Method and device for multi-algorithm mining of viral micro-rnas

Abstract

This invention discloses a method and apparatus for mining viral micro-interference RNA using multiple algorithms, relating to the field of biological detection. The method includes: removing adapters from an input sequence using multiple adapter removal algorithms, merging and deduplicating the resulting sequences to obtain an adapter-removed sequence; mapping the adapter-removed sequence to a ribosomal RNA database using multiple mapping algorithms in a cascaded manner, outputting a first incompletely mapped sequence; mapping the first incompletely mapped sequence to a host genome using the same mapping method, outputting a second incompletely mapped sequence; mapping the second incompletely mapped sequence to a viral genome using multiple mapping algorithms. If the second incompletely mapped sequence is mapped to the viral genome by any mapping algorithm with a mismatch number ≤ 1, it is determined to originate from a viral sequence. This invention significantly improves the efficiency and accuracy of mining viral micro-interference RNA from massive transcriptome data.

Description

Technical Field

[0001] This invention relates to the field of biological detection, and in particular to a method, apparatus, electronic device, and storage medium for mining viral micro-interference RNA using multiple algorithms. Background Technology

[0002] Small interfering RNA (siRNA) is a short double-stranded RNA molecule of 20-25 nucleotides that plays a key role in the RNA interference (RNAi) pathway. It can specifically silence the expression of target genes. In plants and insects, siRNA is a key antiviral defense mechanism: viral double-stranded RNA (dsRNA) is recognized by the Dicer-like (DCL) protein of the host cell and cleaved into viral small interfering RNA (vsiRNA) of 21-23 nucleotides in length. These vsiRNAs are then bound by the RNA-induced silencing complex (RISC) and guided to cleave the viral RNA molecule, thereby inhibiting viral replication.

[0003] RNA sequencing (RNA-seq), as a widely used high-throughput transcriptome analysis technique, has accumulated massive amounts of data. Effectively identifying and extracting vsiRNA fragments from transcriptome data can provide data support for research on antiviral mechanisms and drug development. However, vsiRNA mining faces significant challenges, mainly due to background noise interference from host RNA and the low abundance of vsiRNA itself. Furthermore, using a single algorithm for mining can lead to biases, making accurate detection and extraction of vsiRNA even more difficult. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, the present invention provides a method, apparatus, electronic device and storage medium for mining viral micro-interference RNA using multiple algorithms. By using multiple algorithms for adapter removal and sequence mapping filtering, the comprehensiveness and accuracy of viral micro-interference RNA mining are improved.

[0005] The first aspect of this invention provides a method for mining viral micro-interference RNA using multiple algorithms, comprising:

[0006] 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.

[0007] 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.

[0008] 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.

[0009] 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.

[0010] Furthermore, the process of dejoining the input sequence using various dejoining algorithms includes:

[0011] 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.

[0012] 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.

[0013] Furthermore, the cascading mode includes:

[0014] 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.

[0015] 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.

[0016] The second incompletely mapped subsequence is mapped to the target genome using a third mapping algorithm, and the second incompletely mapped subsequence with a mismatched base number greater than 1 is used as the final output.

[0017] 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.

[0018] Furthermore, the mapping steps for each mapping algorithm include:

[0019] The reference genome is cut into short fragments of fixed length to build an index;

[0020] 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;

[0021] 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.

[0022] The reference genome is a ribosomal RNA database, a host genome, or a viral genome.

[0023] Another aspect of the present invention provides an apparatus for multi-algorithm mining of viral micro-interference RNA, comprising:

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Furthermore, the connector removal module is further used for:

[0029] 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.

[0030] 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.

[0031] Furthermore, the cascading mode includes:

[0032] 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.

[0033] 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.

[0034] The second incompletely mapped subsequence is mapped to the target genome using a third mapping algorithm, and the second incompletely mapped subsequence with a mismatched base number greater than 1 is used as the final output.

[0035] 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.

[0036] Furthermore, the mapping steps for each mapping algorithm include:

[0037] The reference genome is cut into short fragments of fixed length to build an index;

[0038] 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;

[0039] 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.

[0040] The reference genome is a ribosomal RNA database, a host genome, or a viral genome.

[0041] A third aspect of the present invention provides an electronic device, comprising:

[0042] One or more processors;

[0043] One or more storage devices for storing computer programs;

[0044] 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 of the preceding claims.

[0045] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a multi-algorithm method for mining viral micro-interference RNA as described in any of the preceding claims.

[0046] This invention combines multiple adapter removal algorithms to effectively reduce fragment misclassification or residual adapter interference caused by adapter inference errors, providing more accurate adapter-removed sequences for subsequent analysis. By combining multiple mapping algorithms in a cascaded manner, it filters and maps sequentially to remove ribosomal RNA sequences and host RNA sequences to the greatest extent, reducing background noise interference on the mining of low-abundance viral micro-interference RNA sequences. Then, by using multiple mapping algorithms in parallel, it maximizes the mining of viral micro-interference RNA sequences in the transcriptome sequence. This invention effectively solves the problems of false positives and false negatives caused by noise interference, low abundance of viral micro-interference RNA, and the bias of a single algorithm, significantly improving the efficiency and accuracy of mining viral micro-interference RNA from massive transcriptome data. Attached Figure Description

[0047] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0048] Figure 1This is a flowchart illustrating a method for mining viral micro-interference RNA using multiple algorithms, provided in one embodiment of this application.

[0049] Figure 2 This is a schematic diagram of the structure of a device for mining viral micro-interference RNA using multiple algorithms, provided in one embodiment of this application.

[0050] Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0053] It should be understood that although the terms first, second, third, etc., may be used to describe the acquisition modules in the embodiments of the present invention, these acquisition modules should not be limited to these terms. These terms are only used to distinguish the acquisition modules from each other.

[0054] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0055] It should be noted that the directional terms such as "upper," "lower," "left," and "right" used in the embodiments of the present invention are used to describe the angles shown in the accompanying drawings and should not be construed as limiting the embodiments of the present invention. Furthermore, in the context, it should be understood that when it is mentioned that an element is formed "upper" or "lower" of another element, it can not only be formed directly "upper" or "lower" of the other element, but also indirectly "upper" or "lower" of the other element through an intermediate element.

[0056] refer to Figure 1This invention provides a method for mining viral micro-interference RNA using multiple algorithms, comprising:

[0057] Step S101: The input sequence is de-jointed using a variety of de-jointing algorithms, and the sequences obtained by each de-jointing algorithm are merged and deduplicated to obtain a de-jointed sequence;

[0058] The adapter is a special nucleotide sequence added during the construction of a cDNA (complementary DNA) library. This sequence has two main functions: first, it serves as a PCR primer binding site; second, it binds to the flow cell through sequence complementarity. Adapter introduction is an essential step in current commercial sequencing workflows. Since the adapter sequence is artificially introduced and is not part of the gene sequence, it needs to be removed during subsequent data processing. If the adapter sequence is known, mature algorithms such as cutadapt can be used for precise removal; if the adapter sequence is unknown, possible sequences need to be inferred. Currently, different algorithms infer adapter sequences in different ways, resulting in inconsistent errors. Many transcriptome databases, such as those managed by NCBI (National Center for Biotechnology Information), do not provide adapter data, thus requiring inference. Inaccurate adapter data can lead to excessive or insufficient sequence removal, significantly interfering with subsequent analysis. Therefore, this embodiment employs multiple adapter removal algorithms, such as fastp, sRNAminer, and FindAdapt, for adapter removal, and merges the adapter-removed sequences obtained from each algorithm to avoid missing specific lengths of viral microRNAs.

[0059] For example, a first batch of sequences is randomly selected from the input sequence using a first deconnector algorithm such as FASTP. K-mers of the first batch of sequences are generated according to a preset k value. A k-mer refers to a short sequence of length k. By using a sliding window, short sequences of length k are sequentially truncated from one end of the original sequence to the other, generating all possible k-mers. In this embodiment, k=10 is preset to generate the k-mers of the first batch of sequences. Sequences with a frequency higher than a preset frequency are selected from the 10-mers as the first connector seed sequence. Preferably, the preset frequency is 0.0001. The first connector seed sequence is extended forward and backward base by base, and the probability of occurrence of four bases at the extension site is calculated. The probability of occurrence of four bases in the first batch of sequences is statistically analyzed. Given a sequence containing the current first connector seed sequence, identify the bases appearing before and after the current first connector seed sequence. Calculate the probability of different bases appearing at the same position in all sequences. This yields the probability of the four bases appearing at the current extension site. If the base with the highest probability of appearing at the current extension site has a higher probability than a preset probability (preferably 90%), then the base is considered to belong to the connector sequence. Otherwise, the base is considered not to belong to the connector sequence, and extension is stopped. There may be more than one first connector seed sequence, but each first connector seed sequence can be extended independently to obtain the same connector sequence, thus obtaining a complete first connector sequence. Based on the first connector sequence, the input sequence is subjected to connector sequence removal.

[0060] A second-batch sequence is randomly selected from the input sequence using a second-linker algorithm such as sRNAminer. The longest common substring of the second-batch sequence is searched as a candidate sequence. This process is repeated several times, and the frequency of each candidate sequence in the input sequence is counted. The candidate sequence with the highest frequency is selected as the second-linker seed sequence. The second-linker 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 using the same method as above. If the probability of the base with the highest occurrence probability at the current extension site is higher than the preset probability (preferably 90%), then the base is considered to belong to the linker sequence. Otherwise, the base is considered not to belong to the linker sequence, and the extension is stopped. This yields the complete second-linker sequence. The input sequence is then excised based on the second-linker sequence.

[0061] The sequences obtained from the first and second adapter removal algorithms are merged and deduplicated. If the adapter removal algorithm cuts too much, certain siRNAs of specific lengths may be misclassified. For example, the length of vsiRNA is generally 21-23 bp, and that of piRNA is generally 24-31 bp. If it cuts too much, piRNA may be mistaken for vsiRNA. On the other hand, if the adapter removal algorithm does not cut enough, residual adapters may remain on the sequence, preventing it from being mapped to the genome. Therefore, merging the sequences obtained from different adapter removal algorithms and deduplicating them avoids the loss of specific length fragments or residual adapters by a single algorithm, resulting in a more comprehensive and accurate adapter-removed sequence as input for subsequent analysis.

[0062] Step S102: The adapter-decoupled sequences are mapped to the ribosomal RNA database in a cascaded mode using multiple mapping algorithms, and the adapter-decoupled sequences with more than 1 mismatched bases are taken as the first incompletely mapped sequences.

[0063] Specifically, multiple mapping algorithms, including bowtie2, hisat2, and minimap2, are used to map adapter-decoupled sequences to the first target genome: the ribosomal RNA database in a cascaded manner. The specific steps are as follows: using the first mapping algorithm, preferably the minimap2 algorithm, the adapter-decoupled sequences are mapped to the ribosomal RNA database, and the adapter-decoupled sequences with more than 1 mismatched bases are taken as the first incompletely mapped subsequences.

[0064] The second mapping algorithm, preferably the bowtie2 algorithm, maps the first incompletely mapped subsequence to the ribosomal RNA database, and the first incompletely mapped subsequence with more than 1 mismatched bases is used as the second incompletely mapped subsequence.

[0065] The third mapping algorithm, preferably the hisat2 algorithm, maps the second incompletely mapped subsequence to the ribosomal RNA database, and the second incompletely mapped subsequence with more than 1 mismatched bases is taken as the final output: the first incompletely mapped sequence.

[0066] 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.

[0067] Different mapping algorithms have different preferences. This embodiment uses a cascade of multiple mapping algorithms to remove interference from high-abundance ribosomal RNA to the greatest extent possible, avoiding the limitations of a single algorithm. The basic mapping steps of each mapping algorithm include:

[0068] The reference genome is cut into short segments of fixed length to build an index. Different mapping algorithms build indexes of the reference genome in different ways, which leads to different preferences of different mapping algorithms for different reference genomes. For example, the bowtie2 algorithm is based on k-mer hashing and tends to quickly find k-mers, while the minimap2 algorithm is based on minimum hashing and tends to map slightly longer sequences.

[0069] Seed sequences are extracted from the input sequence. Seed sequences are short, fixed subsequences extracted from the input sequence. The seed sequences are matched with the index of the reference genome to obtain candidate regions of the input sequence on the reference genome.

[0070] Based on the candidate regions, each input sequence is then compared, and sequences with more than 1 mismatched bases are determined to be incompletely mapped sequences.

[0071] In this embodiment, the reference genome is a ribosomal RNA database, a host genome, or a viral genome.

[0072] Step S103: The first incompletely mapped sequence is mapped to the host genome in a cascaded mode using the multiple mapping algorithms, and the first incompletely mapped sequence with a mismatch base number greater than 1 is used as the second incompletely mapped sequence.

[0073] Using the same mapping algorithm and method as in step S102, the first incompletely mapped sequence is mapped to the second target genome: the host genome. The first incompletely mapped sequence with more than 1 mismatched bases is used as the second incompletely mapped sequence, thereby minimizing the impact of the host RNA sequence on the discovery of viral micro-interference RNA sequences.

[0074] Step S104: The second incompletely mapped sequence is mapped to the viral genome using the multiple 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.

[0075] Specifically, when a second incompletely mapped sequence is mapped to the viral genome using different mapping algorithms, if any mapping algorithm can ensure that the number of mismatched bases in the sequence is no greater than 1, then the sequence is considered to be from a viral sequence. This minimizes the possibility of missed detections due to the bias of different mapping algorithms. Multiple mapping algorithms are processed in parallel and the results are integrated to effectively avoid the limitations of a single algorithm and maximize the extraction of viral micro-interference RNA sequences from transcriptome data.

[0076] This invention combines multiple adapter removal algorithms to effectively reduce fragment misclassification or adapter residue interference caused by adapter inference errors, providing more accurate adapter-removed sequences for subsequent analysis. By combining multiple mapping algorithms in a cascaded manner, it filters and maps sequentially to remove ribosomal RNA sequences and host RNA sequences to the greatest extent, reducing background noise interference on the mining of low-abundance viral micro-interference RNA sequences. Then, by using multiple mapping algorithms in parallel, it maximizes the mining of viral micro-interference RNA sequences in the transcriptome sequence. This embodiment effectively solves the problems of false detection and false negative detection caused by noise interference, low abundance of viral micro-interference RNA, and the bias of a single algorithm, significantly improving the efficiency and accuracy of mining viral micro-interference RNA from massive transcriptome data.

[0077] refer to Figure 2 This invention also provides a multi-algorithm device 200 for mining viral micro-interference RNA, comprising:

[0078] The de-jointing module 201 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 de-jointing algorithm to obtain a de-jointed sequence.

[0079] The first mapping module 202 is used to map the deadapter sequence to the ribosomal RNA database in a cascaded mode using multiple mapping algorithms, and to take the deadapter sequence with more than 1 mismatched bases as the first incompletely mapped sequence.

[0080] The second mapping module 203 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.

[0081] The third mapping module 204 is used to map the second incompletely mapped sequence to the viral genome through the multiple mapping algorithms. If the second incompletely mapped sequence is mapped to the viral genome by any mapping algorithm with a mismatch base number of no more than 1, it is determined that it comes from a viral sequence.

[0082] Furthermore, the connector removal module 201 is further used for:

[0083] 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.

[0084] 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.

[0085] It should be noted that the technical solutions corresponding to the device 200 provided in this embodiment that can be used to execute various method embodiments are similar in implementation principle and technical effect to the methods, and will not be described again here.

[0086] See Figure 3 Another embodiment of the present invention provides an electronic device 300 for implementing the above-described method for mining viral micro-interference RNA using multiple algorithms. The electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes to implement the methods described in the embodiments of the present invention based on a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0087] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that implementation or possession of all the devices shown is not required. Alternative implementations or possession of more or fewer devices are possible.

[0088] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts, thereby implementing the methods described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a storage device 308, or installed from a ROM 302. When the computer program is executed by the processing device 301, it performs the functions defined above in the methods of the embodiments of the present invention.

[0089] Another embodiment of the present invention also provides a computer-readable storage medium, which may be included in the above-described electronic device or may exist independently and not assembled into the electronic device. The computer program is executable when executed by a processor.

[0090] The above description is merely a preferred embodiment of the present invention. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to the specific combination of the above-described technical features, but should also cover other technical solutions formed by any combination of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

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. 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 target genome using a third mapping algorithm, and the second incompletely mapped subsequence with a mismatched base number greater than 1 is used 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. 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.

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. 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 through the multiple mapping algorithms. If the second incompletely mapped sequence is mapped to the viral genome by any mapping algorithm with a mismatch base number of no more than 1, it is determined that it comes from a viral sequence. 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 target genome using a third mapping algorithm, and the second incompletely mapped subsequence with a mismatched base number greater than 1 is used 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. 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.

4. The apparatus for multi-algorithm mining of viral micro-interference RNA according to claim 3, 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.

5. 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 method for mining viral micro-interference RNA using a multi-algorithm, as described in any one of claims 1 and 2.

6. 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 and 2.

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