CN115948521A - A method for detecting missing chromosome information in aneuploidy - Google Patents

Abstract

The invention discloses a method for detecting aneuploid missing chromosome information, comprising the following steps: extracting the DNA of an organism to be tested and performing whole genome sequencing to obtain a sequencing sequence; comparing the sequencing sequence to a reference genome, And obtain the frequency scatter diagram of each chromosome of the organism to be tested; fit the frequency scatter diagram of each chromosome, and obtain the sequencing depth corresponding to all Gaussian peaks in the fitting curve; Clustering is performed to obtain the chromosomal ploidy of the organism to be tested. Based on the second-generation high-throughput sequencing technology, the present invention can greatly shorten the time compared with traditional detection methods when facing a large number of samples, and can realize automatic operation at the same time, and has the advantages of standardization and repeatability.

Description

A method for detecting aneuploidy missing chromosome information

Technical Field

The present invention belongs to the field of genome sequencing and bioinformatics, and in particular relates to a method for detecting aneuploidy missing chromosome information.

Background Art

In most cases, aneuploidy is fatal to animals and humans, but plants usually show strong tolerance to aneuploidy, especially in allopolyploid plants. Aneuploidy has incomparable advantages in determining the physical location of genes and molecular markers, gene transfer, and establishing the correspondence between linkage groups and chromosomes. It is of great significance to the study of plant genetics and breeding. At the same time, aneuploidy has also achieved many results in the application of actual breeding.

Through aneuploidy research, the genetic laws between various traits of plants can be sorted out more quickly and systematically, and the relationship between its chromosomes and its closely related plants can be determined, so that various special and excellent new varieties can be selected more systematically. However, since this kind of research involves a large number of hybridization experiments, the workload is large and the time is long, research in the field of forestry is somewhat scarce. Since the growth period of forestry is long, it is difficult to adjust the direction during the breeding process, so sufficient chromosome information must be obtained before conducting formal experiments.

Nowadays, karyotype analysis based on individual chromosome sets, such as C-banding method, G-banding method, flow cytometry and fluorescence in situ hybridization (FISH) based on chromosome-specific probes, are relatively common methods for identifying aneuploidy. However, most of the above methods have a strong preference for the type of experimental materials and require a long time for experimental preparation, which will be a bit difficult when faced with the screening of large quantities of experimental materials. The ploidy analyzer can quickly determine whether the created population is aneuploid, but it is difficult to determine the specific chromosome composition of each individual.

In addition, the method of using T test after traditional high-throughput sequencing to detect whether there is a significant difference in the coverage depth between the sample and the standard reference sample has been applied clinically in human diseases such as Down syndrome and 18-trisomy syndrome. However, in plant aneuploid breeding, it is also difficult to carry out due to factors such as the large number of hybrid varieties, large genome differences, large number of chromosome increases and decreases, and difficulty in obtaining standard references. Therefore, it is urgent to provide a method for detecting aneuploid missing chromosome information.

Summary of the invention

The purpose of the present invention is to provide a method for detecting aneuploidy missing chromosome information to solve the problems existing in the above-mentioned prior art.

To achieve the above object, the present invention provides a method for detecting aneuploidy missing chromosome information, comprising the following steps:

Extracting DNA from the organism to be tested and performing whole genome sequencing to obtain a sequencing sequence;

Aligning the sequencing sequence to a reference genome and obtaining a frequency scatter plot of each chromosome of the organism to be tested;

Fitting the frequency scatter plot of each chromosome, and obtaining the sequencing depth corresponding to all Gaussian peaks in the fitting curve;

The sequencing depth is clustered to obtain the chromosome ploidy of the organism to be tested.

Optionally, before whole genome sequencing of the extracted DNA, the method further includes: detecting the integrity of the DNA based on agarose gel electrophoresis, and detecting the concentration of the DNA content using an enzyme marker.

Optionally, the reference genome is selected from the species of the organism to be tested or a genome of a closely related species, and has been mapped to the chromosome level.

Optionally, the process of obtaining the frequency scatter plot of each chromosome includes: obtaining the sequencing depth of each base on each chromosome, and counting the occurrence frequency of each sequencing depth, thereby obtaining the frequency scatter plot of each chromosome.

Optionally, the process of fitting the frequency scatter plot of each chromosome includes: fitting the frequency scatter plot of each chromosome into a single Gaussian curve or a mixed Gaussian model formed by the superposition of x Gaussian curves, where x is the number of peaks in the frequency scatter plot of each chromosome.

Optionally, before clustering the sequencing depths, the method further includes: sorting the sequencing depths to obtain a Gaussian peak with the largest sequencing depth in each chromosome.

Optionally, the process of obtaining the chromosome ploidy of the organism to be tested includes: performing one-dimensional array clustering on the sequencing depth to obtain different clustering groups; obtaining the median of the sequencing depth of different clustering groups based on the ploidy relationship between the medians of the different clustering groups; clustering different chromosomes based on the median of the sequencing depth of the different clustering groups and the Gaussian peak with the largest sequencing depth in each chromosome, thereby obtaining the chromosome ploidy of the organism to be tested.

The technical effects of the present invention are:

The present invention utilizes a method of counting the frequency of occurrence of each sequencing depth on the genome to identify the true sequencing depth of the chromosome, and uses a mixed Gaussian fitting model to split the sequencing depth frequency curve to sort out the various factors that cause the instability of chromosome sequencing depth, and applies a clustering algorithm to group all sequencing depth peaks to determine the sequencing depth of the monomer, thereby improving the detection accuracy of chromosome ploidy.

The present invention is based on the second-generation high-throughput sequencing technology. When faced with large quantities of samples, it can greatly shorten the time compared to traditional detection methods, and can also achieve automated operation, with the advantages of standardization and repeatability.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

FIG1 is a flow chart of a method for detecting aneuploidy missing chromosome information in an embodiment of the present invention;

FIG2 is a frequency scatter plot of 19 chromosomes in an embodiment of the present invention;

FIG. 3 is a fitting curve diagram of sample No. 1 after Gaussian fitting in an embodiment of the present invention.

DETAILED DESCRIPTION

It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

Embodiment 1

As shown in FIG1 , this embodiment provides a method for detecting aneuploidy missing chromosome information, comprising the following steps:

DNA extraction:

According to the characteristics of the animal and plant samples to be tested, select the appropriate DNA extraction plan, and conduct tests on the DNA content and purity. The sample quality shall be based on the official standards for the sequencer.

The embodiment selected 6 aneuploid plants obtained by hybridization as experimental samples and two euploid plants as controls, and used the MGIEasy universal DNA library preparation kit to extract standard DNA. After extraction, agarose gel electrophoresis was used to detect sample integrity, and a microplate reader was used to detect concentration. DNABR was used as the detection kit. The results showed that the quality of the extracted samples met the standards of the sequencing platform.

Whole Genome Sequencing:

Based on the second-generation high-throughput sequencing technology, sequencing library preparation and on-machine detection were performed on the Illumina or BGI sequencing platform according to the official instruction manual. The instrument parameters and operating methods were strictly followed in accordance with the instruction manual of the corresponding sequencing platform.

Based on the second-generation high-throughput sequencing technology, the sequencing library was prepared and tested on the MGISEQ-2000 sequencing platform according to the official instruction manual. The library type was DNBSEQ WGS, the sequencing mode was PE150 whole genome sequencing, and the instrument parameters and operation methods were strictly followed in accordance with the instruction manual of the corresponding sequencing platform.

Alignment of sequencing sequences with reference genome and sequencing depth statistics:

After obtaining the offline data, align the sequences obtained by double-end sequencing to the reference genome. The reference genome can be the genome of the species itself or a closely related species, but it must have been mounted to the chromosome level. In view of the differences in alleles between different strains within the species, the alignment scheme needs to choose a method with a higher error tolerance as much as possible. After the alignment is completed, the sequencing depth of each nucleotide on the reference genome is calculated separately, and the frequency of occurrence of each sequencing depth is counted in units of chromosomes. The results are presented in a scatter plot, with the horizontal axis being the sequencing depth and the vertical axis being the frequency of occurrence corresponding to the sequencing depth.

Taking sample No. 1 as an example, a total of 266.24M double-end sequences were obtained. After filtering out low-quality sequences, the sequenced sequences were aligned to the poplar reference genome using BWA-MEM, with an alignment rate of 92.06%. The sequencing depth of each base on the chromosome was then calculated, and the frequency was counted in units of chromosomes. As shown in Figure 2, a line-connected scatter plot of 19 chromosomes is shown, with the horizontal axis being the sequencing depth and the vertical axis being the frequency of occurrence of this sequencing depth.

Draw the fitting curve on a chromosome basis and calculate the peak value:

The frequency scatter plot of each chromosome is fitted into a single Gaussian curve or a mixed Gaussian model formed by the superposition of x Gaussian curves using the Gaussian fitting principle, where x is the number of peaks in the frequency curve. After the fitting is completed, the sequencing depths corresponding to all Gaussian peaks are recorded and arranged from small to large.

Curve fitting was performed for each chromosome, as shown in Figure 3. Taking chromosome 1 of sample 1 as an example, two normal distribution curves were obtained by Gaussian fitting based on the number of peaks, and R-Square (R ² ) was 0.988. The sequencing depth values 34X and 68X corresponding to the peaks of the fitting curves were recorded, and this operation was repeated for the remaining 18 chromosomes, and finally 38 normal distribution curves were obtained.

Determine the specific ploidy of chromosomes in an organism:

For all the values recorded in the previous step, use DBSCAN or other clustering algorithms that do not require the number of groups to be set in advance to perform one-dimensional array clustering. There should be a ploidy relationship between the medians of different clustering groups. Assuming that the sequencing depth corresponding to the first group of peaks (i.e., monomers) is y, the sequencing depth corresponding to the second group of peaks should be 2y, the sequencing depth corresponding to the third group of peaks should be 3y, and the sequencing depth corresponding to the nth group of peaks should be n×y. Then determine the Gaussian peak with the largest sequencing depth in each chromosome. If its corresponding sequencing depth is clustered to the nth group, the ploidy of the chromosome in the organism is n.

The DBSCAN algorithm is used to perform one-dimensional array clustering on the above record results, and these curves can be divided into three groups, with the median sequencing depths within the groups being 34X, 68X, and 101X, respectively. Among them, the last peak of chromosome 1 (sequencing depth: 68X) was finally clustered into the second group, indicating that the ploidy of this chromosome in the organism is 2, that is, disomy. The last peaks of chromosomes 5, 8, 13, and 19 were all clustered into the third group, indicating that the ploidy of the above chromosomes in the organism is 3, that is, trisomy. In this way, it was finally determined that sample No. 1 was a trisomic plant with chromosomes 5, 8, 13, and 19, with a total of 42 chromosomes in the cell. The test results for the samples in this embodiment are shown in Table 1:

Table 1

It can be seen from Table 1 that the detection results of this embodiment are very consistent with the results of the ploidy analyzer. Since this embodiment is based on the second-generation high-throughput sequencing technology, when facing a large number of samples, it can greatly shorten the time compared to traditional detection methods, and can realize automated operation, with advantages such as standardization and repeatability.

The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (7)

1. A method for detecting aneuploidy deleted chromosome information, comprising the steps of:

extracting DNA of an organism to be detected and carrying out whole genome sequencing to obtain a sequencing sequence;

comparing the sequencing sequences to a reference genome, and acquiring a frequency scatter diagram of each chromosome of the organism to be detected;

fitting the frequency scatter diagram of each chromosome, and acquiring sequencing depths corresponding to all Gaussian peaks in a fitting curve;

and clustering the sequencing depth to further obtain the chromosome ploidy of the organism to be detected.

2. The method for detecting aneuploidy deleted chromosome information according to claim 1,

before whole genome sequencing of the extracted DNA, the method also comprises the following steps: and detecting the integrity of the DNA based on agarose gel electrophoresis, and detecting the concentration of the DNA by using a microplate reader.

3. The method for detecting aneuploidy deleted chromosome information according to claim 1,

the reference genome is selected from the species itself of the organism to be tested or the genome of a closely-derived species, and is mounted to the chromosome level.

4. The method for detecting aneuploidy deleted chromosome information according to claim 1,

the process of obtaining a frequency scattergram for each chromosome includes: and acquiring the sequencing depth of each base on each chromosome, and counting the occurrence frequency of each sequencing depth to further acquire a frequency scatter diagram of each chromosome.

5. The method for detecting aneuploidy deleted chromosome information according to claim 4,

the process of fitting the frequency scatter diagram of each chromosome includes: and fitting the frequency scatter diagram of each chromosome into a mixed Gaussian model formed by superposition of a single Gaussian curve or x Gaussian curves, wherein x is the number of peaks in the frequency scatter diagram of each chromosome.

6. The method for detecting aneuploidy deleted chromosome information according to claim 1,

before the clustering process is carried out on the sequencing depth, the method further comprises the following steps: and sequencing the sequencing depth to obtain a Gaussian peak with the maximum sequencing depth in each chromosome.

7. The method for detecting aneuploidy deleted chromosome information according to claim 6,

the process of obtaining the chromosome ploidy of the test organism comprises: performing one-dimensional array clustering on the sequencing depth to obtain different clustering groups; obtaining the median of the sequencing depths of the different clustering groups based on the ploidy relationship among the medias of the different clustering groups; and clustering different chromosomes based on the median of the sequencing depths of the different clustering groups and the Gaussian peak with the largest sequencing depth in each chromosome, thereby obtaining the ploidy of the chromosome of the organism to be detected.

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