Method and device for identifying species based on full-length 16s rRNA three-generation sequencing data and application

Abstract

The invention provides a species identification method and device based on full-length 16s rRNA three-generation sequencing data and application. The method comprises the following steps: acquiring original nanopore sequencing data of a 16s region of bacteria to be identified; the method comprises the following steps: preprocessing original nanopore sequencing data to obtain a preprocessing sequence in a fastq format, removing joints and barcode sequences at two ends of the preprocessing sequence, then reserving a high-quality sequence, constructing a normalized kmer matrix based on the high-quality sequence, carrying out dimension reduction on the normalized kmer matrix, then carrying out secondary clustering to obtain clustering clusters, merging sequences in the same clustering cluster to obtain a clustering sequence, and finally obtaining a clustering result. Each clustering sequence is compared with a sequence in a full-length 16s sequence database to obtain an optimal species of each clustering cluster, congeneric sequences of the same optimal species in the clustering cluster are reserved, species sequences of the same species are combined to generate a consistent sequence, and identification errors caused by inherent random errors of third-generation sequencing data are reduced; and highly similar species are combined by adopting a similarity evaluation technology, so that the reliability of an identification result is improved.

Description

Technical Field

[0001] The present application relates to the field of species identification, and in particular to a species identification method, device and application based on full-length 16s rRNA third-generation sequencing data. Background Art

[0002] The 16s rRNA gene is the DNA sequence that encodes rRNA on bacteria and exists in the genomes of all bacteria. It is highly conserved and specific, and the gene sequence is long enough (containing about 50 functional domains). With the emergence of PCR technology and the continuous improvement of nucleic acid research technology, 16s rRNA gene detection technology has become a powerful tool for pathogen detection and identification.

[0003] At present, second-generation sequencing data and third-generation sequencing data are commonly used for species identification of 16S rRNA genes. Among them, second-generation sequencing technology usually produces shorter reads. Researchers can use these short reads to analyze species diversity in microbial samples. However, these short reads usually only cover a small part of the 16S rRNA gene, which limits the resolution of species identification. Common analysis tools for second-generation sequencing data are QIIME (Quantitative Insights Into MicrobialEcology) and Mothur, which can be used to analyze microbial communities based on second-generation sequencing technology. It provides a complete set of workflows from raw DNA sequencing data to species identification and ecological statistical analysis, but these methods rely on short reads for specific 16S rRNA regions (such as V3-V4 regions) are compared. Although it is suitable for processing a large number of samples, it may not be able to distinguish highly similar species. Among them, the third-generation sequencing technology, especially nanopore sequencing technology, can read long DNA sequences in real time at the single molecule level. This technology uses a tiny nanopore to detect DNA molecules passing through the hole, thereby generating longer reads than the second-generation sequencing technology. Due to the long read length of the third-generation sequencing data, there are two main identification methods for the existing third-generation full-length 16s sequencing data: 1. The method based on global alignment, the commonly used software is minimap2, and because nanopore sequencing has a high random error, minimap2 is extremely sensitive to sequence insertions and deletions during alignment, so species with high similarity often cannot be distinguished; 2. The method based on kmer LCA, the commonly used software is kraken2, and although Kraken2 is extremely efficient in rapid species classification, due to its unique kmer LCA algorithm, its accuracy and running time are also limited by the complexity and error rate of long read data.

[0004] In addition, the current bioinformatics tools on the market tend to focus on solving specific problems, such as data quality control, alignment, species identification, etc., but lack an integrated process. From the input of sequencing data to the output of species identification, users need to learn and operate multiple software tools, which increases the complexity and time cost of operation. For researchers and technicians without a bioinformatics background, they not only need to understand the functions and operation methods of each scattered tool, but also need to have the knowledge and skills to effectively combine them. This has become an important obstacle in practical applications, limiting the popularization and application of advanced sequencing technologies and species identification methods. Summary of the invention

[0005] The embodiments of the present application provide a species identification method, device and application based on full-length 16s rRNA third-generation sequencing data, and provide a one-click solution that uses dimensionality reduction clustering technology to reduce identification errors caused by inherent random errors in third-generation sequencing data, and uses similarity evaluation technology to merge highly similar species to improve the reliability of identification results.

[0006] In a first aspect, the present application provides a method for species identification based on full-length 16S rRNA nanopore sequencing data, comprising the following steps:

[0007] Obtaining raw nanopore sequencing data of the 16S region of the bacteria to be identified;

[0008] Preprocess the raw nanopore sequencing data to obtain preprocessed sequences in fastq format, remove the adapters and barcode sequences at both ends of the preprocessed sequences, and retain high-quality sequences with a sequence length between 1300-1900 bp;

[0009] A normalized kmer matrix is ​​constructed based on high-quality sequences, where the rows of the normalized kmer matrix are a sequence, the columns are kmers of each type, and the matrix values ​​are the normalized values ​​of the number of kmers that appear in the sequence;

[0010] After reducing the dimension of the normalized kmer matrix, secondary clustering is performed to obtain clusters, and the sequences in the same cluster are merged to obtain cluster sequences;

[0011] Compare each cluster sequence with the sequences in the full-length 16s sequence database to obtain the optimal species of each cluster and retain the same genus sequence as the optimal species in the cluster. If different species in the cluster cannot be distinguished, the sequences of different species are merged to obtain the species sequence of the current cluster.

[0012] Species sequences from the same species were merged to generate consensus sequences.

[0013] In a second aspect, an embodiment of the present application provides a device for species identification based on full-length 16S rRNA nanopore sequencing data, comprising:

[0014] A nanopore sequencing data acquisition unit, used to acquire raw nanopore sequencing data of the 16S region of the bacteria to be identified;

[0015] A nanopore sequencing data processing unit is used to preprocess the raw nanopore sequencing data to obtain a preprocessed sequence in fastq format, remove the adapters and barcode sequences at both ends of the preprocessed sequence, and retain a high-quality sequence with a sequence length between 1300-1900 bp;

[0016] Kmer matrix processing unit, used to construct a normalized kmer matrix based on high-quality sequences, where the normalized kmer matrix has a row as a sequence, a column as each type of kmer, and a matrix value as the normalized value of the number of times the kmer appears in the sequence;

[0017] The dimension reduction clustering unit is used to perform secondary clustering on the normalized kmer matrix after dimension reduction to obtain cluster clusters, and to merge the sequences in the same cluster to obtain cluster sequences;

[0018] The comparison unit is used to compare each cluster sequence with the sequences in the full-length 16s sequence database to obtain the optimal species of each cluster and retain the same genus sequence as the optimal species in the cluster. If different species in the cluster cannot be distinguished, the sequences of different species are merged to obtain the species sequence of the current cluster;

[0019] The consensus sequence generation unit is used to merge species sequences of the same species to generate a consensus sequence.

[0020] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods for species identification of full-length 16S rRNA nanopore sequencing data.

[0021] In a fourth aspect, an embodiment of the present application provides a readable storage medium, wherein a computer program is stored in the readable storage medium, wherein the computer program includes a program code for controlling a process to execute a process, wherein the process includes a method for species identification of full-length 16S rRNA nanopore sequencing data according to any of the above methods.

[0022] The main contributions and innovations of the present invention are as follows:

[0023] The scheme proposed in the embodiment of the present application provides a one-click solution, which greatly simplifies the entire species identification process, allowing researchers to easily process and analyze third-generation 16S nanopore sequencing data. Secondly, by using dimensionality reduction clustering technology to identify different 16S rRNA copies in the same species, the impact of random errors inherent in third-generation sequencing on species identification is effectively reduced to improve the accuracy of identification, and the user is allowed to manually remove noise in the data during dimensionality reduction to further optimize and refine the identification results. In addition, the method also includes a step of evaluating the similarity between species of different species in the reference database, and by merging highly similar species in the identification results, the reliability and practicality of the identification results are further improved. This scheme provides a powerful, accurate and user-friendly tool for species identification of third-generation sequencing data. This comprehensive approach can be used to understand microbial diversity and evolution, and has important scientific research and application value.

[0024] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0026] Figure 1 It is a flow chart of a species identification method based on full-length 16s rRNA third-generation sequencing data according to an embodiment of the present application;

[0027] Figure 2 This is a logic diagram of a species identification method based on full-length 16s rRNA third-generation sequencing data according to an embodiment of the present application.

[0028] Figure 3 This is a structural framework diagram of a species identification device based on full-length 16s rRNA third-generation sequencing data according to an embodiment of the present application;

[0029] Figure 4 It is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0031] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0032] Embodiment 1

[0033] like Figure 1 As shown, this scheme provides a method for species identification based on full-length 16S rRNA nanopore sequencing data, and the species to which it belongs is identified after processing and analyzing the full-length 16S rRNA generated by the third-generation nanopore sequencing, including the following steps:

[0034] Obtaining raw nanopore sequencing data of the 16S region of the bacteria to be identified;

[0035] Preprocess the raw nanopore sequencing data to obtain preprocessed sequences in fastq format, remove the adapters and barcode sequences at both ends of the preprocessed sequences, and retain high-quality sequences with a sequence length between 1300-1900 bp;

[0036] A normalized kmer matrix is ​​constructed based on high-quality sequences, where the rows of the normalized kmer matrix are a sequence, the columns are kmers of each type, and the matrix values ​​are the normalized values ​​of the number of kmers that appear in the sequence;

[0037] After reducing the dimension of the normalized kmer matrix, secondary clustering is performed to obtain clusters, and the sequences in the same cluster are merged to obtain cluster sequences;

[0038] Compare each cluster sequence with the sequences in the full-length 16s sequence database to obtain the optimal species of each cluster and retain the same genus sequence as the optimal species in the cluster. If different species in the cluster cannot be distinguished, the sequences of different species are merged to obtain the species sequence of the current cluster.

[0039] Species sequences from the same species were merged to generate consensus sequences.

[0040] The length of 16S rRNA sequence is about 1542bp, including 10 conserved regions and 9 variable regions (V1-V9). The sequence of the conserved region reflects the relationship between species, while the sequence of the variable region can reflect the differences between species. Among them, V3, V4, and V5 regions have good specificity and complete database information, which is the best choice for bacterial diversity analysis and annotation. This scheme uses the method of dimensionality reduction clustering to identify different 16s rRNA copies of the same species, aiming to improve the accuracy and efficiency of species identification of third-generation nanopore sequencing data.

[0041] In the step of "obtaining raw nanopore sequencing data of the 16s region of the bacteria to be identified", the 16s region of the bacteria to be identified is subjected to targeted enrichment sequencing by nanopore sequencing to obtain raw nanopore sequencing data. However, the raw nanopore sequencing data is electrical signal data in the fast5 or pod5 format, and the data of multiple barcodes (multiple samples) are together, so this solution needs to preprocess the raw nanopore sequencing data to obtain a preprocessed sequence in the fastq format.

[0042] In the step of "preprocessing raw nanopore sequencing data to obtain preprocessed sequences in fastq format", the raw nanopore sequencing data is converted into fastq format data using a data conversion tool, the barcode on the fastq format data is found and the cross-linked sequences are filtered out, and the filtered sequences are split according to the barcode to obtain the preprocessed sequences in fastq format.

[0043] It should be noted that if the original nanopore sequencing data is in fast5 format, guppy is used to convert the original nanopore sequencing data into fastq format data; if the original nanopore sequencing data is in pod5 format, dorado is used to convert the original nanopore sequencing data into fastq format data. In order to ensure the accuracy of the sequence single base as high as possible, sup mode (super accurate mode) is used as the conversion configuration file.

[0044] In the step of "searching for barcodes on fastq format data and filtering out cross-linked sequences", the barcodes on fastq format data are searched under the condition of allowing one base error tolerance, and cross-linked sequences containing different barcodes on a sequencing sequence, cross-linked sequences where the barcode sequence is not at both ends of the sequencing sequence, and cross-linked sequences that do not contain barcodes are removed. The advantage of doing this is that the influence of sequence cross-linking on identification in the experiment can be removed. In the embodiment of this scheme, a barcode.fa file is self-built, and seqkit locate is used to search for barcodes on fastq format data under the condition of allowing one base error tolerance.

[0045] In addition, since the sequence length and the accuracy of the single base of the sequence will directly affect the accuracy of the identification, the sequence needs to be strictly quality controlled to ensure the accuracy of the subsequent identification results. Therefore, this solution also needs to filter and quality control the pre-processed sequence. Correspondingly, in the step of "removing the connectors and barcode sequences at both ends of the pre-processed sequence and retaining the high-quality sequence with a sequence length between 1300-1900bp", the connectors and barcode sequences at both ends of the pre-processed sequence are identified and removed, and the sequence fragments with a length between 1300-1900bp in the sequence after removing the connectors and barcode sequences are retained, and the high-quality sequence is obtained after removing the low-quality sequence fragments.

[0046] This scheme uses porechop to automatically identify and remove the adapters and barcode sequences at both ends of the preprocessed sequence, and uses seqkit seq-m 1300-M 1900 to retain the sequence fragments between 1300-1900bp in length after removing the adapters and barcode sequences, and then uses seqkit seq-Q 12 to remove low-quality sequence fragments and retain high-quality sequences. It should be noted that the reason why this scheme chooses sequence fragments between 1300-1900bp is because the length of bacterial 16s is distributed in this range. If the sequence is too short, the accuracy of the alignment will decrease. If the sequence is too long, it means that the sequence has cross-linking or non-specific amplification, and low-quality sequence fragments need to be removed to avoid generating a series of false positives and false negatives.

[0047] In the step of "constructing a normalized kmer matrix based on high-quality sequences", a kmer matrix is ​​constructed for high-quality sequences to obtain a normalized kmer matrix, and the kmer length is set to 5. This scheme sets the kmer length to 5 to ensure clustering accuracy and obtain a normalized kmer matrix in a shorter time.

[0048] It should be noted that the kmer matrix constructed in this scheme is normalized to obtain a normalized kmer matrix for subsequent dimensionality reduction. Each sequence in the normalized kmer matrix constructed in this scheme is a row, and each type of kmer is a column. The number of times each type of kmer exists in the sequence is calculated and then normalized to obtain the value of the matrix.

[0049] In the step of "reducing the dimension of the normalized kmer matrix", the dimension reduction result after the dimension reduction of the normalized kmer matrix is ​​displayed as a visual scatter plot. In some embodiments, umap / tsne is used to perform data dimension reduction on the normalized kmer matrix to obtain the dimension reduction result, and the minimum number of adjacent samples for dimension reduction is 100 and the minimum distance is 0.01. This solution can use R ggplot and jsecharts to visualize the scatter plot corresponding to the dimension reduction result. The user can manually check whether there are significant outlier sequences on the visualized scatter plot, and process these significant outlier sequences to increase the subsequent clustering accuracy.

[0050] After the normalized kmer matrix is ​​reduced in dimension, the reduced sequences need to be clustered. This solution uses secondary clustering to achieve clustering. Specifically, in the step of "secondary clustering to obtain cluster clusters after reducing the dimension of the normalized kmer matrix", the dimension reduction results generated after the normalized kmer matrix is ​​reduced in dimension are clustered once to obtain preliminary clusters, and cluster outliers are removed with the centroid of the preliminary cluster as the center to obtain cluster clusters.

[0051] In some embodiments, the present solution sets the clustering parameters as the minimum number of clusters to 30, the minimum number of samples to 30, and the cluster separation distance to 0.3, and uses hdbscan to perform the first step of clustering on the dimensionality reduction results to obtain preliminary clusters. Since there will be some cluster outliers in the preliminary clusters, secondary clustering is required to exclude these cluster outliers. The method adopted by the present solution is to calculate the x, y median of the preliminary cluster as the centroid of the preliminary cluster, and remove the points that exceed twice the average centroid distance as cluster outliers.

[0052] In this scheme, the sequences in the clusters obtained after secondary clustering are merged into one fastq file for subsequent analysis. Correspondingly, in "Merge sequences in the same cluster to obtain cluster sequences", the sequences in the same cluster are merged to obtain the same fastq file as the cluster sequence.

[0053] After obtaining the clustered sequence, this scheme evaluates it with the data in the full-length 16s sequence database for species identification, so the file quality of the selected full-length 16s sequence database is also crucial to the quality of species identification. This scheme downloads the 16s-23s amplicon database from MirrorDB, compares the fasta file in the 16s-23s amplicon database with the 16s blast database on NCBI, filters the data with a comparison length of more than 1000 and an evalue less than 1e-5, and retains the position with the highest comparison score as the corresponding 16s position, and uses bedtools getfasta to obtain the full-length 16s sequence from MirrorDB as the full-length 16s sequence database. In other words, the full-length 16s sequence in the full-length 16s sequence database of this scheme is the sequence corresponding to the 16s position with a high score after the 16s-23s amplification data of MirrorDB is compared with the 16s blast on NCBI.

[0054] In addition, due to the high similarity of different species under the same genus level, 16s has limited distinguishing ability, and is prone to false positives or false negatives, so it is necessary to evaluate different species under the same genus, and mark the species that cannot be distinguished to carry out special solutions in the subsequent identification process. Therefore, this scheme calculates similarity for the genus of more than two species under all genus levels in the full-length 16s sequence database, and statistics the similarity between different species of the same genus. If the similarity between different species is higher than the intra-species similarity, it is determined that 16s cannot distinguish the two species. In the embodiment of the present scheme, pairwise2 under Biopython is used to calculate the similarity of the comparison length of any two sequences in the same species under the same genus as the intra-species similarity, and the similarity from the comparison length of any two sequences between different species under the same genus is calculated as the inter-species similarity, if the similarity between different species is higher than the intra-species similarity, it is believed that 16s cannot distinguish the two species, otherwise it is believed that 16s can distinguish the two species.

[0055] Specifically, the calculation formula of the similarity of this solution is: similarity = comparison length / average length of sequence 1 and sequence 2.

[0056] In the step of "aligning each cluster sequence with the sequences in the full-length 16s sequence database to obtain the optimal species of each cluster cluster", the cluster sequence is aligned with the sequences in the full-length 16s sequence database, the alignment results whose sequence alignment length exceeds the set threshold are retained, and the optimal alignment result in the alignment result is taken as the optimal species. In some embodiments, the -O2,12 parameter set by minimap2 is used to align each cluster sequence with the sequence in the full-length 16s sequence database to reduce the soft clip caused by insertions and deletions at both ends of the sequence to restore the true sequence alignment score. This scheme can obtain the optimal species of each cluster sequence through this alignment, and then obtain the species identification result of each cluster sequence.

[0057] It should be noted that, when calculating the alignment score of some sequences, the alignment software's built-in softclip and hardclip algorithms will avoid the penalties caused by mismatches and insertions and deletions at both ends of the sequence. As a result, the alignment length is very short, but the alignment score is very high. Because the alignment length is short, the alignment position may not contain the area that distinguishes species differences, which will lead to false positive detection. Therefore, in order to avoid these "false high-scoring" sequences, this solution adopts two methods. The first is to force the alignment length to be long enough and greater than the set threshold. In some embodiments, the threshold is set to 85%; the second is to set the -O2,12 parameter for the alignment software to reduce the penalty for insertions and deletions and reduce the generation of softclip.

[0058] In the step of "If different species within a cluster cannot be distinguished, the sequences of different species are merged to obtain the species sequence of the current cluster", the inter-species similarity of different species within the same cluster and the intra-species similarity within the species are calculated. If the inter-species similarity is higher than the intra-species similarity, it is considered that the different species within the cluster cannot be distinguished, and the sequences of different species are merged to obtain the species sequence of the current cluster.

[0059] In addition, since bacteria may contain multiple 16s copies in their genome, different clusters may be different 16s copies. Therefore, when generating the final statistical results, clusters identified as the same species need to be merged. In addition to merging clusters of the same species, this step also requires merging different species of the same genus that are difficult to distinguish to generate a species identification statistics table.

[0060] Correspondingly, in the step of "merging species sequences of the same species to generate a consensus sequence", the species sequences of the same species are merged, and a consensus sequence generation method is used to generate a consensus sequence for each merged species sequence. In some embodiments, for each merged cluster, amplicon_sorter-sc 0.99 (similarity higher than 99) is used to generate a consensus sequence for species sequences with a similarity higher than 99%, so that an accurate 16s single copy sequence can be obtained as much as possible to achieve species identification of the bacteria to be identified.

[0061] Figure 2 A logical flow chart of the method for species identification of full-length 16S rRNA nanopore sequencing data of this scheme is given. It can be seen that the implementation logic of this scheme is: processing the MirrorDB database and the ncbi 16s database to obtain a full-length 16s sequence database, judging whether there are multiple species under the same genus in the full-length 16s sequence data, and if so, statistically analyzing the intra-species similarity and inter-species similarity; obtaining the original nanopore sequencing data of the bacteria to be identified, pre-processing the nanopore sequencing data to obtain a pre-processed sequence, performing quality control on the pre-processed sequence to obtain a high-quality sequence, performing kmer5 normalization on the high-quality sequence to obtain a normalized kmer matrix, performing dimensionality reduction clustering on the normalized kmer matrix to obtain a cluster cluster, comparing the cluster cluster with the sequence in the full-length 16s sequence database to obtain an intra-cluster species identification table, and judging whether species under multiple species of the same genus can be distinguished by intra-species similarity and inter-species similarity to obtain a species identification table, and then merging the species sequences of the same species to obtain an identification result table and generate a consensus sequence.

[0062] Embodiment 2

[0063] Based on the same idea, refer to Figure 3 The present application also proposes a device for species identification based on full-length 16S rRNA nanopore sequencing data, comprising:

[0064] A nanopore sequencing data acquisition unit, used to acquire raw nanopore sequencing data of the 16S region of the bacteria to be identified;

[0065] A nanopore sequencing data processing unit is used to preprocess the raw nanopore sequencing data to obtain a preprocessed sequence in fastq format, remove the adapters and barcode sequences at both ends of the preprocessed sequence, and retain a high-quality sequence with a sequence length between 1300-1900 bp;

[0066] Kmer matrix processing unit, used to construct a normalized kmer matrix based on high-quality sequences, where the normalized kmer matrix has a row as a sequence, a column as each type of kmer, and a matrix value as the normalized value of the number of times the kmer appears in the sequence;

[0067] The dimension reduction clustering unit is used to perform secondary clustering on the normalized kmer matrix after dimension reduction to obtain cluster clusters, and to merge the sequences in the same cluster to obtain cluster sequences;

[0068] The comparison unit is used to compare each cluster sequence with the sequences in the full-length 16s sequence database to obtain the optimal species of each cluster and retain the same genus sequence as the optimal species in the cluster. If different species in the cluster cannot be distinguished, the sequences of different species are merged to obtain the species sequence of the current cluster;

[0069] The consensus sequence generation unit is used to merge species sequences of the same species to generate a consensus sequence.

[0070] The contents of the second embodiment that are the same as those of the first embodiment will not be described in detail here.

[0071] Embodiment 3

[0072] This embodiment also provides an electronic device, referring to Figure 4 , including a memory 304 and a processor 302, wherein the memory 304 stores a computer program, and the processor 302 is configured to run the computer program to execute any of the steps in the above-mentioned method embodiment for species identification of full-length 16S rRNA nanopore sequencing data.

[0073] Specifically, the processor 302 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0074] Among them, the memory 304 may include a large capacity memory 304 for data or instructions. By way of example and not limitation, the memory 304 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 304 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 304 may be inside or outside the data processing device. In a specific embodiment, the memory 304 is a non-volatile memory. In a specific embodiment, the memory 304 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (Programmable Read-Only Memory, PROM for short), an erasable PROM (Erasable Programmable Read-Only Memory, EPROM for short), an electrically erasable PROM (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), an electrically alterable ROM (Electrically Alterable Read-Only Memory, EAROM for short) or a flash memory (FLASH) or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (Static Random-Access Memory, abbreviated as SRAM) or a dynamic random access memory (Dynamic Random Access Memory, abbreviated as DRAM), wherein the DRAM can be a fast page mode dynamic random access memory 304 (Fast Page Mode Dynamic Random Access Memory, abbreviated as FPMDRAM), an extended data output dynamic random access memory (Extended Date Out Dynamic Random Access Memory, abbreviated as EDODRAM), a synchronous dynamic random access memory (Synchronous Dynamic Random-Access Memory, abbreviated as SDRAM), etc.

[0075] The memory 304 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 302 .

[0076] The processor 302 reads and executes the computer program instructions stored in the memory 304 to implement any one of the methods for species identification on full-length 16S rRNA nanopore sequencing data in the above embodiments.

[0077] Optionally, the electronic device may further include a transmission device 306 and an input / output device 308 , wherein the transmission device 306 is connected to the processor 302 , and the input / output device 308 is connected to the processor 302 .

[0078] The transmission device 306 can be used to receive or send data via a network. Specific examples of the above-mentioned network may include a wired or wireless network provided by a communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 306 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0079] The input / output device 308 is used to input or output information. In this embodiment, the input information may be raw nanopore sequencing data, etc., and the output information may be species identification results, consensus sequences, etc.

[0080] Optionally, in this embodiment, the processor 302 may be configured to perform the following steps through a computer program:

[0081] Obtaining raw nanopore sequencing data of the 16S region of the bacteria to be identified;

[0082] Preprocess the raw nanopore sequencing data to obtain preprocessed sequences in fastq format, remove the adapters and barcode sequences at both ends of the preprocessed sequences, and retain high-quality sequences with a sequence length between 1300-1900 bp;

[0083] A normalized kmer matrix is ​​constructed based on high-quality sequences, where the rows of the normalized kmer matrix are a sequence, the columns are kmers of each type, and the matrix values ​​are the normalized values ​​of the number of kmers that appear in the sequence;

[0084] After reducing the dimension of the normalized kmer matrix, secondary clustering is performed to obtain clusters, and the sequences in the same cluster are merged to obtain cluster sequences;

[0085] Compare each cluster sequence with the sequences in the full-length 16s sequence database to obtain the optimal species of each cluster and retain the same genus sequence as the optimal species in the cluster. If different species in the cluster cannot be distinguished, the sequences of different species are merged to obtain the species sequence of the current cluster.

[0086] Species sequences from the same species were merged to generate consensus sequences.

[0087] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0088] In general, various embodiments may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the boxes, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0089] Embodiments of the present invention can be implemented by computer software, which is executable by a data processor of a mobile device, such as in a processor entity, or implemented by hardware, or implemented by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros can be stored in any device readable data storage medium, and they include program instructions for performing specific tasks. Computer program products can include one or more computer executable components configured to perform embodiments when the program is running. One or more computer executable components can be at least one software code or a part thereof. In addition, at this point, it should be noted that any box of the logic flow in the figure can represent a program step, or interconnected logic circuits, boxes and functions, or a combination of program steps and logic circuits, boxes and functions. Software can be stored in physical media such as memory chips or storage blocks implemented in processors, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and data variants thereof, CDs. Physical media are non-transient media.

[0090] Those skilled in the art should understand that the technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for species identification based on full-length 16S rRNA nanopore sequencing data, characterized in that: The following steps are involved: Obtaining raw nanopore sequencing data of the 16S region of the bacteria to be identified; Preprocess the raw nanopore sequencing data to obtain preprocessed sequences in fastq format, remove the adapters and barcode sequences at both ends of the preprocessed sequences, and retain high-quality sequences with a sequence length between 1300-1900 bp; A normalized kmer matrix is ​​constructed based on high-quality sequences, where the rows of the normalized kmer matrix are a sequence, the columns are kmers of each type, and the matrix values ​​are the normalized values ​​of the number of kmers that appear in the sequence; After reducing the dimension of the normalized kmer matrix, secondary clustering is performed to obtain clusters, and the sequences in the same cluster are merged to obtain cluster sequences; Compare each cluster sequence with the sequences in the full-length 16s sequence database to obtain the optimal species of each cluster and retain the same genus sequence as the optimal species in the cluster. If different species in the cluster cannot be distinguished, the sequences of different species are merged to obtain the species sequence of the current cluster. Species sequences from the same species were merged to generate consensus sequences.

2. The method for species identification based on full-length 16S rRNA nanopore sequencing data according to claim 1, characterized in that: In the step "Preprocessing raw nanopore sequencing data to obtain preprocessed sequences in fastq format", use the data conversion tool to convert the raw nanopore sequencing data into fastq format data, find the barcode on the fastq format data and filter out the cross-linked sequences, and split the filtered sequences according to the barcode to obtain the preprocessed sequences in fastq format.

3. The method for species identification based on full-length 16S rRNA nanopore sequencing data according to claim 1, characterized in that: In the step "Search for barcodes on fastq format data and filter out cross-linked sequences", the barcodes on fastq format data are searched with a one-base tolerance condition, and cross-linked sequences containing different barcodes on a sequencing sequence, cross-linked sequences whose barcode sequences are not at both ends of the sequencing sequence, and cross-linked sequences that do not contain barcodes are removed.

4. The method for species identification based on full-length 16S rRNA nanopore sequencing data according to claim 1, characterized in that: In the step of "retaining high-quality sequences with a sequence length between 1300-1900 bp after removing the adapters and barcode sequences at both ends of the preprocessed sequence", the adapters and barcode sequences at both ends of the preprocessed sequence are identified and removed, and sequence fragments with a length between 1300-1900 bp in the sequence after removing the adapters and barcode sequences are retained, and high-quality sequences are obtained after removing low-quality sequence fragments.

5. The method for species identification based on full-length 16S rRNA nanopore sequencing data according to claim 1, characterized in that: In the step of "performing secondary clustering on the normalized kmer matrix after dimensionality reduction to obtain cluster clusters", the dimension reduction results generated after the normalized kmer matrix is ​​reduced in dimension are clustered once to obtain preliminary clusters, and cluster outliers are removed with the centroid of the preliminary cluster as the center to obtain cluster clusters.

6. The method for species identification based on full-length 16S rRNA nanopore sequencing data according to claim 1, characterized in that: The full-length 16S sequence in the full-length 16S sequence database is the sequence corresponding to the 16S position with the highest score after the 16S-23S amplification data of MirrorDB is aligned with the 16S blast on NCBI.

7. The method for species identification based on full-length 16S rRNA nanopore sequencing data according to claim 1, characterized in that: In the step "If different species within a cluster cannot be distinguished, the sequences of different species are merged to obtain the species sequence of the current cluster", the inter-species similarity of different species within the same cluster and the intra-species similarity within the species are calculated. If the inter-species similarity is higher than the intra-species similarity, it is considered that the different species within the cluster cannot be distinguished, and the sequences of different species are merged to obtain the species sequence of the current cluster.

8. A device for species identification based on full-length 16S rRNA nanopore sequencing data, characterized in that: include: A nanopore sequencing data acquisition unit, used to acquire raw nanopore sequencing data of the 16S region of the bacteria to be identified; A nanopore sequencing data processing unit is used to preprocess the raw nanopore sequencing data to obtain a preprocessed sequence in fastq format, remove the adapters and barcode sequences at both ends of the preprocessed sequence, and retain a high-quality sequence with a sequence length between 1300-1900 bp; Kmer matrix processing unit, used to construct a normalized kmer matrix based on high-quality sequences, where the normalized kmer matrix has a row as a sequence, a column as each type of kmer, and a matrix value as the normalized value of the number of times the kmer appears in the sequence; The dimension reduction clustering unit is used to perform secondary clustering on the normalized kmer matrix after dimension reduction to obtain cluster clusters, and to merge the sequences in the same cluster to obtain cluster sequences; The comparison unit is used to compare each cluster sequence with the sequences in the full-length 16s sequence database to obtain the optimal species of each cluster and retain the same genus sequence as the optimal species in the cluster. If different species in the cluster cannot be distinguished, the sequences of different species are merged to obtain the species sequence of the current cluster; The consensus sequence generation unit is used to merge species sequences of the same species to generate a consensus sequence.

9. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the method for species identification of full-length 16SrRNA nanopore sequencing data according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which includes a program code for controlling a process to execute a process, wherein the process includes a method for species identification of full-length 16S rRNA nanopore sequencing data according to any one of claims 1 to 7.

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