads of the same genus, that is, according to the species annotation results at the genus level in the previous step, the sequencing reads of all species aligned to the same genus are extracted separately, and then the canu software (a software specially designed to process long read data and can efficiently generate high-quality genome assemblies) is used to assemble the consensus sequences separately to obtain all representative sequencing reads under the genus reads. The representative reads are the sequences after assembly, which eliminates random errors in sequencing and better represents the true genome sequence of the species. Subsequent use of representative sequences for microbial species annotation can obtain more accurate results.

[0052] The microbial database comparison calculation module 8 is used to compare and compare each representative sequencing read with any genome sequence in the microbial database. When the first comparison condition is met, it is output that the species to which the representative sequencing read belongs is consistent with the genome sequence that meets the first comparison condition. When the second comparison condition is met, it is output that the species to which the representative sequencing read belongs is homologous to the genome sequence that meets the second comparison condition. When the representative sequencing read and each genome sequence in the microbial database do not meet the first and second comparison conditions, it is output that the representative sequencing read is not compared in the microbial database, prompting the user that an unknown pathogen may exist.

[0053] Among them, the first calculation comparison condition: similarity> first similarity threshold, and coverage> first coverage threshold; the second calculation comparison condition: second similarity threshold<similarity≤first similarity threshold, and second coverage threshold<coverage≤first coverage threshold. In this embodiment, the first similarity threshold is 97% and the first coverage threshold is 95%.

[0054] Similarity = the number of bases that a representative sequencing read has in common with a genome sequence in the microbial database / total length of the representative sequencing read.

[0055] Coverage = length of the region where the representative sequencing read is mapped to a certain genome sequence in the microbial database / total length of the representative sequencing read.

[0056] The microbial abundance statistics module 9 is used to count the number of representative sequencing reads of each species under species consistency, and calculate the microbial abundance of a species under species consistency = the number of representative sequencing reads corresponding to the species / the cumulative sum of the number of representative sequencing reads of each species under species consistency.

[0057] like Figure 2 As shown, this embodiment also provides a method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing, which includes the following steps:

[0058] Step 101, constructing a microbial database: obtaining multiple microbial species information based on a public database, each microbial species information at least including a genome sequence, performing integrity assessment on each genome sequence to remove genome sequences of poor quality, removing redundant genome sequences based on the similarity principle for each retained genome sequence, identifying and merging multiple genome sequences with a similarity greater than a set threshold, and retaining only one genome sequence of the same species or similar genome sequence after redundancy removal, and constructing a microbial database based on the microbial species information to which the retained genome sequence belongs.

[0059] Step 102, standardized annotation of microbial information: classify according to the level attributes of kingdom, phylum, class, order, family, genus, and species in turn, and perform standardized classification annotation on the species for each microbial species information in the microbial database, and after traversing the information of each microbial species, issue a manual correction reminder message to remind manual correction of the automatically annotated microbial database.

[0060] After steps 101 and 102, the microbial database is updated and the information is standardized and annotated: after detecting that the public database has been updated, the microbial database is reconstructed using the updated public database, and the information of each microbial species in the reconstructed microbial database is standardized and classified.

[0061] Step 103, nanopore sequencing analysis: extract all nucleic acids in the sample to be tested, the nucleic acid is DNA or RNA, the DNA is terminal-repaired or the RNA is reverse transcribed and terminal-repaired to obtain products, a nanopore sequencing platform is used to build a library and sequence the obtained products, and the sequencing directory of the nanopore sequencing process is monitored in real time, and whenever a new sequencing sequence file is generated, it is used as the sequencing sequence file to be analyzed until the conditions for the completion of nanopore sequencing are met or no new sequencing sequence file is generated within a set time, and the sequencing sequence file to be analyzed includes multiple sequencing reads.

[0062] Step 104, sequencing data quality control: for each sequencing sequence file to be analyzed, operations of removing read adapter contamination, removing low-quality reads, and removing short reads are performed on each sequencing read in the sequencing sequence file to be analyzed, so as to obtain a plurality of sequencing reads retained in the sequencing sequence file to be analyzed; quality checks are performed on the sequencing reads retained in the sequencing sequence file to be analyzed to confirm that adapter contamination, low-quality reads, and short reads have been effectively removed.

[0063] Step 105, host sequencing read removal: for each sequencing read retained in each sequencing sequence file to be analyzed, each sequencing read is aligned with the host genome sequence, and the sequencing reads that are not aligned to the host genome sequence are extracted.

[0064] Step 106, comparison and annotation with microbial database: each extracted sequencing read is compared with the microbial database. When the similarity between the sequencing read and the read in the genome sequence of a species reaches a set similarity, the sequencing read is annotated with species at the species level and genus level of the species. When the similarity between the sequencing read and the read in the genome sequence of a species does not reach the set similarity, the sequencing read is removed, so as to obtain each retained sequencing read and its species annotation at the species level and genus level.

[0065] Step 107, assembling sequencing reads of the same genus: for the retained sequencing reads, all sequencing reads annotated to the same genus are taken as a read set, and all sequencing reads in the read set of the same genus are assembled with consensus sequences to obtain several representative sequencing reads, thereby obtaining representative sequencing reads under each genus.

[0066] Step 108, microbial database comparison calculation: for each representative sequencing read, the representative sequencing read is compared with any genome sequence in the microbial database. When the first comparison condition is met, the representative sequencing read is output to be consistent with the species to which the genome sequence that meets the first comparison condition belongs. When the second comparison condition is met, the representative sequencing read is output to be homologous to the species to which the genome sequence that meets the second comparison condition belongs. When the representative sequencing read and each genome sequence in the microbial database do not meet the first and second comparison conditions, the information that the representative sequencing read is not compared in the microbial database is output.

[0067] Among them, the first calculation and comparison condition is: similarity>first similarity threshold, and coverage>first coverage threshold; the second calculation and comparison condition is: second similarity threshold<similarity≤first similarity threshold, and second coverage threshold<coverage≤first coverage threshold.

[0068] Similarity = the number of bases that the representative sequencing read has in common with a genome sequence in the microbial database / the total length of the representative sequencing reads; Coverage = the length of the region where the representative sequencing read is aligned to a genome sequence in the microbial database / the total length of the representative sequencing reads.

[0069] Step 109, microbial abundance statistics: Count the number of representative sequencing reads of each species under species consistency, and calculate the microbial abundance of a species under species consistency = the number of representative sequencing reads corresponding to the species / the cumulative sum of the number of representative sequencing reads of each species under species consistency.

[0070] This analysis system can be used to quickly and accurately identify pathogenic microorganisms, including bacteria, fungi and viruses. First, the random error rate of nanopore sequencing is high. When performing species identification, it is easy to produce erroneous comparison results for homologous microorganisms, resulting in subsequent errors in the identification of microbial species. Therefore, we have specially developed an analysis process to solve species comparison errors. Secondly, the nanopore sequencing platform can sequence in real time. This analysis system can capture and analyze nanopore sequencing data in real time, shortening the waiting cycle for sequencing and analysis; there are multiple algorithms and software for microbial database comparison. We have optimized the analysis process and can quickly obtain analysis results in a short time. Finally, the analysis system will perform microbial analysis at the genus level, analyze the comparison results of reads in the species contained in the genus, and if there is an unknown pathogen, its reads will be compared to other species of the same genus, but the comparison results will be relatively poor, thus prompting users that there may be unknown pathogens.

[0071] Although the specific embodiments of the present invention are described above, those skilled in the art should understand that these are only examples, and the protection scope of the present invention is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but these changes and modifications all fall within the protection scope of the present invention.

Claims

1. A nanopore sequencing-based unknown pathogenic microorganism identification and analysis system, characterized in that: It includes a microbial database construction and update module, a microbial information standardization and annotation module, a nanopore sequencing analysis module, a sequencing data quality control module, a host sequencing read removal module, a microbial database comparison and annotation module, a same-genus sequencing read assembly module and a microbial database comparison and calculation module; The microbial database construction and update module is used to obtain multiple microbial species information based on a public database, each microbial species information includes at least a genome sequence, perform integrity assessment on each genome sequence to remove genome sequences of poor quality, remove redundant genome sequences according to the similarity principle for each retained genome sequence, identify and merge multiple genome sequences with similarities greater than a set threshold, and retain only one genome sequence of the same species or similar genome sequence after redundancy removal, construct a microbial database based on the microbial species information to which the retained genome sequences belong, and regularly update the microbial database; The microbial information standardization and annotation module is used to classify the information of each microbial species in the microbial database according to the level attributes of kingdom, phylum, class, order, family, genus and species in turn, and perform standardized classification and annotation of species, and after traversing each microbial species information, issue a manual correction reminder message to remind manual correction of the automatically annotated microbial database; The nanopore sequencing analysis module is used to extract all nucleic acids in the sample to be tested, the nucleic acid is DNA or RNA, the DNA is terminally repaired or the RNA is reverse transcribed and terminally repaired to obtain products, a nanopore sequencing platform is used to build a library and sequence the obtained products, and the sequencing directory of the nanopore sequencing process is monitored in real time, and whenever a new sequencing sequence file is generated, it is used as a sequencing sequence file to be analyzed until the condition for the completion of nanopore sequencing is met or no new sequencing sequence file is generated within a set time, and the sequencing sequence file to be analyzed includes multiple sequencing reads; The sequencing data quality control module is used to perform operations of removing read segment adapter contamination, removing low-quality read segments, and removing short read segments on each sequencing read segment in each sequencing sequence file to be analyzed, thereby obtaining a plurality of sequencing read segments retained in the sequencing sequence file to be analyzed; The host sequencing read removal module is used to compare each sequencing read retained in each sequencing sequence file to be analyzed with the host genome sequence, and extract the sequencing reads that are not aligned to the host genome sequence; The microbial database comparison and annotation module is used to compare each extracted sequencing read with the microbial database, and when the similarity between the sequencing read and the read in the genome sequence of a species reaches a set similarity, the sequencing read is annotated with species at the species level and genus level of the species, and when the similarity between the sequencing read and the read in the genome sequence of a species does not reach the set similarity, the sequencing read is removed, so that each retained sequencing read and its species annotation at the species level and genus level can be obtained; The same-genus sequencing read assembly module is used to assemble all sequencing reads annotated to the same genus as a read set for the retained sequencing reads, and to perform consensus sequence assembly on all sequencing reads in the read set of the same genus to obtain a number of representative sequencing reads, thereby obtaining representative sequencing reads under each genus; The microbial database comparison calculation module is used to compare the representative sequencing read with any genome sequence in the microbial database for each representative sequencing read. When a first calculation comparison condition is met, it is output that the species to which the representative sequencing read belongs is consistent with the genome sequence that meets the first calculation comparison condition. When a second calculation comparison condition is met, it is output that the species to which the representative sequencing read belongs is homologous to the genome sequence that meets the second calculation comparison condition. When the representative sequencing read and each genome sequence in the microbial database do not meet the first calculation comparison condition and the second calculation comparison condition, information that the representative sequencing read is not compared in the microbial database is output.

2. The unknown pathogenic microorganism identification and analysis system based on nanopore sequencing according to claim 1, characterized in that: The first calculation comparison condition: similarity>first similarity threshold, and coverage>first coverage threshold; The second calculation and comparison condition: the second similarity threshold <similarity ≤ the first similarity threshold, and the second phase coverage threshold <coverage ≤ the first coverage threshold; Similarity = the number of bases that a representative sequencing read has in common with a genome sequence in the microbial database / total length of the representative sequencing read; Coverage = length of the region where the representative sequencing read is mapped to a certain genome sequence in the microbial database / total length of the representative sequencing read.

3. The unknown pathogenic microorganism identification and analysis system based on nanopore sequencing according to claim 1, characterized in that: The system also includes a microbial abundance statistics module, which is used to count the number of representative sequencing reads of each species under species consistency, and calculate the microbial abundance of a species under species consistency = the number of representative sequencing reads corresponding to the species / the cumulative sum of the number of representative sequencing reads of each species under species consistency.

4. The unknown pathogenic microorganism identification and analysis system based on nanopore sequencing according to claim 1, characterized in that: The sequencing data quality control module is used to perform a quality check on the sequencing reads retained in the sequencing sequence file to be analyzed to confirm that adapter contamination, low-quality reads, and short reads have been effectively removed.

5. The unknown pathogenic microorganism identification and analysis system based on nanopore sequencing according to claim 1, characterized in that: The microbial database construction and update module is used to reconstruct the microbial database using the updated public database after detecting that the public database has been updated, and call the microbial information standardization and annotation module to perform species standardized classification annotation on each microbial species information in the reconstructed microbial database.

6. A method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing, characterized in that: It includes the following steps: S1. Based on a public database, multiple pieces of microbial species information are obtained, each piece of microbial species information includes at least a genome sequence, each genome sequence is evaluated for integrity to remove genome sequences of poor quality, and for each retained genome sequence, redundant genome sequences are removed according to the similarity principle, and multiple genome sequences with similarities greater than a set threshold are identified and merged. After redundancy removal, only one genome sequence of the same species or similar genome sequence is retained, and a microbial database is constructed based on the microbial species information to which the retained genome sequence belongs; S2, classify in sequence according to the level attributes of kingdom, phylum, class, order, family, genus, and species, perform standardized classification annotation on each microbial species information in the microbial database, and after traversing each microbial species information, issue a manual correction reminder message to remind manual correction of the automatically annotated microbial database; S3, extracting all nucleic acids in the sample to be tested, the nucleic acid is DNA or RNA, the DNA is terminal-repaired or the RNA is reverse transcribed and terminal-repaired to obtain products, using a nanopore sequencing platform to build a library and sequence the obtained products, and monitoring the sequencing directory of the nanopore sequencing process in real time, and whenever a new sequencing sequence file is generated, it is used as a sequencing sequence file to be analyzed until the condition for the completion of nanopore sequencing is met or no new sequencing sequence file is generated within a set time, and the sequencing sequence file to be analyzed includes multiple sequencing reads; S4. For each sequencing sequence file to be analyzed, operations of removing read segment adapter contamination, removing low-quality read segments, and removing short read segments are performed on each sequencing read segment in the sequencing sequence file to be analyzed, thereby obtaining a plurality of sequencing read segments retained in the sequencing sequence file to be analyzed; S5. For each sequencing read segment retained in each sequencing sequence file to be analyzed, each sequencing read segment is aligned with the host genome sequence, and the sequencing read segments that are not aligned to the host genome sequence are extracted; S6. Compare each extracted sequencing read with the microbial database. When the similarity between the sequencing read and the read in the genome sequence of a species reaches a set similarity, use the species level and genus level of the species to annotate the sequencing read. When the similarity between the sequencing read and the read in the genome sequence of a species does not reach the set similarity, remove the sequencing read, so as to obtain each retained sequencing read and its species annotation at the species level and genus level; S7. For the retained sequencing reads, all sequencing reads annotated to the same genus are taken as a read set, and consensus sequences are assembled for all sequencing reads in the read set of the same genus to obtain several representative sequencing reads, thereby obtaining representative sequencing reads under each genus; S8. For each representative sequencing read, the representative sequencing read is compared with any genome sequence in the microbial database. When the first comparison condition is met, the representative sequencing read is output to be consistent with the species to which the genome sequence that meets the first comparison condition belongs. When the second comparison condition is met, the representative sequencing read is output to be homologous to the species to which the genome sequence that meets the second comparison condition belongs. When the representative sequencing read and each genome sequence in the microbial database do not meet the first comparison condition and the second comparison condition, the information that the representative sequencing read is not compared in the microbial database is output.

7. The method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing according to claim 6, characterized in that: The first calculation comparison condition: similarity>first similarity threshold, and coverage>first coverage threshold; The second calculation and comparison condition: the second similarity threshold <similarity ≤ the first similarity threshold, and the second phase coverage threshold <coverage ≤ the first coverage threshold; Similarity = the number of bases that a representative sequencing read has in common with a genome sequence in the microbial database / total length of the representative sequencing read; Coverage = length of the region where the representative sequencing read is mapped to a certain genome sequence in the microbial database / total length of the representative sequencing read.

8. The method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing according to claim 6, characterized in that: S9. Count the number of representative sequencing reads of each species under species consistency, and calculate the microbial abundance of a species under species consistency = the number of representative sequencing reads corresponding to the species / the cumulative sum of the number of representative sequencing reads of each species under species consistency.

9. The method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing according to claim 6, characterized in that: In step S4, the quality of the sequencing reads retained in the sequencing sequence file to be analyzed is checked to confirm that adapter contamination, low-quality reads and short reads have been effectively removed.

10. The method for identifying and analyzing unknown pathogenic microorganisms based on nanopore sequencing according to claim 6, characterized in that: After steps S1 and S2, when it is detected that the public database has been updated, the microbial database is reconstructed using the updated public database, and the species-standardized classification annotations are performed on the information of each microbial species in the reconstructed microbial database.

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