Method for performing hybridization detection by singly using whole genome re-sequencing data

Abstract

The invention discloses a method for performing hybridization detection by singly using whole genome re-sequencing data, and belongs to the technical field of molecular biology. In order to solve the technical problems that a hybridization detection method in the prior art is high in cost and low in accuracy and needs pure male and female parent reference genome information, the invention provides a method for detecting hybrid individuals based on nuclear mitochondrial DNA resequencing data, and the method is based on whole genome resequencing data and nuclear mitochondrial DNA fragments of target individuals. And judging a hybridization detection result according to a broken line graph drawn by a genetic distance data set between the nuclear mitochondrial DNA fragment and the mitochondrial homologous fragment thereof. The hybrid individual detection method provided by the invention has relatively high accuracy, avoids the technical problem that genetic markers effective to both male and female parents need to be designed, does not need to detect pure male and female parent reference genomes of target individuals, reduces the detection cost and expands the application range.

Description

Technical Field

[0001] The present invention belongs to the technical field of molecular biology, and in particular relates to a method for performing hybridization detection using whole genome resequencing data alone. Background Art

[0002] Hybridization is the process of mating between closely related species and is a natural phenomenon. Studies have found evidence of introgressive hybridization in many groups, including amphibians, birds, and mammals, where hybrids backcross with parental species, thereby achieving gene flow between groups. This provides clues for a deeper understanding of the formation process of species boundaries and whether the gene exchange pattern is consistent. The introduction of non-native species is the main impact of human activities on biodiversity. It also promotes hybridization and has become the second largest threat to biodiversity after habitat loss, partly because they have the potential to hybridize with native populations, thereby affecting genetic integrity, inducing out-breeding depression, and even replacing native populations genetically and phenotypically, thereby increasing the risk of species extinction and posing a challenge to biodiversity conservation. Therefore, it is very important to propose an efficient and accurate hybridization detection method for ecological and evolutionary research and conservation practice.

[0003] Hybrids inherit the traits of both parental species. With successive generations of backcrossing, hybrids gradually lose their obvious traits and become similar to purebred individuals, eventually presenting native morphology, which poses a great challenge to morphological identification. Molecular biological identification is a sensitive detection method. Traditionally, molecular biological identification methods use mitochondrial DNA (mtDNA) to identify the maternal lineage and use genomic markers such as microsatellites (also known as short tandem repeats, STRs) or single nucleotide polymorphisms (SNPs) to estimate the genomic contribution of parental species. However, the entire detection method has technical problems such as complex process, high cost and low accuracy. At present, whole genome sequencing technology allows the use of a large number of SNPs with whole genome coverage, which improves the accuracy of hybridization detection results. However, the use of genetic markers requires reference samples of known pure maternal and pure paternal parents. Microsatellite and SNP markers must be valid for both parental species. Otherwise, genotyping errors will also weaken the accuracy of identification. Verification of marker panels is both time-consuming and costly. For the same reason, whole-genome sequencing (WGS) methods also require pure maternal and pure paternal reference genomes. Although genome assembly is becoming more common, cheap and easy, it still has the problem of purity authentication of individuals used for genome assembly, which is expensive and technical.

[0004] Therefore, those skilled in the art are eager to develop a method for hybridization detection of target individuals that is sensitive, accurate, low-cost, and does not require pure maternal and pure paternal reference genome information. Summary of the invention

[0005] In order to solve the technical problems of the prior art hybridization detection methods having high cost, low accuracy and requiring pure parental reference genome information, the present invention provides a method for hybridization detection using whole genome resequencing data alone.

[0006] One of the purposes of the present invention is to provide a method for hybridization detection using whole genome resequencing data alone, wherein the detection method specifically comprises the following steps:

[0007] Step 1: Collect samples from the target individual, transport them under dry ice conditions and store them at a temperature below -20°C, and use a kit to extract the DNA of the samples; construct a DNBSEQ whole-genome resequencing conventional library for sequencing to obtain the resequencing data of the target individual;

[0008] Step 2: Download the cytochrome b sequence of the species to which the target individual belongs from NCBI as the seed for de novo assembly of the mitochondrial genome, and use NOVOplasty v4.3.1 to assemble the mitochondrial genome to obtain the mitochondrial reference genome;

[0009] Step 3: Identification of nuclear mitochondrial DNA fragments;

[0010] Step 3.1: Remove the reads used to assemble the mitochondrial genome from the resequencing data reads obtained in step 1 to generate a new fastq file. Use BWA0.7.17-r1188 to align the fastq file obtained above with the mitochondrial reference genome assembled in step 2 to generate a bam file to obtain the aligned bam file;

[0011] Step 3.2: Use samtools 1.17 to filter the bam file obtained in step 3.1 to obtain potential nuclear mitochondrial DNA sequences;

[0012] Step 3.3: Convert the potential nuclear mitochondrial DNA sequences obtained in step 3.2 into BED format using bamtobed ​​of bedtools 2.30.0, cluster the overlapping reads within 1000 bp using Bedtools Cluster, and then convert the above sequences back into fastq format using Bedtools Bamtofastq;

[0013] Step 3.4: Perform BLAST comparison between the mitochondrial reference genome in step 2 and the fastq obtained in step 3.3. Use the -dust no parameter to not mask low-complexity regions. The Python script extracts the highest-scoring BLAST matches. The above nuclear mitochondrial DNA sequences are compared with each other to obtain the confirmed nuclear mitochondrial DNA sequence.

[0014] Step 4: Align the nuclear mitochondrial DNA sequence determined in step 3 with the mitochondrial reference genome in step 2 separately without filtering, calculate the genetic distance between each nuclear mitochondrial DNA sequence and its mitochondrial homologous fragment; and draw a line graph, and judge the results based on the line graph.

[0015] In a preferred embodiment of the present invention, when the sample in step 1 is a blood or tissue sample, whole genome resequencing can be performed directly after DNA extraction, and the depth of whole genome resequencing is >30X.

[0016] In a preferred embodiment of the present invention, the resequencing data reads in step 1 are clean-data obtained after quality control.

[0017] In a preferred embodiment of the present invention, the genetic distance in step 4 is calculated by the Kimura2 parameter model in MEGAx.

[0018] In a preferred embodiment of the present invention, the horizontal axis of the line graph in step 4 represents the genetic distance, and the vertical axis represents the frequency of each genetic distance interval.

[0019] In a preferred embodiment of the present invention, the criterion for judging the result in step 4 is: if the frequency of the genetic distance interval decreases steadily as the X-axis increases, it indicates that the target individual is a purebred; if the frequency of the genetic distance interval shows a frequency peak as the X-axis increases, it indicates that the target individual is a hybrid.

[0020] Beneficial effects of the present invention: The present invention is based on the theory that a part of the mitochondrial genome can be transferred to the nuclear genome, namely, nuclear mitochondrial DNA fragments (NUMTs), and provides a method for hybridization detection using whole genome resequencing data alone; after the whole genome of the target individual is resequenced, the detection method assembles the mitochondrial genome using the cytochrome b fragment of the species to which the target individual belongs downloaded from NCBI as a seed, and then compares the obtained resequencing data reads with the assembled mitochondrial genome using BWA, and the comparison results are screened to obtain determined NUMTs, and the genetic distance data set between the NUMTs and their mitochondrial homologous fragments is plotted into a line graph, and the target individual is identified as a hybrid individual based on the relationship between the genetic distance on the horizontal axis and the frequency of each genetic distance interval on the vertical axis in the line graph.

[0021] The present invention filters bam files by samtools 1.17 to delete low-quality reads (mapping quality <30), unmapped reads and reads of secondary alignment; converts clustering results back to fastq format by using Bedtools Bamtofastq to minimize redundant hits; shields targeted reads by BLAST alignment, uses the "-dust no" parameter to not shield low-complexity regions, avoids missing information that may be useful for research, and improves the accuracy of the detection results of the method described in the present invention.

[0022] The present invention adopts molecular means to perform hybridization detection, which has higher credibility and improves the accuracy of hybridization detection results compared with morphological hybridization detection methods; the present invention does not need to design microsatellite and SNP markers, and compared with traditional molecular hybridization detection methods, it effectively avoids the technical difficulty of needing to design simultaneous effective markers for pure parents, thereby reducing detection costs; and the detection method provided by the present invention does not need to detect the reference genome at the chromosome level of the pure parent population of the target individual, thereby expanding the scope of application of the detection method provided by the present invention and providing technical support for biodiversity conservation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The figure is the test result of purebred sika deer in Example 1; the horizontal axis Genetic Distance is the genetic distance, and the vertical axis Frequency is the frequency;

[0024] Figure 2 The figure is the test result of the hybrid of sika deer and red deer in Example 1;

[0025] Figure 3 The figure is the test result of the purebred Siberian tiger in Example 2;

[0026] Figure 4The test result diagram of the Siberian tiger and Bengal tiger hybrid in Example 2;

[0027] Figure 5 This is a test result diagram of the purebred Golden Pheasant in Example 3;

[0028] Figure 6 This is a test result diagram of the hybrid of the red-bellied pheasant and the white-bellied pheasant in Example 3;

[0029] Figure 7 This is a graph showing the test results of the purebred green turtle in Example 4;

[0030] Figure 8 This is a graph showing the test results of the hybrid of the green turtle and the loggerhead turtle in Example 4;

[0031] Fig. 9 The test result diagram of purebred blue catfish in Example 5;

[0032] Fig.10 This is a graph showing the detection results of the blue catfish and channel catfish hybrid in Example 5. DETAILED DESCRIPTION

[0033] Those skilled in the art can refer to the content of this article and appropriately improve the process parameters. It should be particularly noted that all similar replacements and modifications are obvious to those skilled in the art, and they are all considered to be included in the present invention. The method and application of the present invention have been described through preferred embodiments, and relevant personnel can obviously modify or appropriately change and combine the methods and applications described herein without departing from the content and scope of the present invention to implement and apply the technology of the present invention.

[0034] In order to make the purpose, technical scheme and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with specific embodiments and the accompanying drawings of the specification. The test methods used in the following examples are conventional methods unless otherwise specified, and the materials, reagents, methods and instruments used are conventional materials, reagents, methods and instruments in the art unless otherwise specified, and can be obtained by those skilled in the art through commercial channels.

[0035] The English names involved in the following embodiments are:

[0036] Nuclear mitochondrial DNA fragments: NUMTs;

[0037] Cytochrome b: Cyt b.

[0038] Example 1: Detection of purebred sika deer and sika-red deer hybrids

[0039] Step 1: Ten blood samples of sika deer and eight blood samples of hybrid red deer and sika deer were obtained from deer farms, and DNA was extracted using the AxyPrep Blood Genomic DNA Miniprep Kit (Axygen Biosciences, USA). The extracted DNA was used to prepare the library using the MGIEasy Universal DNA Library Prepared Kit (MGI, China), and then sequenced on the DNBSEQ-T1 sequencer. After quality control, clean-data was obtained. The above clean-data is the resequencing data reads of the target individual;

[0040] Step 2: Download the Cyt b sequence of purebred sika deer species from NCBI as the seed for de novo assembly of the mitochondrial genome, and use NOVOplasty v4.3.1 to assemble the mitochondrial genome to obtain the mitochondrial reference genome;

[0041] Step 3: Identification of nuclear mitochondrial DNA fragments (NUMTs);

[0042] Step 3.1: Remove the reads used to assemble the mitochondrial genome from the resequencing data reads obtained in step 1 to generate a new fastq file. Use BWA0.7.17-r1188 to align the fastq file obtained above with the mitochondrial reference genome assembled in step 2 to generate a bam file to obtain the aligned bam file;

[0043] Step 3.2: Use samtools 1.17 to filter the bam files obtained in step 3.1 to remove low-quality reads (mapping quality < 30), unmapped reads, and reads with secondary alignments to obtain potential NUMTs;

[0044] Step 3.3: Convert the potential NUMTs obtained in step 3.2 into BED format using bamtobed ​​of bedtools 2.30.0, cluster the overlapping reads within 1000 bp using Bedtools Cluster, and then convert the above sequences back into fastq format using BedtoolsBamtofastq to minimize redundant hits;

[0045] Step 3.4: Perform BLAST comparison between the mitochondrial reference genome in step 2 and the fastq obtained in step 3.3 to mask the targeted reads; use the -dust no parameter to not mask low-complexity regions to avoid missing information that may be useful for the study; Python script extracts the highest-scoring BLAST matches, and the above NUMTs are compared with each other to obtain the confirmed NUMTs;

[0046] Step 4: Align the NUMTs determined in step 3 with the mitochondrial reference genome in step 2 separately without filtering, calculate the genetic distance between each NUMTs and its mitochondrial homologous fragment through the Kimura 2 parameter model in MEGA x, and draw a line graph to judge the results based on the line graph.

[0047] The horizontal axis of the line graph represents the genetic distance, and the vertical axis represents the frequency of each genetic distance interval. The genetic distance increases along the horizontal axis, such as Figure 1 As shown in the figure, the frequency of the purebred sika deer genetic distance interval gradually decreases with the increase of the X-axis genetic distance interval, and finally tends to a stable state, indicating that the target individual (purebred sika deer) is purebred; Figure 2 As shown, the frequency of the genetic distance interval of the sika deer-red deer hybrid increases with the increase of the genetic distance interval on the X-axis. Two frequency peaks appear at the farther genetic distance interval. The presence of a genome fragment of another species or subspecies in the target individual indicates that the target individual (sika deer-red deer hybrid) is a hybrid. It can be seen that the hybridization detection method provided by the present invention using whole genome resequencing data alone is accurate for the hybridization detection results of purebred sika deer and sika deer-red deer hybrid. Example 2: Detection of purebred Siberian tiger and Siberian tiger-Bengal tiger hybrid

[0048] Step 1: Obtain five Siberian tiger blood samples from the zoo, use the AxyPrep Blood Genomic DNA Miniprep Kit (Axygen Biosciences, USA) to extract DNA, use the MGIEasyUniversal DNA Library Prepared Kit (MGI, China) to prepare the library, and then sequence it on the DNBSEQ-T1 sequencer. After quality control, clean-data is obtained. The above clean-data is the resequencing data reads of the target individual; then download the resequencing data of 7 Siberian tiger-Bengal tiger hybrid individuals from NCBI, and the NCBI numbers are NC_056660.1, NC_056661.1, NC_056662.1, NC_056663.1, NC_056664.1, NC_056665.1, and NC_056666.1 respectively;

[0049] Step 2: Download the Cyt b sequence of purebred sika deer species from NCBI as the seed for de novo assembly of the mitochondrial genome, and use NOVOplasty v4.3.1 to assemble the mitochondrial genome to obtain the mitochondrial reference genome;

[0050] Step 3: Identification of nuclear mitochondrial DNA fragments (NUMTs);

[0051] Step 3.1: Remove the reads used to assemble the mitochondrial genome from the resequencing data reads obtained in step 1 to generate a new fastq file. Use BWA0.7.17-r1188 to align the fastq file obtained above with the mitochondrial reference genome assembled in step 2 to generate a bam file to obtain the aligned bam file;

[0052] Step 3.2: Use samtools 1.17 to filter the bam files obtained in step 3.1 to remove low-quality reads (mapping quality < 30), unmapped reads, and reads with secondary alignments to obtain potential NUMTs;

[0053] Step 3.3: Convert the potential NUMTs obtained in step 3.2 into BED format using bamtobed ​​of bedtools 2.30.0, cluster the overlapping reads within 1000 bp using Bedtools Cluster, and then convert the above sequences back into fastq format using BedtoolsBamtofastq to minimize redundant hits;

[0054] Step 3.4: Perform BLAST comparison between the mitochondrial reference genome in step 2 and the fastq obtained in step 3.3 to mask the targeted reads; use the -dust no parameter to not mask low-complexity regions to avoid missing information that may be useful for the study; Python script extracts the highest-scoring BLAST matches, and the above NUMTs are compared with each other to obtain the confirmed NUMTs;

[0055] Step 4: Align the NUMTs determined in step 3 with the mitochondrial reference genome in step 2 separately without filtering, calculate the genetic distance between each NUMTs and its mitochondrial homologous fragment through the Kimura 2 parameter model in MEGA x, and draw a line graph to judge the results based on the line graph.

[0056] like Figure 3 As shown in the figure, the frequency of the genetic distance interval of the purebred Siberian tiger gradually decreases with the increase of the genetic distance interval on the X axis, and finally tends to a stable state, indicating that the target individual (purebred Siberian tiger) is purebred; Figure 4 As shown, the frequency of the genetic distance interval of the Siberian tiger and Bengal tiger hybrid increases with the increase of the genetic distance interval on the X axis, and the presence of a genome fragment of another species or subspecies in the target individual indicates that the target individual (Siberian tiger and Bengal tiger hybrid) is a hybrid. It can be seen that the method for detecting hybrid individuals provided by the present invention is accurate in detecting the hybridization of purebred Siberian tigers and Siberian tiger and Bengal tiger hybrids.

[0057] Example 3: Detection of purebred Golden Pheasant and hybrid of Golden Pheasant and White Pheasant

[0058] Step 1: Obtain 2 blood gauze samples of purebred Golden Pheasant and 3 blood gauze samples of hybrid individuals of Golden Pheasant and White Pheasant from the farm, use AxyPrep Blood Genomic DNA Miniprep Kit (Axygen Biosciences, USA) to extract DNA, use MGIEasy Universal DNA Library Prepared Kit (MGI, China) to prepare the library of the extracted DNA, and then use the sequencing kit to sequence on the DNBSEQ-T1 sequencer, and obtain clean-data after quality control. The above clean-data is the resequencing data reads of the target individual;

[0059] Step 2: Download the Cyt b sequence of purebred sika deer species from NCBI as the seed for de novo assembly of the mitochondrial genome, and use NOVOplasty v4.3.1 to assemble the mitochondrial genome to obtain the mitochondrial reference genome;

[0060] Step 3: Identification of nuclear mitochondrial DNA fragments (NUMTs);

[0061] Step 3.1: Remove the reads used to assemble the mitochondrial genome from the resequencing data reads obtained in step 1 to generate a new fastq file. Use BWA0.7.17-r1188 to align the fastq file obtained above with the mitochondrial reference genome assembled in step 2 to generate a bam file to obtain the aligned bam file;

[0062] Step 3.2: Use samtools 1.17 to filter the bam files obtained in step 3.1 to remove low-quality reads (mapping quality < 30), unmapped reads, and reads with secondary alignments to obtain potential NUMTs;

[0063] Step 3.3: Convert the potential NUMTs obtained in step 3.2 into BED format using bamtobed ​​of bedtools 2.30.0, cluster the overlapping reads within 1000 bp using Bedtools Cluster, and then convert the above sequences back into fastq format using BedtoolsBamtofastq to minimize redundant hits;

[0064] Step 3.4: Perform BLAST comparison between the mitochondrial reference genome in step 2 and the fastq obtained in step 3.3 to mask the targeted reads; use the -dust no parameter to not mask low-complexity regions to avoid missing information that may be useful for the study; Python script extracts the highest-scoring BLAST matches, and the above NUMTs are compared with each other to obtain the confirmed NUMTs;

[0065] Step 4: Align the NUMTs determined in step 3 with the mitochondrial reference genome in step 2 separately without filtering, calculate the genetic distance between each NUMTs and its mitochondrial homologous fragment through the Kimura 2 parameter model in MEGA x, and draw a line graph to judge the results based on the line graph.

[0066] like Figure 5 As shown in the figure, the frequency of the genetic distance interval of the purebred Golden Pheasant gradually decreases with the increase of the genetic distance interval on the X axis, and finally tends to a stable state, indicating that the target individual (purebred Golden Pheasant) is purebred; Figure 6 As shown in the figure, the frequency of the genetic distance interval of the hybrid of Golden Pheasant and White-bellied Pheasant increases with the increase of the genetic distance interval on the X-axis. Four frequency peaks appear at the farther genetic distance interval. If there is a genome fragment of another species or subspecies in the target individual, it means that the target individual (the hybrid of Golden Pheasant and White-bellied Pheasant) is a hybrid. It can be seen that the method for detecting hybrid individuals provided by the present invention is accurate for the hybridization detection results of purebred Golden Pheasant and Golden Pheasant and White-bellied Pheasant hybrids.

[0067] Example 4: Detection of purebred green turtles and hybrids of green turtles and loggerhead turtles

[0068] Step 1: Download the resequencing data of 8 pure green turtles and 7 green turtle-loggerhead hybrids from NCBI. The NCBI numbers are SRX21466357, SRX21466356, SRX21466348, SRX21466347, SRX8673235, SRX8673234, SRX8673232, SRX8673227, NC_057849.1, NC_057850.1, NC_057851.1, NC_057852.1, NC_051245.2, NC_051246.2, and NC_057853.1 respectively;

[0069] Step 2: Download the Cyt b sequence of purebred sika deer species from NCBI as the seed for de novo assembly of the mitochondrial genome, and use NOVOplasty v4.3.1 to assemble the mitochondrial genome to obtain the mitochondrial reference genome;

[0070] Step 3: Identification of nuclear mitochondrial DNA fragments (NUMTs);

[0071] Step 3.1: Remove the reads used to assemble the mitochondrial genome from the resequencing data reads obtained in step 1 to generate a new fastq file. Use BWA0.7.17-r1188 to align the fastq file obtained above with the mitochondrial reference genome assembled in step 2 to generate a bam file to obtain the aligned bam file;

[0072] Step 3.2: Use samtools 1.17 to filter the bam files obtained in step 3.1 to remove low-quality reads (mapping quality < 30), unmapped reads, and secondary aligned reads to obtain potential NUMTs;

[0073] Step 3.3: Convert the potential NUMTs obtained in step 3.2 into BED format using bamtobed ​​of bedtools 2.30.0, cluster the overlapping reads within 1000 bp using Bedtools Cluster, and then convert the above sequences back into fastq format using BedtoolsBamtofastq to minimize redundant hits;

[0074] Step 3.4: Perform BLAST comparison between the mitochondrial reference genome in step 2 and the fastq obtained in step 3.3 to mask the targeted reads; use the -dust no parameter to not mask low-complexity regions to avoid missing information that may be useful for the study; Python script extracts the highest-scoring BLAST matches, and the above NUMTs are compared with each other to obtain the confirmed NUMTs;

[0075] Step 4: Align the NUMTs determined in step 3 with the mitochondrial reference genome in step 2 separately without filtering, calculate the genetic distance between each NUMTs and its mitochondrial homologous fragment through the Kimura 2 parameter model in MEGAx, and draw a line graph to judge the results based on the line graph.

[0076] like Figure 7 As shown in the figure, the frequency of the genetic distance interval of purebred green turtles gradually decreases with the increase of the genetic distance interval on the X axis, and finally tends to a stable state, indicating that the target individual (purebred green turtle) is purebred; Figure 8 As shown, the frequency of the genetic distance interval of the hybrid of green turtle and loggerhead turtle increases with the increase of the genetic distance interval on the X axis, and two frequency peaks appear at the farther genetic distance interval. If there is a genome fragment of another species or subspecies in the target individual, it means that the target individual (green turtle and loggerhead turtle hybrid) is a hybrid. It can be seen that the method for detecting hybrid individuals provided by the present invention is accurate for the hybridization detection results of purebred green turtles and green turtle and loggerhead turtle hybrids.

[0077] Example 5: Detection of purebred blue catfish and blue catfish-channel catfish hybrids

[0078] Step 1: Download the resequencing data of 9 pure blue catfish and 8 blue catfish and channel catfish hybrids from NCBI. The NCBI numbers are SRX19313580, SRX19313577, SRX19313578, SRX19313576, SRX19313575, SRX19313574, SRX19313573, SRX19313572, SRX19313571, NC_071255.1, NC_071256.1, NC_071257.1, NC_071258.1, NC_071259.1, NC_071260.1, NC_071261.1, and NC_071262.1 respectively;

[0079] Step 2: Download the Cyt b sequence of purebred sika deer species from NCBI as the seed for de novo assembly of the mitochondrial genome, and use NOVOplasty v4.3.1 to assemble the mitochondrial genome to obtain the mitochondrial reference genome;

[0080] Step 3: Identification of nuclear mitochondrial DNA fragments (NUMTs);

[0081] Step 3.1: Remove the reads used to assemble the mitochondrial genome from the resequencing data reads obtained in step 1 to generate a new fastq file. Use BWA0.7.17-r1188 to align the fastq file obtained above with the mitochondrial reference genome assembled in step 2 to generate a bam file to obtain the aligned bam file;

[0082] Step 3.2: Use samtools 1.17 to filter the bam files obtained in step 3.1 to remove low-quality reads (mapping quality < 30), unmapped reads, and reads with secondary alignments to obtain potential NUMTs;

[0083] Step 3.3: Convert the potential NUMTs obtained in step 3.2 into BED format using bamtobed ​​of bedtools 2.30.0, cluster the overlapping reads within 1000 bp using Bedtools Cluster, and then convert the above sequences back into fastq format using BedtoolsBamtofastq to minimize redundant hits;

[0084] Step 3.4: Perform BLAST comparison between the mitochondrial reference genome in step 2 and the fastq obtained in step 3.3 to mask the targeted reads; use the -dust no parameter to not mask low-complexity regions to avoid missing information that may be useful for the study; Python script extracts the highest-scoring BLAST matches, and the above NUMTs are compared with each other to obtain the confirmed NUMTs;

[0085] Step 4: Align the NUMTs determined in step 3 with the mitochondrial reference genome in step 2 separately without filtering, calculate the genetic distance between each NUMTs and its mitochondrial homologous fragment through the Kimura 2 parameter model in MEGAx, and draw a line graph to judge the results based on the line graph.

[0086] like Fig. 9 As shown in the figure, the frequency of the purebred green turtle genetic distance interval gradually decreases with the increase of the X-axis genetic distance interval, and finally tends to a stable state, indicating that the target individual (purebred blue catfish) is purebred; Fig.10 As shown, the frequency of the genetic distance interval of the hybrid of green turtle and loggerhead turtle increases with the increase of the genetic distance interval on the X axis, and two frequency peaks appear at the farther genetic distance interval. The presence of a genome fragment of another species or subspecies in the target individual indicates that the target individual (blue catfish-channel catfish hybrid) is a hybrid. It can be seen that the method for detecting hybrid individuals provided by the present invention is accurate for the hybridization detection results of purebred blue catfish and blue catfish-channel catfish hybrid.

[0087] The contents not described in detail in the specification of the present invention are well-known technologies for those skilled in the art. Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Anyone familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be based on the definition of the claims.

Claims

1. A method for hybridization detection using whole genome resequencing data alone, characterized in that: The detection method specifically comprises the following steps: Step 1: Collect samples from the target individual, transport them under dry ice conditions and store them at a temperature below -20°C, and use a kit to extract the DNA of the samples; construct a DNBSEQ whole-genome resequencing conventional library for sequencing to obtain the resequencing data of the target individual; Step 2: Download the cytochrome b sequence of the species to which the target individual belongs from NCBI as the seed for de novo assembly of the mitochondrial genome, and use NOVOplasty v4.3.1 to assemble the mitochondrial genome to obtain the mitochondrial reference genome; Step 3: Identification of nuclear mitochondrial DNA fragments; Step 3.1: Remove the reads used to assemble the mitochondrial genome from the resequencing data reads obtained in step 1 to generate a new fastq file. Use BWA0.7.17-r1188 to align the fastq file obtained above with the mitochondrial reference genome assembled in step 2 to generate a bam file to obtain the aligned bam file; Step 3.2: Use samtools 1.17 to filter the bam file obtained in step 3.1 to obtain potential nuclear mitochondrial DNA sequences; Step 3.3: Convert the potential nuclear mitochondrial DNA sequences obtained in step 3.2 into BED format using bamtobed ​​of bedtools 2.30.0, cluster the overlapping reads within 1000 bp using Bedtools Cluster, and then convert the above sequences back into fastq format using Bedtools Bamtofastq; Step 3.4: Perform BLAST comparison between the mitochondrial reference genome in step 2 and the fastq obtained in step 3.

3. Use the -dust no parameter to not mask low-complexity regions. The Python script extracts the highest-scoring BLAST matches. The above nuclear mitochondrial DNA sequences are compared with each other to obtain the confirmed nuclear mitochondrial DNA sequence. Step 4: Align the nuclear mitochondrial DNA sequence determined in step 3 with the mitochondrial reference genome in step 2 separately without filtering, calculate the genetic distance between each nuclear mitochondrial DNA sequence and its mitochondrial homologous fragment; and draw a line graph, and judge the results based on the line graph.

2. The method according to claim 1, characterized in that When the sample described in step 1 is a blood or tissue sample, whole genome resequencing can be performed directly after DNA extraction, and the depth of whole genome resequencing is >30X.

3. The method according to claim 1, characterized in that: The resequencing data reads described in step 1 are clean-data obtained after quality control.

4. The method according to claim 1, characterized in that The genetic distance described in step 4 was calculated using the Kimura 2-parameter model in MEGA x.

5. The method according to claim 1, characterized in that The horizontal axis of the line graph in step 4 represents the genetic distance, and the vertical axis represents the frequency of each genetic distance interval.

6. The method according to claim 1, characterized in that The criteria for judging the results in step 4 are: if the frequency of the genetic distance interval decreases steadily as the X-axis increases, it means that the target individual is a purebred; if the frequency of the genetic distance interval reaches a peak as the X-axis increases, it means that the target individual is a hybrid.

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