A method, device, computer readable storage medium and application for making simulated variant data

Abstract

The application discloses a method, device, computer readable storage medium and application for making simulated variant data. The method comprises the following steps: (1) reading an input bed file; (2) reading an input variant site; (3) making a wild-type simulated genome FASTA file and a mutant-type simulated genome FASTA file; (4) intercepting a FASTQ file from the wild-type simulated genome FASTA file and the mutant-type simulated genome FASTA file; and (5) making a BAM file of simulated data. The method can read different levels of annotation forms of variant sites, simulate all forms of genomic variations except structural variations, make corresponding simulated data according to the average depth and mutation frequency of simulated data required by a user, and is convenient to use.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics technology and relates to a method, apparatus, computer-readable storage medium and application for generating simulated variation data, and particularly to a method, apparatus, computer-readable storage medium and application for generating high-throughput sequencing simulated data based on hybridization capture. Background Technology

[0002] Next-generation sequencing (NGS) technologies for clinical molecular diagnostics, including whole-genome sequencing (WGS), whole-exome sequencing, and targeted gene sequencing, have become important tools for cancer diagnosis and treatment. However, when detecting variants from raw NGS data, the results from different bioinformatics workflows do not yet achieve satisfactory stability and standardization. Therefore, it is necessary to evaluate the performance of bioinformatics workflows before they are used for clinical sample testing. However, standardized data for evaluating bioinformatics workflows is currently lacking.

[0003] Aside from structural variations, genomic variations are categorized into single nucleotide variants (SNVs), small insertions / deletions (InDels), multiple nucleotide variants (MNVs), and complex deletions / insertions. Current mainstream simulation software can only simulate some forms of genomic variations, and no software can currently simulate MNVs and complex variations. Strategies that read and edit raw sequencing reads include BAMSurgen, insiM, and MutationMaker. These software programs require editing and modifying existing sequencing data, making them unsuitable for users without readily available sequencing data. De novo synthesis strategies include VarSim, SVEngine, and Wessim; however, these programs can only simulate SNVs and small insertions / deletions (InDels). Furthermore, common tumor variants (EGFR L858R, BRAF V600E, etc.) are typically represented by amino acid changes, but current software requires input of the genomic level changes and cannot directly read the RNA or amino acid level changes and output corresponding simulation data.

[0004] In summary, developing novel methods for constructing simulated variation data to meet the needs of evaluating bioinformatics processes is one of the most pressing issues to be addressed in the diagnostics field alone. Summary of the Invention

[0005] To address the shortcomings of existing technologies and practical needs, this invention provides a method, apparatus, computer-readable storage medium, and application for generating simulated variation data to meet the current requirements of bioinformatics evaluation processes.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for generating simulated mutation data, the method comprising the following steps:

[0008] (1) Read the input .bed file and extract the list of genes covered by the .bed file and the genomic coordinates of the genes;

[0009] (2) Read the input mutation sites and obtain genome-level mutation information, including chromosomes, start and end coordinates, and mutation sequences;

[0010] (3) Based on the gene list covered by the bed file and the genomic coordinates of the genes and the information of mutation sites at the genomic level, create wild-type simulated genome FASTA files and mutant simulated genome FASTA files;

[0011] (4) Extract FASTQ files from wild-type and mutant FASTA genome files;

[0012] (5) Using alignment software, the wild-type FASTQ file and the mutant FASTQ file are aligned with the reference genome to obtain the initial wild-type BAM file and the mutant BAM file. The part of the bed file region is extracted from the initial wild-type BAM file and the mutant BAM file to obtain the wild-type BAM file and the mutant BAM file.

[0013] (6) Read the mutation frequency of the target mutation site, and mix the wild-type BAM file and the mutant BAM file according to the mutation frequency to generate a BAM file of simulated mutation data.

[0014] In this invention, to address the problems of current simulation methods being unable to read the changes in mutations at the RNA or amino acid level and output corresponding simulation data, as well as the inability to simulate multi-base variations and complex variations in the genome, a de novo synthesis strategy is adopted to design and create a method for simulating mutation data. This method can directly read the changes in mutations in the genome, RNA, or amino acids in HGVS format, simulate all forms of genomic variation, and output the corresponding BAM files.

[0015] In this invention, a BED file refers to a text file format containing at least three columns: chromosome, start coordinates, and end coordinates. HGVS format annotation rules: The currently accepted mutation naming rules established by the Human Genome Variation Society (HGVS). FASTA format: In bioinformatics, FASTA format (also known as Pearson format) is a text-based format used to represent nucleotide or amino acid sequences. The first line of the sequence file begins with any text description starting with a greater-than sign ">" or a semicolon (">" is commonly used as the starting point), used for sequence labeling. From the second line onwards, the sequence itself is displayed, allowing only the use of established nucleotide or amino acid coding symbols. Nucleotide symbols are usually case-insensitive, while amino acids are typically uppercase. FASTQ format: FASTQ is a text format that stores biological sequences (usually nucleic acid sequences) and corresponding quality assessments. FASTQ format is a common sequence format. FASTQ sequences typically consist of four lines. The first line begins with '@' followed by sequence description information, similar to FASTA format. The second line is the sequence itself. The third line begins with '+', also followed by sequence description information. The fourth line contains quality values ​​(presumably sequencing quality assessments), with the same number of characters as the second line of sequence data. BAM files refer to the binary format of SAM files. SAM files are the default standard format for short sequence alignment. They are text formats delimited by tabs and are primarily used to represent sequencing sequence mapping results on the genome. They can also represent other multiple alignment results. Common alignment software typically aligns FASTQ files to a reference genome, resulting in a BAM file.

[0016] Preferably, step (1) specifically includes:

[0017] The first database is used to annotate the BED file, extract the list of genes covered by the BED file, and the chromosome, start, and end coordinates of all transcripts corresponding to each gene in the genome. The second database is used to extract the unique transcript of each gene, and thus obtain the chromosome, start, and end coordinates of each gene in the genome.

[0018] Preferably, the first database includes a refGene database or an ensemble database.

[0019] Preferably, the second database includes the HGNC database.

[0020] In this invention, the HGNC database, short for HUGO Gene Nomenclature Committee, is responsible for providing unique, standardized, and widely disseminated symbols for all genes in the human genome, including protein-coding genes, ncRNA genes, pseudogenes, and other genes. This database was jointly selected by NCBI and EMBL for each gene, and the most representative transcript was included in the database.

[0021] Preferably, step (2) specifically includes:

[0022] (2-1) Read the input variant sites, determine the annotation format of the input variant sites, use Transvar software to transform the input variant sites, and extract the genome-level annotations;

[0023] (2-2) Based on the genome-level annotations in the Transvar annotation results, obtain the genome-level variation information, and extract the chromosome, mutation start coordinates, mutation end coordinates and mutation sequence from the genome-level variation information;

[0024] (2-2-1) The number following the string "chr" in the genome-level annotation information is the chromosome where the mutation site is located;

[0025] (2-2-2) If the genome-level annotation information contains a single number, then that number is determined to be both the initial and final coordinates of the mutation. Continue reading the string following the number. If the string contains "delins", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following "delins". If the string contains "del", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is "" (indicating an empty mutation sequence). If the string contains "dup", then the mutation start coordinate is the same as the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following "dup". If the string contains ">", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following ">".

[0026] (2-2-3) If the genome-level annotation information contains two numbers separated by "_", then the first number is determined to be the initial start coordinate of the mutation, and the second number is determined to be the initial end coordinate of the mutation. Continue reading the string following the numbers. If the string contains "delins", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is the sequence string after "delins". If the string contains "del", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is "" (indicating that the mutation sequence is empty). If the string contains "dup", then the mutation start coordinate is the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is the sequence string after "dup". If the string contains "ins", then the mutation start coordinate is the initial mutation coordinate, and the mutation end coordinate is the initial and end mutation coordinate. The mutation sequence is the sequence string after "ins".

[0027] Preferably, step (3) specifically includes:

[0028] (3-1) Traverse the gene list covered by the bed file. If the gene does not contain the mutation site to be simulated, use the extraction software to extract the sequence information of the gene from the genome according to the chromosome coordinates of the gene and output it to the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file respectively.

[0029] (3-2) If the gene contains the mutation site that needs to be simulated, firstly, based on the chromosome coordinates of the gene, use the extraction software to extract the sequence information of the gene from the genome and output it to the wild-type simulated genome FASTA file; secondly, based on the start coordinates of the gene on the genome and the start coordinates of the mutation site, extract the first sequence from the genome, take the mutation sequence of the mutation site as the second sequence, and based on the end coordinates of the gene mutation site and the end coordinates on the genome, extract the third sequence from the genome. Combine the first, second and third sequences together and output them to the mutant simulated genome FASTA file;

[0030] (3-3) Process the wild-type and mutant FASTA simulated genome files so that the number of bases in each line of the sequence portion in the file is equal.

[0031] Preferably, the interception software includes samtools software or twoBitToFa software;

[0032] Preferably, the software for adjusting the number of bases per line in the FASTA file includes Picard software.

[0033] Preferably, step (4) specifically includes:

[0034] (4-1) Read the target average depth of the input simulation data. Based on the start and end coordinates of all genes in the gene list covered by the BED file, calculate the sum of the differences between the start and end coordinates of all genes. Multiply the average depth by the sum of the differences to get the required amount of data.

[0035] (4-2) Extract the required amount of wild-type FASTQ files and mutant FASTQ files from the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file, respectively. Preferably, this step can be performed using wgsim software.

[0036] Preferably, step (5) specifically includes:

[0037] (5-1) Use alignment software to align the wild-type FASTQ file and the mutant FASTQ file with the reference genome to obtain the initial wild-type BAM file and the mutant BAM file. Extract the part of the bed file region from the initial wild-type BAM file and the mutant BAM file to obtain the wild-type BAM file and the mutant BAM file (the intersect module of bedtools software can be used).

[0038] (5-2) Read the mutation frequency of the target mutation site, and mix the wild-type BAM file and the mutant BAM file according to the ratio to generate a BAM file of simulated mutation data.

[0039] In a second aspect, the present invention provides an apparatus for generating simulated mutation data, the apparatus being used to perform the steps of the method for generating simulated mutation data described in the first aspect, the apparatus comprising:

[0040] The module for reading BED files is used to perform the following:

[0041] Read the input BED file and extract the list of genes covered by the BED file and the genomic coordinates of the genes;

[0042] The module for reading variant sites is used to perform the following:

[0043] Read the input mutation sites to obtain genome-level mutation information, including chromosome, start and end coordinates, and mutation sequence;

[0044] Create a FASTA file module for execution including:

[0045] Based on the gene list covered by the BED file and the genomic coordinates of the genes and the information on mutation sites at the genomic level, create wild-type and mutant FASTA simulated genome files.

[0046] Create a FASTQ file module for execution including:

[0047] Extract FASTQ files from wild-type and mutant FASTA genome files;

[0048] Create a simulated mutation data module to perform the following:

[0049] The mutation frequency of the target mutation site is read, and BAM files of simulated variation data are generated using wild-type FASTQ files and mutant FASTQ files, genome files, and bed files.

[0050] Preferably, the module for reading the BED file is used to perform the following:

[0051] The first database is used to annotate the BED file, extract the list of genes covered by the BED file, and the chromosome, start, and end coordinates of all transcripts corresponding to each gene in the genome. The second database is used to extract the unique transcript of each gene, and thus obtain the chromosome, start, and end coordinates of each gene in the genome.

[0052] The module for reading variant sites is used to perform the following:

[0053] (2-1) Read the input variant sites, determine the annotation format of the input variant sites, use Transvar software to transform the input variant sites, and extract the genome-level annotations;

[0054] (2-2) Based on the genome-level annotations in the transvar software annotation results, obtain genome-level variation information, and extract chromosomes, mutation start coordinates, mutation end coordinates, and mutation sequences from the genome-level variation information;

[0055] (2-2-1) The number following the string "chr" in the genome-level annotation information is the chromosome where the mutation site is located;

[0056] (2-2-2) If the genome-level annotation information contains a single number, then that number is determined to be both the initial and final coordinates of the mutation. Continue reading the string following the number. If the string contains "delins", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following "delins". If the string contains "del", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is "". If the string contains "dup", then the mutation start coordinate is the same as the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following "dup". If the string contains ">", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following ">".

[0057] (2-2-3) If the genome-level annotation information contains two numbers separated by "_", then the first number is determined to be the initial start coordinate of the mutation, and the second number is determined to be the initial end coordinate of the mutation. Continue reading the string following the numbers. If the string contains "delins", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is the sequence string after "delins". If the string contains "del", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is "". If the string contains "dup", then the mutation start coordinate is the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is the sequence string after "dup". If the string contains "ins", then the mutation start coordinate is the initial mutation coordinate, and the mutation end coordinate is the initial and end mutation coordinate. The mutation sequence is the sequence string after "ins".

[0058] The module for creating FASTA files is used to perform the following:

[0059] (3-1) Traverse the gene list covered by the bed file. If the gene does not contain the mutation site to be simulated, use the extraction software to extract the sequence information of the gene from the genome according to the chromosome coordinates of the gene and output it to the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file respectively.

[0060] (3-2) If the gene contains the mutation site that needs to be simulated, firstly, based on the chromosome coordinates of the gene, use the extraction software to extract the sequence information of the gene from the genome and output it to the wild-type simulated genome FASTA file; secondly, based on the start coordinates of the gene on the genome and the start coordinates of the mutation site, extract the first sequence from the genome, take the mutation sequence of the mutation site as the second sequence, and based on the end coordinates of the gene mutation site and the end coordinates on the genome, extract the third sequence from the genome. Combine the first, second and third sequences together and output them to the mutant simulated genome FASTA file;

[0061] (3-3) Process the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file so that the number of bases in each line of the sequence portion in the file is equal. Preferably, Picard software can be used to perform this step.

[0062] The FASTQ file creation module is used to perform the following:

[0063] (4-1) Read the target average depth of the input simulation data. Based on the start and end coordinates of all genes in the gene list covered by the BED file, calculate the sum of the differences between the start and end coordinates of all genes. Multiply the average depth by the sum of the differences to get the required amount of data.

[0064] (4-2) Extract the required amount of wild-type FASTQ files and mutant FASTQ files from the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file, respectively. Preferably, this step can be performed using wgsim software.

[0065] The module for generating simulated mutation data is used to perform the following:

[0066] (5-1) Use alignment software to align the wild-type FASTQ file and the mutant FASTQ file with the reference genome to obtain the initial wild-type BAM file and the mutant BAM file. Extract the part of the bed file region from the initial wild-type BAM file and the mutant BAM file to obtain the wild-type BAM file and the mutant BAM file (the intersect module of bedtools software can be used).

[0067] (5-2) Read the mutation frequency of the target mutation site, and mix the wild-type BAM file and the mutant BAM file according to the ratio to generate a BAM file of simulated mutation data.

[0068] Thirdly, the present invention provides a computer-readable storage medium storing a computer program that causes a computer to establish and / or run the steps of a method for producing simulated mutation data as described in the first aspect, or a module of an apparatus for producing simulated mutation data as described in the second aspect.

[0069] Fourthly, the present invention provides the application of the method for generating simulated variant data as described in the first aspect, the apparatus for generating simulated variant data as described in the second aspect, or the computer-readable storage medium storing computer programs as described in the third aspect in the preparation of bioinformatics analysis standard reference data for biological sequencing data, or in the testing of bioinformatics analysis software or processes.

[0070] Compared with the prior art, the present invention has the following beneficial effects:

[0071] This method employs a de novo synthesis strategy to design and create simulated variant data. It can read annotations of variant sites at different levels (genome, RNA, amino acids) based on HGVS format annotation rules and convert them into genome-level mutation start and end coordinates and mutation sequences. It can simulate all forms of genomic variation except structural variation, including single nucleotide variants (SNVs), multiple nucleotide variants (MNVs), insertions and deletions (InDels), and complex deletion-insertion. It can generate corresponding simulated data according to the average depth and mutation frequency required by the user, making it convenient to use. Attached Figure Description

[0072] Figure 1 A correlation graph showing the actual mutation frequency and the expected mutation frequency of a single base variation (MET: c.3028+3A>C);

[0073] Figure 2 A correlation plot of the actual mutation frequency and the expected mutation frequency for the insertion variant (chr7:g.116412036_116412037insAA);

[0074] Figure 3 A correlation plot showing the actual mutation frequency versus the expected mutation frequency of the deletion variant (MET: c.2888-18_2888-2del);

[0075] Figure 4 Box plots showing the actual effective average depth versus the expected effective average depth for single-base variants (MET: c.3028+3A>C), insertion variants (chr7: g.116412036_116412037insAA), and deletion variants (MET: c.2888-18_2888-2del). Detailed Implementation

[0076] To further illustrate the technical means and effects of this invention, the following description, in conjunction with embodiments and accompanying drawings, provides a further explanation of the invention. It is understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it.

[0077] Where specific techniques or conditions are not specified in the examples, they shall be performed in accordance with the techniques or conditions described in the literature in this field, or in accordance with the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0078] Example 1

[0079] This embodiment generates simulated mutation data.

[0080] To verify the simulation effect of the method of the present invention on different types of genomic variants, single base variants (MET: c.3028+3A>C), multibase variants (chr7: g.116412045_116412046delinsCC), insertion mutations (chr7: g.116412036_116412037insAA), deletion mutations (MET: c.2888-18_2888-2del), and complex variants (MET: c.3028+2_3028+4delinsACC) were selected.

[0081] The method for creating simulated mutation data includes the following steps:

[0082] (1) Read the input .bed file and extract the gene list and gene genomic coordinates covered by the .bed file; (2) Read the input variant sites and obtain the variant information at the genomic level, including chromosome, start and end coordinates, and mutation sequence; (3) Based on the gene list and gene genomic coordinates covered by the .bed file and the information of the mutation sites at the genomic level, create a wild-type simulated genome FASTA file and a mutant simulated genome FASTA file; (4) Extract the FASTQ file from the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file; (5) Use alignment software to align the wild-type FASTQ file and the mutant FASTQ file with the reference genome to obtain the initial wild-type BAM file and the mutant BAM file, extract the part of the .bed file region from the initial wild-type BAM file and the mutant BAM file to obtain the wild-type BAM file and the mutant BAM file; (6) Read the mutation frequency of the target mutation site, mix the wild-type BAM file and the mutant BAM file according to the ratio, and generate a BAM file of simulated variant data.

[0083] The process of generating simulation data is serialized using Python code and encapsulated in a script named `simulate.py`. Simulation data can be generated via the command line: `python simulate.py -I<mutation site> -p <prefix for generating simulation data> -d <effective average depth> -f <mutation frequency>`. Simulation data for the different types of mutation sites selected above can be generated using the following command line:

[0084] a.python3 simulate.py-i "MET:c.3028+3A>C" -p ad 1000-f0.05;

[0085] b.python3 simulate.py -i "chr7:g.116412045_116412046delinsCC" -p b-d1000-f0.05;

[0086] c.python3 simulate.py -i "chr7:g.116412036_116412037insAA" -p cd 1000-f0.05;

[0087] d.python3 simulate.py -i "MET:c.2888-18_2888-2del" -p dd 1000-f0.05;

[0088] e.python3 simulate.py -i "MET:c.3028+2_3028+4delinsACC" -p ed 1000-f0.05.

[0089] The generated BAM file was detected using VarDict software, and the detection results of VarScan were annotated using transvar software. The results were compared with the expected results. The results are shown in Table 1.

[0090] Table 1

[0091] Input mutation form Expected codon changes at the detected sites Actual codon changes at detected sites MET:c.3028+3A>C c.3028+3A>C c.3028+3A>C chr7:g.116412045_116412046delinsCC c.3028+2_3028+3delinsCC c.3028+2_3028+3delinsCC chr7:g.116412036_116412037insAA c.3021_3022insAA c.3021_3022insAA MET:c.2888-18_2888-2del c.2888-18_2888-2del17 c.2888-18_2888-2del17 MET:c.3028+2_3028+4delinsACC c.3028+2_3028+4delinsACC c.3028+2_3028+4delinsACC

[0092] As shown in Table 1, the method of the present invention can effectively simulate single-base mutations, multi-base mutations, insertion mutations, deletion mutations and complex mutations.

[0093] Example 2

[0094] The simulation data generated in Example 1 was tested, including the effective average depth of the data, the mutation frequency, and the accuracy compared with the input parameters.

[0095] (1) Select single base mutation (MET: c.3028+3A>C), insertion mutation (chr7: g.116412036_116412037insAA), and deletion mutation (MET: c.2888-18_2888-2del) to create BAM files respectively. The depth is set to three gradients: 500X, 1000X and 2000X respectively. The mutation frequency ranges from 0.01 to 0.2, with each step being 0.01. Create 60 BAM files for each mutation.

[0096] (2) The effective average depth of different BAM files was calculated using the bamdst software. The correlation between the actual average depth and the expected average depth of different BAM files was statistically analyzed. The BAM files were detected using the VarDict software. The correlation between the actual mutation frequency and the expected mutation frequency of different mutation sites was statistically analyzed. In this way, the accuracy of the effective average depth and mutation frequency of the simulated data compared with the input parameters was evaluated.

[0097] (3) The correlation between the actual mutation frequency and the expected mutation frequency of the three mutation types was statistically analyzed, and the results are as follows: Figures 1-3 As shown, the correlation coefficients all reached above 0.95.

[0098] (4) Statistically analyze the relationship between the actual effective average depth and the expected effective average depth for the three types of variation, and plot a box plot. The results are as follows: Figure 4 As shown, the actual effective average depth fluctuates around the expected effective average depth.

[0099] Based on the above results, it can be seen that the effective average depth and mutation frequency of the simulation data generated by the method of the present invention are more accurate than the input parameters.

[0100] Example 3

[0101] Simulated data carrying 17 variant sites were generated using simulation data software according to Table 2, with an effective average depth of 1400X and a mutation frequency of 0.3%. Each site was replicated 10 times, resulting in a total of 17 x 10 = 170 simulated data. The simulated data were analyzed using the "Human EGFR / KRAS / BRAF / NRAS / PIK3CA Gene Mutation Combined Detection Kit Analysis Software" from Beijing Youxun, and the detection rate was statistically analyzed. The detection rate for each site was 100%. Therefore, this invention verifies that the "Human EGFR / KRAS / BRAF / NRAS / PIK3CA Gene Mutation Combined Detection Kit Analysis Software" is reliable.

[0102] Table 2

[0103]

[0104] In summary, this invention employs a de novo synthesis strategy to design and create simulated variant data. It can read annotations of variant sites at different levels (genomics, RNA, amino acids) based on HGVS format annotation rules and convert them into genome-level mutation start and end coordinates and mutation sequences. It can simulate all forms of genomic variation except structural variations, including single-base variations, multi-base variations, insertions and deletions, and complex variations. Furthermore, it can create corresponding simulated data based on the average depth and mutation frequency required by the user, making it convenient to use.

[0105] The applicant declares that the detailed method of the present invention is illustrated by the above embodiments, but the present invention is not limited to the above detailed method, that is, it does not mean that the present invention must rely on the above detailed method to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions of the raw materials of the product of the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method for generating simulated mutation data, characterized in that, The method includes the following steps: (1) Read the input .bed file and extract the list of genes covered by the .bed file and the genomic coordinates of the genes; (2) Read the input mutation sites and obtain genome-level mutation information, including chromosomes, start and end coordinates, and mutation sequences; (3) Based on the gene list covered by the BED file and the genomic coordinates of the genes and the information of mutation sites at the genomic level, create wild-type simulated genome FASTA files and mutant simulated genome FASTA files; (4) Extract FASTQ files from wild-type and mutant FASTA genome files; (5) Using alignment software, the wild-type FASTQ file and the mutant FASTQ file are aligned with the reference genome to obtain the initial wild-type BAM file and the mutant BAM file. The part of the bed file region in the initial wild-type BAM file and the mutant BAM file is extracted to obtain the wild-type BAM file and the mutant BAM file. (6) Read the mutation frequency of the target mutation site, mix the wild-type BAM file and the mutant BAM file to generate a BAM file of simulated mutation data; Step (2) specifically includes: (2-1) Read the input variant sites, determine the annotation format of the input variant sites, use Transvar software to transform the input variant sites, and extract the genome-level annotations; (2-2) Based on the genome-level annotations in the Transvar software annotation results, obtain genome-level variation information, and extract chromosomes, mutation start coordinates, mutation end coordinates, and mutation sequences from the genome-level variation information; (2-2-1) The number following the string "chr" in the genome-level annotation information is the chromosome where the mutation site is located; (2-2-2) If the genome-level annotation information contains a single number, then that number is determined to be both the initial and final coordinates of the mutation. Continue reading the string following the number. If the string contains "delins", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following "delins". If the string contains "del", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is " ". If the string contains "dup", then the mutation start coordinate is the same as the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following "dup". If the string contains ">", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following ">". (2-2-3) If the genome-level annotation information contains two numbers separated by "_", then the first number is determined to be the initial start coordinate of the mutation, and the second number is determined to be the initial end coordinate of the mutation. Continue reading the string following the numbers. If the string contains "delins", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is the sequence string after "delins". If the string contains "del", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is " ". If the string contains "dup", then the mutation start coordinate is the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is the sequence string after "dup". If the string contains "ins", then the mutation start coordinate is the initial mutation coordinate, and the mutation end coordinate is the initial and end mutation coordinate. The mutation sequence is the sequence string after "ins".

2. The method for generating simulated mutation data according to claim 1, characterized in that, Step (1) specifically includes: The first database is used to annotate the BED file, extract the list of genes covered by the BED file, and the chromosome, start, and end coordinates of all transcripts corresponding to each gene in the genome. The second database is used to extract the unique transcript of each gene, and thus obtain the chromosome, start, and end coordinates of each gene in the genome.

3. The method for generating simulated mutation data according to claim 2, characterized in that, The first database includes either the refGene database or the ensemble database.

4. The method for generating simulated mutation data according to claim 2, characterized in that, The second database includes the HGNC database.

5. The method for generating simulated mutation data according to claim 1, characterized in that, Step (3) specifically includes: (3-1) Traverse the gene list covered by the bed file. If the gene does not contain the mutation site to be simulated, use the extraction software to extract the sequence information of the gene from the genome according to the chromosome coordinates of the gene and output it to the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file respectively. (3-2) If the gene contains the mutation site that needs to be simulated, firstly, according to the chromosome coordinates of the gene, use the extraction software to extract the sequence information of the gene from the genome and output it to the wild-type simulated genome FASTA file; secondly, according to the start coordinates of the gene on the genome and the start coordinates of the mutation site, extract the first sequence from the genome, take the mutation sequence of the mutation site as the second sequence, and according to the end coordinates of the gene mutation site and the end coordinates on the genome, extract the third sequence from the genome, merge the first, second and third sequences together and output them to the mutant simulated genome FASTA file; (3-3) Process the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file so that the number of bases in each line of the sequence portion in the file is equal.

6. The method for generating simulated mutation data according to claim 5, characterized in that, The interception software includes samtools or twoBitToFa.

7. The method for generating simulated variation data according to claim 5, characterized in that, Software that can adjust the number of bases per line in a FASTA file includes Picard.

8. The method for generating simulated mutation data according to claim 1, characterized in that, Step (4) specifically includes: (4-1) Read the target average depth of the input simulation data. Based on the start and end coordinates of all genes in the gene list covered by the BED file, calculate the sum of the differences between the start and end coordinates of all genes. Multiply the average depth by the sum of the differences to get the required amount of data. (4-2) Extract the required amount of wild-type FASTQ files and mutant FASTQ files from the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file, respectively.

9. An apparatus for generating simulated mutation data, characterized in that, The apparatus is used to perform the steps of the method for generating simulated variation data according to any one of claims 1-8, the apparatus comprising: The module for reading BED files is used to perform the following: Read the input BED file and extract the list of genes covered by the BED file and the genomic coordinates of the genes; The module for reading variant sites is used to perform the following: Read the input mutation sites to obtain genome-level mutation information, including chromosome, start and end coordinates, and mutation sequence; Create a FASTA file module for execution including: Based on the gene list covered by the BED file and the genomic coordinates of the genes and the information on mutation sites at the genomic level, create wild-type and mutant FASTA simulated genome files. Create a FASTQ file module for execution including: Extract FASTQ files from wild-type and mutant FASTA genome files; Create a simulated mutation data module to perform the following: The mutation frequency of the target mutation site is read, and BAM files of simulated variation data are generated using wild-type FASTQ files and mutant FASTQ files, genome files, and bed files.

10. The apparatus for generating simulated mutation data according to claim 9, characterized in that, The module for reading BED files is used to perform the following: The first database is used to annotate the BED file, extract the gene list covered by the BED file, and the chromosome, start and end coordinates of all transcripts corresponding to each gene in the genome; the second database is used to extract the unique transcript of each gene, and thus obtain the chromosome, start and end coordinates of each gene in the genome. The module for reading variant sites is used to perform the following: (2-1) Read the input variant sites, determine the annotation format of the input variant sites, use Transvar software to transform the input variant sites, and extract the genome-level annotations; (2-2) Based on the genome-level annotations in the transvar software annotation results, obtain the genome-level variation information, and extract the chromosome, mutation start coordinates, mutation end coordinates, and mutation sequence from the genome-level variation information; (2-2-1) The number following the string "chr" in the genome-level annotation information is the chromosome where the mutation site is located; (2-2-2) If the genome-level annotation information contains a single number, then that number is determined to be both the initial and final coordinates of the mutation. Continue reading the string following the number. If the string contains "delins", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following "delins". If the string contains "del", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is " ". If the string contains "dup", then the mutation start coordinate is the same as the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following "dup". If the string contains ">", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and final mutation coordinates. The mutation sequence is the sequence string following ">". (2-2-3) If the genome-level annotation information contains two numbers separated by "_", then the first number is determined to be the initial start coordinate of the mutation, and the second number is determined to be the initial end coordinate of the mutation. Continue reading the string following the numbers. If the string contains "delins", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is the sequence string after "delins". If the string contains "del", then the mutation start coordinate is reduced by 1 from the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is " ". If the string contains "dup", then the mutation start coordinate is the initial mutation coordinate, and the mutation end coordinate is increased by 1 from the initial and end mutation coordinates. The mutation sequence is the sequence string after "dup". If the string contains "ins", then the mutation start coordinate is the initial mutation coordinate, and the mutation end coordinate is the initial and end mutation coordinate. The mutation sequence is the sequence string after "ins". The module for creating FASTA files is used to perform the following: (3-1) Traverse the gene list covered by the bed file. If the gene does not contain the mutation site to be simulated, use the extraction software to extract the sequence information of the gene from the genome according to the chromosome coordinates of the gene and output it to the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file respectively. (3-2) If the gene contains the mutation site that needs to be simulated, firstly, according to the chromosome coordinates of the gene, use the extraction software to extract the sequence information of the gene from the genome and output it to the wild-type simulated genome FASTA file; secondly, according to the start coordinates of the gene on the genome and the start coordinates of the mutation site, extract the first sequence from the genome, take the mutation sequence of the mutation site as the second sequence, and according to the end coordinates of the gene mutation site and the end coordinates on the genome, extract the third sequence from the genome, merge the first, second and third sequences together and output them to the mutant simulated genome FASTA file; (3-3) Process the wild-type and mutant FASTA simulated genome files so that the number of bases in each line of the sequence portion in the file is equal; The FASTQ file creation module is used to perform the following: (4-1) Read the target average depth of the input simulation data. Based on the start and end coordinates of all genes in the gene list covered by the BED file, calculate the sum of the differences between the start and end coordinates of all genes. Multiply the average depth by the sum of the differences to get the required amount of data. (4-2) Extract the required amount of wild-type FASTQ files and mutant FASTQ files from the wild-type simulated genome FASTA file and the mutant simulated genome FASTA file, respectively; The module for generating simulated mutation data is used to perform the following: (5-1) Using alignment software, the wild-type FASTQ file and the mutant FASTQ file are aligned with the reference genome to obtain the initial wild-type BAM file and the mutant BAM file. The bed file region is extracted from the initial wild-type BAM file and the mutant BAM file to obtain the wild-type BAM file and the mutant BAM file. (5-2) Read the mutation frequency of the target mutation site, and mix the wild-type BAM file and the mutant BAM file according to the ratio to generate a BAM file of simulated mutation data.

11. A computer-readable storage medium storing a computer program, characterized in that, The computer program enables the computer to establish and / or run the steps of the method for producing simulated variation data as described in any one of claims 1-8, or the computer program enables the computer to establish and / or run the modules of the apparatus for producing simulated variation data as described in claim 9 or 10.

12. The method for generating simulated variant data as described in any one of claims 1-8, the apparatus for generating simulated variant data as described in claim 9 or 10, or the computer-readable storage medium storing a computer program as described in claim 11, in the preparation of standard reference data for bioinformatics analysis of biological sequencing data, or in the testing of bioinformatics analysis software or processes.

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