Viral genome assembly methods and systems combining reference sequence and de novo assembly

Abstract

The application provides a virus genome splicing method and system combining a reference sequence and de novo splicing, which comprises the following steps: after quality control is performed on sequencing data of a virus genome sequence, host genome sequences in the sequencing data are removed based on a k-mer strategy; the sequencing data is spliced de novo to generate a virus genome preliminary assembly sequence; the virus genome preliminary assembly sequence is annotated based on a blastn tool to perform virus classification annotation, and highly similar virus genome sequences are selected from a virus library as reference sequences according to the virus classification annotation; a dataset is generated according to the reference sequences and the virus genome preliminary assembly sequence, a plurality of sequence clusters are divided according to the similarity between sequences in the dataset, and a consensus sequence is generated; the reads after removing the host are compared with the consensus sequence, the consensus sequence is adjusted according to the comparison result, and a final virus genome sequence is obtained. The application improves the accuracy of virus genome splicing and has strong universality.

Description

Technical Field

[0001] The present invention relates to the technical field of viral genome splicing, and in particular to a viral genome splicing method and system combining reference sequence and de novo splicing. Background Art

[0002] The sequencing and assembly of viral genomes are core tasks in virology research. With the rapid development of next-generation sequencing (NGS) technology, researchers can quickly generate large amounts of viral genome data, promoting research in areas such as viral evolution, pathogenicity, and epidemiology. The precise assembly of viral genomes is fundamental to understanding viral genetic information, predicting its host range, and developing antiviral drugs and vaccines. For example, in recent years, genome sequencing technology has enabled scientists to quickly identify new viruses and reveal their transmission pathways and potential pathogenicity mechanisms through the comparison and annotation of genetic sequences.

[0003] However, although high-throughput sequencing technology has significantly reduced sequencing costs and can generate massive amounts of data in a short period of time, these data are usually short reads that need to be assembled to restore the complete viral genome sequence. Splicing viral genomes often faces the following challenges:

[0004] Short read lengths and complex assembly: Viral genomes are typically small, but their genomic structure is complex and diverse, including highly repetitive regions, non-coding regions (such as 5' and 3' non-coding regions), and overlapping open reading frames. These features increase the difficulty of assembly. Short read sequencing data (typically 100 to 300bp) cannot cover all regions of the viral genome, especially when sequencing depth is insufficient or viral sequence diversity is high, which can easily lead to splicing errors.

[0005] Host genome contamination: Viral sequencing data often come from host samples (such as blood or tissue samples), resulting in a significant amount of host genome sequences contaminating the data. Efficiently removing these host sequences while preserving the viral sequences is a critical step in achieving high-quality assembly. Host sequence contamination not only complicates data processing but also interferes with viral genome assembly and annotation.

[0006] Viral genome diversity and assembly of non-coding regions: Viral genomes are highly diverse, with significant genomic sequence variation between different virus species and strains. Even within the same virus, different regions can have varying mutation rates. This is particularly true for the 5' and 3' non-coding regions, where length and sequence uncertainty are high. Existing de novo assembly tools struggle to accurately determine the length and sequence of these regions, leading to errors in viral genome assembly. Summary of the Invention

[0007] The present invention provides a method and system for assembling viral genomes that combines reference sequences with de novo assembly. This approach addresses the limitations of existing de novo assembly tools and the low accuracy of viral genome assembly. It implements an extended assembly strategy that effectively incorporates reference sequences to reduce errors in de novo assembly and automatically selects appropriate reference sequences to ensure assembly accuracy. Furthermore, the system features efficient host sequence removal and the ability to process low-quality reference sequences.

[0008] The present invention provides a method for assembling viral genomes by combining reference sequences and de novo assembly, comprising:

[0009] After quality control of the sequencing data of the viral genome sequence, the host genome sequence in the sequencing data is removed based on the k-mers strategy;

[0010] De novo assembly of the sequencing data after removal of the host genome sequence to generate a preliminary assembled sequence of the viral genome;

[0011] Performing virus classification annotation on the preliminary assembled viral genome sequence based on the blastn tool, selecting a viral genome sequence from a virus library as a reference sequence based on the virus classification annotation, wherein the similarity between the reference sequence and the preliminary assembled viral genome sequence is greater than a first preset threshold;

[0012] Generating a data set based on the reference sequence and the preliminary assembled viral genome sequence, dividing the data set into multiple sequence clusters based on the similarity between sequences in the data set, with each sequence cluster containing at least one preliminary assembled viral genome sequence, and generating a consensus sequence based on the base with the largest proportion at the same position in the sequence in each sequence cluster;

[0013] The reads in the sequencing data after removing the host genome sequence are aligned with the consensus sequence, and the consensus sequence is adjusted according to the alignment result to obtain the final viral genome sequence.

[0014] According to the present invention, a method for assembling viral genomes by combining reference sequences and de novo assembly, before selecting a viral genome sequence as a reference sequence from a virus library according to the virus classification annotation, further comprises:

[0015] The virus-related sequences in the preliminary assembled viral genome sequence are retained, and the sequences unrelated to the virus are removed.

[0016] According to a viral genome assembly method provided by the present invention that combines reference sequences and de novo assembly, sequences related to the virus in the preliminary assembled viral genome sequence are retained, and sequences unrelated to the virus are removed, comprising:

[0017] Retaining sequences in the preliminary assembled viral genome sequence whose nucleotide similarity is greater than a second preset threshold and whose nucleotide coverage is greater than a third preset threshold as virus-related sequences;

[0018] The sequences other than the virus-related sequences in the preliminary assembled sequence of the viral genome are removed as sequences unrelated to the virus.

[0019] According to a viral genome assembly method combining reference sequence and de novo assembly provided by the present invention, a viral genome sequence is selected from a virus library as a reference sequence according to the virus classification annotation, comprising:

[0020] Selecting, from the virus library according to the virus classification annotation, a viral genome sequence whose similarity to the preliminary assembled viral genome sequence is greater than a first preset threshold;

[0021] After replacing the non-ATGC bases in the selected viral genome sequence with N bases, filtering out viral genome sequences in which the N base ratio exceeds a fourth preset threshold or the number of N bases is greater than a fifth preset threshold;

[0022] The filtered viral genome sequence is used as the reference sequence.

[0023] According to a viral genome assembly method provided by the present invention that combines reference sequences and de novo assembly, quality control of sequencing data of viral genome sequences is performed, comprising:

[0024] Adapter sequences, reads with a quality value less than a sixth preset threshold, and sequences with consecutive repeated bases in the sequencing data are removed.

[0025] According to a viral genome assembly method combining reference sequence and de novo assembly provided by the present invention, the sequencing data is double-end sequencing data or single-end sequencing data.

[0026] The present invention also provides a viral genome assembly system combining reference sequence and de novo assembly, comprising:

[0027] A quality control module, used to perform quality control on the sequencing data of the viral genome sequence;

[0028] A host removal module, used to remove host genome sequences from the sequencing data based on a k-mers strategy;

[0029] A de novo assembly module is used to assemble the sequencing data from scratch after removing the host genome sequence to generate a preliminary assembly sequence of the viral genome;

[0030] A classification annotation module, used for performing virus classification annotation on the preliminary assembled sequence of the viral genome based on the blastn tool;

[0031] A reference sequence pull-down module is used to select a viral genome sequence from the virus library as a reference sequence according to the virus classification annotation, wherein the similarity between the reference sequence and the preliminary assembled viral genome sequence is greater than a first preset threshold;

[0032] A consensus sequence generation module is used to generate a data set based on the reference sequence and the preliminary assembled viral genome sequence, divide the data set into multiple sequence clusters based on the similarity between the sequences in the data set, and each sequence cluster contains at least one preliminary assembled viral genome sequence, and generate a consensus sequence based on the base with the largest proportion at the same position in the sequence in each sequence cluster;

[0033] A reads alignment module, for aligning the reads in the sequencing data after removing the host genome sequence with the consensus sequence;

[0034] The sequence adjustment module is used to adjust the consensus sequence according to the comparison results to obtain the final viral genome sequence.

[0035] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for assembling viral genomes combining reference sequences and de novo assembly as described above is implemented.

[0036] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for assembling viral genomes by combining reference sequences and de novo assembly.

[0037] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described methods for assembling viral genomes by combining reference sequences and de novo assembly.

[0038] The present invention provides a method and system for assembling viral genomes by combining reference sequences and de novo splicing. By combining the reference sequence for extended splicing, de novo splicing is first used to obtain a preliminary assembled sequence of the viral genome. Then, a reference sequence with high similarity to the preliminary assembled sequence of the viral genome is automatically selected. By integrating the preliminary assembled sequence of the viral genome and the reference sequence, base differences in the de novo splicing are reduced, ensuring that the spliced ​​sequence is closer to the actual viral genome and significantly improving the accuracy of the splicing. In addition, the extended splicing of the reference sequence can accurately determine the lengths of the 5' and 3' non-coding regions of the virus, which is of great significance for the functional research of the viral genome. The sequence clustering method based on similarity ensures that each cluster contains at least one preliminary assembled sequence of the viral genome that has been spliced ​​from scratch, and a consensus sequence is generated through multiple sequence alignment. It not only improves the accuracy of assembly, but also generates more consistent splicing results, which is suitable for the splicing of diverse virus strains; by aligning the host-free reads to the generated consensus sequence and correcting the consensus sequence based on the alignment results, the base errors in the splicing are further reduced; compared with the completely de novo splicing method, by combining the extended splicing of the reference sequence, the splicing time and computing resource requirements are significantly reduced, and when processing data with low sequencing depth or poor sequencing quality, high-quality splicing results can still be quickly generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 This is one of the flow diagrams of the viral genome assembly method provided by the present invention that combines reference sequences and de novo assembly;

[0041] Figure 2 This is the second flow chart of the viral genome assembly method combining reference sequence and de novo assembly provided by the present invention;

[0042] Figure 3 Schematic diagram of the viral genome assembly method provided by the present invention combining reference sequence and de novo assembly;

[0043] Figure 4 Schematic diagram of the comparison between the final viral genome sequence and Sanger first-generation sequencing in the viral genome assembly method combining reference sequence and de novo assembly provided by the present invention;

[0044] Figure 5is a structural schematic diagram of a virus genome splicing system provided by the present application, which combines a reference sequence and de novo assembly;

[0045] Figure 6 is a structural schematic diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0047] Currently, virus genome assembly tools based on de novo assembly, such as SPAdes, Velvet, IDBA-UD, etc., have been widely used in virus genome assembly. These tools try to reconstruct the complete genome through complex graph algorithms (such as Hamiltonian path or Eulerian path). However, these tools have the following limitations when dealing with virus genomes:

[0048] Poor splicing accuracy: De novo assembly tools are prone to introduce incorrect splicing results when the sequencing depth is insufficient or the sequencing data quality is poor, especially in the highly repetitive regions or non-coding regions of the virus genome, the difference between the splicing results and the true sequence is large.

[0049] Non-coding region length is difficult to determine: The 5' and 3' non-coding regions of the virus genome are crucial for virus replication and regulation, but the length and sequence of these regions are often difficult to accurately assemble through de novo assembly. In addition, due to the lack of obvious reading frame or protein coding signal in the non-coding region, existing splicing tools are difficult to accurately identify and assemble these regions.

[0050] Incomplete removal of host sequences: Although existing tools provide the function of removing host sequences, the effect is limited, especially for incompletely annotated host genomes, the removal effect is not ideal. This leads to the fact that virus genome assembly data often contains a large amount of host background sequences, which interferes with the accurate identification of viral sequences.

[0051] Reference sequence selection problem: In the reference sequence-based splicing method, how to select a suitable reference sequence is an important factor affecting the splicing accuracy. The difference between the reference sequence and the sequence to be spliced may lead to incorrect sequence splicing, especially in the case of large virus genome diversity, selecting the wrong reference sequence will introduce incorrect base information, affecting the accuracy of the final assembly result.

[0052] To improve assembly accuracy when sequencing depth is insufficient, many studies have begun to incorporate reference sequences for assembly. For example, the QUAST software can assess the quality of assembly results and identify assembly errors by comparing them with reference sequences. However, existing reference sequence-based assembly tools still face several problems: First, how to select a reference sequence similar to the target sequence remains a challenge; second, the differences between the reference sequence and the sequence to be assembled can introduce splicing bias, which can easily lead to incorrect splicing, especially when dealing with virus strains with large variations.

[0053] Removing host genome sequences is a crucial step in viral genome assembly. Common methods for removing host genome sequences rely on alignment tools, such as BWA and Bowtie2, to remove host sequences by aligning sequencing data to a host genome reference sequence. However, this method has drawbacks in the following situations:

[0054] Incomplete annotation of host genomes: For some incompletely annotated host genomes, especially those of novel or rare host species, existing host removal methods are ineffective, resulting in a large number of host sequences not being effectively removed, affecting the splicing and annotation of viral genomes.

[0055] Limitations of the alignment strategy: Some low-complexity regions or sequences with high similarity are prone to alignment errors, and host sequences cannot be accurately eliminated and mixed into the viral splicing results.

[0056] To address these issues, researchers have developed a variety of host removal strategies, such as the k-mers-based host removal method, which, through more efficient alignment and filtering algorithms, can improve host removal to a certain extent. This approach not only processes known host genomes but also effectively removes some unannotated genomes. However, existing technologies still need further optimization to improve their ability to handle complex host backgrounds.

[0057] De novo assembly is a common method for assembling complete viral genomes, particularly when no suitable reference sequence is available or when the viral sequence is unknown. Common de novo assembly tools, such as SPAdes and Velvet, rely on the coverage depth and sequencing quality of short reads. However, low sequencing depth, short read lengths, and highly repetitive regions can lead to assembly errors, particularly in non-coding and low-complexity regions of the viral genome, where assembly results can differ significantly from the true sequence.

[0058] In addition, de novo assembly is particularly difficult for the 5' and 3' non-coding regions, which are usually regulatory regions of the viral genome and have large length and sequence variations. Traditional assembly tools have difficulty accurately determining the length and sequence of these regions.

[0059] To address the shortcomings of de novo assembly, many studies have proposed reference sequence-based assembly methods, which align sequencing data with known reference sequences for extended assembly. This approach has important applications in viral genome assembly, particularly when sequencing depth is insufficient, as reference sequences can be used to correct assembly results. However, selecting the appropriate reference sequence is crucial for successful assembly.

[0060] Regarding the selection of reference sequences, the sequences of different viral strains vary greatly. If the selected reference sequence differs too much from the viral sequence to be assembled, it is easy to introduce incorrect base information, resulting in splicing deviation. Therefore, the reference sequence-based splicing method needs to be able to automatically select a reference sequence with high similarity to the sequence to be spliced ​​and perform quality control on the reference sequence itself.

[0061] Regarding reference sequence quality control, known reference sequences may contain low-quality regions, such as those with non-ATGC bases or a high number of N bases. These low-quality regions can negatively impact the splicing results. Therefore, strict quality control and filtering of the reference sequence must be performed to ensure that the reference sequence used for splicing has a high degree of accuracy.

[0062] The following combination Figure 1 The present invention describes a method for assembling viral genomes by combining reference sequences and de novo assembly, comprising:

[0063] Step 101, after quality control of the sequencing data of the viral genome sequence, the host genome sequence in the sequencing data is removed based on the k-mers strategy;

[0064] First, the sequencing data of the viral genome sequence is quality controlled to ensure the quality of the data used for subsequent analysis.

[0065] Then, based on the k-mers strategy, the host genome sequences in the sequencing data were removed to ensure that the retained sequences were mainly derived from viruses.

[0066] The host removal method based on the k-mers strategy uses host genome reference sequences for alignment to remove host contamination sequences and ensure the integrity of the viral sequence. The host genome reference sequence HOSTdb is a designated host sequence and can include all host sequences at the order, family, and genus levels.

[0067] Host sequence removal technology based on the k-mers strategy can effectively remove host genome sequences during the alignment step, especially for incompletely annotated host species. This technology can handle complex host backgrounds, ensuring that only viral sequences are retained, and reducing the interference of host contamination on subsequent splicing.

[0068] Step 102, performing de novo assembly on the sequencing data after removing the host genome sequence to generate a preliminary assembled sequence of the viral genome;

[0069] After removing the host data, the remaining viral data is spliced ​​from scratch to generate a preliminary assembly sequence of the viral genome.

[0070] Step 103, performing virus classification annotation on the preliminary assembled viral genome sequence based on the blastn tool, selecting a viral genome sequence from the virus library as a reference sequence based on the virus classification annotation, wherein the similarity between the reference sequence and the preliminary assembled viral genome sequence is greater than a first preset threshold;

[0071] The de novo assembled preliminary viral genome was classified and annotated using the blastn tool to ensure that the assembly results were consistent with the viral genome.

[0072] Based on the similarity scores between the preliminary viral genome assembly sequence from the classification annotation results and viral sequences in the NTDB virus database, known viral sequences with high similarity to the preliminary viral genome assembly sequence, such as those with a similarity greater than 99%, are automatically pulled down as reference sequences. Further quality control can be performed on the pulled-down reference sequences to ensure their high quality.

[0073] Step 104: generating a data set based on the reference sequence and the preliminary assembled viral genome sequence, dividing the data set into multiple sequence clusters based on the similarity between sequences in the data set, with each sequence cluster containing at least one preliminary assembled viral genome sequence, and generating a consensus sequence based on the base with the largest proportion at the same position in each sequence cluster;

[0074] The reference sequence and the de novo assembled viral genome sequences were combined into a single dataset and divided into different sequence clusters based on the similarity between sequences in the dataset. For example, sequences with a similarity greater than 90% were grouped into the same sequence cluster to ensure diversity in the assembly.

[0075] Each sequence cluster contains at least one de novo assembled sequence, and through multiple sequence alignment within each sequence cluster, a consensus sequence is generated based on the most common bases at the same position. This not only improves assembly accuracy but also produces more consistent assembly results, making it suitable for assembling diverse viral strains.

[0076] Step 105 , aligning the reads in the sequencing data after removing the host genome sequence with the consensus sequence, adjusting the consensus sequence according to the alignment result, and obtaining the final viral genome sequence.

[0077] The host reads were removed from the sequencing data and aligned to the generated consensus sequence, and the duplicate reads in the consensus sequence were marked to ensure the accuracy of the correction splicing results.

[0078] The consensus sequence is corrected based on the alignment results to ensure the accuracy of the final viral genome sequence output. Specifically, the continuity of reads in the consensus sequence is determined based on repeated reads, and N bases are inserted into the consensus sequence where continuity is insufficient. The base coverage in the consensus sequence is determined, and base substitutions are performed on the consensus sequence where base coverage is insufficient to generate the final viral genome sequence.

[0079] By accurately adding N bases, regions that were not sequenced or could not be accurately assembled are replaced with N bases, effectively concatenating the fragmented assembled sequence. This strategy not only maintains sequence continuity but also provides clear guidance for subsequent further sequencing and genome completion, ensuring that the generated sequence does not contain splicing breaks or missing markers.

[0080] This embodiment uses extended splicing in conjunction with a reference sequence, first using de novo splicing to obtain a preliminary assembled sequence of the viral genome, then automatically selects a reference sequence with high similarity to the preliminary assembled sequence of the viral genome, and integrates the preliminary assembled sequence of the viral genome with the reference sequence, thereby reducing base differences in de novo splicing, ensuring that the spliced ​​sequence is closer to the actual viral genome, and significantly improving the accuracy of splicing. In addition, through extended splicing of the reference sequence, the lengths of the 5' and 3' non-coding regions of the virus can be accurately determined, which is of great significance for functional studies of the viral genome. The similarity-based sequence clustering method ensures that each cluster contains at least one de novo assembled viral genome sequence, and generates a consensus sequence through multiple sequence alignment. It not only improves the accuracy of assembly, but also generates more consistent splicing results, which is suitable for the splicing of diverse virus strains; by aligning the host-free reads to the generated consensus sequence and correcting the consensus sequence based on the alignment results, the base errors in the splicing are further reduced; compared with the completely de novo splicing method, by combining the extended splicing of the reference sequence, the splicing time and computing resource requirements are significantly reduced, and when processing data with low sequencing depth or poor sequencing quality, high-quality splicing results can still be quickly generated.

[0081] Based on the above embodiment, this embodiment further includes, before selecting a viral genome sequence as a reference sequence from the virus library according to the virus classification annotation:

[0082] The virus-related sequences in the preliminary assembled viral genome sequence are retained, and the sequences unrelated to the virus are removed.

[0083] Based on the above embodiment, this embodiment retains virus-related sequences in the preliminary assembled viral genome sequence and removes sequences unrelated to the virus, including:

[0084] Retaining sequences in the preliminary assembled viral genome sequence whose nucleotide similarity is greater than a second preset threshold and whose nucleotide coverage is greater than a third preset threshold as virus-related sequences;

[0085] The sequences other than the virus-related sequences in the preliminary assembled sequence of the viral genome are removed as sequences unrelated to the virus.

[0086] In this embodiment, based on the customized nucleotide similarity (default is greater than 90%) and coverage (default is greater than 50%), sequences related to the virus are retained and other sequences not related to the virus are removed. That is, the second preset threshold can be 90%, and the third preset threshold can be 50%, but are not limited thereto.

[0087] Based on the above embodiments, in this embodiment, a viral genome sequence is selected from a virus library as a reference sequence according to the virus classification annotation, including:

[0088] Selecting, from the virus library according to the virus classification annotation, a viral genome sequence whose similarity to the preliminary assembled viral genome sequence is greater than a first preset threshold;

[0089] After replacing the non-ATGC bases in the selected viral genome sequence with N bases, filtering out viral genome sequences in which the N base ratio exceeds a fourth preset threshold or the number of N bases is greater than a fifth preset threshold;

[0090] The filtered viral genome sequence is used as the reference sequence.

[0091] The fourth preset threshold may be 1%, and the fifth preset threshold may be 100, but are not limited thereto.

[0092] This embodiment provides a strict quality control mechanism for known reference sequences, such as replacing non-ATGC bases with N and filtering out sequences with a large proportion of N bases or a large number of N bases, to ensure the high quality of the reference sequence and effectively avoid the negative impact of low-quality sequences on the splicing results.

[0093] Based on the above embodiments, in this embodiment, the quality control of the sequencing data of the viral genome sequence is performed, including:

[0094] Adapter sequences, reads with a quality value less than a sixth preset threshold, and sequences with consecutive repeated bases in the sequencing data are removed.

[0095] Perform quality control on the input sequencing data, remove adapter sequences, low-quality reads, and low-complexity sequences, and improve the accuracy of subsequent splicing.

[0096] Low-quality reads are reads whose quality values ​​are less than a sixth preset threshold, which may be Q20. Low-complexity sequences include sequences with consecutive repeated bases.

[0097] Based on the above embodiments, the sequencing data in this embodiment is double-end sequencing data or single-end sequencing data.

[0098] This embodiment supports multiple sequencing format inputs, including paired-end and single-end sequencing data, ensuring the compatibility of input data and supporting the acquisition of raw reads from paired-end and single-end sequencing data.

[0099] In practical applications, this embodiment is used to assemble the whole genome sequencing data of a new virus. The specific process is as follows Figure 2 As shown, quality control removed adapter sequences and low-quality regions from the sequencing data, and the host removal module was used to remove host contamination sequences. A preliminary viral genome was generated by de novo assembly, and high-quality assembly results were obtained by extending the reference sequence.

[0100] Figure 3 How to obtain the viral genome by combining the preliminary assembled sequence of the viral genome and the reference sequence. Figure 3 The preliminary assembly shown consists of multiple, discontinuous fragments of varying lengths. By combining highly similar reference genomes of varying lengths, the preliminary assembly can be extended and corrected to generate the final viral genome sequence. The final viral genome sequence can be obtained with a single command-line operation.

[0101] Compared with traditional de novo splicing tools, this embodiment shows higher accuracy and efficiency when processing low-depth sequencing data. By extending the splicing of the reference sequence, it can effectively avoid base differences in splicing and accurately determine the length of the 5' and 3' non-coding regions of the viral genome. The splicing results are highly consistent with the real viral genome (first-generation sequencing results), such as Figure 3 shown.

[0102] Figure 4 The lower line segment in the middle is the result of first-generation sequencing, and the upper line segment is the final viral genome sequence obtained. It can be seen that the 5' and 3' non-coding regions in the final viral genome sequence are longer than those of the first-generation sequencing.

[0103] The virus genome splicing system provided by the present application is described below, and the virus genome splicing system described below can be correspondingly referred to the virus genome splicing method described above.

[0104] As shown in Figure 5 The system comprises a quality control module 501, a host removal module 502, a de novo splicing module 503, a classification annotation module 504, a reference sequence pull-down module 505, a consensus sequence generation module 506, a reads alignment module 507, and a sequence adjustment module 508, wherein:

[0105] The quality control module 501 is configured to perform quality control on the sequencing data of the virus genome sequence;

[0106] The host removal module 502 is configured to remove the host genome sequence in the sequencing data based on a k-mers strategy;

[0107] The de novo splicing module 503 is configured to perform de novo splicing on the sequencing data after removing the host genome sequence, to generate a virus genome preliminary assembly sequence;

[0108] The classification annotation module 504 is configured to perform virus classification annotation on the virus genome preliminary assembly sequence based on a blastn tool;

[0109] The reference sequence pull-down module 505 is configured to select a virus genome sequence from a virus library as a reference sequence according to the virus classification annotation, and the similarity between the reference sequence and the virus genome preliminary assembly sequence is greater than a first preset threshold;

[0110] The consensus sequence generation module 506 is configured to generate a data set according to the reference sequence and the virus genome preliminary assembly sequence, divide the data set into a plurality of sequence clusters according to the similarity between the sequences in the data set, and each sequence cluster contains at least one virus genome preliminary assembly sequence, and generate a consensus sequence according to the base with the highest proportion at the same position in each sequence cluster;

[0111] The reads alignment module 507 is configured to align the reads in the sequencing data after removing the host genome sequence with the consensus sequence;

[0112] The sequence adjustment module 508 is configured to adjust the consensus sequence according to the alignment result to obtain a final virus genome sequence.

[0113] This embodiment uses extended splicing in conjunction with a reference sequence, first using de novo splicing to obtain a preliminary assembled sequence of the viral genome, then automatically selects a reference sequence with high similarity to the preliminary assembled sequence of the viral genome, and integrates the preliminary assembled sequence of the viral genome with the reference sequence, thereby reducing base differences in de novo splicing, ensuring that the spliced ​​sequence is closer to the actual viral genome, and significantly improving the accuracy of splicing. In addition, through extended splicing of the reference sequence, the lengths of the 5' and 3' non-coding regions of the virus can be accurately determined, which is of great significance for functional studies of the viral genome. The similarity-based sequence clustering method ensures that each cluster contains at least one de novo assembled viral genome sequence, and generates a consensus sequence through multiple sequence alignment. It not only improves the accuracy of assembly, but also generates more consistent splicing results, which is suitable for the splicing of diverse virus strains; by aligning the host-free reads to the generated consensus sequence and correcting the consensus sequence based on the alignment results, the base errors in the splicing are further reduced; compared with the completely de novo splicing method, by combining the extended splicing of the reference sequence, the splicing time and computing resource requirements are significantly reduced, and when processing data with low sequencing depth or poor sequencing quality, high-quality splicing results can still be quickly generated.

[0114] Figure 6 An example of a physical structure diagram of an electronic device is shown below. Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communication interface 620, and the memory 630 communicate with each other via the communication bus 640. The processor 610 can call the logic instructions in the memory 630 to execute a viral genome assembly method that combines a reference sequence and de novo assembly, the method comprising: after quality control of the sequencing data of the viral genome sequence, removing the host genome sequence in the sequencing data based on the k-mers strategy; de novo assembly of the sequencing data to generate a preliminary viral genome assembly sequence; annotating the preliminary viral genome assembly sequence with virus classification based on the blastn tool, selecting a viral genome sequence with high similarity from the virus library as a reference sequence based on the virus classification annotation; generating a data set based on the reference sequence and the preliminary viral genome assembly sequence, dividing multiple sequence clusters based on the similarity between the sequences in the data set, and generating a consensus sequence; aligning the reads after removing the host genome sequence with the consensus sequence, adjusting the consensus sequence based on the alignment result, and obtaining a final viral genome sequence.

[0115] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0116] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the viral genome splicing method provided by the above methods that combines reference sequences and de novo splicing. The method includes: after quality control of the sequencing data of the viral genome sequence, removing the host genome sequence in the sequencing data based on the k-mers strategy; de novo splicing the sequencing data to generate a preliminary assembled sequence of the viral genome; annotating the preliminary assembled sequence of the viral genome with virus classification based on the blastn tool, and selecting a viral genome sequence with high similarity from the virus library as a reference sequence based on the virus classification annotation; generating a data set based on the reference sequence and the preliminary assembled sequence of the viral genome, dividing multiple sequence clusters according to the similarity between the sequences in the data set, and generating a consensus sequence; aligning the reads after removing the host genome sequence with the consensus sequence, adjusting the consensus sequence according to the alignment results, and obtaining the final viral genome sequence.

[0117] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for splicing a viral genome by combining a reference sequence and de novo splicing, the method comprising: after quality control of sequencing data of a viral genome sequence, removing host genome sequences in the sequencing data based on a k-mers strategy; de novo splicing the sequencing data to generate a preliminary assembled sequence of the viral genome; performing viral classification annotation on the preliminary assembled sequence of the viral genome based on a blastn tool, and selecting a viral genome sequence with high similarity from a virus library as a reference sequence according to the viral classification annotation; generating a dataset according to the reference sequence and the preliminary assembled sequence of the viral genome, dividing a plurality of sequence clusters according to the similarity between sequences in the dataset, and generating a consensus sequence; aligning reads after removing the host genome sequences with the consensus sequence, and adjusting the consensus sequence according to the alignment result to obtain a final viral genome sequence.

[0118] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0119] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and necessary universal hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0120] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for viral genome assembly combining reference sequence and de novo assembly, characterized in that: include: After quality control of the sequencing data of the viral genome sequence, the host genome sequence in the sequencing data is removed based on the k-mers strategy; De novo assembly of the sequencing data after removal of the host genome sequence to generate a preliminary assembled sequence of the viral genome; Performing virus classification annotation on the preliminary assembled viral genome sequence based on the blastn tool, selecting a viral genome sequence from a virus library as a reference sequence based on the virus classification annotation, wherein the similarity between the reference sequence and the preliminary assembled viral genome sequence is greater than a first preset threshold; Generating a data set based on the reference sequence and the preliminary assembled viral genome sequence, dividing the data set into multiple sequence clusters based on the similarity between sequences in the data set, with each sequence cluster containing at least one preliminary assembled viral genome sequence, and generating a consensus sequence based on the base with the largest proportion at the same position in the sequence in each sequence cluster; The reads in the sequencing data after removing the host genome sequence are aligned with the consensus sequence, and the consensus sequence is adjusted according to the alignment result to obtain the final viral genome sequence.

2. The method for viral genome assembly combining reference sequence and de novo assembly according to claim 1, characterized in that Before selecting a viral genome sequence as a reference sequence from a virus library according to the virus classification annotation, the method further comprises: The virus-related sequences in the preliminary assembled viral genome sequence are retained, and the sequences unrelated to the virus are removed.

3. The method for viral genome assembly combining reference sequence and de novo assembly according to claim 2, characterized in that The virus-related sequences in the preliminary assembled viral genome sequence are retained, and sequences unrelated to the virus are removed, including: Retaining sequences in the preliminary assembled viral genome sequence whose nucleotide similarity is greater than a second preset threshold and whose nucleotide coverage is greater than a third preset threshold as virus-related sequences; The sequences other than the virus-related sequences in the preliminary assembled sequence of the viral genome are removed as sequences unrelated to the virus.

4. The method for viral genome assembly combining reference sequence and de novo assembly according to claim 1, characterized in that Selecting a viral genome sequence as a reference sequence from a virus library according to the virus classification annotation includes: Selecting, from the virus library according to the virus classification annotation, a viral genome sequence whose similarity to the preliminary assembled viral genome sequence is greater than a first preset threshold; After replacing the non-ATGC bases in the selected viral genome sequence with N bases, filtering out viral genome sequences in which the N base ratio exceeds a fourth preset threshold or the number of N bases is greater than a fifth preset threshold; The filtered viral genome sequence is used as the reference sequence.

5. The method for assembling viral genomes by combining reference sequences and de novo assembly according to any one of claims 1 to 4, characterized in that Perform quality control on the sequencing data of viral genome sequences, including: Adapter sequences, reads with a quality value less than a sixth preset threshold, and sequences with consecutive repeated bases in the sequencing data are removed.

6. The method for viral genome assembly combining reference sequence and de novo assembly according to any one of claims 1 to 4, characterized in that The sequencing data is double-end sequencing data or single-end sequencing data.

7. A viral genome assembly system combining reference sequence and de novo assembly, characterized in that: include: A quality control module, used to perform quality control on the sequencing data of the viral genome sequence; A host removal module, used to remove host genome sequences from the sequencing data based on a k-mers strategy; A de novo assembly module is used to assemble the sequencing data from scratch after removing the host genome sequence to generate a preliminary assembly sequence of the viral genome; A classification annotation module, used for performing virus classification annotation on the preliminary assembled sequence of the viral genome based on the blastn tool; A reference sequence pull-down module is used to select a viral genome sequence from the virus library as a reference sequence according to the virus classification annotation, wherein the similarity between the reference sequence and the preliminary assembled viral genome sequence is greater than a first preset threshold; A consensus sequence generation module is used to generate a data set based on the reference sequence and the preliminary assembled viral genome sequence, divide the data set into multiple sequence clusters based on the similarity between the sequences in the data set, and each sequence cluster contains at least one preliminary assembled viral genome sequence, and generate a consensus sequence based on the base with the largest proportion at the same position in the sequence in each sequence cluster; A reads alignment module, for aligning the reads in the sequencing data after removing the host genome sequence with the consensus sequence; The sequence adjustment module is used to adjust the consensus sequence according to the comparison results to obtain the final viral genome sequence.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the viral genome assembly method combining reference sequence and de novo assembly is implemented as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for assembling a viral genome combining a reference sequence and de novo assembly is implemented as claimed in any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for assembling a viral genome combining a reference sequence and de novo assembly is implemented as claimed in any one of claims 1 to 6.

Images