Method, system, storage medium and device for annotating short-read sequences of macrovirome raw sequencing data

Abstract

The present invention discloses a method, system, storage medium, and device for annotating short-read sequences of raw macrovirome sequencing data. The method, based on a Windows system, comprises the following steps: obtaining raw sequencing data, decompressing the raw sequencing data and extracting short-read sequences, generating a short-read sequence dataset, and calling the BLASTN program to align the dataset into a database; retaining the best alignment result for each short-read sequence, removing those with poor alignment quality from the best alignment results, and then adding the virus species name according to the gene ID; and using a script library to count the number of short-read sequences annotated to each virus species, calculating the standard deviation of the alignment position of the short-read sequences of each virus species on the genome, extracting the corresponding short-read sequence sequences according to the annotated virus species, and outputting a FASTA dataset. The present invention localizes and streamlines the annotation of raw macrovirome data, making it easier for researchers without bioinformatics background knowledge to use.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to a method, system, storage medium and device for annotating short-read sequences of raw sequencing data of a macrovirome. Background Art

[0002] The human microbiome, known as the second genome of the human body, is the general term for the genetic material carried by all microorganisms on the internal and external surfaces of the human body (Sender R, et al, 2016). Countries around the world attach great importance to this emerging field and have launched a number of microbiome research programs. For example, the International Human Microbiome Consortium was initiated by the French Agricultural Research Institute in 2005 and formally established in 2008; in 2008, the Human Microbiome Project (HMP) was launched under the leadership of the National Institutes of Health of the United States (Methé Barbara A, et al, 2012); and the "Metagenomics of Human Intestinal Tract MetaHIT" program launched by the European Union in 2008. Under its Seventh Framework Program, the European Union focused on specific microbiome fields. The implementation of these programs has revealed the complex composition of human microorganisms ( Figure 1 ), including bacteria, fungi, human-infecting viruses, bacteriophages that specifically infect bacteria, etc. These microorganisms interact with each other, maintain the dynamic balance of the human body's internal environment, and affect human health.

[0003] Viral metagenomics is a branch of metagenomics, but differs from traditional metagenomics in that it builds on the concept of metagenomics and applies metagenomic methods to the field of virology, incorporating the characteristics of viruses themselves. In 2002, Breitbart et al. (2002) applied metagenomics to the study of marine viral communities and discovered that bacteriophages were the predominant virome in seawater. This study marked the official application of metagenomics in scientific research. Viral metagenomic sequencing, also known as macroviromes, is a new discipline that has emerged based on metagenomic theory and combined with existing viral molecular biology detection technologies. Macroviromes directly study the genetic material of all viruses in the environment, enabling rapid and accurate identification of the full range of viral components. It plays a crucial role in virus discovery, virus tracing, and microbial early warning. Macroviromes can be applied to human or animal intestinal or blood samples, oceans, soils, and other areas to uncover potential hazards to humans and the environment.

[0004] The raw data of macrovirome sequencing is a compressed text file containing the nucleic acid sequence of the tested library and the sequencing quality score. The nucleic acid sequence needs to be further annotated into the database for virus species identification and subsequent quantitative analysis.

[0005] Since many microbial researchers or clinical workers do not have bioinformatics knowledge and many grassroots laboratories do not have servers, most sequence alignment programs such as metaWRAP, drVM, and VirMAP need to run on the Linux platform and require high computing resources. In other words, the annotation and extraction of macrovirome sequencing data cannot be implemented on the Windows platform. Researchers may spend weeks waiting for third-party companies to issue species identification reports, which has the disadvantages of long cycles and high costs. It is impossible to "quickly identify microbial species using high-throughput sequencing", which greatly hinders scientific research progress and slows down clinical testing. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method, system, storage medium and device for annotating short-read sequences of raw sequencing data of macroviromes.

[0007] The object of the present invention is achieved through the following technical solutions:

[0008] In a first aspect of the present invention, a method for annotating short-read sequences of macrovirome raw sequencing data is provided, based on a Windows system, comprising the following steps:

[0009] Obtain the raw sequencing data, decompress the raw sequencing data and extract the short read sequences to generate a short read sequence dataset, and call the blastn program to align the dataset to the database;

[0010] The best alignment result of each short-read sequence was retained, and the results with poor alignment quality were removed from the best alignment results, and then the virus species name was added according to the gene ID;

[0011] Based on the script library, the number of short read sequences annotated to each virus species is counted, the standard deviation of the alignment position of the short read sequences of each virus species on the genome is calculated, and the corresponding short read sequence sequences are extracted according to the annotated virus species, and the fasta dataset is output.

[0012] Furthermore, the method further includes the following initialization steps:

[0013] Check whether there is a blastn index in a specific directory. If no blastn index exists, further determine whether there is a user-provided fasta dataset in the specific directory. If there is a user-provided fasta dataset, build a new blastn index based on the fasta dataset. Otherwise, further determine whether there is a RefSeq virus database in the specific directory. If there is a RefSeq virus database, build a new blastn index based on the RefSeq virus database. Otherwise, report an error. If a blastn index exists, complete the subsequent annotation steps.

[0014] Furthermore, the script library includes a database self-check script, a strain name addition script and a short read sequence extraction script.

[0015] Furthermore, the quality difference is that the comparison length is less than a first length and / or the similarity is less than a first threshold.

[0016] A second aspect of the present invention provides a system for annotating short-read sequences of macrovirome raw sequencing data, based on a Windows system, comprising:

[0017] Blastn analysis module: used to obtain raw sequencing data, decompress the raw sequencing data and extract short read sequences, generate a short read sequence dataset, call the blastn program to align the dataset into the database; retain the best alignment result of each short read sequence, remove the results with poor alignment quality in the best alignment results, and then add the virus species name according to the gene ID;

[0018] Short read sequence extraction and statistical analysis module: Based on the script library, it is used to count the number of short read sequences annotated to each virus species, calculate the standard deviation of the alignment position of the short read sequences of each virus species on the genome, and extract the corresponding short read sequence sequences according to the annotated virus species, and output the fasta data set.

[0019] Furthermore, the system further comprises:

[0020] The database update module is used to detect whether there is a blastn index in a specific directory. If no blastn index exists, it further determines whether there is a user-provided fasta dataset in the specific directory. If the user-provided fasta dataset exists, a new blastn index is constructed based on the fasta dataset. Otherwise, it further determines whether there is a RefSeq virus database in the specific directory. If the RefSeq virus database exists, a new blastn index is constructed based on the RefSeq virus database. Otherwise, an error is reported. If the blastn index exists, the subsequent annotation steps are completed.

[0021] Furthermore, the script library includes a database self-check script, a strain name addition script and a short read sequence extraction script.

[0022] Furthermore, the quality difference is that the comparison length is less than a first length and / or the similarity is less than a first threshold.

[0023] A third aspect of the present invention provides a storage medium having computer instructions stored thereon, which, when executed, execute the steps of the method for annotating short-read sequences of macrovirome raw sequencing data.

[0024] In a fourth aspect of the present invention, a device is provided, comprising a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, and when the processor executes the computer instructions, the steps of the method for annotating short read sequences of macrovirome raw sequencing data are performed.

[0025] The beneficial effects of the present invention are:

[0026] (1) In an exemplary embodiment of the present invention, the localization and portability of macrovirome raw data annotation are achieved, making it easy for researchers without bioinformatics background to use. In the current situation where mainframe computers are not yet popular, the problem of waiting for a long analysis period and high fees from third-party companies is solved, providing a fast, simple and cost-effective method for macrovirome raw data annotation. Specifically, short-read annotation of viral sequences from raw macrovirome sequencing data can be performed on a Windows PC without queuing on third-party servers, saving significant turnaround time and facilitating rapid testing. The system boasts a very high recall rate: 100% for two viral mock communities (VMCs) (BioProject IDs PRJNA431646 and PRJNA395784), consistent with Nadim J Ajami's VirMap, meeting comprehensive testing requirements. The system is easy to use and fully automated, making it suitable for researchers or clinicians without a bioinformatics background. Short-read sequences are automatically extracted based on annotated viral species, facilitating subsequent research. The system requires minimal computing resources, enabling analysis on most Windows PCs. On a Windows 7 PC equipped with a 2.8GHz dual-core Intel(R) Celeron(R) CPU G3900 and 4Gb of memory, analyzing 10Gb of macrovirome sequencing data takes only 1.7 hours.

[0027] (2) In another exemplary embodiment of the present invention, before use, the system checks whether the user-provided FASTA dataset exists in a specific directory. If not, the system uses the built-in RefSeq virus database. If it exists, a new Blastn index is constructed for the user-provided FASTA dataset. This approach facilitates database updates and allows users to easily construct databases using their own datasets.

[0028] (3) In another exemplary embodiment of the present invention, the script library includes a database self-check script, a strain name addition script, and a short read sequence extraction script. The script is used to achieve matching of multiple steps, which is used to better realize automated processing and clearer conditioning.

[0029] (4) In another exemplary embodiment of the present invention, the quality difference is that the comparison length is less than the first length and / or the similarity is less than the first threshold, which has the effect of filtering out comparison results with poor matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The present invention is a flowchart of a method provided in an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention is described clearly and completely below with reference to the accompanying drawings. It is apparent that the embodiments described are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0032] In the description of the present invention, it should be noted that the directions or positional relationships indicated by "center", "up", "down", "left", "right", "vertical", "horizontal", "inside" and "outside" are directions or positional relationships based on the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0033] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention.

[0034] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0035] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of" or "when" or "in response to a determination". In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance.

[0036] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0037] The raw data of macrovirome sequencing is a compressed text file, the content of which contains the nucleic acid sequence of the tested library and the sequencing quality score. The nucleic acid sequence needs to be further annotated into the database for the identification of virus species and subsequent quantitative analysis. In the prior art, since many microbial researchers or clinical workers do not have bioinformatics knowledge, many grassroots laboratories do not have servers. Most sequence alignment programs such as metaWRAP, drVM, and VirMAP need to be run on the Linux platform and require higher computing resources. That is, the annotation and extraction of macrovirome sequencing data cannot be implemented on the Windows platform. Researchers may spend weeks waiting for a third-party company to issue a species identification report, which has the disadvantages of long cycle and high cost. It is impossible to achieve the ability to "quickly identify microbial species using high-throughput sequencing", which greatly hinders scientific research progress and slows down clinical testing. Therefore, the following exemplary embodiments will explain the above problems:

[0038] See also Figure 1 , Figure 1 A flowchart of a method for annotating short-read sequences of macrovirome raw sequencing data is shown in accordance with an exemplary embodiment of the present invention, based on a Windows system, comprising the following steps:

[0039] S02: Obtain the original sequencing data, decompress the original sequencing data and extract the short read sequence to generate a short read sequence dataset, and call the blastn program to align the dataset to the database;

[0040] S04: retain the best alignment result of each short-read sequence, remove the results with poor alignment quality in the best alignment results, and then add the virus species name according to the gene ID;

[0041] S06: Based on the script library, count the number of short read sequences annotated to each virus species, calculate the standard deviation of the alignment position of the short read sequences of each virus species on the genome, and extract the corresponding short read sequence sequences according to the annotated virus species, and output the fasta dataset.

[0042] In step S02, the raw sequencing data is in fastq.gz format (a gzip-compressed text file), and the decompression includes decompression and format conversion from fastq.gz to fasta. After extracting short read sequences (without extracting sequencing quality scores), a short read sequence dataset (fasta format) is generated, and finally the blastn program is called to align the dataset to the database;

[0043] Then in step S04, since there may be multiple alignment results for a short read sequence, in this exemplary embodiment, the best alignment result of each short read sequence is retained, and the results with poor alignment quality in the best alignment results are removed (it should be noted that even for the best alignment result, the alignment quality (i.e., alignment length and alignment similarity) may be very low. If the results with low alignment quality are not removed, the false positive rate of identification will increase. For example, 133106–133255bp of chromosome 11q of the human genome has certain similarities with the genome of the BeAn58058 virus and may be annotated as the virus, but the alignment quality is usually very low), and then the virus species name (strain name) is added according to the gene ID (it should be noted that geneID is the unique strain identity ID given by NCBI, which is a string of numbers; the result output after the blastn program alignment only contains the gene ID, not the virus strain name, so the strain name needs to be added separately).

[0044] Finally, in step S06, based on the script library, the number of short read sequences annotated to each virus species is counted, and the standard deviation of the alignment position of the short read sequences of each virus species on the genome is calculated (it should be noted that if the short read sequences are evenly distributed on the reference genome, the standard deviation of the alignment position will be large; if the short read sequence abundance is high and the standard deviation of the alignment position is small, it means that these short read sequences may have been mistakenly aligned to a highly homologous fragment. The purpose of calculating the standard deviation is to help the user determine whether the result is a false positive. However, this method only calculates the standard deviation, and does not automatically determine and remove false positives, because the actual detection is more complicated, such as some important pathogens. (such as avian influenza) may be carried in small quantities by certain wild animals but not reproduced in large quantities, so fewer short read sequences are measured, or segmented double-stranded RNA viruses (such as rotavirus) are prone to failure in adding connectors during library construction, resulting in only a few short read sequences being measured... These low-abundance species may be ignored as contamination. Therefore, the method of this exemplary embodiment does not determine and screen out false positives, and aims to retain more information. It retains more information by reporting the average alignment similarity, the number of short read sequences, and the standard deviation of the alignment position to meet the subsequent "comprehensive detection, all that should be tested" requirements), and extracts the corresponding short read sequence sequences according to the annotated virus species, and outputs the FASTA data set. This facilitates subsequent primer design, and classic programs such as Seqman can also be used to assemble short read sequences.

[0045] Among them, in this exemplary embodiment, the method realizes a pipeline for fully automatic annotation of macrovirome sequencing short read sequences in a Windows system, which is written in shell and perl, and provides all the required environments through Gitbash on Windows (that is, the shell and perl environments can be provided by the Git for Windows program), thereby solving the problem that the existing technology cannot be run on Windows computers, and the programs such as diamond, bowtie, and virfinder in the pipeline must be run on the Linux platform, which is not convenient for researchers without computer background knowledge to use. In other words, this exemplary embodiment realizes the localization and lightweight annotation of macrovirome raw data, making it convenient for researchers without bioinformatics background knowledge to use; in the current situation where mainframes are not yet popular, it solves the problems of long analysis cycles and high fees for third-party companies, and provides a fast, simple, and cost-saving method for annotating macrovirome raw data.

[0046] Among them, it should be noted that the second generation sequencing has been around for 17 years, but there is still no software that can realize the annotation of macrovirome sequencing data on the Windows platform. The reason is that the Linux system without a desktop has higher performance than Windows. The problem of platform conversion has not been solved. Since most users use the Windows system, although the annotation of macrovirome raw data is realized in the Windows system, it does not have technical differences or advantages, but it has application necessity. There are many programs called by pipelines in the prior art, such as using cutadapt and trimmomatic software to remove the linker sequence. This step significantly improves the assembly quality of contigs, but has no effect on the basic local alignment search of short read sequences. The pipeline of this exemplary embodiment is not based on assembly, so this step is not required. The pipeline of the prior art uses the soap.coverage program to count the abundance, while the method of this exemplary embodiment directly counts the number of short read sequences aligned to each viral genome in the Blastn results to obtain the abundance, with fewer redundant steps.

[0047] Therefore, this exemplary embodiment has the following advantages: 1. Annotation of viral short-read sequences from raw macrovirome sequencing data can be performed on a Windows personal computer without queuing on a third-party company's server, saving a lot of turnaround time and facilitating the satisfaction of "rapid detection" requirements; 2. A very high recall rate: the recall rate for two viral mock communities (VMCs) (BioProject IDs PRJNA431646 and PRJNA395784) was 100%, consistent with Nadim J Ajami's VirMap, facilitating the satisfaction of "comprehensive detection" requirements; 3. Simple and fully automatic operation, making it convenient for researchers or clinicians without a bioinformatics background to use; 4. Short-read sequences are automatically extracted according to the annotated viral species, facilitating subsequent research; 5. Low computing resource requirements: analysis can be performed on most Windows personal computers. Analysis of 10Gb of macrovirome sequencing data took only 1.7 hours on a Windows 7 computer equipped with a 2.8GHz dual-core Intel(R) Celeron(R) CPU G3900 and 4Gb of memory.

[0048] It should be noted that Perl stands for Practical Extraction and Report Language. It was designed, continuously updated, and maintained by Larry Wall for programming in the UNIX environment. Perl possesses the power and flexibility of high-level languages ​​such as C. In fact, many of its features are borrowed from C. Like scripting languages, Perl does not require a compiler or linker to run code; all you need to do is write the program and tell Perl to run it. This makes Perl ideal for quickly solving small programming problems and for prototyping and testing potential solutions for larger projects. Perl offers all the functionality of scripting languages ​​such as sed and awk, but also many that they lack. Perl also supports translators from sed to Perl and from awd to Perl. Perl is as powerful as C and as convenient as scripting languages ​​like awk and sed. The shell is the user interface, providing an environment for entering commands and parameters and obtaining the results of command execution. Generally speaking, shell is an interpretive "program" that is specifically used to interpret and execute various commands; perl is a scripting language, and script programs written in it can be interpreted and executed by shell.

[0049] Git For Windows focuses on providing a lightweight, native toolset that brings the full feature set of Git SCM to Windows while providing a suitable user interface for Git users to use Git on Windows. Git For Windows mainly provides two tools: one is the Git Bash command line and the other is the Git GUI graphical user interface.

[0050] The fastq format is a sequence file containing quality values, where q stands for quality. It is generally used to store raw sequencing data and has an extension of fastq or fq. The following is a common sequence format for the fastq format: The first line: Begins with '@' and is the name of the short read sequence. This string is converted from the status information during sequencing and has no spaces in between. It is a unique identifier for each short read sequence and will not appear repeatedly in the same FASTQ file or even in different fastq files. The second line: The sequence of the sequencing short read sequence, composed of the five letters A, C, G, T, and N, is the DNA sequence of real interest. N represents the bases that could not be identified during sequencing. The third line: Begins with '+'. In older versions of fastq files, the information on the first line was directly repeated, but now it is generally not added (to save storage space). The fourth line: The quality value of the sequencing short read sequence. This is as important as the base information on the second line. It describes the reliability of each sequenced base and is represented by an ASCII code. Currently, most software can directly process compressed formats. Therefore, the general fastq format is presented in compressed format with the extension fq.gz. If compression or decompression is required, it can be performed.

[0051] The fasta format is a text-based format for representing nucleic acid sequences or polypeptide sequences. Nucleic acids or amino acids are represented by single letters, and sequence names and comments can be added before the sequence. This format has become a standard in the field of bioinformatics. The first line of a fasta format file is an arbitrary text description starting with a greater than sign ">" (more commonly used) or a semicolon ";", which is used for sequence labeling. Starting from the second line is the sequence itself, only established nucleotide or amino acid coding symbols are allowed (see supported code types). Usually nucleotide symbols can be uppercase or lowercase, while amino acids are often capitalized. When using, please note that some programs have clear requirements for uppercase and lowercase letters. Generally, there are 60 to 80 letters per line.

[0052] The blast program includes the following five: (1) blastp: query the protein sequence to be queried and its complementary sequence together against the protein sequence database; (2) blastn: query the nucleic acid sequence to be queried and its complementary sequence together against the nucleic acid sequence database; (3) blastx: first translate the nucleic acid sequence to be queried into a protein sequence according to six readable frames (read three bases forward and three bases backward one by one), and then query the protein sequence database with the translation result; (4) tblastn: first translate the nucleic acid sequence in the nucleic acid sequence database into a protein sequence according to six readable frames, and then query the translation result with the protein sequence to be queried and its complementary sequence; (5) tblastx: first translate the nucleic acid sequence to be queried and the nucleic acid sequence in the nucleic acid sequence database into a protein sequence according to six readable frames, and then query the two translation results at the protein level. Among them, step S02 of this exemplary embodiment adopts the method in (2).

[0053] In addition, the prior art uses an assembly method, which consumes a lot of computation and is not convenient to run on most personal computers. In this exemplary embodiment, this method realizes the annotation of short-read sequences in macrovirome sequencing data, which retains richer information than the annotation based on assembly. The short-read sequences of some low-abundance viruses are discontinuous, and there are no overlaps between these short-read sequences, so they cannot be assembled, and the resulting contigs will not contain this part of the virus. If annotation is performed based on the assembly method, the information of these viruses will be lost, and the computational cost is relatively high. The main purpose of assembly in the prior art is to obtain a complete gene (group) sequence and perform subsequent analysis, such as predicting viral sequences...which is different from the purpose of this pipeline.

[0054] More preferably, in an exemplary embodiment, Figure 1 As shown, the method further includes the following steps:

[0055] S01: Check whether there is a blastn index in a specific directory. If no blastn index exists, further determine whether there is a user-provided fasta dataset in the specific directory. If there is a user-provided fasta dataset, build a new blastn index based on the fasta dataset. Otherwise, further determine whether there is a RefSeq virus database in the specific directory. If there is a RefSeq virus database, build a new blastn index based on the RefSeq virus database. Otherwise, report an error. If a blastn index exists, complete the subsequent annotation steps.

[0056] Specifically, in this step, the specific directory can be the / databases directory created by the Windows system, and the RefSeq virus database can be the RefSeq virus gene sequence library of the year (for example, the 2021 RefSeq virus gene sequence library, which can include all non-bacteriophage viruses); and the construction of a new Blastn index for the fasta data set provided by the user can be implemented using the makeblastdb program.

[0057] Among them, there can be the following types: if there is a blastn index (that is, a database file) in the / databases directory, proceed directly to the next step (step S02); if there is no blastn index, the program uses its own 2021 RefSeq viral gene sequence dataset to build a blastn index, and then proceeds to the next step (step S02); if there is no blastn index and there is a user-provided fasta dataset with gene id, the program will integrate this dataset and the original dataset to build a blastn index, and then proceed to the next step (step S02); if there is neither any dataset (that is, there is no user-provided fasta dataset with gene id, nor is there a self-provided 2021 RefSeq viral gene sequence dataset) nor a blastn index, the program will report an error (caused by user deletion).

[0058] Specifically, in this step, it is extremely convenient to update and make your own database: delete the original database in the / databases directory and put your own data set (Fasta format, with gene ID) into the directory to automatically integrate the original data set the next time you run the program. The purpose of updating the database is to annotate newly emerging viruses more accurately; there are many application scenarios for homemade databases. For example, some viruses (such as porcine astrovirus) have rich genetic diversity. When studying mixed infections of the virus, users can download different strains of the same type from NCBI to make their own indexes, which can increase the resolution of virus identification; for example, when it is necessary to search for virulence genes of highly toxic strains or key genes that determine special phenotypes, using a homemade database can greatly reduce computing power and increase search accuracy. For example, the phage sequence data set can be directly placed in the folder where the database is stored to complete the database replacement, and then the phages in the high-throughput sequencing raw data can be directly annotated.

[0059] More preferably, in an exemplary embodiment, the script library includes a database self-check script, a strain name addition script, and a short read sequence extraction script.

[0060] The database-built script implements step S01, the strain name addition script implements step S04, and the short-read sequence extraction script implements step S02. Furthermore, each script is required for extracting short-read sequences in step S06. Because this exemplary embodiment is implemented on a Windows system, using a Perl script is efficient, and using script matching provides a more organized approach.

[0061] More preferably, in an exemplary embodiment, the quality difference is that the comparison length is less than a first length and / or the similarity is less than a first threshold.

[0062] In one exemplary embodiment, the specific condition that the comparison length is less than the first length is that the comparison length is less than 80 bp, and the specific condition that the similarity is less than the first threshold is that the similarity is less than 80%; the length and similarity can be implemented in the form of "and / or".

[0063] It should be noted that short-read sequences for next-generation sequencing are generally 100-250bp in length. Based on experience, alignments with short-read lengths less than 80bp and nucleotide similarity less than 80% are generally false positives. Similarity is calculated as: number of identical bases in the aligned segment / length of the aligned segment × 100%.

[0064] With the same inventive concept as the above exemplary embodiment, another exemplary embodiment of the present invention provides a system for annotating short-read sequences of raw sequencing data of a macrovirus group, based on a Windows system, comprising:

[0065] Blastn analysis module: used to obtain raw sequencing data, decompress the raw sequencing data and extract short read sequences, generate a short read sequence dataset, call the blastn program to align the dataset into the database; retain the best alignment result of each short read sequence, remove the results with poor alignment quality in the best alignment results, and then add the virus species name according to the gene ID;

[0066] Short read sequence extraction and statistical analysis module: Based on the script library, it is used to count the number of short read sequences annotated to each virus species, calculate the standard deviation of the alignment position of the short read sequences of each virus species on the genome, and extract the corresponding short read sequence sequences according to the annotated virus species, and output the fasta data set.

[0067] Similar to the exemplary embodiment of the method, a fully automated pipeline for annotating short-read sequences of macrovirome sequencing is implemented in a Windows system. It is written in shell and perl, and all required environments are provided by Gitbash on Windows (i.e., the shell and perl environments can be provided by the Git for Windows program). This solves the problem that the existing technology cannot be run on Windows computers, and programs such as diamond, bowtie, and virfinder in the pipeline must be run on a Linux platform, which is not convenient for researchers without computer background knowledge to use. In other words, this exemplary embodiment realizes the localization and lightweight annotation of macrovirome raw data, making it easy for researchers without bioinformatics background knowledge to use; in the current situation where mainframes are not yet popular, it solves the problems of waiting for long analysis cycles and high fees from third-party companies, and provides a fast, simple, and cost-effective method for annotating macrovirome raw data.

[0068] Therefore, this exemplary embodiment has the following advantages: 1. Annotation of viral short read sequences from raw macrovirome sequencing data can be performed on a Windows personal computer without queuing on a third-party company's server, saving a lot of turnaround time and facilitating the satisfaction of "rapid detection" requirements; 2. A very high recall rate: the recall rate for two viral mock communities (VMCs) (BioProject IDs PRJNA431646 and PRJNA395784) was 100%, consistent with Nadim J Ajami's VirMap, facilitating the satisfaction of "comprehensive detection" requirements; 3. Simple and fully automatic operation, making it easy for researchers or clinicians without a bioinformatics background to use; 4. Short read sequences are automatically extracted according to the annotated virus species, facilitating subsequent research; 5. Low computing resource requirements: analysis can be performed on most Windows personal computers. Analysis of 10Gb of macrovirome sequencing data took only 1.7 hours on a Windows 7 computer equipped with a 2.8GHz dual-core Intel(R) Celeron(R) CPU G3900 and 4Gb of memory.

[0069] Correspondingly, in an exemplary embodiment, the system further includes:

[0070] The database update module is used to detect whether the user-provided FASTA dataset exists in a specific directory: if it does not exist, the built-in RefSeq virus database is used as the database; if it exists, a new Blastn index is built for the user-provided FASTA dataset as the database.

[0071] Correspondingly, in an exemplary embodiment, the script library includes a database self-check script, a strain name addition script, and a short read sequence extraction script.

[0072] Correspondingly, in an exemplary embodiment, the quality difference is that the comparison length is less than a first length and / or the similarity is less than a first threshold.

[0073] With the same inventive concept as the above exemplary embodiment, another exemplary embodiment of the present invention provides a storage medium having computer instructions stored thereon, which, when executed, execute the steps of the method for annotating short-read sequences of raw sequencing data of a macrovirus group.

[0074] Having the same inventive concept as the above exemplary embodiment, another exemplary embodiment of the present invention provides a device including a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, and when the processor executes the computer instructions, it executes the steps of the method for annotating short read sequences of macrovirus group raw sequencing data.

[0075] Based on this understanding, the technical solution of this embodiment, 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 a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention.

[0076] The memory is used to store various types of data to support the operation of the device. Such data may include, for example, instructions for any application or method operating on the device, as well as application-related data, such as contact information, sent and received messages, pictures, audio, video, etc. The memory may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0077] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications can be made based on the above descriptions. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications derived therefrom remain within the scope of protection of the present invention.

Claims

1. A method for annotating short-read sequences of macrovirome raw sequencing data, based on Windows system, written in shell and perl, characterized by: The following steps are involved: Detect whether there is a Blastn index in a specific directory. If there is no Blastn index, further determine whether there is a user-provided fasta dataset in the specific directory. If there is a user-provided fasta dataset, build a new Blastn index based on the fasta dataset. Otherwise, further determine whether there is a RefSeq virus database in the specific directory. If there is a RefSeq virus database, build a new Blastn index based on the RefSeq virus database, otherwise report an error; if there is a Blastn index, complete the subsequent annotation steps; the specific directory is the / databases directory created by the Windows system; build a new Blastn index for the user-provided fasta dataset, and use the makeblastdb program to implement it; if there is a Blastn index in the / databases directory, that is, the database file, proceed directly to the next step; if there is no Blastn index, the program uses its own 2021 RefSeq virus gene sequence dataset to build a Blastn index, and then proceed to the next step; if there is no Blastn index and there is a user-provided fasta dataset with gene id, the program will integrate this dataset and the original dataset to build a Blastn index, and then proceed to the next step; If there is no dataset, that is, no user-provided FASTA dataset with gene ID, no built-in 2021 RefSeq viral gene sequence dataset, and no Blastn index, the program will report an error; Obtain the raw sequencing data, decompress the raw sequencing data and extract the short read sequences to generate a short read sequence dataset, and call the Blastn program to align the dataset to the database; The best alignment result of each short-read sequence was retained, and the results with poor alignment quality were removed from the best alignment results, and then the virus species name was added according to the gene ID; Based on the script library, the number of short read sequences annotated to each virus species is counted, the standard deviation of the short read sequence alignment position of each virus species on the genome is calculated, and the corresponding short read sequences are extracted according to the annotated virus species, and the fasta dataset is output; The system is written in shell and perl, and all required environments are provided by Gitbash on Windows. That is, the shell and perl environments are provided by the Git for Windows program. The abundance can be obtained by directly counting the number of short read sequences aligned to each viral genome in the Blastn results. The script library includes a database self-check script, a strain name addition script and a short read sequence extraction script.

2. A system for annotating short-read sequences of macrovirome raw sequencing data, based on Windows system, written in shell and Perl, characterized by: include: The database update module detects whether there is a Blastn index in a specific directory. If there is no Blastn index, it further determines whether there is a user-provided fasta dataset in the specific directory. If there is a user-provided fasta dataset, a new Blastn index is constructed based on the fasta dataset. Otherwise, it further determines whether there is a RefSeq virus database in the specific directory. If there is a RefSeq virus database, a new Blastn index is constructed based on the RefSeq virus database, otherwise an error is reported; if there is a Blastn index, the subsequent annotation steps are completed; the specific directory is the / databases directory created by the Windows system; a new Blastn index is constructed for the user-provided fasta dataset, and the makeblastdb program is used to implement it; if there is a Blastn index in the / databases directory, that is, the database file, proceed directly to the next step; if there is no Blastn index, the program uses its own 2021 RefSeq virus gene sequence dataset to construct a Blastn index, and then proceeds to the next step; if there is no Blastn index and there is a user-provided fasta dataset with gene id, the program will integrate this dataset and the original dataset to construct a Blastn index, and then proceed to the next step; If there is no dataset, that is, no user-provided FASTA dataset with gene ID, no built-in 2021 RefSeq viral gene sequence dataset, and no Blastn index, the program will report an error; Blastn analysis module: used to obtain raw sequencing data, decompress the raw sequencing data and extract short-read sequences, generate a short-read sequence dataset, and call the Blastn program to align the dataset to the database; The best alignment result of each short-read sequence was retained, and the results with poor alignment quality were removed from the best alignment results, and then the virus species name was added according to the gene ID; Short-read sequence extraction and statistical analysis module: Based on the script library, it counts the number of short-read sequences annotated to each virus species, calculates the standard deviation of the short-read sequence alignment position of each virus species on the genome, extracts the corresponding short-read sequences according to the annotated virus species, and outputs the FASTA dataset; The system is written in shell and perl, and all required environments are provided by Gitbash on Windows. That is, the shell and perl environments are provided by the Git for Windows program. The abundance can be obtained by directly counting the number of short read sequences aligned to each viral genome in the Blastn results. The script library includes a database self-check script, a strain name addition script and a short read sequence extraction script.

3. A storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed, the steps of the method for annotating short-read sequences of macrovirome raw sequencing data according to claim 1 are executed.

4. A device comprising a memory and a processor, wherein the memory stores computer instructions that can be executed on the processor, characterized in that: When the processor runs the computer instructions, it performs the steps of the method for annotating short-read sequences of macrovirome raw sequencing data according to claim 1.

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