Second-generation sequencing data analysis device, method and computer-readable storage medium based on the combination of fuzzy search and exact matching

Abstract

The present invention discloses a second-generation sequencing data analysis device, method and computer-readable storage medium based on a combination of fuzzy search and precise matching. The device or method of the present invention obtains a preliminary matching sequencing read segment by targeting a reference sequence, based on a PCR-like precise matching reference genome and a parsed sequence library of 7nt nucleotide length of sequencing data, and then calculates the similarity between the preliminary matching sequencing read segment and the reference sequence based on the Linestein distance to obtain a sequence with insertion / deletion mutations, and then obtains specific information of the mutation based on the Hamming distance comparison and the Smith-Waterman algorithm comparison, and finally realizes the comparison, assembly and identification of mutations of the second-generation sequencing data. The device or method of the present invention does not rely on a large server during operation, can be effectively operated on a local computer workstation, is easy to operate, has a fast calculation speed, and can be applied to sequencing data analysis to ensure the confidentiality and security of genetic data.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to a second-generation sequencing data analysis device, method and computer-readable storage medium based on a combination of fuzzy search and exact matching. Background Art

[0002] DNA is the main carrier of genetic information in organisms, and the determination and interpretation of DNA sequences is also one of the main tasks of current bioinformatics. With the rapid development of sequencing technology, second-generation sequencing technology has gradually become popular, and the sequencing time and cost have been reduced again and again, which has directly led to the exponential growth of the amount of second-generation sequencing data. In recent years, with the repeated emergence of emerging infectious diseases, second-generation sequencing has become an important tool for identifying emerging infectious disease pathogens and their evolutionary relationships. For example, in the identification of SARS-CoV-2 coronavirus and subsequent evolutionary trend analysis, as well as the identification of monkeypox virus subtypes, the pathogen can only be quickly and accurately judged through the analysis of second-generation sequencing data. This means that in the face of the current massive sequencing data, subsequent analysis methods are particularly important. Only by fully, quickly and accurately interpreting the sequencing results can the advantages of high accuracy of second-generation sequencing be truly brought into play.

[0003] Commonly used second-generation sequencing data analysis software include BWA, SOAPdenovo, SPAdes, Velvet and Bowtie 2. Different software focuses on different functions. For example, BWA is often used for mapping DNA sequences of large reference genomes (such as human genomes), including BWA-backtrack, BWA-SW, BWA-MEM, etc.; SOAPdenovo uses a method to assemble short reads, with kerm as the node unit, and uses the de Bruijn graph method to achieve whole genome assembly; SPAdes is a de novo genome assembly software suitable for the assembly of small genomes such as bacteria and fungi. It not only supports illumina sequencing data, but can also be used for the splicing of sequencing data such as Ion Torrent, PacBio, Sanger, Nanopore, etc.; Velvet is suitable for de novo assembly of genomes, and has a good splicing effect on short sequences, and the assembly results have good integrity; and Bowtie2 is a tool for aligning sequencing reads to reference sequences, suitable for lengths of about 50 to 1000bp, and is a classic tool for assembling sequencing reads based on reference sequences. The operation of the above software often requires professional bioinformatics analysts to operate on a bioinformatics analysis server in a Linux working environment. However, local computer workstations are often unable to effectively run most assembly and analysis software due to hardware problems or limitations of the software itself. This also limits the development of second-generation sequencing data analysis in many laboratories. Data analysis by professional sequencing analysis companies cannot well guarantee the confidentiality and security of genetic data. Summary of the invention

[0004] The technical problem to be solved by the present invention is how to analyze sequencing data without relying on large servers and / or how to use local computer workstations to analyze sequencing data and / or how to use local computer workstations to assemble sequencing data.

[0005] In order to solve the above technical problems, the present invention first provides a method for analyzing second-generation sequencing data, which may include the following steps:

[0006] In order to solve the above technical problems, the present invention first provides a device for analyzing second-generation sequencing data, which may include the following modules:

[0007] B1) Reference genome parsed sequence library establishment module: used to establish a reference genome parsed sequence library of the organism to be analyzed, wherein the length of the reference genome nucleotide sequence is Nbp, and the reference genome parsed sequence library contains N-6 parsed sequences, and the length of the parsed sequence is 7nt; the parsed sequence of the first parsed sequence is the 1st to 7th nucleotides from the 5' end of the reference genome sequence, the parsed sequence of the second parsed sequence is the 2nd to 8th nucleotides from the 5' end of the reference genome sequence, and the parsed sequence of the nth parsed sequence is the nth to (n+6)th nucleotides from the 5' end of the reference genome sequence; n is a natural number greater than 0, and n≤N-6;

[0008] B2) Sequencing data analysis sequence library establishment module: used to establish a sequencing data analysis sequence library, the sequencing data analysis sequence library is composed of the PF1 sequence, PF2 sequence, PR1 sequence and PR2 sequence of each sequencing read segment in the sequencing data of the organism to be analyzed, and the rPF1 sequence, rPF2 sequence, rPR1 sequence and rPR2 sequence of the reverse complementary sequence of each sequencing read segment; the PF1 sequence is the 1st to 7th nucleotides from the 5' end of each sequencing read segment, and the PF2 sequence is the 8th to 14th nucleotides from the 5' end of each sequencing read segment, The PR1 sequence is the 1st to 7th nucleotides from the 3' end of each sequencing read, and the PR2 sequence is the 8th to 14th nucleotides from the 3' end of each sequencing read; the rPF1 sequence is the 1st to 7th nucleotides from the 5' end of the reverse complementary sequence, and the rPF2 sequence is the 8th to 14th nucleotides from the 5' end of each sequencing read, the rPR1 sequence is the 1st to 7th nucleotides from the 3' end of the reverse complementary sequence, and the rPR2 sequence is the 8th to 14th nucleotides from the 3' end of the reverse complementary sequence;

[0009] B3) Accurate matching module: used to match the sequencing data parsing sequence library with the reference genome parsing sequence library, the PF1 sequence of each sequencing read segment and the sequence matched with the reference genome parsing sequence library are recorded as PF1-M, the PF2 sequence of each sequencing read segment and the sequence matched with the reference genome parsing sequence library are recorded as PF2-M, the relative distance D1 is obtained by calculating the difference between the positions of PF1-M and PF2-M on the reference genome; the PR1 sequence of each sequencing read segment and the sequence matched with the reference genome parsing sequence library are recorded as PF2-M. The obtained sequence is recorded as PR1-M, the PR2 sequence of each sequencing read segment and the sequence matched in the reference genome analysis sequence library are recorded as PR2-M, the difference between the positions of PR1-M and PR2-M on the reference genome is calculated to obtain the relative distance D2; the difference between the positions of PF1-M and PR1-M on the reference genome is calculated to obtain the relative distance D3; the nucleotide length of the sequencing read segment in the sequencing data is recorded as D4; the sequencing read segment with D1=7, D2=7 and 0.5×D4≤D3≤1.5×D4 is selected to obtain the preliminary matching sequencing read segment;

[0010] Alternatively, the sequencing data parsed sequence library is matched with the reference genome parsed sequence library, the rPF1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF1-M, the rPF2 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF2-M, and the relative distance rD1 is obtained by calculating the difference between the positions of rPF1-M and rPF2-M on the reference genome; the rPR1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF1-M. rPR1-M, the rPR2 sequence of each sequencing read segment and the sequence matched in the reference genome parsed sequence library are recorded as rPR2-M, the difference between the positions of rPR1-M and rPR2-M on the reference genome is calculated to obtain the relative distance rD2; the difference between the positions of rPF1-M and rPR1-M on the reference genome is calculated to obtain the relative distance rD3; the nucleotide length of the sequencing read segment in the sequencing data is recorded as D4; the sequencing read segment with rD1=7, rD2=7 and 0.5×D4≤rD3≤1.5×D4 is selected to obtain the preliminary matching sequencing read segment;

[0011] B4) A coarse-grained alignment module: used to record the sequence in the reference genome between the first nucleotide from the 5' end of the PF1-M of each of the sequencing reads and the first nucleotide from the 5' end of the PR1-M, or the sequence in the reference genome between the first nucleotide from the 5' end of the rPF1-M of each of the sequencing reads and the first nucleotide from the 5' end of the rPR1-M as the reference genome seq sequence of each preliminary matching sequencing read; calculate the Levenshtein distance LD value between each of the preliminary matching sequencing reads and the reference genome seq sequence of each of the preliminary matching sequencing reads based on the Levenshtein distance algorithm, and determine whether each of the preliminary matching sequencing reads is a sequencing read without indel mutation or a sequencing read with indel mutation based on the LD value; the sequencing read without indel mutation is a sequencing read without mutation or a read with SNP mutation;

[0012] B5) a fine-grained alignment module: used to align the sequencing read with indel mutations with the corresponding reference genome seq sequence based on the Hamming distance and the Smith-Waterman algorithm respectively to obtain the genomic position information of the indel mutation in the sequencing read with indel mutations;

[0013] B6) Assembly output module for sequencing reads: assembling and splicing all the sequencing reads without indel mutations and all the sequencing reads with indel mutations to obtain assembly analysis results of the sequencing data of the organism to be analyzed; the assembly analysis results include mutation information in the sequencing data of the organism to be analyzed and genomic location information of the mutation; the mutation includes SNP and / or indel mutation.

[0014] In the above device, in module B4), the preliminary matching sequencing reads with LD values ​​less than or equal to 0.05 can be output as sequencing reads without indel mutations, and the preliminary matching sequencing reads with LD values ​​greater than 0.05 are output as sequencing reads with indel mutations.

[0015] The calculation formula of the LD can be as follows:

[0016]

[0017] In Formula 1, i represents the length of seq, j represents the length of tmp, tmp represents the template sequence, and seq represents sequencing reads.

[0018] In the above device, B5) may include the following submodules:

[0019] B5-1) A Hamming distance calculation module is used to calculate the similarity score between the m-(m+6)th nucleotide from the 5' segment or from the 3' end in the sequencing read with indel mutation and the m-(m+6)th nucleotide from the 5' end in the corresponding reference genome seq sequence using the Hamming distance, and obtain the preliminary mutation position in the sequencing read with indel mutation according to the similarity score; m is a natural number greater than 7, m is less than or equal to M-7, and M is the length of the sequencing read;

[0020] B5-2) Smith-Waterman algorithm-based alignment module: used to use the Smith-Waterman algorithm to perform local sequence alignment on the preliminary mutation position and the reference genome seq sequence to obtain the genomic position information of the indel mutation in the sequencing read with the indel mutation.

[0021] The M may be greater than or equal to 70.

[0022] In the above device, the module B6) can be implemented by a method comprising the following steps: naming all the sequencing reads without indel mutations and all the sequencing reads with indel mutations as qualified sequencing reads, mapping the qualified sequencing reads to (aligning) the reference group sequence of the organism to be analyzed; calculating the nucleotide type with the largest number at each nucleotide site in all the mapped qualified sequencing reads starting from the first nucleotide at the 5' end of the reference group sequence, as the nucleotide type at that site in the sequencing data of the organism to be analyzed, and finally obtaining the nucleotide type at that nucleotide site in the sequencing data of the organism to be analyzed corresponding to each nucleotide site in the reference group sequence, and obtaining the assembly analysis result of the sequencing data of the organism to be analyzed; the type of the nucleotide is A, T, C, G or missing.

[0023] B6) In the module, calculating the nucleotide type with the largest number in all the mapped (aligned) qualified sequencing reads at each nucleotide site starting from the first nucleotide at the 5' end of the reference group sequence can be achieved by a method comprising the following steps: using one-hot encoding to characterize the nucleotide number of the nucleotide type in each qualified sequencing read to obtain a site nucleotide number characterization parallel matrix, and completing the site nucleotide number characterization of all the qualified sequencing reads. Subsequently, calculating the sum of various nucleotide types corresponding to the site in all the qualified sequencing reads at each nucleotide site in the reference sequence, and obtaining the nucleotide type with the largest number in the site in the qualified sequencing read.

[0024] In order to solve the above technical problems, the present invention also provides a method for analyzing second-generation sequencing data, which may include the following steps:

[0025] A1) establishing a reference genome sequence library of the organism to be analyzed, wherein the length of the reference genome nucleotide sequence is Nbp, and the reference genome sequence library contains N-6 sequences, and the length of the sequences is 7nt; the first sequence is the 1st to 7th nucleotides from the 5' end of the reference genome sequence, the second sequence is the 2nd to 8th nucleotides from the 5' end of the reference genome sequence, and the nth sequence is the nth to (n+6)th nucleotides from the 5' end of the reference genome sequence; n is a natural number greater than 0, and n≤N-6;

[0026] A2) establishing a sequencing data analysis sequence library, wherein the sequencing data analysis sequence library is composed of the PF1 sequence, PF2 sequence, PR1 sequence and PR2 sequence of each sequencing read in the sequencing data of the organism to be analyzed, and the rPF1 sequence, rPF2 sequence, rPR1 sequence and rPR2 sequence of the reverse complementary sequence of each sequencing read; the PF1 sequence is the 1st to 7th nucleotides from the 5' end of each sequencing read, the PF2 sequence is the 8th to 14th nucleotides from the 5' end of each sequencing read, and the PR1 sequence is The 1st to 7th nucleotides from the 3' end of each sequencing read, and the PR2 sequence is the 8th to 14th nucleotides from the 3' end of each sequencing read; the rPF1 sequence is the 1st to 7th nucleotides from the 5' end of the reverse complementary sequence, the rPF2 sequence is the 8th to 14th nucleotides from the 5' end of each sequencing read, the rPR1 sequence is the 1st to 7th nucleotides from the 3' end of the reverse complementary sequence, and the rPR2 sequence is the 8th to 14th nucleotides from the 3' end of the reverse complementary sequence;

[0027] A3) Exact matching: the sequencing data parsed sequence library is matched with the reference genome parsed sequence library, the PF1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as PF1-M, the PF2 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as PF2-M, the relative distance D1 is obtained by calculating the difference between the positions of PF1-M and PF2-M on the reference genome; the PR1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as PF2-M. The sequence is recorded as PR1-M, the PR2 sequence of each sequencing read segment and the sequence matched in the reference genome parsed sequence library are recorded as PR2-M, the difference between the positions of PR1-M and PR2-M on the reference genome is calculated to obtain the relative distance D2; the difference between the positions of PF1-M and PR1-M on the reference genome is calculated to obtain the relative distance D3; the nucleotide length of the sequencing read segment in the sequencing data is recorded as D4; the sequencing read segment with D1=7, D2=7 and 0.5×D4≤D3≤1.5×D4 is selected to obtain the preliminary matching sequencing read segment;

[0028] Alternatively, the sequencing data parsed sequence library is matched with the reference genome parsed sequence library, the rPF1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF1-M, the rPF2 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF2-M, and the relative distance rD1 is obtained by calculating the difference between the positions of rPF1-M and rPF2-M on the reference genome; the rPR1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF1-M. rPR1-M, the rPR2 sequence of each sequencing read segment and the sequence matched in the reference genome parsed sequence library are recorded as rPR2-M, the difference between the positions of rPR1-M and rPR2-M on the reference genome is calculated to obtain the relative distance rD2; the difference between the positions of rPF1-M and rPR1-M on the reference genome is calculated to obtain the relative distance rD3; the nucleotide length of the sequencing read segment in the sequencing data is recorded as D4; the sequencing read segment with rD1=7, rD2=7 and 0.5×D4≤rD3≤1.5×D4 is selected to obtain the preliminary matching sequencing read segment;

[0029] A4) Coarse-grained alignment: record the sequence in the reference genome between the first nucleotide from the 5' end of the PF1-M and the first nucleotide from the 5' end of the PR1-M of each sequencing read, or, record the sequence in the reference genome between the first nucleotide from the 5' end of the rPF1-M and the first nucleotide from the 5' end of the rPR1-M of each sequencing read as the reference genome seq sequence of each preliminary matching sequencing read; calculate the Levenshtein distance LD value between each preliminary matching sequencing read and the reference genome seq sequence of each preliminary matching sequencing read based on the Levenshtein distance algorithm, and determine whether each preliminary matching sequencing read is a sequencing read without indel mutation or a sequencing read with indel mutation based on the LD value; the sequencing read without indel mutation is a sequencing read without mutation or a read with SNP mutation;

[0030] A5) fine-grained alignment: aligning the sequencing read with indel mutations with the corresponding reference genome seq sequence based on Hamming distance and Smith-Waterman algorithm respectively to obtain the genomic position information of the indel mutation in the sequencing read with indel mutations;

[0031] A6) Assembly output of sequencing reads: assembling and splicing all the sequencing reads without indel mutations and all the sequencing reads with indel mutations to obtain an assembly analysis result of the sequencing data of the organism to be analyzed; the assembly analysis result includes mutation information in the sequencing data of the organism to be analyzed and genomic location information of the mutation; the mutation includes SNP and / or indel mutation.

[0032] In the above method, in A4), the preliminary matching sequencing reads with an LD value less than or equal to 0.05 are determined as sequencing reads without indel mutations, and the preliminary matching sequencing reads with an LD value greater than 0.05 are determined as sequencing reads with indel mutations.

[0033] The calculation formula of the LD can be as follows:

[0034]

[0035] In Formula 1, i represents the length of seq, j represents the length of tmp, tmp represents the template sequence, and seq represents sequencing reads.

[0036] In the above method, A5) may include the following steps:

[0037] A5-1) using the Hamming distance to calculate the similarity score between the m-(m+6)th nucleotides from the 5' segment or from the 3' end in the sequencing read with the indel mutation and the m-(m+6)th nucleotides from the 5' end in the corresponding reference genome seq sequence, and obtaining the preliminary mutation position in the sequencing read with the indel mutation according to the similarity score; m is a natural number greater than 7, m is less than or equal to M-7, and M is the length of the sequencing read;

[0038] The M may be greater than or equal to 70.

[0039] A5-2) Using the Smith-Waterman algorithm, the preliminary mutation position is locally aligned with the reference genome seq sequence to obtain the genomic position information of the indel mutation in the sequencing read with the indel mutation.

[0040] In the above method, in A6), all the sequencing reads without indel mutations and all the sequencing reads with indel mutations are named qualified sequencing reads, and the qualified sequencing reads are mapped to (aligned with) the reference group sequence of the organism to be analyzed; the nucleotide type with the largest number at each nucleotide site in all the mapped qualified sequencing reads starting from the first nucleotide at the 5' end of the reference group sequence is calculated as the nucleotide type at that site in the sequencing data of the organism to be analyzed, and finally the nucleotide type at that nucleotide site in the sequencing data of the organism to be analyzed corresponding to each nucleotide site in the reference group sequence is obtained, and the assembly analysis result of the sequencing data of the organism to be analyzed is obtained; the type of the nucleotide is A, T, C, G or missing.

[0041] A6), calculating the nucleotide type with the largest number in all the mapped (aligned) qualified sequencing reads at each nucleotide site starting from the first nucleotide at the 5' end of the reference group sequence can be achieved by a method comprising the following steps: using one-hot encoding to perform nucleotide number characterization on the nucleotide types in each qualified sequencing read to obtain a site nucleotide number characterization parallel matrix, and completing the site nucleotide number characterization of all the qualified sequencing reads. Subsequently, calculating the sum of various nucleotide types corresponding to the site in all the qualified sequencing reads at each nucleotide site in the reference sequence, and obtaining the nucleotide type with the largest number in the site in the qualified sequencing read.

[0042] In order to solve the above technical problem, the present invention also provides a computer-readable storage medium, which can enable a computer to execute the steps of the method described above.

[0043] Any of the following applications of the method described above and / or the device described above and / or the computer-readable storage medium described above also falls within the protection scope of the present invention:

[0044] C1) Application in the preparation of localized computer-operated sequencing data analysis products;

[0045] C2) Application in the preparation of localized computer-operated disease diagnosis products.

[0046] The purpose of the present invention is to provide a method for analyzing second-generation sequencing data. The method is based on the principle of combining fuzzy search with exact matching. By targeting reference sequences, the method realizes the comparison, assembly and identification of mutations (including single nucleotide polymorphisms, insertion and / or deletion mutations) of second-generation sequencing data. In addition, the method does not rely on large servers during operation and can be effectively run on a local computer workstation. The method is easy to operate and has a fast computing speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The process of establishing the parsed sequence library for the reference genome and the parsed sequence library for sequencing data.

[0048] Figure 2 It is an exact match and coarse-grained comparison based on fuzzy search.

[0049] Figure 3 For fine-grained comparison.

[0050] Figure 4 It is the assembly output of the alignment results. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below in conjunction with specific embodiments, and the examples provided are only for illustrating the present invention, rather than for limiting the scope of the present invention. The examples provided below can be used as a guide for further improvements by those of ordinary skill in the art, and do not constitute a limitation of the present invention in any way.

[0052] The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can all be obtained from commercial channels.

[0053] Example 1: Establishment of a second-generation sequencing data analysis method based on the combination of fuzzy search and exact matching

[0054] Establishment of 1.7 nt probe library

[0055] There are four types of nucleotides in the probe library: A, T, C, and G. Therefore, there are 4 probes with a length of 7 nt. 7There are 16,384 probes in the probe library. Each probe in the probe library is 7 nt long and is a single-stranded DNA molecule composed of four nucleotides, A, T, C and G, arranged randomly. Figure 1 ). This probe library is embedded in the program. During each use, the subsequent reference genome analysis sequence library and sequencing data analysis sequence library can directly retrieve sequences from the probe library to improve operation efficiency.

[0056] 2. Establishment of reference genome analysis sequence library

[0057] Input the fasta file of the reference genome sequence (the length of the reference genome sequence is N), parse the reference sequence, and obtain the reference genome parsed sequence library. The parsing process is: starting from the first nucleotide from the 5' end of the reference genome sequence, split it into 1 sliding window of every 7 nucleotides to obtain 1 parsed sequence, and the length of each parsed sequence is 7. For example: the first parsed sequence in the parsed sequence library is the 1st to 7th nucleotides from the 5' end of the reference genome sequence, the second parsed sequence is the 2nd to 8th nucleotides from the 5' end of the reference genome sequence, and the third parsed sequence is the 3rd to 9th nucleotides from the 5' end of the reference genome sequence. The parsed sequence library contains N-6 sequences, and so on. The N-6th parsed sequence is the (N-6)th to Nth nucleotides from the 5' end of the reference genome sequence ( Figure 1 ).

[0058] 3. Establishment of Sequencing Data Analysis Sequence Library

[0059] 3.1 Filtering of sequencing data

[0060] Input the fastq format file of the second-generation sequencing data results, filter the sequencing reads in the second-generation sequencing data by length conditions, and retain reads with sequencing length ≥ 70bp to obtain valid sequencing data for subsequent analysis.

[0061] 3.2 Sequencing data analysis and acquisition of sequence library

[0062] The sequencing reads in the valid sequencing data were parsed, and the nucleotide sequences of the 1st to 7th positions (7nt) from the 5' end of each read sequence were extracted and recorded as the forward primer 1 (PF1) sequence; the nucleotide sequences of the 8th to 14th positions (7nt) from the 5' end of each read sequence were extracted and recorded as the forward primer 2 (PF2) sequence. Similarly, the nucleotide sequences of the 1st to 7th positions (7nt) from the 3' end of each read sequence were extracted and recorded as the reverse primer 1 (PR1) sequence; the nucleotide sequences of the 8th to 14th positions (7nt) from the 3' end of each read sequence were extracted and recorded as the reverse primer 2 (PR2) sequence.

[0063] At the same time, reverse complementation operation is performed on each read in the sequencing reads to obtain the reverse complementary sequence of each read, and the reverse complementary sequence is parsed in the same way as the above algorithm. The four primer sequences obtained are recorded as rPF1, rPF2, rPR1 and rPR2. The eight primer sequences (PF1, PF2, PR1, PR2, rPF1, rPF2, rPR1 and rPR2) of all sequencing reads constitute the second-generation sequencing data parsing sequence library ( Figure 2 ).

[0064] 4. Coarse-grained comparison of sequencing data with reference genome data

[0065] 4.1 Exact Match

[0066] 4.1.1 14nt primer matching between PCR-based sequencing reads and reference genome sequence

[0067] The primer (PF1, PF2, PR1 and PR2) sequences of each read in the second-generation sequencing data analysis sequence library are accurately matched with the analysis sequences in the reference genome analysis sequence library obtained in step 2. When an accurate match can be achieved, the relative distance D1 between PF1-M and PF2-M is calculated based on the positioning of the reference genome analysis sequences PF1-M and PF2-M that accurately match the PF1 sequence and PF2 sequence in the reference genome (the first nucleotide from the 5' end of PF1-M and the first nucleotide from the 5' end of PF2-M). The relative distance is calculated by the difference in the position of the reference genome sequence of PF1 and PR1-M (the relative distance is calculated by the difference in the position of the reference genome sequence of PF1 and PR2-M). When the relative distance is 7 nt nucleotides, it indicates a successful match; similarly, according to the positioning of the reference genome analysis sequences PR1-M and PR2-M that accurately match PR1 and PR2 in the reference genome, the relative distance D2 between PR1-M and PR2-M is calculated. When the distance is 7 nt nucleotides, it indicates a successful match. At this time, the relative distance (the relative distance between PF1-M and PR1-M in the reference genome) can be calculated as seqlen (D3) according to the positioning of PF1 and PR1 in the reference sequence. Considering the possible nucleotide insertion or deletion of reads, when the length of seqlen is between 0.5 and 1.5 times the read length (denoted as D4), the next step of the algorithm is performed, that is, the sequencing reads with D1=7, D2=7 and 0.5×D4≤D3≤1.5×D4 are selected as the preliminary matching sequencing reads for the next step of analysis.

[0068] If any primer cannot accurately match the template, the nucleotide sequence of the 15th to 21st (7nt) from the 5' end of the read sequence is extracted and recorded as PF1, and the nucleotide sequence of the 21st to 28th (7nt) from the 5' end is extracted and recorded as PF2; similarly, the nucleotide sequence of the 15th to 21st (7nt) from the 3' end of the read sequence is extracted and recorded as PR1, and the nucleotide sequence of the 21st to 28th (7nt) from the 3' end is extracted and recorded as PR2, and the above matching steps are repeated.

[0069] At the same time, the rPF1, rPF2, rPR1, and rPR2 obtained after reverse complementation of the reads were precisely matched with the reference sequence according to the above steps. According to the positioning of the reference genome parsed sequences rPF1-M and rPF2-M that exactly match the rPF1 sequence and rPF2 sequence in the reference genome, the relative distance rD1 between rPF1-M and rPF2-M is calculated (the relative distance is calculated as the difference between the positions of the first nucleotide from the 5' end of rPF1-M and the first nucleotide from the 5' end of rPF2-M in the reference genome sequence). When the relative distance is 7 nt nucleotides, it indicates a successful match; similarly, according to the positioning of the reference genome parsed sequences rPR1-M and rPR2-M that exactly match rPR1 and rPR2 in the reference genome, the relative distance rD2 between rPR1-M and rPR2-M is calculated. When the distance is 7 nt nucleotides, it indicates a successful match. At this time, the relative distance (the relative distance between rPF1-M and rPR1-M in the reference genome) can be calculated as seqlen(rD3) based on the positioning of rPF1 and rPR1 in the reference sequence. Similarly, sequencing reads with rD1=7, rD2=7 and 0.5×D4≤rD3≤1.5×D4 were selected as preliminary matching sequencing reads for the next step of analysis.

[0070] The sequence in the reference genome between the first nucleotide from the 5' end of PF1-M and the first nucleotide from the 5' end of PR1-M, or the sequence in the reference genome between the first nucleotide from the 5' end of rPF1-M and the first nucleotide from the 5' end of rPR1-M is recorded as seq sequence.

[0071] 4.1.2 Calculating the match based on the Lewenstein distance between sequencing reads and reference genome parsed sequences

[0072] The seq sequence obtained in step 4.1.1 (as a template reference genome sequence) is aligned with the corresponding reads sequence. The alignment is performed using the Levenshtein distance (LD) algorithm. LD is used to measure the similarity between two strings (preliminary matching sequencing reads and reference genome sequence seq sequences) ( Figure 2 ), which is specifically defined as "the number of deletion, insertion, and substitution operations required to transform string a into string b". The LD of two strings a and b is denoted by lev a,b (|a|,|b|), specifically in this algorithm, the LD calculation formula for the sequencing reads sequence and the template reference genome sequence is as follows:

[0073]

[0074] In Formula 1, i represents the length of the sequencing read sequence seq, j represents the length of tmp, tmp represents the template sequence, and seq represents sequencing reads.

[0075] The LD threshold is set to 0.05. If the LD value is less than or equal to 0.05, the sequencing read is a sequencing read without indel mutation (but may include SNP mutation), and the sequencing read is directly assembled and output in step 6; if the LD value is greater than 0.05, it indicates that the sequencing read has a mutation (deletion or insertion (indels)) and is a sequencing read with indel mutation. The sequencing reads with indel mutation are subjected to fine-grained alignment search in step 5.

[0076] 5. Fine-grained comparison search

[0077] Perform a fine-grained alignment of each read in the sequencing reads with indel mutations obtained in step 4.1.2 with the reference genome seq sequence (template sequences) corresponding to the read obtained in step 4.1.1 ( Figure 3 ).

[0078] 5.1 Comparison based on Hamming distance

[0079] First, the Hamming distance is used to calculate the similarity between the sequencing reads with indel mutations and the template sequences. The Hamming distance represents the number of different characters in the corresponding positions of two strings of the same length. The algorithm is to align the reads and template sequences from the 5' and 3' ends through the exact match of the primer in step 4.1.1, take 7nt nucleotide length as the sliding window length, slide inward from both ends (from the 5' end and from the 3' end), and slide 1nt nucleotide length each time. The Hamming distance is used to calculate the similarity score between the two after each window sliding, that is, the similarity score between the mth to (m+6)th nucleotides from the 5' end or from the 3' end of the sequencing reads with indel mutations obtained in step 4 and the mth to (m+6)th nucleotides from the 5' end of the reference genome (the reference genome seq sequence corresponding to the read obtained in step 4.1.1) is calculated (m is a natural number greater than 7, m is less than or equal to M-7, M is the length of the sequencing read, M≥70):

[0080]

[0081] When the score is equal to 0, it means that there is 0 nucleotide difference between the two sequences. Similarly, set the threshold to 3. When the score is equal to 3, record the position of the sliding window at this time:

[0082] loc mis-match =loc i if Score window = = 3

[0083] Finally, by continuously calculating the Hamming distance between the two in the sliding window, we further find out the positions with large sequence differences between the indel mutation sequencing reads and template sequences to obtain the preliminary mutation positions, thereby shortening the length of the sequence to be accurately aligned.

[0084] 5.2 Comparison based on Smith-Waterman algorithm

[0085] Fine-grained alignment of the reads and template sequences obtained in step 5.1 that need to be further accurately aligned is performed using the classic Smith-Waterman algorithm-dependent alignment. This algorithm uses local sequence alignment to find highly similar fragments in two sequences. The specific algorithm is as follows:

[0086] Assume that the sequencing reads are sequence A = a1a2…a n , template sequences are sequences B = b1b2…b m , where n and m are the lengths of sequences A and B respectively;

[0087] Determine the substitution matrix and gap penalty scheme:

[0088] S(a,b) - similarity score between the elements that make up the sequence;

[0089] W k -Gap penalty of length k.

[0090] Create a score matrix and initialize its first row and first column. The size of the matrix is ​​n+1 rows and m+1 columns (note that the count starts from 0):

[0091] H k0 =H 0l =0,(0≤k≤n,0≤l≤m)

[0092] Score from left to right and from top to bottom to fill in the remaining part of the score matrix H:

[0093]

[0094] In formula 2, H i-1,j-1 +s(a i ,b j ) means to convert a i ,b j The similarity score of the alignment,

[0095] H i-k,j -W k Indicates a i The score for being at the end of a deletion of length k,

[0096] H i,j-l -W l Indicates b j The score for being at the end of a deletion of length l,

[0097] 0 means a i ,b j So far no similarity.

[0098] Backtrack, starting from the element with the highest score in matrix H, backtrack to the previous position according to the source of the score, and repeat this process until an element with a score of 0 is encountered.

[0099] Through the above steps, the indels in the mismatched regions between the reads and template sequences can be accurately found, and the genomic location information of the indel mutation in the sequencing read with the indel mutation can be obtained.

[0100] 6. Assembly output of sequencing reads

[0101] Through the above steps, all qualified sequencing reads (including sequencing reads without indel mutations (including sequencing reads with SNP mutations and sequencing reads without mutations) and sequencing reads with indel mutations) are mapped to the reference sequence. Starting from the first nucleotide at the 5' end of the reference sequence, the number of each of the five nucleotides "A", "T", "G", "C" and "-" (missing) in all reads of the nucleotide site in the qualified sequencing reads on the mapped (mapped or aligned) is calculated. First, the nucleotide type of each qualified sequencing read is characterized by the number of nucleotides to obtain a parallel matrix of nucleotide number representation for each nucleotide site. For example, if a read is at position i to n in the reference sequence, and i is "A", then "A" is recorded as "1", "T", "G", "C" and "-" are recorded as 0, and so on, and the nucleotide numbers of all reads are characterized. Subsequently, the sum of the numbers of various types of nucleotides at each nucleotide site corresponding to all qualified sequencing reads in each nucleotide site of the reference sequence is calculated, and the sequence is output according to the type of nucleotide with the largest number of nucleotides in each nucleotide (A, T, C, G or deletion (indicated by -)) as the final splicing result; at the same time, the proportion of the five types of nucleotides (A, T, C, G and deletion (indicated by -)) in each bit is visualized and output to intuitively display the high-frequency area of ​​SNP.

[0102] Example 2: Comparison of the analysis results of the method of the present invention and the prior art on the second generation sequencing data

[0103] 1. Download of next generation sequencing data

[0104] The sequencing data were downloaded from the Sequence Read Archive (SRA) database in the National Center for Biotechnology Information (NCBI), and accession NO. "PRJNA817870" was entered (Related literature: Reference: Bal A, Simon B, Destras G, et al. Detection and prevalence of SARS-CoV-2 co-infections during the Omicron variant circulation in France. Nat Commun. 2022 Oct 23; 13 (1): 6316). 189 sequencing data were retrieved, which were SARS-CoV-2 virus sequencing data. All 189 sequencing data were downloaded.

[0105] 2. Comparison of sequencing data analysis results

[0106] The sequencing data downloaded in step 1 are compared and analyzed with the reference genome sequence (MN908947.3) using the existing BowTie2 comparison method and the method established in Example 1 of the present invention, and the ratio of the number of reads of the 189 sequencing data matched to the reference gene to the total number of sequencing reads (MappingRate) and the time taken for the comparison are output.

[0107] Among them, the analysis process based on the method of the present invention is operated on a local computer workstation, and the analysis process based on BowTie2 software is operated on a bioinformatics analysis server under a Linux working environment. The analysis results of the two methods are shown in Table 1. The assembly comparison rate of the method of the present invention for the second-generation sequencing data is higher than that of BowTie2, and most of the time used for the analysis of the method of the present invention is less than that of BowTie2. At the same time, the method of the present invention does not need to be operated on a bioinformatics analysis server, so it can be applied to the laboratory to carry out the analysis of the second-generation sequencing data, thereby ensuring the confidentiality and security of the genetic data.

[0108] Table 1. Comparison of the analysis results of the method of the present invention and BowTie2 on the second generation sequencing data

[0109]

[0110]

[0111]

[0112]

[0113] The present invention has been described in detail above. For those skilled in the art, without departing from the purpose and scope of the present invention, and without the need to carry out unnecessary experimental conditions, the present invention can be implemented in a wide range under equivalent parameters, concentrations and conditions. Although the present invention provides specific embodiments, it should be understood that the present invention can be further improved. In a word, according to the principles of the present invention, the application is intended to include any changes, uses or improvements to the present invention, including departure from the disclosed scope in the application, and changes made with conventional techniques known in the art.

Claims

1. A device for analyzing second-generation sequencing data, characterized in that: The device comprises the following modules: B1) Reference genome parsed sequence library establishment module: used to establish a reference genome parsed sequence library of the organism to be analyzed, wherein the length of the reference genome nucleotide sequence is Nbp, and the reference genome parsed sequence library contains N-6 parsed sequences, and the length of the parsed sequence is 7nt; the parsed sequence of the first parsed sequence is the 1st to 7th nucleotides from the 5' end of the reference genome sequence, the parsed sequence of the second parsed sequence is the 2nd to 8th nucleotides from the 5' end of the reference genome sequence, and the parsed sequence of the nth parsed sequence is the nth to (n+6)th nucleotides from the 5' end of the reference genome sequence; n is a natural number greater than 0, and n≤N-6; B2) Sequencing data analysis sequence library establishment module: used to establish a sequencing data analysis sequence library, the sequencing data analysis sequence library is composed of the PF1 sequence, PF2 sequence, PR1 sequence and PR2 sequence of each sequencing read segment in the sequencing data of the organism to be analyzed, and the rPF1 sequence, rPF2 sequence, rPR1 sequence and rPR2 sequence of the reverse complementary sequence of each sequencing read segment; the PF1 sequence is the 1st to 7th nucleotides from the 5' end of each sequencing read segment, and the PF2 sequence is the 8th to 14th nucleotides from the 5' end of each sequencing read segment, The PR1 sequence is the 1st to 7th nucleotides from the 3' end of each sequencing read, and the PR2 sequence is the 8th to 14th nucleotides from the 3' end of each sequencing read; the rPF1 sequence is the 1st to 7th nucleotides from the 5' end of the reverse complementary sequence, and the rPF2 sequence is the 8th to 14th nucleotides from the 5' end of each sequencing read, the rPR1 sequence is the 1st to 7th nucleotides from the 3' end of the reverse complementary sequence, and the rPR2 sequence is the 8th to 14th nucleotides from the 3' end of the reverse complementary sequence; B3) Accurate matching module: used for matching the sequencing data parsing sequence library with the reference genome parsing sequence library, the sequence obtained by matching the PF1 sequence of each sequencing read segment with the sequence obtained by matching the reference genome parsing sequence library is recorded as PF1-M, the sequence obtained by matching the PF2 sequence of each sequencing read segment with the sequence obtained by matching the reference genome parsing sequence library is recorded as PF2-M, the relative distance D1 is obtained by calculating the difference between the positions of PF1-M and PF2-M on the reference genome; the sequence obtained by matching the PR1 sequence of each sequencing read segment with the sequence obtained by matching the reference genome parsing sequence library is recorded as PR1-M, the sequence obtained by matching the PR2 sequence of each sequencing read segment with the sequence obtained by matching the reference genome parsing sequence library is recorded as PR2-M, the relative distance D2 is obtained by calculating the difference between the positions of PR1-M and PR2-M on the reference genome; Calculate the difference between the positions of the PF1-M and PR1-M on the reference genome to obtain a relative distance D3; record the nucleotide length of the sequencing read in the sequencing data as D4; select the sequencing read with D1=7, D2=7 and 0.5×D4≤D3≤1.5×D4 to obtain a preliminary matching sequencing read; Alternatively, the sequencing data parsed sequence library is matched with the reference genome parsed sequence library, the rPF1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF1-M, the rPF2 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF2-M, and the relative distance rD1 is obtained by calculating the difference between the positions of rPF1-M and rPF2-M on the reference genome; the rPR1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF1-M. rPR1-M, the rPR2 sequence of each sequencing read segment and the sequence matched in the reference genome parsed sequence library are recorded as rPR2-M, the difference between the positions of rPR1-M and rPR2-M on the reference genome is calculated to obtain the relative distance rD2; the difference between the positions of rPF1-M and rPR1-M on the reference genome is calculated to obtain the relative distance rD3; the nucleotide length of the sequencing read segment in the sequencing data is recorded as D4; the sequencing read segment with rD1=7, rD2=7 and 0.5×D4≤rD3≤1.5×D4 is selected to obtain the preliminary matching sequencing read segment; B4) A coarse-grained alignment module: used to record the sequence in the reference genome between the first nucleotide from the 5' end of the PF1-M of each of the sequencing reads and the first nucleotide from the 5' end of the PR1-M, or the sequence in the reference genome between the first nucleotide from the 5' end of the rPF1-M of each of the sequencing reads and the first nucleotide from the 5' end of the rPR1-M as the reference genome seq sequence of each preliminary matching sequencing read; calculate the Levenshtein distance LD value between each of the preliminary matching sequencing reads and the reference genome seq sequence of each of the preliminary matching sequencing reads based on the Levenshtein distance algorithm, and determine whether each of the preliminary matching sequencing reads is a sequencing read without indel mutation or a sequencing read with indel mutation based on the LD value; the sequencing read without indel mutation is a sequencing read without mutation or a read with SNP mutation; B5) a fine-grained alignment module: used to align the sequencing read with indel mutations with the corresponding reference genome seq sequence based on the Hamming distance and the Smith-Waterman algorithm respectively to obtain the genomic position information of the indel mutation in the sequencing read with indel mutations; B6) Assembly output module for sequencing reads: assembling and splicing all the sequencing reads without indel mutations and all the sequencing reads with indel mutations to obtain assembly analysis results of the sequencing data of the organism to be analyzed; the assembly analysis results include mutation information in the sequencing data of the organism to be analyzed and genomic location information of the mutation; the mutation includes SNP and / or indel mutation.

2. The device according to claim 1, characterized in that: In B4), the preliminary matching sequencing reads with LD values ​​less than or equal to 0.05 are output as sequencing reads without indel mutations, and the preliminary matching sequencing reads with LD values ​​greater than 0.05 are output as sequencing reads with indel mutations.

3. The device according to claim 1 or 2, characterized in that: B5) includes the following sub-modules: B5-1) A Hamming distance calculation module is used to calculate the similarity score between the m-(m+6)th nucleotide from the 5' segment or from the 3' end in the sequencing read with indel mutation and the m-(m+6)th nucleotide from the 5' end in the corresponding reference genome seq sequence using the Hamming distance, and obtain the preliminary mutation position in the sequencing read with indel mutation according to the similarity score; m is a natural number greater than 7, m is less than or equal to M-7, and M is the length of the sequencing read; B5-2) Smith-Waterman algorithm-based alignment module: used to use the Smith-Waterman algorithm to perform local sequence alignment on the preliminary mutation position and the reference genome seq sequence to obtain the genomic position information of the indel mutation in the sequencing read with the indel mutation.

4. The device according to claim 1 or 2, characterized in that: B6) The module is implemented by a method comprising the following steps: naming all the sequencing reads without indel mutations and all the sequencing reads with indel mutations as qualified sequencing reads, and mapping the qualified sequencing reads to the reference group sequence of the organism to be analyzed; calculating the nucleotide type with the largest number at each nucleotide site in all the mapped qualified sequencing reads starting from the first nucleotide at the 5' end of the reference group sequence, as the nucleotide type at the site in the sequencing data of the organism to be analyzed, and finally obtaining the nucleotide type at the nucleotide site in the sequencing data of the organism to be analyzed corresponding to each nucleotide site in the reference group sequence, and obtaining the assembly analysis result of the sequencing data of the organism to be analyzed; the type of the nucleotide is A, T, C, G or missing.

5. A method for analyzing second generation sequencing data, characterized in that: The method comprises the following steps: A1) establishing a reference genome sequence library of the organism to be analyzed, wherein the length of the reference genome nucleotide sequence is Nbp, and the reference genome sequence library contains N-6 sequences, and the length of the sequences is 7nt; the first sequence is the 1st to 7th nucleotides from the 5' end of the reference genome sequence, the second sequence is the 2nd to 8th nucleotides from the 5' end of the reference genome sequence, and the nth sequence is the nth to (n+6)th nucleotides from the 5' end of the reference genome sequence; n is a natural number greater than 0, and n≤N-6; A2) establishing a sequencing data analysis sequence library, wherein the sequencing data analysis sequence library is composed of the PF1 sequence, PF2 sequence, PR1 sequence and PR2 sequence of each sequencing read in the sequencing data of the organism to be analyzed, and the rPF1 sequence, rPF2 sequence, rPR1 sequence and rPR2 sequence of the reverse complementary sequence of each sequencing read; the PF1 sequence is the 1st to 7th nucleotides from the 5' end of each sequencing read, the PF2 sequence is the 8th to 14th nucleotides from the 5' end of each sequencing read, and the PR1 sequence is The 1st to 7th nucleotides from the 3' end of each sequencing read, and the PR2 sequence is the 8th to 14th nucleotides from the 3' end of each sequencing read; the rPF1 sequence is the 1st to 7th nucleotides from the 5' end of the reverse complementary sequence, the rPF2 sequence is the 8th to 14th nucleotides from the 5' end of each sequencing read, the rPR1 sequence is the 1st to 7th nucleotides from the 3' end of the reverse complementary sequence, and the rPR2 sequence is the 8th to 14th nucleotides from the 3' end of the reverse complementary sequence; A3) Exact matching: matching the sequencing data parsed sequence library with the reference genome parsed sequence library, the PF1 sequence of each sequencing read segment and the sequence matched with the reference genome parsed sequence library are recorded as PF1-M, the PF2 sequence of each sequencing read segment and the sequence matched with the reference genome parsed sequence library are recorded as PF2-M, and the relative distance D1 is obtained by calculating the difference between the positions of PF1-M and PF2-M on the reference genome; the PR1 sequence of each sequencing read segment and the sequence matched with the reference genome parsed sequence library are recorded as PR1-M, and the PR2 sequence of each sequencing read segment and the sequence matched with the reference genome parsed sequence library are recorded as PR2-M, and the relative distance D2 is obtained by calculating the difference between the positions of PR1-M and PR2-M on the reference genome; Calculate the difference between the positions of the PF1-M and PR1-M on the reference genome to obtain a relative distance D3; record the nucleotide length of the sequencing read in the sequencing data as D4; select the sequencing read with D1=7, D2=7 and 0.5×D4≤D3≤1.5×D4 to obtain a preliminary matching sequencing read; Alternatively, the sequencing data parsed sequence library is matched with the reference genome parsed sequence library, the rPF1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF1-M, the rPF2 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF2-M, and the relative distance rD1 is obtained by calculating the difference between the positions of rPF1-M and rPF2-M on the reference genome; the rPR1 sequence of each sequencing read segment is matched with the sequence in the reference genome parsed sequence library as rPF1-M. rPR1-M, the rPR2 sequence of each sequencing read segment and the sequence matched in the reference genome parsed sequence library are recorded as rPR2-M, the difference between the positions of rPR1-M and rPR2-M on the reference genome is calculated to obtain the relative distance rD2; the difference between the positions of rPF1-M and rPR1-M on the reference genome is calculated to obtain the relative distance rD3; the nucleotide length of the sequencing read segment in the sequencing data is recorded as D4; the sequencing read segment with rD1=7, rD2=7 and 0.5×D4≤rD3≤1.5×D4 is selected to obtain the preliminary matching sequencing read segment; A4) Coarse-grained alignment: record the sequence in the reference genome between the first nucleotide from the 5' end of the PF1-M and the first nucleotide from the 5' end of the PR1-M of each sequencing read, or, record the sequence in the reference genome between the first nucleotide from the 5' end of the rPF1-M and the first nucleotide from the 5' end of the rPR1-M of each sequencing read as the reference genome seq sequence of each preliminary matching sequencing read; calculate the Levenshtein distance LD value between each preliminary matching sequencing read and the reference genome seq sequence of each preliminary matching sequencing read based on the Levenshtein distance algorithm, and determine whether each preliminary matching sequencing read is a sequencing read without indel mutation or a sequencing read with indel mutation based on the LD value; the sequencing read without indel mutation is a sequencing read without mutation or a read with SNP mutation; A5) fine-grained alignment: aligning the sequencing read with indel mutations with the corresponding reference genome seq sequence based on Hamming distance and Smith-Waterman algorithm respectively to obtain the genomic position information of the indel mutation in the sequencing read with indel mutations; A6) Assembly output of sequencing reads: assembling and splicing all the sequencing reads without indel mutations and all the sequencing reads with indel mutations to obtain an assembly analysis result of the sequencing data of the organism to be analyzed; the assembly analysis result includes mutation information in the sequencing data of the organism to be analyzed and genomic location information of the mutation; the mutation includes SNP and / or indel mutation.

6. The method according to claim 5, characterized in that: A4), the preliminary matching sequencing reads with LD values ​​less than or equal to 0.05 are determined as sequencing reads without indel mutations, and the preliminary matching sequencing reads with LD values ​​greater than 0.05 are determined as sequencing reads with indel mutations.

7. The method according to claim 5 or 6, characterized in that: A5) comprises the following steps: A5-1) using the Hamming distance to calculate the similarity score between the m-(m+6)th nucleotides from the 5' segment or from the 3' end in the sequencing read with the indel mutation and the m-(m+6)th nucleotides from the 5' end in the corresponding reference genome seq sequence, and obtaining the preliminary mutation position in the sequencing read with the indel mutation according to the similarity score; m is a natural number greater than 7, m is less than or equal to M-7, and M is the length of the sequencing read; A5-2) Using the Smith-Waterman algorithm, the preliminary mutation position is locally aligned with the reference genome seq sequence to obtain the genomic position information of the indel mutation in the sequencing read with the indel mutation.

8. The method according to claim 5 or 6, characterized in that: A6) naming all the sequencing reads without indel mutations and all the sequencing reads with indel mutations as qualified sequencing reads, mapping the qualified sequencing reads to the reference group sequence of the organism to be analyzed; calculating the nucleotide type with the largest number at each nucleotide site in all the mapped qualified sequencing reads starting from the first nucleotide at the 5' end of the reference group sequence, as the nucleotide type at the site in the sequencing data of the organism to be analyzed, and finally obtaining the nucleotide type at the nucleotide site in the sequencing data of the organism to be analyzed corresponding to each nucleotide site in the reference group sequence, and obtaining the assembly analysis result of the sequencing data of the organism to be analyzed; the type of the nucleotide is A, T, C, G or missing.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium enables a computer to execute the steps of the method according to any one of claims 5 to 8.

10. Any of the following applications of the apparatus of any one of claims 1 to 4 and / or the method of any one of claims 5 to 8 and / or the computer-readable storage medium of claim 9: C1) Application in the preparation of localized computer-operated sequencing data analysis products; C2) Application in the preparation of localized computer-operated disease diagnosis products.

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