Method and device for mining virus small interfering RNA (Ribonucleic Acid) through multiple algorithms

Abstract

The invention discloses a method and a device for mining virus small interfering RNA (Ribonucleic Acid) through multiple algorithms, and relates to the field of biological detection.The method comprises the following steps: removing linkers of input sequences through multiple linker removing algorithms, and merging and de-duplicating the obtained sequences to obtain linker-removed sequences; mapping the delinker sequence to a ribosome RNA database in a cascade mode through a plurality of mapping algorithms, and outputting a first incompletely mapped sequence; mapping the first incompletely-mapped sequence to a host genome by adopting a same mapping method, and outputting a second incompletely-mapped sequence; and mapping the second incompletely-mapped sequence to the viral genome through a plurality of mapping algorithms, and judging that the second incompletely-mapped sequence is from the viral sequence when the second incompletely-mapped sequence is mapped to the viral genome by any mapping algorithm in a way that the mismatched base number is less than or equal to 1. According to the method, the efficiency and the accuracy of mining the virus small interfering RNA from massive transcriptome data are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biological detection, and particularly relates to a method and device for mining virus small interfering RNA by using multiple algorithms, an electronic device and a storage medium. BACKGROUND

[0002] Small interfering RNA (siRNA) is a short double-stranded RNA molecule with 20-25 nucleotides, which plays a key role in the RNA interference (RNAi) pathway and can specifically silence target gene expression. In plants and insects, siRNA is a key antiviral defense mechanism: viral double-stranded RNA (dsRNA) is recognized and cut by the host cell's Dicer-like (DCL) protein into 21-23 nucleotide length virus small interfering RNA (vsiRNA), which is then combined with the RNA-induced silencing complex (RISC) and guides the cutting of viral RNA molecules, thereby inhibiting viral replication.

[0003] RNA sequencing (RNA-seq) is a widely used high-throughput transcriptome analysis technique that has accumulated vast amounts of data. Effective identification and acquisition of vsiRNA fragments from transcriptome data can provide data support for the study of antiviral mechanisms and drug development. However, the mining of vsiRNA faces significant challenges, mainly due to the background noise interference of host RNA and the low content of vsiRNA itself, and the use of a single algorithm for mining can have a preference, making it more difficult to accurately detect and extract vsiRNA. SUMMARY

[0004] In view of the above-mentioned defects or deficiencies in the prior art, the present application provides a method and device for mining virus small interfering RNA by using multiple algorithms, an electronic device and a storage medium, which improves the comprehensiveness and accuracy of virus small interfering RNA mining through multiple algorithm processing for adapter removal and sequence mapping filtering.

[0005] The first aspect of the present application provides a method for mining virus small interfering RNA by using multiple algorithms, comprising: removing adapters from input sequences by multiple adapter removal algorithms, and merging and deduplicating the sequences obtained by each of the adapter removal algorithms to obtain adapter-removed sequences; mapping the adapter-removed sequences to a ribosomal RNA database in a cascading mode by multiple mapping algorithms, and taking adapter-removed sequences with more than 1 number of mismatched bases as first incompletely mapped sequences; mapping the first incompletely mapped sequences to the host genome by the plurality of mapping algorithms in a cascade mode, taking the first incompletely mapped sequences with the number of mismatched bases greater than 1 as second incompletely mapped sequences; mapping the second incompletely mapped sequences to the viral genome by the plurality of mapping algorithms, and determining that the second incompletely mapped sequences come from the viral sequences if the second incompletely mapped sequences are mapped to the viral genome by any mapping algorithm with the number of mismatched bases not greater than 1.

[0006] Further, the de-ligating the input sequences by the plurality of de-ligating algorithms comprises: randomly selecting a first batch of sequences from the input sequences by a first de-ligating algorithm, generating k-mers of the first batch of sequences according to a preset k value, screening sequences with a frequency higher than a preset frequency from the k-mers as first ligase seed sequences, performing forward and backward base-by-base extension on the first ligase seed sequences, calculating the occurrence probability of four bases at the extension site, and if the base with the highest occurrence probability at the current extension site has an occurrence probability higher than a preset probability, considering that the base belongs to a ligase sequence, otherwise, considering that the base does not belong to a ligase sequence and stopping the extension, thereby obtaining complete first ligase sequences, and cutting the ligase sequences from the input sequences based on the first ligase sequences; randomly selecting a second batch of sequences from the input sequences by a second de-ligating algorithm, searching for the longest common substring of the second batch of sequences as a candidate sequence, repeating the process for several times, counting the frequency of each candidate sequence in the input sequences, selecting the candidate sequence with the highest frequency as a second ligase seed sequence, performing forward and backward base-by-base extension on the second ligase seed sequence, calculating the occurrence probability of four bases at the extension site, and if the base with the highest occurrence probability at the current extension site has an occurrence probability higher than a preset probability, considering that the base belongs to a ligase sequence, otherwise, considering that the base does not belong to a ligase sequence and stopping the extension, thereby obtaining complete second ligase sequences, and cutting the ligase sequences from the input sequences based on the second ligase sequences.

[0007] Further, the cascade mode comprises: mapping the to-be-mapped sequences to the target genome by a first mapping algorithm, and taking the to-be-mapped sequences with the number of mismatched bases greater than 1 as first incompletely mapped subsequences; mapping the first incompletely mapped subsequences to the target genome by a second mapping algorithm, and taking the first incompletely mapped subsequences with the number of mismatched bases greater than 1 as second incompletely mapped subsequences; mapping the second incompletely mapped subsequences to the first reference genome by a third mapping algorithm, and taking the second incompletely mapped subsequences with the number of mismatched bases greater than 1 as final outputs; The first mapping algorithm is faster than the second and third mapping algorithms in comparison speed, the second mapping algorithm is more accurate than the first mapping algorithm in comparison accuracy, and the third mapping algorithm is more accurate than the second mapping algorithm in comparison accuracy; and the target genome is a ribosomal RNA database or a host genome.

[0008] Further, the mapping step of each mapping algorithm comprises: cutting the reference genome into short fragments of a fixed length to construct an index; extracting seed sequences from the input sequences, matching the seed sequences with the index of the reference genome, and obtaining candidate regions of the input sequences on the reference genome; aligning each of the input sequences based on the candidate regions, and determining a sequence with more than one mismatched base as an incomplete mapping sequence; The reference genome is a ribosomal RNA database, a host genome, or a viral genome.

[0009] In another aspect, the application provides a device for mining viral micro-interference RNA by using multiple algorithms, comprising: a de-adaptor module configured to de-adapt the input sequences by using multiple de-adaptor algorithms, and to combine and remove duplicates of the sequences obtained by each of the de-adaptor algorithms to obtain de-adapted sequences; a first mapping module configured to map the de-adapted sequences to a ribosomal RNA database by using multiple mapping algorithms in a cascade mode, and to determine a de-adapted sequence with more than one mismatched base as a first incomplete mapping sequence; a second mapping module configured to map the first incomplete mapping sequence to a host genome by using the multiple mapping algorithms in the cascade mode, and to determine a first incomplete mapping sequence with more than one mismatched base as a second incomplete mapping sequence; a third mapping module configured to map the second incomplete mapping sequence to a viral genome by using the multiple mapping algorithms, and to determine that the second incomplete mapping sequence is from a viral sequence if the second incomplete mapping sequence is mapped to the viral genome by any of the mapping algorithms with no more than one mismatched base.

[0010] Further, the de-adaptor module is further configured to: The first batch of sequences is randomly selected from the input sequence by the first de-ligating algorithm, the k-mers of the first batch of sequences are generated according to the preset k value, the sequences with a frequency higher than a preset frequency are screened from the k-mers as first ligating seed sequences, the first ligating seed sequences are extended base by base in the forward and backward directions, the occurrence probability of four bases is calculated at the extension site, if the base with the highest occurrence probability at the current extension site has an occurrence probability higher than a preset probability, it is considered that the base belongs to the ligating sequence, otherwise, it is considered that the base does not belong to the ligating sequence and the extension is stopped, thereby obtaining the complete first ligating sequence, and the input sequence is ligated based on the first ligating sequence. The second batch of sequences is randomly selected from the input sequence by the second ligating algorithm, the longest common substring of the second batch of sequences is searched as a candidate sequence, the process is repeated for several times, the frequency of each candidate sequence appearing in the input sequence is counted, the candidate sequence with the highest frequency is selected as the second ligating seed sequence, the second ligating seed sequence is extended base by base in the forward and backward directions, the occurrence probability of four bases is calculated at the extension site, if the base with the highest occurrence probability at the current extension site has an occurrence probability higher than a preset probability, it is considered that the base belongs to the ligating sequence, otherwise, it is considered that the base does not belong to the ligating sequence and the extension is stopped, thereby obtaining the complete second ligating sequence, and the input sequence is ligated based on the second ligating sequence.

[0011] Further, the cascade mode comprises: The first mapping algorithm is used to map the to-be-mapped sequence to the target genome, and the to-be-mapped sequence with a number of mismatched bases greater than 1 is taken as a first incomplete mapping subsequence; The second mapping algorithm is used to map the first incomplete mapping subsequence to the target genome, and the first incomplete mapping subsequence with a number of mismatched bases greater than 1 is taken as a second incomplete mapping subsequence; The third mapping algorithm is used to map the second incomplete mapping subsequence to the first reference genome, and the second incomplete mapping subsequence with a number of mismatched bases greater than 1 is taken as a final output; The first mapping algorithm is faster than the second and third mapping algorithms in terms of alignment speed, the second mapping algorithm is more accurate than the first mapping algorithm in terms of alignment accuracy, and the third mapping algorithm is more accurate than the second mapping algorithm in terms of alignment accuracy; the target genome is a ribosomal RNA database or a host genome.

[0012] Further, the mapping step of each mapping algorithm comprises: The reference genome is cut into short fragments of a fixed length to construct an index; extracting a seed sequence from the input sequence, matching the seed sequence with an index of the reference genome to obtain a candidate region of the input sequence on the reference genome; aligning each of the input sequences based on the candidate region, and determining a sequence with a number of mismatched bases greater than 1 as an incomplete mapping sequence; The reference genome is a ribosomal RNA database, a host genome, or a viral genome.

[0013] The third aspect of the present application provides an electronic device, comprising: one or more processors; one or more storage devices for storing a computer program; When the computer program is executed by the processor, the processor implements the method for mining viral micro-interference RNA by multiple algorithms as described in any one of the preceding aspects.

[0014] The fourth aspect of the present application provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for mining viral micro-interference RNA by multiple algorithms as described in any one of the preceding aspects.

[0015] The present application effectively reduces the fragment misclassification or adapter residue interference caused by incorrect adapter inference by combining multiple adapter removal algorithms, and provides more accurate adapter removal sequences for subsequent analysis; by combining multiple mapping algorithms, the ribosomal RNA sequences and host RNA sequences are removed to the greatest extent in a cascade mode, the background noise is reduced, the interference of low-abundance viral micro-interference RNA sequence mining is reduced, and the viral micro-interference RNA sequences in the transcriptome sequence are mined to the greatest extent by parallel mapping of multiple mapping algorithms, the present application effectively solves the problems of noise interference, low-abundance viral micro-interference RNA and false detection and missed detection caused by single algorithm bias, and significantly improves the efficiency and accuracy of mining viral micro-interference RNA from massive transcriptome data. BRIEF DESCRIPTION OF DRAWINGS

[0016] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings: Figure 1 is a flowchart of a method for mining viral micro-interference RNA by multiple algorithms according to an embodiment of the present application; Figure 2 is a structural diagram of a device for mining viral micro-interference RNA by multiple algorithms according to an embodiment of the present application; Figure 3 is a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some, but not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0018] The terms used in the embodiments of the present application are only for the purpose of describing particular embodiments and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0019] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present application to describe obtaining modules, these obtaining modules should not be limited by these terms. These terms are only used to distinguish the obtaining modules from each other.

[0020] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting". Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" can be interpreted to mean "upon being determined" or "in response to determining" or "upon detecting [the stated condition or event]" or "in response to detecting [the stated condition or event]".

[0021] It should be noted that the terms "upper", "lower", "left", "right", etc. described in the embodiments of the present application are described from the angle shown in the drawings, and should not be understood as a limitation on the embodiments of the present application. In addition, in the context, it should also be understood that when referring to an element being formed "on" or "under" another element, it can be directly formed "on" or "under" another element, or indirectly formed "on" or "under" another element through an intermediate element.

[0022] Reference Figure 1 The embodiments of the present application provide a method for mining virus micro-interference RNA by multiple algorithms, comprising: In step S101, the input sequence is de-ligated by multiple de-ligation algorithms, and the sequences obtained by each de-ligation algorithm are merged and de-duplicated to obtain de-ligated sequences. The adapter is a special nucleotide sequence added in the process of cDNA (complementary DNA) library construction. This sequence has two main functions: the first function is as a PCR primer binding site, and the second function is to bind to the flow cell through sequence complementarity. The introduction of the adapter is a necessary step in the current commercial sequencing process. Since the adapter sequence is artificially introduced and does not belong to the sequence of the gene, it needs to be removed in the subsequent data processing. If the adapter sequence is known, mature algorithms such as cutadapt can be used for accurate removal; if the adapter sequence is unknown, the possible sequence needs to be inferred. Different algorithms infer the adapter sequence in different ways, so the errors introduced by different algorithms are inconsistent. Some transcriptome databases, such as the transcriptome database managed by NCBI (National Center for Biotechnology Information, U.S. National Center for Biotechnology Information), do not provide adapter data, so the adapter data needs to be inferred. If the adapter data is not accurate, it will cause the sequence to be cut more or less, which will greatly interfere with subsequent analysis. Therefore, the present embodiment adopts multiple adapter removal algorithms such as fastp, sRNAminer and FindAdapt for adapter removal tasks, and combines and de-duplicates the adapter-removed sequences obtained by each algorithm, which can avoid missing specific length viral microRNAs; For example, a first batch of sequences is randomly selected from the input sequences by a first adapter removal algorithm such as fastp, and k-mers of the first batch of sequences are generated according to a preset k value. The k-mer refers to a short sequence with a length of k. By using a sliding window, short sequences with a length of k are sequentially cut from one end of the original sequence to the other end to generate all possible k-mers. In the present embodiment, k=10 is preset to generate k-mers of the first batch of sequences. Sequences with a frequency higher than a preset frequency are selected from the 10-mers as first adapter seed sequences. Preferably, the preset frequency is 0.0001. The first adapter seed sequences are extended in the forward and backward directions on a base-by-base basis. The occurrence probability of four bases at the extension site is calculated: the bases appearing before and after the current first adapter seed sequence in all sequences containing the current first adapter seed sequence are counted, and the occurrence probability of different bases at the same position is calculated. Thus, the occurrence probability of four bases at the current extension site is obtained. If the base with the highest occurrence probability at the current extension site has an occurrence probability higher than a preset probability, preferably 90%, it is considered that the base belongs to the adapter sequence, otherwise it is considered that the base does not belong to the adapter sequence and the extension is stopped. There can be more than one first adapter seed sequence, but the same adapter sequence can be obtained after independent extension of each first adapter seed sequence. Thus, a complete first adapter sequence is obtained, and the input sequence is subjected to adapter sequence removal based on the first adapter sequence. The second batch of sequences is randomly selected from the input sequences by a second joint algorithm such as sRNAminer, the longest common substring of the second batch of sequences is searched as a candidate sequence, the process is repeated several times, the frequency of each candidate sequence appearing in the input sequence is counted, the candidate sequence with the highest frequency is selected as the second joint seed sequence, the second joint seed sequence is extended by base in the forward and backward directions, the probability of the occurrence of four bases at the extension site is calculated, and the calculation method is the same as above, if the base with the highest probability of occurrence at the current extension site has a probability of occurrence higher than the preset probability, preferably the preset probability is 90%, it is considered that the base belongs to the joint sequence, otherwise it is considered that the base does not belong to the joint sequence and the extension is stopped, thereby obtaining the complete second joint sequence, and the input sequence is cut by the joint sequence based on the second joint sequence.

[0023] The sequences obtained by the first and second joint removal algorithms are merged and de-duplicated, if the joint removal algorithm is cut too much, it will cause some siRNAs of specific lengths to be misclassified, for example, the length of vsiRNA is generally 21-23bp, and the length of piRNA is generally 24-31bp, if it is cut too much, it will cause the situation that piRNA is mistakenly considered as vsiRNA; on the other hand, if the joint removal algorithm is not cut enough, the residual joint will be retained on the sequence, which will cause the final mapping to the genome, therefore, the sequences obtained by different joint removal algorithms are merged and de-duplicated to avoid the loss of specific length fragments or residual joints by a single algorithm, and more comprehensive and accurate joint removal sequences are obtained as the input for subsequent analysis.

[0024] In step S102, the joint removal sequences are mapped to the ribosome RNA database by a plurality of mapping algorithms in a cascade mode, and the joint removal sequences with more than one mismatched bases are taken as first incomplete mapping sequences. Specifically, the plurality of mapping algorithms includes bowtie2, hisat2 and minimap2, the joint removal sequences are mapped to the first target genome, i.e. the ribosome RNA database in a cascade mode, and the specific steps are as follows: the joint removal sequences are mapped to the ribosome RNA database by a first mapping algorithm, preferably the minimap2 algorithm, and the joint removal sequences with more than one mismatched bases are taken as first incomplete mapping sub-sequences. The first incomplete mapping sub-sequences are mapped to the ribosome RNA database by a second mapping algorithm, preferably the bowtie2 algorithm, and the first incomplete mapping sub-sequences with more than one mismatched bases are taken as second incomplete mapping sub-sequences. The second incomplete mapping sub-sequences are mapped to the ribosome RNA database by a third mapping algorithm, preferably the hisat2 algorithm, and the second incomplete mapping sub-sequences with more than one mismatched bases are taken as the final output, i.e. the first incomplete mapping sequences. The first mapping algorithm is faster than the second and third mapping algorithms, the second mapping algorithm is more accurate than the first mapping algorithm, and the third mapping algorithm is more accurate than the second mapping algorithm.

[0025] Different mapping algorithms have different preferences. The embodiment removes the interference of high-abundance ribosomal RNA to the greatest extent and avoids the limitations of a single algorithm by cascading mapping of multiple mapping algorithms. The basic mapping steps of each mapping algorithm include: The reference genome is cut into short fragments of a fixed length to construct an index. Different mapping algorithms construct different indexes of the reference genome, which results in different preferences of different mapping algorithms for different reference genomes. For example, the bowtie2 algorithm is based on k-mer hashing to construct an index and tends to quickly find k-mers. The minimap2 algorithm is based on minimum hashing to construct an index and tends to map slightly longer sequences. Seed sequences are extracted from the input sequences. The seed sequences are short fixed subsequences extracted from the input sequences. The seed sequences are matched with the index of the reference genome to obtain candidate regions of the input sequences on the reference genome. Each of the input sequences is aligned based on the candidate regions, and sequences with more than one mismatched base are determined as incompletely mapped sequences. In the embodiment, the reference genome is a ribosomal RNA database, a host genome, or a viral genome.

[0026] In step S103, the first incompletely mapped sequences are mapped to the host genome in a cascading mode by using the multiple mapping algorithms. The first incompletely mapped sequences with more than one mismatched base are determined as second incompletely mapped sequences. The same mapping algorithm and mapping method as in step S102 are used to map the first incompletely mapped sequences to the second target genome, i.e., the host genome. The first incompletely mapped sequences with more than one mismatched base are determined as second incompletely mapped sequences, thereby minimizing the influence of host RNA sequences on the mining of viral micro-interference RNA sequences.

[0027] In step S104, the second incompletely mapped sequences are mapped to the viral genome by using the multiple mapping algorithms. If the second incompletely mapped sequences are mapped to the viral genome with no more than one mismatched base by using any of the mapping algorithms, it is determined that the second incompletely mapped sequences come from viral sequences.

[0028] Specifically, when a certain second incomplete mapping sequence is mapped to a viral genome by using different mapping algorithms, if any mapping algorithm can make the number of mismatched bases of the sequence not more than 1, it is considered that the sequence comes from a viral sequence, thereby reducing the missed detection caused by the preference of different mapping algorithms to the greatest extent. Multiple mapping algorithms are processed in parallel and the results are integrated, thereby effectively avoiding the limitations of a single algorithm and maximizing the mining of viral micro-interference RNA sequences from transcriptome data.

[0029] The embodiment of the present application effectively reduces the fragment misclassification or adapter residue interference caused by incorrect adapter inference by combining multiple de-adapting algorithms, thereby providing more accurate de-adapting sequences for subsequent analysis. By combining multiple mapping algorithms and sequentially mapping and filtering in a cascade mode, ribosomal RNA sequences and host RNA sequences are removed to the greatest extent, thereby reducing the interference of background noise on the mining of low-abundance viral micro-interference RNA sequences. Then, the viral micro-interference RNA sequences in the transcriptome sequences are mined to the greatest extent by parallel mapping of multiple mapping algorithms. The embodiment effectively solves the problems of noise interference, low abundance of viral micro-interference RNA, and false positives and false negatives caused by the preference of a single algorithm, thereby significantly improving the efficiency and accuracy of mining viral micro-interference RNA from massive transcriptome data.

[0030] Reference Figure 2 The embodiment of the present application also provides a device 200 for mining viral micro-interference RNA by using multiple algorithms, which comprises: A de-adapting module 201 is configured to de-adapt input sequences by using multiple de-adapting algorithms, and to combine and remove duplicates of the sequences obtained by each de-adapting algorithm, thereby obtaining de-adapted sequences. A first mapping module 202 is configured to map the de-adapted sequences to a ribosomal RNA database by using multiple mapping algorithms in a cascade mode, and to regard the de-adapted sequences with more than 1 number of mismatched bases as first incomplete mapping sequences. A second mapping module 203 is configured to map the first incomplete mapping sequences to a host genome by using the multiple mapping algorithms in a cascade mode, and to regard the first incomplete mapping sequences with more than 1 number of mismatched bases as second incomplete mapping sequences. A third mapping module 204 is configured to map the second incomplete mapping sequences to a viral genome by using the multiple mapping algorithms, and to determine that the second incomplete mapping sequences come from viral sequences if the second incomplete mapping sequences are mapped to the viral genome by any mapping algorithm with not more than 1 number of mismatched bases.

[0031] Further, the de-adapting module 201 is further configured to: The first batch of sequences is randomly selected from the input sequences by a first de-ligating algorithm, k-mers of the first batch of sequences are generated according to a preset k value, sequences with a frequency higher than a preset frequency are screened from the k-mers as first ligating seed sequences, the first ligating seed sequences are extended base by base in the forward and backward directions, the occurrence probability of four bases is calculated at the extension site, if the base with the highest occurrence probability at the current extension site has an occurrence probability higher than a preset probability, it is considered that the base belongs to the ligating sequence, otherwise, it is considered that the base does not belong to the ligating sequence and the extension is stopped, thereby obtaining a complete first ligating sequence, and the input sequences are ligating sequence excised based on the first ligating sequence. The second batch of sequences is randomly selected from the input sequences by a second ligating algorithm, the longest common substring of the second batch of sequences is searched as a candidate sequence, the process is repeated for several times, the frequency of each candidate sequence appearing in the input sequences is counted, the candidate sequence with the highest frequency is selected as a second ligating seed sequence, the second ligating seed sequence is extended base by base in the forward and backward directions, the occurrence probability of four bases is calculated at the extension site, if the base with the highest occurrence probability at the current extension site has an occurrence probability higher than a preset probability, it is considered that the base belongs to the ligating sequence, otherwise, it is considered that the base does not belong to the ligating sequence and the extension is stopped, thereby obtaining a complete second ligating sequence, and the input sequences are ligating sequence excised based on the second ligating sequence.

[0032] It should be noted that the device 200 provided in the embodiment corresponds to the technical solution which can be used to execute the method embodiments, and the implementation principle and technical effects are similar to those of the method, which will not be described here.

[0033] Referring to Figure 3 Another embodiment of the present application further provides an electronic device 300 for implementing the method of mining viral micro-interference RNA by using multiple algorithms. The electronic device 300 can include a processing device (such as a central processor, a graphics processor, etc.) 301, which can perform various appropriate actions and processes to implement the method of the embodiments as described in the present application according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage device 308 to the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302 and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0034] Generally, the following devices can be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 can allow the electronic device 300 to communicate wirelessly or wired with other devices to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it is understood that not all of the shown devices are required to be implemented or present. More or fewer devices can alternatively be implemented or present.

[0035] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program comprising program code for performing the methods illustrated by the flowcharts, thereby implementing the methods as described above. In such embodiments, the computer program can be downloaded and installed from a network via the communication devices 309, or installed from the storage devices 308, or installed from the ROM 302. When the computer program is executed by the processing devices 301, the above-described functions defined in the methods of embodiments of the present application are performed.

[0036] Another embodiment of the present application also provides a computer readable storage medium, which can be included in the electronic device described above, or can exist separately without being assembled into the electronic device. The computer program can be executed by the processor to achieve the above.

[0037] The above description is merely preferred embodiments of the present application. It is understood by those skilled in the art that the disclosed scope of the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combinations of the above technical features or their equivalent features without departing from the disclosed concept. For example, the above technical features can be replaced with technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

Claims

1. A method for mining viral micro-interference RNA using multiple algorithms, characterized in that, include: The input sequence is de-jointed by a variety of de-joining algorithms, and the sequences obtained by each de-joining algorithm are merged and deduplicated to obtain a de-jointed sequence. The adapter-decoupled sequences were mapped to the ribosomal RNA database using a cascaded mapping algorithm, with the adapter-decoupled sequences having more than 1 mismatched bases as the first incompletely mapped sequences. The first incompletely mapped sequence is mapped to the host genome in a cascaded mode using the various mapping algorithms, and the first incompletely mapped sequence with a mismatched base number greater than 1 is used as the second incompletely mapped sequence. The second incompletely mapped sequence is mapped to the viral genome using the various mapping algorithms. If the second incompletely mapped sequence is mapped to the viral genome by any mapping algorithm with a mismatch number of no more than 1, it is determined that it comes from a viral sequence.

2. The method for mining viral micro-interference RNA using multiple algorithms according to claim 1, characterized in that, The process of removing the connector from the input sequence using various de-connection algorithms includes: The first batch of sequences is randomly selected from the input sequence using a first deconnection algorithm. k-mers of the first batch of sequences are generated according to a preset k value. Sequences with a frequency higher than a preset frequency are selected from the k-mers as first connector seed sequences. The first connector seed sequence is extended base by base in both forward and backward directions. The occurrence probability of four bases at the extension site is calculated. If the probability of the base with the highest occurrence probability at the current extension site is higher than the preset probability, then the base is considered to belong to the connector sequence. Otherwise, the base is considered not to belong to the connector sequence and the extension is stopped. Thus, a complete first connector sequence is obtained. The input sequence is then deconnected based on the first connector sequence. The second connector algorithm randomly selects a second batch of sequences from the input sequence, searches for the longest common substring of the second batch of sequences as a candidate sequence, and repeats this process several times. The frequency of each candidate sequence in the input sequence is counted, and the candidate sequence with the highest frequency is selected as the second connector seed sequence. The second connector seed sequence is extended base by base in both forward and backward directions. The occurrence probability of four bases at the extension site is calculated. If the probability of the base with the highest occurrence probability at the current extension site is higher than the preset probability, then the base is considered to belong to the connector sequence; otherwise, the base is considered not to belong to the connector sequence, and the extension is stopped. Thus, a complete second connector sequence is obtained. The input sequence is then subjected to connector sequence removal based on the second connector sequence.

3. The method for mining viral micro-interference RNA using multiple algorithms according to claim 1, characterized in that, The cascading modes include: The first mapping algorithm maps the sequence to be mapped to the target genome, and the sequence to be mapped with more than 1 mismatched base is regarded as the first incompletely mapped subsequence. The first incompletely mapped subsequence is mapped to the target genome using a second mapping algorithm, and the first incompletely mapped subsequence with a mismatch base number greater than 1 is used as the second incompletely mapped subsequence. The second incompletely mapped subsequence is mapped to the first reference genome using a third mapping algorithm, and the second incompletely mapped subsequence with a mismatched base number greater than 1 is taken as the final output. The first mapping algorithm is faster than the second and third mapping algorithms, the second mapping algorithm is more accurate than the first mapping algorithm, and the third mapping algorithm is more accurate than the second mapping algorithm; the target genome is a ribosomal RNA database or a host genome.

4. The method for mining viral micro-interference RNA using multiple algorithms according to claim 3, characterized in that, The mapping steps for each mapping algorithm include: The reference genome is cut into short fragments of fixed length to build an index; A seed sequence is extracted from the input sequence, and the seed sequence is matched with the index of the reference genome to obtain candidate regions of the input sequence on the reference genome; Based on the candidate region, each input sequence is compared, and sequences with more than 1 mismatched bases are determined to be incompletely mapped sequences. The reference genome is a ribosomal RNA database, a host genome, or a viral genome.

5. A device for mining viral micro-interference RNA using multiple algorithms, characterized in that, include: The de-jointing module is used to de-join the input sequence using multiple de-jointing algorithms, and to merge and remove duplicates from the sequences obtained by each of the de-jointing algorithms to obtain a de-jointed sequence. The first mapping module is used to map the adapter-decoupled sequence to the ribosomal RNA database in a cascaded mode using multiple mapping algorithms, and to take the adapter-decoupled sequence with more than 1 mismatched base as the first incompletely mapped sequence. The second mapping module is used to map the first incompletely mapped sequence to the host genome in a cascaded mode using the multiple mapping algorithms, and to use the first incompletely mapped sequence with a mismatched base number greater than 1 as the second incompletely mapped sequence. The third mapping module is used to map the second incompletely mapped sequence to the viral genome using the various mapping algorithms. If the second incompletely mapped sequence is mapped to the viral genome by any mapping algorithm with a mismatch number of no more than 1, it is determined that it comes from a viral sequence.

6. The apparatus for multi-algorithm mining of viral micro-interference RNA according to claim 5, characterized in that, The connector removal module is further used for: The first batch of sequences is randomly selected from the input sequence using a first deconnection algorithm. k-mers of the first batch of sequences are generated according to a preset k value. Sequences with a frequency higher than a preset frequency are selected from the k-mers as first connector seed sequences. The first connector seed sequence is extended base by base in both forward and backward directions. The occurrence probability of four bases at the extension site is calculated. If the probability of the base with the highest occurrence probability at the current extension site is higher than the preset probability, then the base is considered to belong to the connector sequence. Otherwise, the base is considered not to belong to the connector sequence and the extension is stopped. Thus, a complete first connector sequence is obtained. The input sequence is then deconnected based on the first connector sequence. The second connector algorithm randomly selects a second batch of sequences from the input sequence, searches for the longest common substring of the second batch of sequences as a candidate sequence, and repeats this process several times. The frequency of each candidate sequence in the input sequence is counted, and the candidate sequence with the highest frequency is selected as the second connector seed sequence. The second connector seed sequence is extended base by base in both forward and backward directions. The occurrence probability of four bases at the extension site is calculated. If the probability of the base with the highest occurrence probability at the current extension site is higher than the preset probability, then the base is considered to belong to the connector sequence; otherwise, the base is considered not to belong to the connector sequence, and the extension is stopped. Thus, a complete second connector sequence is obtained. The input sequence is then subjected to connector sequence removal based on the second connector sequence.

7. The apparatus for multi-algorithm mining of viral micro-interference RNA according to claim 5, characterized in that, The cascading modes include: The first mapping algorithm maps the sequence to be mapped to the target genome, and the sequence to be mapped with more than 1 mismatched base is regarded as the first incompletely mapped subsequence. The first incompletely mapped subsequence is mapped to the target genome using a second mapping algorithm, and the first incompletely mapped subsequence with a mismatch base number greater than 1 is used as the second incompletely mapped subsequence. The second incompletely mapped subsequence is mapped to the first reference genome using a third mapping algorithm, and the second incompletely mapped subsequence with a mismatched base number greater than 1 is taken as the final output. The first mapping algorithm is faster than the second and third mapping algorithms, the second mapping algorithm is more accurate than the first mapping algorithm, and the third mapping algorithm is more accurate than the second mapping algorithm; the target genome is a ribosomal RNA database or a host genome.

8. The apparatus for multi-algorithm mining of viral micro-interference RNA according to claim 7, characterized in that, The mapping steps for each mapping algorithm include: The reference genome is cut into short fragments of fixed length to build an index; A seed sequence is extracted from the input sequence, and the seed sequence is matched with the index of the reference genome to obtain candidate regions of the input sequence on the reference genome; Based on the candidate region, each input sequence is compared, and sequences with more than 1 mismatched bases are determined to be incompletely mapped sequences. The reference genome is a ribosomal RNA database, a host genome, or a viral genome.

9. An electronic device, characterized in that, include: One or more processors; One or more storage devices for storing computer programs; When the computer program is executed by the processor, the processor implements a multi-algorithm method for mining viral micro-interference RNA as described in any one of claims 1-4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a multi-algorithm method for mining viral micro-interference RNA as described in any one of claims 1-4.

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