Method, apparatus and storage medium for analyzing a chromosome karyotype based on sequencing data

Abstract

The application discloses a method and device for analyzing chromosome karyotype based on sequencing data and a storage medium, and relates to the technical field of bioinformatics. The method comprises the following steps: performing quality control and error correction on sequencing data obtained through high-throughput sequencing to obtain gene sequence data to be analyzed; performing telomere query and screening on the gene sequence data to be analyzed to screen out telomere sequence; performing position filtering, fixed end alignment, telomere sequence trimming and interval value taking on the telomere sequence to obtain gene fragments with a fixed length; arranging and combining the gene fragments with the fixed length two by two, and performing global alignment to obtain a global alignment result; performing similarity scoring based on the global alignment result to construct a global alignment score matrix; and performing similarity clustering analysis on the global alignment score matrix to identify the corresponding chromosome karyotype information of the species. Through the application, the chromosome karyotype of a species can be analyzed by using high-throughput sequencing data, and the application is not limited by the research species.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bioinformatics, and in particular to a method and device for analyzing chromosome karyotype based on sequencing data and a storage medium. BACKGROUND

[0002] With the development of science and technology, chromosome karyotype analysis has gradually combined high-throughput omics data from traditional microscopic observation, promoting the in-depth study of chromosomes from morphology to molecular level. At present, it is difficult to integrate chromosome analysis with genomic data, and chromosome karyotype analysis mainly focuses on macroscopic chromosome structure and behavior, while bioinformatics usually deals with fine genetic sequences or genomic data. How to efficiently integrate large-scale (chromosome level) and small-scale (gene, nucleotide level) information is still a challenge. The standardization of heterogeneous data is insufficient, and the diversity of sample sources, technical means (such as banding technology, FISH, whole genome sequencing) and analysis platforms makes it difficult to unify the data, affecting the comparison and reuse of results. The algorithm accuracy and efficiency trade-off, bioinformatics algorithms often need to find a balance point between high-resolution analysis and computational rate, but this is difficult to achieve in large chromosome data sets (such as high-throughput sequencing data). The processing capacity of high-noise data is insufficient, and chromosome data usually contains experimental noise (such as banding image artifacts, sequencing errors, etc.), and the existing bioinformatics methods have limited ability in denoising and accurate identification of abnormalities.

[0003] With the development of high-throughput sequencing data, people in the field of bioinformatics are eager to develop a method for chromosome karyotype analysis based on bioinformatics means, and at present, there is no mature method for chromosome karyotype analysis based on bioinformatics means. SUMMARY

[0004] The purpose of the present application is to provide a method and device for analyzing chromosome karyotype based on sequencing data and a storage medium, to realize the prediction and determination of species chromosome karyotype information based on bioinformatics means.

[0005] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0006] According to one aspect of the present application, a method for analyzing chromosome karyotype based on sequencing data is provided, comprising: performing quality control and error correction on sequencing data obtained by high-throughput sequencing to obtain gene sequences to be analyzed; performing telomere query and screening on the gene sequences to be analyzed to screen out telomere-containing sequences; performing position filtering, fixed-end alignment, telomere sequence trimming and intervalization on the telomere-containing sequences to obtain gene fragments of a fixed length; arranging and combining the gene fragments of the fixed length two by two, and performing global alignment to obtain a global alignment result; performing similarity scoring based on the global alignment result, and constructing a global alignment score matrix according to the similarity score; performing similarity clustering analysis on the global alignment score matrix, and identifying the chromosome karyotype information of the species corresponding to the gene sequences to be analyzed based on the clustering analysis result.

[0007] Optionally, the quality control and error correction on the sequencing data obtained by high-throughput sequencing comprises: using quality control software fastp to perform quality control on the sequencing data, and using BWA or Pilon software to perform error correction on the sequencing data.

[0008] Optionally, the telomere query and screening on the gene sequences to be analyzed to screen out telomere-containing sequences comprises: traversing the gene sequences to be analyzed, identifying and retaining six-nucleotide sequences containing tandem repeats and having the number of guanine in each repeat unit exceeding a preset value, thereby screening out telomere-containing sequences.

[0009] Optionally, the position filtering, fixed-end alignment, telomere sequence trimming and intervalization on the telomere-containing sequences to obtain gene fragments of a fixed length comprises: based on the position information that telomeres of eukaryotes are located at the ends of chromosomes, traversing the telomere-containing sequences, checking whether the telomere coordinates of each sequence conform to the position information, and filtering out sequences not conforming to the position information; taking the head end of the telomere-containing sequence as a fixed end and the tail end as a non-fixed end, wherein the head end refers to the 5' end and the tail end refers to the 3' end; checking whether the telomere of each telomere-containing sequence is located at the fixed end, and for sequences with telomeres located at the non-fixed end, performing reverse complement operation to move the telomere to the fixed end; performing position alignment on all telomere-containing sequences based on the fixed end, and trimming and deleting the telomere located at the fixed end; taking the termination position of the telomere as a starting point, and taking a fragment of a target length from the rear to obtain gene fragments of a fixed length.

[0010] Optionally, the arranging and combining the gene fragments of the fixed length two by two, and performing global alignment to obtain a global alignment result comprises: arranging the gene fragments of the fixed length, taking each two gene fragments as a combination, the combination including two-by-two combinations between different gene fragments, and also including combinations of a gene fragment with itself; performing global alignment on all gene fragment combinations using a multiple sequence alignment tool to obtain global alignment results of all combinations.

[0011] Optionally, the similarity score is based on the global alignment result, and constructing a global alignment score matrix according to the similarity score comprises: designing a similarity score rule, wherein, for each base of the global alignment result, if the base types of the two gene fragments at the same coordinate position are the same, one point is obtained, if the base types at the same coordinate position are different, no point is obtained, and if there is a base vacancy at the same coordinate position, one point is deducted; traversing each base of the global alignment result, and scoring each two gene fragments according to the similarity score rule; and constructing a score matrix according to the scores between all the gene fragments, wherein the number of rows and the number of columns of the matrix are equal, and are equal to the number of gene fragments of the fixed length, and each element of the matrix represents the similarity score between two gene fragments of the fixed length, and the higher the score, the higher the similarity between the two gene fragments in the alignment.

[0012] Optionally, the similarity clustering analysis of the global alignment score matrix comprises: delimiting the similarity degree based on the score distribution of the global alignment score matrix; and gathering the gene fragments of the fixed length with the same similarity degree into a class.

[0013] Optionally, the chromosome karyotype information of the species corresponding to the gene sequence to be analyzed is identified based on the clustering analysis result, comprising: counting the number of classes in the clustering result; and obtaining the number of chromosomes of the species corresponding to the gene sequence to be analyzed according to the number of classes.

[0014] According to another aspect of the present application, there is also provided a device for analyzing chromosome karyotype based on sequencing data, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program is executed by the processor to implement the steps of the method described above.

[0015] According to another aspect of the present application, there is also provided a storage medium, wherein the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method described above.

[0016] The present application uses high-throughput sequencing data to analyze the chromosome karyotype of a species, can predict and determine the chromosome karyotype of a species, is not limited by the species to be studied, and can provide important karyotype information for further research and analysis of the species. In addition, the large amount of gene sequences to be analyzed obtained based on high-throughput sequencing are all clear sequence information and operable. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0018] Figure 1 is a flow chart of the method for analyzing chromosome karyotype based on sequencing data in Embodiment 1 of the present application;

[0019] Figure 2 is a flow chart of the method for analyzing chromosome karyotype based on sequencing data in Embodiment 2 of the present application;

[0020] Figure 3 is a schematic diagram of the clustering analysis result of high-throughput sequencing data of Pleurotus eryngii in Embodiment 2 of the present application. DETAILED DESCRIPTION

[0021] In order to enable persons skilled in the art to better understand the schemes of the present application, the technical schemes in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without making creative efforts shall fall within the scope of protection of the present application. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0022] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent of or selected from other embodiments.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily mean a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] Embodiment 1

[0025] In this embodiment, a method for analyzing chromosome karyotype based on sequencing data is provided.

[0026] Reference Figure 1 , Figure 1is a flowchart of a method for analyzing chromosome karyotype based on sequencing data in an embodiment of the present application, as shown in Figure 1 The method comprises the following steps:

[0027] S1. Quality control and error correction of sequencing data obtained by high-throughput sequencing to obtain gene sequences to be analyzed;

[0028] Quality control of sequencing data can be achieved by bioinformatics quality control techniques such as fastp, fastqC, and other bioinformatics software. Error correction of sequencing data can be achieved by BWA and Pilon software. Through quality control and error correction, low-quality bases are removed, sequencing errors are corrected, and high-quality, high-accuracy gene sequence data to be analyzed is obtained.

[0029] In addition, in other embodiments, the sequencing data obtained by high-throughput sequencing can also be compared to the reference sequence, and the errors, deletions and variations existing in the sequencing data compared with the reference sequence are identified by comparison and checking with the reference sequence, so as to quality control and error correction of sequencing data.

[0030] S2. Telomere query and screening of the gene sequence to be analyzed to screen out telomere sequences;

[0031] Telomere (English name: Telomere) is a nucleoprotein complex located at the end of eukaryotic chromosome. The genetic component of telomere contains a tandem repeat of G-rich (guanine) hexanucleotide sequence (TTAGGG / CCCTAA)n, wherein n represents the number of repetitions. The typical telomere hexanucleotide repeat unit is (TTAGGG), each unit contains six nucleotides, and the number of G accounts for half, i.e. three Gs. In this step, "rich" is defined as the number of guanine (G) in each repeat unit exceeding a preset value. Based on the typical characteristics of telomere sequence, the preset value can be set to three, i.e. each repeat unit contains at least three Gs. The programming language or bioinformatics tools (such as BLAST, bowtie2, etc.) are used to traverse the gene sequence to be analyzed, and the hexanucleotide sequence meeting the above standard is checked segment by segment, and when the sequence meeting the standard is found, it is retained, thereby screening out telomere sequences from the gene sequence to be analyzed.

[0032] S3. Position filtering, fixed end alignment, telomere sequence trimming and intervalization of the telomere sequence to obtain gene fragments of fixed length;

[0033] The position filtering can be implemented by using a sequence alignment tool such as BLAST or Bowtie; the fixed-end alignment can be implemented by using a multiple sequence alignment tool such as ClustalW or MAFFT; the telomere sequence trimming can be implemented by using a programming language such as Python in combination with a regular expression tool; and the intervalization and value extraction can be implemented by using a programming language such as Python in combination with a string processing function.

[0034] Further, in the embodiment of the present application, the step S3 comprises:

[0035] S31, based on the position information of the telomere of the eukaryote located at the end of the chromosome, traversing the telomere sequence, checking whether the telomere coordinate of each sequence is consistent with the position information, and filtering out the sequence inconsistent with the position information;

[0036] The position information of the telomere of the eukaryote on the chromosome is obtained from a related database or literature, the telomere sequence is aligned with the known telomere position information by using a bioinformatics software or script, it is checked whether the telomere coordinate of each sequence is consistent with the known position information of the end of the chromosome, the sequence with the consistent telomere position is screened out, the sequence with the inconsistent position is removed, the position filtering of the telomere sequence is realized, and the gene sequence with the guanine-rich hexanucleotide sequence of the tandem repeat but not the telomere sequence is filtered out.

[0037] S32, taking the head end of the telomere sequence as a fixed end and the tail end as a non-fixed end;

[0038] The telomere sequence is divided into a head end (5' end) and a tail end (3' end), and the head end is defined as the fixed end and the tail end is defined as the non-fixed end in the embodiment.

[0039] S33, checking whether the telomere of each telomere sequence is located at the fixed end, and performing a reverse complement operation on the sequence with the telomere located at the non-fixed end so that the telomere is transposed to the fixed end;

[0040] If the telomere is found to be located at the non-fixed end (3' end), the reverse complement operation is performed on the sequence to ensure that the telomere is adjusted to the fixed end. The reverse complement operation is a common technique in bioinformatics, which is used to arrange the complementary strand of a DNA sequence in reverse.

[0041] S34, performing position alignment on all telomere sequences based on the fixed end, and trimming and deleting the telomere located at the fixed end;

[0042] Considering that high-throughput sequencing technology has a large error in processing repetitive sequences (whether by detecting fluorescence intensity or current size), the length of the gene obtained by high-throughput sequencing is different, and the copy number of telomere sequences (tandem repeat number) between chromosomes of different species is different, cutting the telomere sequence can avoid the influence of the high error rate of repetitive sequence sequencing on subsequent sequence alignment scoring, and then affect the subsequent clustering results. In addition, due to the large difference in telomere length of different species, and the telomere length of cells at different growth stages and different cells at the same growth stage is also different, and high-throughput sequencing is directly sequencing all cells, if the telomere is also globally aligned, it will affect the alignment result. Therefore, the telomere is cut in this step to improve the accuracy of the alignment result and increase the adaptability and universality of the method of the application, so that it can be applied to more sequencing data of different species and cell types.

[0043] After the reverse complement operation in step S33, the telomere of each sequence is located at the fixed end. Based on the fixed end, all telomere sequences are aligned by position. After alignment, the telomere part located at the fixed end is cut and deleted, and only the gene sequence after the telomere is retained.

[0044] S35, taking the terminal position of the telomere as the starting point, taking a fragment of a target length, obtaining a fixed length gene fragment.

[0045] The sequence with the cut fixed end telomere is intervalized and valued at the fixed end to determine the target fixed length fragment. The terminal position of the telomere (i.e. the starting position of the sequence after cutting) is taken as the starting point, and a fragment of a target length is taken from the starting point to obtain a fixed length gene fragment.

[0046] Due to the difference between species, the range of intervalization and value is not fixed, that is, the intervalization and value also vary with species, and the target length can be set according to research needs.

[0047] S4, arranging and combining the fixed length gene fragments two by two, and performing global alignment to obtain a global alignment result;

[0048] All fixed length gene fragments are combined two by two, and global alignment is performed on each pair of gene fragment combinations by using a multiple sequence alignment tool such as ClustalW or Muscle software, to obtain a global alignment result. The global alignment result usually includes alignment score (used to quantify the degree of similarity between two gene fragments), base matching condition (showing which position base is matched), similarity and difference (usually in the form of percentage, showing the proportion of matched bases in the total number of bases, and the number or proportion of mismatches, insertions and deletions), and the like.

[0049] Further, in the embodiment of the application, step S4 includes:

[0050] S41, arranging the fixed-length gene fragments, taking each pair of gene fragments as a combination, the combination including pairwise combinations between different gene fragments (such as A and B, A and C, B and C, etc., where A, B, and C represent different gene fragments), and combinations of a gene fragment with itself (i.e., A and A, B and B, C and C, etc.);

[0051] S42, performing global alignment on all combinations of gene fragments using a multiple sequence alignment tool to obtain global alignment results of all combinations.

[0052] Each pair of gene fragment combinations in S41 is globally aligned using a selected multiple sequence alignment tool. Global alignment attempts to find the best alignment between two sequences to maximize the number of matching bases and minimize the number of mismatches, insertions, and deletions.

[0053] S5, performing similarity scoring based on the global alignment results, and constructing a global alignment score matrix according to the similarity score;

[0054] A two-dimensional matrix is created using a programming language (such as Python), with rows and columns representing each gene fragment. If the number of fixed-length gene fragments is n, a score matrix of size nxn is constructed. For each pair of gene fragment combinations, their similarity score is filled into the corresponding position of the two-dimensional matrix to obtain the score matrix.

[0055] Further, in an embodiment of the first embodiment of the present application, step S5 includes:

[0056] S51, designing a similarity scoring rule. For each base in the global alignment result, if the base types at the same coordinate position of the two gene fragments are the same, one point is scored, if the base types at the same coordinate position are different, no points are scored, and if there is a base vacancy at the same coordinate position, one point is penalized.

[0057] A base vacancy refers to a situation where one gene fragment has a base at that position while the other does not, or neither of them has a base but the algorithm considers that there should be a base at that position.

[0058] S52, traversing each base of the global alignment result, and scoring each pair of gene fragments using the similarity scoring rule;

[0059] According to the similarity scoring rule designed in S51, the score of each base position in the two gene fragments is calculated, and then the scores of each base position are accumulated to obtain the score of each pair of gene fragments, which represents the degree of similarity of the pair of gene fragments in global alignment.

[0060] S53, constructing a score matrix according to the scores between all gene fragments, wherein the number of rows and the number of columns of the matrix are equal, and equal to the number of gene fragments of the fixed length, and each element of the matrix represents the similarity score between two gene fragments of the fixed length, and the higher the score, the higher the similarity of the two gene fragments in alignment.

[0061] The rows and columns of the score matrix respectively represent different gene fragments, and each element of the matrix represents the similarity score between two gene fragments, and the higher the score in the score matrix, the higher the similarity of the corresponding two gene fragments in alignment.

[0062] S6, performing similarity clustering analysis on the global alignment score matrix, and identifying the chromosome karyotype information of the species corresponding to the gene sequence to be analyzed based on the clustering analysis result.

[0063] Select a suitable clustering algorithm, such as K-Means, or use MEGA software to perform similarity clustering analysis on the global alignment score matrix, and the number of categories obtained by clustering analysis can obtain the number of chromosomes of the species.

[0064] Further, in an embodiment of the first embodiment of the present application, step S6 comprises:

[0065] S61, based on the score distribution of the global alignment score matrix, the similarity degree is determined;

[0066] The score distribution in the global alignment score matrix is analyzed, and one or more similarity thresholds are set to divide the gene fragments into different similarity degrees, and these thresholds can be determined according to experiments or expert experience.

[0067] S62, the fixed length gene fragments with the same similarity degree are gathered into a category;

[0068] According to the similarity degree, the clustering is performed, and after the clustering is completed, the gene fragments with the same similarity degree are gathered into the same category.

[0069] S63, the number of categories in the clustering result is counted, and the number of chromosomes of the species corresponding to the gene sequence to be analyzed is obtained according to the number of categories.

[0070] The number of categories in the clustering result is the number of categories (or clusters) into which the fixed length gene fragments are gathered, that is, how many categories the fixed length gene fragments are gathered into.

[0071] The chromosome karyotype information includes the number of chromosomes. The number of categories of the clustering result is half of the number of categories, which represents the number of chromosomes of the species corresponding to the gene sequences to be analyzed. This is because: the method of the present application first performs screening of telomere sequences on the sequencing data obtained by sequencing, and the gene sequences obtained by screening are mostly sequence information from the ends of chromosomes. The telomere sequence information between different chromosomes is basically the same (there are differences in copy number), but the sequence information remaining after the telomere sequence is cut off (i.e. the main part of the chromosome) is significantly different. Only the gene sequences from the same end of the chromosome have high similarity (the same end of the chromosome, although the length of the gene sequence obtained by sequencing is different, but the sequence information is exactly the same). Therefore, after global alignment and scoring of these sequences, a score matrix is obtained, and then clustering is performed according to the score. Gene sequences with similar scores are clustered into a category. Gene sequences from the same end of the chromosome have high similarity, so the scores obtained by aligning these gene sequences are similar or the same. The clustering result obtained by similarity clustering through scoring shows that each category represents an end of the chromosome of the species. By analogy, the number of categories obtained by clustering can be used to calculate the number of chromosomes, and it is known that a chromosome has two ends (the telomere sequence is located at the end of the chromosome). Therefore, half of the number of categories obtained by clustering is the number of chromosomes. Assuming that the high-throughput sequencing data of a species is analyzed by the method of the present application, and the clustering result obtained is 20 categories, it indicates that the data comes from 20 ends of chromosomes. Therefore, the number of chromosomes of the species can be calculated to be 10.

[0072] The present application uses high-throughput sequencing data to analyze the chromosome karyotype of a species, can predict and determine the number of chromosomes of the species, and can provide important karyotype information for further research and analysis of the species, such as detecting whether the number of chromosomes is abnormal, whether the chromosomes are missing, and whether there are abnormal gene sequences.

[0073] Example 2

[0074] The technical solutions of the present application will be further described in detail below in combination with a specific embodiment and the accompanying drawings. It should be understood that the following embodiments are only used to explain the present application and do not limit the present application.

[0075] Pleurotus eryngii, also known as Pleurotus eryngii, is a kind of edible mushroom growing in the Middle East, North Africa and some parts of Asia. Pleurotus eryngii grows alone or in groups. The cap is slightly arched when young, then gradually flat, and the mature central depression is funnel-shaped, with a diameter of 2-12 cm. Generally, single individuals are larger, and group individuals are smaller. The cap is gray-black when young, gradually lightens with age, and turns into light brown, light yellow and white after maturity, with radial brown stripes around the center and a silky luster. The flesh is white, with a distinct almond flavor. The gill is not uniform, white, and the same as the ordinary Pleurotus ostreatus. The stem is 5-20 cm long and 3-10 cm in diameter, with a bulbous base. Pleurotus eryngii is the largest species of the genus Pleurotus.

[0076] This example analyzes the gene sequence obtained by high-throughput sequencing of Pleurotus eryngii to explain the specific analysis steps and methods of the present application. In order to verify the feasibility and accuracy of the method, this example uses the reference genome of Pleurotus eryngii for analysis, as shown in Figure 2 The method for analyzing chromosome karyotype based on sequencing data in the embodiment of the present application includes the following steps:

[0077] Step 1, construct the sequencing data obtained by high-throughput sequencing, analyze and control the quality of the sequencing data, based on the gene sequence data research and analysis, obtain the gene sequence data quality evaluation value, based on the quality evaluation value, use fastp to quality control the sequencing data, and use BWA or Pilon to correct the data, determine the high-quality, high-accuracy gene sequence to be analyzed;

[0078] Step 2, write a telomere sequence query algorithm, the algorithm logic is: telomere is a nucleoprotein complex located at the end of eukaryotic chromosome, the gene composition of telomere contains tandem repeat of G-rich (guanine) hexanucleotide sequence (TTAGGG / CCCTAA)n, traverse the gene sequence to be analyzed, realize the query of telomere sequence, the length of telomere sequence varies with species; based on the telomere sequence query algorithm, the telomere sequence query and screening of the gene sequence to be analyzed is carried out, and 2771 telomere sequences are obtained by query and screening;

[0079] Step 3, based on the coordinate information that the telomere of eukaryotes is located at the end of the chromosome, the telomere sequence is filtered, and the gene sequence with the telomere sequence of guanine-rich hexanucleotide sequence of tandem repeat but not the telomere sequence is filtered out. After coordinate position filtering of 2771 telomere sequences, it is determined that 2688 telomere sequences meet the coordinate position; based on the coordinate information of the telomere sequence, the head (5' end) of the telomere sequence is set as the fixed end, and the tail (3' end) of the telomere sequence is set as the non-fixed end. The telomere sequence of the fixed end is aligned for all telomere sequences. For the telomere sequence located at the non-fixed end, the telomere is transposed to the fixed end after the reverse complement operation, and then the position alignment is performed. After alignment, the telomere of the fixed end is cut and deleted; then the fixed end interval value is obtained, and the fixed length value is set as 1000bp. The end position of the fixed end telomere is set as the starting position of the target fixed length fragment. Based on the position coordinate information, the fixed length of 1000bp is taken from the starting position, and the fixed length gene fragment is obtained. Due to the species difference, the interval value range is not fixed, that is, the interval value is different for different species;

[0080] Step 4, the fixed length 1000bp gene fragment is arranged and counted, and each two fragments are a combination, including the combination of itself and itself, to obtain 7225344 two gene fragment combinations. For all two combinations, ClustalW or Muscle software is used for global alignment to obtain the global alignment result of all combinations;

[0081] Step 5, based on the global alignment result of all combinations of the fixed length 1000bp gene fragment, a scoring rule of the global alignment result is designed, each base of the global alignment result is traversed, if the base types at the same coordinate position are the same, one point is obtained, if the base types at the same coordinate position are different, no points are obtained, and if the base at the same coordinate position is missing, one point is deducted. According to the above scoring rule, the global alignment result of 7225344 combinations is operated to determine the score data set of the global alignment result of all combinations of the fixed length 1000bp fragment, and 7225344 two gene fragment combinations correspond to 7225344 scores;

[0082] Step 6, based on 7225344 scores, a score matrix corresponding to the global alignment result of 7225344 combinations is constructed, the row and column of the matrix are gene fragment names, the number of rows and columns are the number of fixed length gene fragments, and the number of fixed length 1000bp gene fragments is 2688. Therefore, the size of the constructed score matrix is 2688x2688;

[0083] Step 7, the score value based on the score matrix is used to determine the degree of similarity, and the score of the global alignment is similar, which indicates that the characteristics of the gene fragments are similar, and the characteristics are similar, and are clustered into a class. Similarity clustering is performed using MEGA software, and a clustering diagram is drawn.

[0084] Step 8, according to the number of clusters, that is, the number of classes into which the fixed-length gene fragments are clustered, it is indicated that the species corresponding to the analyzed gene sequence has how many chromosomes, that is, the karyotype of the species can be determined. According to this method, 2688 fixed-length gene fragments of 1000bp are clustered into 24 clusters, and each cluster contains about 112 gene sequences, so it can be predicted and determined that Pleurotus eryngii contains 12 chromosomes, and thus the karyotype of Pleurotus eryngii can be determined.

[0085] Reference Figure 3 , Figure 3 It is a schematic diagram of the clustering analysis result of the high-throughput sequencing data of Pleurotus eryngii in the embodiment of the present application. The high-throughput sequencing data of Pleurotus eryngii is analyzed by using the method of the present application, and the number of chromosomes is predicted and determined. Figure 3 As can be seen from the figure, the gene sequences processed are clustered into 24 classes through clustering analysis, and the number of chromosomes of Pleurotus eryngii can be calculated and determined to be 12. It should be noted that there is a professional knowledge point here, and the number of chromosomes can only be counted to be 23, but it should be noted that the number of gene sequences in one of the classes is obviously greater than that in other classes and is approximately twice the number, which indicates that there is a high similarity between the two ends of the chromosomes of the species, which does not affect the scientificity of the method of the present application, and at the same time proves that the present application is also applicable to these special cases. In addition, Figure 3 There are also some single or several branches in the figure, which are regarded as noise that cannot be avoided by the present application. The existence of noise is due to the presence of gene sequences similar to telomere sequences located in the middle of the chromosomes on the chromosomes of the species. However, the noise has been minimized as much as possible in the interval value step, but due to the limitation of high-throughput sequencing technology, the noise cannot be completely eliminated.

[0086] In the embodiment, a device for analyzing chromosome karyotype based on sequencing data is also provided, which is used to realize the above-mentioned embodiments and preferred embodiments, and will not be described again.

[0087] Alternatively, specific examples in the embodiment can refer to the examples described in the above-mentioned embodiments and optional embodiments, and the embodiment will not be described again here.

[0088] The embodiment of the present application also provides a storage medium, and the storage medium stores a computer program, wherein the computer program is set to execute the steps in any one of the above-mentioned method embodiments when running.

[0089] The specific examples in the embodiment can refer to the examples described in the above embodiments and optional implementation manners, and the embodiment will not be described here again.

[0090] Optionally, in the embodiment, the storage medium can include but is not limited to a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various storage media that can store computer programs.

[0091] The serial numbers of the above embodiments of the application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0092] In the above embodiments of the application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can refer to the related description of other embodiments.

[0093] The steps in the above embodiment methods of the application can be adjusted in sequence, combined and reduced according to actual needs. Each technical feature can be combined arbitrarily, and in order to make the description simple, all possible combinations of each technical feature in the embodiments are not described, however, as long as the combinations of these technical features do not exist contradictory, they should be considered as the range disclosed by the application.

[0094] The above is only the preferred embodiment of the application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the application, a number of improvements and refinements can be made, and these improvements and refinements should be considered as the protection scope of the application.

Claims

1. A method for analyzing chromosome karyotypes based on sequencing data, characterized in that, include: Quality control and error correction are performed on the sequencing data obtained from high-throughput sequencing to obtain the gene sequences to be analyzed. The gene sequences to be analyzed are subjected to telomere query and screening to identify sequences with telomeres. The telomere-bearing sequence is subjected to position filtering, fixed end alignment, telomere sequence trimming, and interval-based value extraction to obtain a gene fragment of fixed length; Based on the location information of eukaryotic telomeres at the ends of chromosomes, the telomere-bearing sequences are traversed, and the telomere coordinates of each sequence are checked to see if they conform to the location information. Sequences that do not conform to the location information are then filtered out. The head end of the telomere sequence is taken as the fixed end, and the tail end is taken as the non-fixed end, wherein the head end refers to the 5' end and the tail end refers to the 3' end; Check whether the telomeres of each telomere sequence are located at the fixed end. For sequences with telomeres located at non-fixed ends, perform an inverse complementation operation to transpose the telomeres to the fixed end. All telomere sequences are aligned based on the fixed ends, and telomeres located at the fixed ends are pruned and deleted. Starting from the termination position of the telomere, a segment of the target length is taken to obtain a gene segment of fixed length. The fixed-length gene fragments are arranged in pairs and combined, and a global alignment is performed to obtain the global alignment results. A similarity score is calculated based on the global comparison results, and a global comparison score matrix is ​​constructed based on the similarity score. The similarity scoring rules are designed so that for each base in the global alignment result, if the base types at the same coordinate position of the two gene fragments are the same, one point is awarded; if the base types at the same coordinate position are different, no points are awarded; and if there is a base gap at the same coordinate position, one point is deducted. Traverse every base in the global alignment results and score each pair of gene fragments using the aforementioned similarity scoring rules; A score matrix is ​​constructed based on the scores between all gene fragments. The number of rows and columns in the matrix are equal, which is equal to the number of gene fragments of fixed length. Each element of the matrix represents the similarity score between two gene fragments of fixed length. The higher the score, the higher the similarity between the two gene fragments in the comparison. A similarity clustering analysis is performed on the global alignment score matrix, and based on the clustering analysis results, the chromosomal karyotype information of the species corresponding to the gene sequence to be analyzed is identified.

2. The method for analyzing chromosome karyotypes based on sequencing data according to claim 1, characterized in that, Quality control and error correction of sequencing data obtained from high-throughput sequencing includes: The sequencing data were quality controlled using the FastP quality control software, and errors were corrected using BWA or Pilon software.

3. The method for analyzing chromosome karyotypes based on sequencing data according to claim 1, characterized in that, The gene sequence to be analyzed was subjected to telomere query and screening, and the telomere-bearing sequences included: The gene sequence to be analyzed is traversed to identify and retain hexanucleotide sequences containing tandem repeats and where the number of guanine in each repeat unit exceeds a preset value, thereby screening out sequences with telomeres.

4. The method for analyzing chromosome karyotypes based on sequencing data according to claim 1, characterized in that, The fixed-length gene fragments are arranged in pairs and combined, and then globally aligned to obtain the global alignment results, including: The fixed-length gene fragments are arranged so that each pair of gene fragments is a combination. The combination includes pairwise combinations between different gene fragments, as well as combinations of gene fragments themselves. For all gene fragment combinations, a multiple sequence alignment tool was used to perform a global alignment to obtain the global alignment results for all combinations.

5. The method for analyzing chromosome karyotypes based on sequencing data according to claim 1, characterized in that, The similarity clustering analysis performed on the global alignment score matrix includes: The degree of similarity is determined based on the score distribution of the global comparison score matrix. Gene fragments of fixed length with consistent similarity are grouped into one class.

6. The method for analyzing chromosome karyotypes based on sequencing data according to claim 1, characterized in that, Based on the cluster analysis results, the chromosome karyotype information of the species corresponding to the gene sequence to be analyzed was identified, including: Count the number of clusters in the clustering results; The number of chromosomes in the species corresponding to the gene sequence to be analyzed is obtained based on the number of categories.

7. A device for analyzing chromosome karyotypes based on sequencing data, characterized in that, The apparatus for analyzing chromosome karyotypes based on sequencing data includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 6.

8. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 6.

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