Method and device for analyzing karyotype based on sequencing data and storage medium

Abstract

The invention discloses a method and device for analyzing chromosome karyotypes 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 by high-throughput sequencing to obtain gene sequence data to be analyzed; carrying out telomere query and screening on the to-be-analyzed gene sequence data, and screening out a sequence with telomeres; performing position filtering, fixed end alignment, telomere sequence cutting and interval valuing on the sequence with the telomere to obtain a gene segment with a fixed length; permutation and combination are carried out on the gene segments with the fixed lengths in pairs, global comparison is carried out, and a global comparison result is obtained; performing similarity scoring based on the global comparison result, and constructing a global comparison score matrix; and performing similarity clustering analysis on the global comparison score matrix, and identifying chromosome karyotype information of the corresponding species. According to the method, the chromosome karyotype of the species can be analyzed by using high-throughput sequencing data, and the method is not limited by research species.

Description

Technical Field

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

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

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

[0004] The purpose of the present invention is to provide a method, device and storage medium for analyzing chromosome karyotype based on sequencing data, so as to predict and determine the chromosome karyotype information of species based on bioinformatics means.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, 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 a gene sequence to be analyzed; performing telomere query and screening on the gene sequence to be analyzed to screen out sequences with telomeres; performing position filtering, fixed-end alignment, telomere sequence clipping and interval value taking on the sequences with telomeres to obtain gene fragments of fixed length; arranging and combining the gene fragments of fixed length in pairs, 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 based on the similarity scoring; performing similarity cluster analysis on the global alignment score matrix, and based on the cluster analysis result, identifying the chromosome karyotype information of the species corresponding to the gene sequence to be analyzed.

[0007] Optionally, performing quality control and error correction on the sequencing data obtained by high-throughput sequencing includes: 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, telomere query and screening are performed on the gene sequence to be analyzed, and screening out sequences with telomeres includes: traversing the gene sequence to be analyzed, identifying and retaining hexanucleotide sequences containing tandem repeats and the number of guanines in each repeating unit exceeding a preset value, thereby screening out sequences with telomeres.

[0009] Optionally, the telomere sequence is subjected to position filtering, fixed-end alignment, telomere sequence clipping and interval value taking to obtain a gene fragment of a fixed length, including: based on the position information of the eukaryotic telomere at the end of the chromosome, traversing the telomere sequence, checking whether the telomere coordinates of each sequence conform to the position information, and filtering out sequences that do not conform to the position information; taking the head end of the telomere sequence as the fixed end and the tail end as the 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 sequence is located at the fixed end, and for sequences whose telomeres are located at the non-fixed end, performing a reverse complementation operation to transpose the telomeres to the fixed end; performing position alignment on all telomere sequences based on the fixed end, and clipping and deleting the telomeres located at the fixed end; taking the end position of the telomere as the starting point, taking fragments of the target length backward to obtain a gene fragment of a fixed length.

[0010] Optionally, the fixed-length gene fragments are arranged and combined in pairs, and a global alignment is performed to obtain a global alignment result, which includes: arranging the fixed-length gene fragments, taking every two gene fragments as a combination, wherein the combination includes pairwise combinations between different gene fragments, and also includes combinations of gene fragments themselves; for all gene fragment combinations, a global alignment is performed using a multiple sequence alignment tool to obtain global alignment results of all combinations.

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

[0012] Optionally, performing similarity cluster analysis on the global comparison score matrix includes: defining the degree of similarity based on the score distribution of the global comparison score matrix; and clustering gene fragments of fixed length with consistent similarity into one category.

[0013] Optionally, based on the cluster analysis results, identifying the chromosome karyotype information of the species corresponding to the gene sequence to be analyzed includes: counting the number of cluster categories in the clustering results; and obtaining the number of chromosomes of the species corresponding to the gene sequence to be analyzed according to the number of categories.

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

[0015] According to another aspect of the present invention, a storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described above are implemented.

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

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

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

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

[0020] Figure 3 This is a schematic diagram of the clustering analysis results of the high-throughput sequencing data of Pleurotus eryngii in Example 2 of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should 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 term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with 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-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, 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 "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] Example 1

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

[0026] Reference Figure 1 , Figure 1FIG. 1 is a flow chart of a method for analyzing chromosome karyotype based on sequencing data in an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0027] S1, quality control and error correction of sequencing data obtained by high-throughput sequencing to obtain the gene sequence to be analyzed;

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

[0029] In addition, in other embodiments, the sequencing data obtained by high-throughput sequencing can be compared to a reference sequence, and by comparing and checking with the reference sequence, errors, deletions and variations in the sequencing data compared to the reference sequence can be identified, thereby performing quality control and error correction on the sequencing data.

[0030] S2, performing telomere query and screening on the gene sequence to be analyzed, and screening out sequences with telomeres;

[0031] Telomere (English name: Telomere) is a nucleoprotein complex located at the end of the chromosome of eukaryotic organisms. The genetic components of telomeres contain tandemly repeated hexanucleotide sequences rich in G (guanine) (TTAGGG / CCCTAA)n, where n represents the number of repetitions. The typical telomere hexanucleotide repeating unit is (TTAGGG), each unit contains six nucleotides, of which the number of Gs accounts for half, that is, three Gs. In this step, "rich" is defined as the number of guanines (G) in each repeating unit exceeding the preset value. Based on the typical characteristics of telomere sequences, the preset value can be set to three, that is, each repeating unit contains at least three Gs. Use programming languages ​​or bioinformatics tools (such as BLAST, bowtie2, etc.) to traverse the gene sequence to be analyzed, check the hexanucleotide sequences that meet the above standards segment by segment, and when a sequence that meets the standards is found, it will be retained, thereby screening out sequences with telomeres from the gene sequence to be analyzed.

[0032] S3, performing position filtering, fixed-end alignment, telomere sequence clipping and interval value selection on the telomere sequence to obtain a gene fragment of fixed length;

[0033] Position filtering can be achieved using sequence alignment tools such as BLAST and Bowtie; fixed-end alignment can be performed using multiple sequence alignment tools such as ClustalW and MAFFT; telomere sequence trimming can be achieved using programming languages ​​such as Python scripts in combination with tools such as regular expressions; interval value extraction can be performed using programming languages ​​such as Python in combination with string processing functions for fragment extraction.

[0034] Further, in an implementation manner of the embodiment of the present invention, step S3 includes:

[0035] S31, based on the position information of the eukaryotic telomere at the end of the chromosome, traverse the telomere sequences, check whether the telomere coordinates of each sequence conform to the position information, and filter out the sequences that do not conform to the position information;

[0036] Obtain the position information of eukaryotic telomeres on chromosomes from relevant databases or literature, use bioinformatics software or scripts to compare telomere sequences with known telomere position information, check whether the telomere coordinates of each sequence are consistent with the known chromosome end position information, screen out sequences whose telomere positions are consistent with the known information, and eliminate sequences whose positions do not match, to achieve position filtering of telomere sequences, and filter out gene sequences that have tandemly repeated guanine-rich hexanucleotide sequences but are non-telomere sequences.

[0037] S32, the head end with the telomere sequence was used as the fixed end, and the tail end as the non-fixed end;

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

[0039] S33, checking whether the telomere of each telomere sequence is located at the fixed end, and for the sequence with the telomere located at the non-fixed end, performing a reverse complementation operation to transfer the telomere to the fixed end;

[0040] If the telomere is found to be located at the non-fixed end (3' end), a reverse complementation operation is performed on the sequence to ensure that the telomere is adjusted to the fixed end. Among them, the reverse complementation operation is a commonly used technique in bioinformatics to reversely align the complementary strands of a DNA sequence.

[0041] S34, align all telomere sequences based on the fixed ends, and trim and delete the telomeres located at the fixed ends;

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

[0043] After the reverse complementation operation in step S33, the telomere of each sequence is located at the fixed end, and all sequences with telomeres are aligned based on the fixed end. After alignment, the telomere part located at the fixed end is trimmed and deleted, and only the gene sequence after the telomere is retained.

[0044] S35, taking the end position of the telomere as the starting point, taking fragments of the target length backward to obtain a gene fragment of a fixed length.

[0045] The fixed-end telomere sequence with trimmed fixed-end is intervalized to determine the target fixed-length fragment. The end position of the telomere (i.e. the starting position of the sequence after trimming) is used as the starting point, and the target length is taken from the starting point to obtain a fixed-length gene fragment.

[0046] Due to species differences, the range of interval values ​​is not fixed, that is, the interval values ​​also vary from species to species, and the target length can be set according to research needs.

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

[0048] All gene fragments of fixed length are combined in pairs, and each pair of gene fragment combinations is globally aligned by using multiple sequence alignment tools, such as ClustalW or Muscle software, to obtain global alignment results. The global alignment results usually include alignment scores (used to quantify the degree of similarity between two gene fragments), base matching (showing which bases are matched), similarities and differences (usually expressed as a percentage of the proportion of matching bases to the total number of bases, and the number or proportion of mismatches, insertions and deletions) and other information.

[0049] Further, in an implementation of the embodiment of the present invention, step S4 includes:

[0050] S41, arranging the fixed-length gene fragments, taking every two gene fragments as a combination, wherein the combination includes combinations of two different gene fragments (such as A and B, A and C, B and C, etc., wherein A, B, and C represent different gene fragments), and also includes combinations of gene fragments themselves (i.e., A and A, B and B, C and C, etc.);

[0051] S42, use a multiple sequence alignment tool to perform a global alignment of all gene fragment combinations to obtain global alignment results of all combinations.

[0052] Each pair of gene fragments in S41 is globally aligned using the selected multiple sequence alignment tool. The global alignment attempts to find the best alignment between the 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 comparison results, and constructing a global comparison score matrix according to the similarity scoring;

[0054] Use a programming language (such as Python) to create a two-dimensional matrix, in which the rows and columns are each gene fragment. That is, if the number of gene fragments of fixed length is n, the size of the constructed score matrix is ​​nxn. For each pair of gene fragment combinations, fill the similarity scores between them into the corresponding positions of the two-dimensional matrix to obtain the score matrix.

[0055] Further, in an implementation manner of the embodiment of the present invention, step S5 includes:

[0056] S51, design similarity scoring rules. 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 point is awarded. If there is a base vacancy at the same coordinate position, one point is penalized.

[0057] A base gap means that one gene fragment has a base at that position but the other does not, or neither has a base but the algorithm thinks there should be a base there.

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

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

[0060] S53, construct a score matrix based on the scores between all gene fragments, where the number of rows and columns of the matrix is ​​equal, both equal to the number of gene fragments of fixed length, and 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.

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

[0062] S6, performing similarity cluster analysis on the global comparison score matrix, and identifying the chromosome karyotype information of the species corresponding to the gene sequence to be analyzed based on the cluster 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. The number of chromosomes of the species can be obtained by counting the number of categories obtained by clustering analysis.

[0064] Further, in an implementation of the embodiment of the present invention, step S6 includes:

[0065] S61, defining a degree of similarity based on the score distribution of the global comparison score matrix;

[0066] Analyze the score distribution in the global comparison score matrix, set one or more similarity thresholds, and divide the gene fragments into different similarity levels. These thresholds can be determined based on experiments or expert experience.

[0067] S62, clustering fixed-length gene fragments with the same degree of similarity into one category;

[0068] Clustering is performed according to the degree of similarity. After clustering is completed, gene fragments with the same degree of similarity are clustered into the same category.

[0069] S63, counting the number of cluster categories 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 categories.

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

[0071] Chromosome karyotype information includes the number of chromosomes. There are as many categories as the clustering results, and half of the number of categories represents the number of chromosomes of the species corresponding to the gene sequence to be analyzed. This is because: the method of the present invention first screens the sequencing data obtained by sequencing with telomere sequences, and most of the gene sequences obtained by screening are sequence information from the ends of chromosomes. The telomere sequence information between different chromosomes is basically the same (there are differences in the number of copies), but the sequence information (i.e., the main part of the chromosome) left after cutting off the telomere sequence is significantly different. Only the gene sequences from the same chromosome end have high similarity (at the same chromosome end, although the length of the gene sequence obtained by sequencing is different, the sequence information is exactly the same). Therefore, these sequences are scored after global alignment to obtain a score matrix, and then clustered according to the scores. Gene sequences with similar scores are clustered into one category. Gene sequences from the same chromosome end have high similarity, so the scores obtained by comparing these gene sequences are similar or the same. The clustering result obtained by similarity clustering based on the score shows that each category represents a certain end of the chromosome of the species. By analogy, the number of categories obtained by clustering can be calculated to obtain the number of chromosomes. It is known that chromosomes have two ends (telomere sequences are located at the ends of chromosomes), so 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 invention, the clustering result obtained is clustered into 20 categories, indicating that these data come from 20 chromosome ends, so the number of chromosomes of this species can be calculated to be 10.

[0072] The present invention uses high-throughput sequencing data to perform chromosome karyotype analysis on species, which 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 chromosome number is abnormal, whether there are chromosome deletions, whether there are abnormal gene sequences, etc.

[0073] Example 2

[0074] The technical solution of the present invention is further described in detail below in conjunction with a specific embodiment and the accompanying drawings. It should be understood that the following embodiment is only used to explain the present invention, and is not used to limit the present invention.

[0075] Pleurotus eryngii, also known as Pleurotus eryngii, is an edible mushroom that grows in the Middle East, North Africa and parts of Asia. Pleurotus eryngii grows singly or in groups. The cap is slightly arched when young, then gradually flattens. When mature, it is concave in the center and funnel-shaped, with a diameter ranging from 2 to 12 cm. Generally, solitary individuals are larger, while those growing in groups are smaller. The cap is gray-black when young, gradually becomes lighter as the mushroom ages, and becomes light earthy yellow or light yellow-white when mature. There are radiating brown stripes around the center and a filamentous luster. The flesh is pure white with a distinct almond flavor. The gills are uneven and white, the same as ordinary oyster mushrooms. The stem is 5 to 20 cm long and 3 to 10 cm thick in diameter. The base is swollen and bowling-ball-shaped. Pleurotus eryngii is the largest species of the genus Pleurotus.

[0076] In this example, the gene sequence obtained by high-throughput sequencing of Pleurotus eryngii is analyzed for chromosome karyotype to illustrate the specific analysis steps and methods of the present invention. In order to verify the feasibility and accuracy of the present method, the reference genome of Pleurotus eryngii is used for analysis. Figure 2 As shown, the method for analyzing chromosome karyotype based on sequencing data in an embodiment of the present invention comprises the following steps:

[0077] Step 1: construct sequencing data obtained by high-throughput sequencing, analyze and control the quality of sequencing data, obtain the quality assessment value of gene sequence data based on the survey and analysis of gene sequence data, use fastp to perform quality control on sequencing data based on the quality assessment value, and use BWA or Pilon to perform data error correction to determine high-quality and high-accuracy gene sequences 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 the chromosome of eukaryotic organisms, and the genetic components of telomeres all contain tandemly repeated hexanucleotide sequences rich in G (guanine) (TTAGGG / CCCTAA)n. Traverse the gene sequence to be analyzed to query the telomere sequence. The length of the telomere sequence varies from species to species. Based on the telomere sequence query algorithm, query and screen the gene sequence with telomere sequences to be analyzed, and 2771 telomere sequences are queried and screened.

[0079] Step 3: Based on the coordinate information of eukaryotic telomeres at the ends of chromosomes, the sequences with telomeres were filtered to filter out gene sequences with tandemly repeated guanine-rich hexanucleotide sequences but non-telomere sequences. The 2771 sequences with telomeres were filtered by coordinate position, and 2688 sequences with telomeres that met the coordinate position were determined. Based on the coordinate information of the telomere sequences, the head end (5' end) of the sequence with telomeres was set as the fixed end, and the tail end (3' end) of the sequence with telomeres was set as the non-fixed end. The telomere sequences of all sequences with telomeres were aligned with the fixed end. For the sequences with telomeres located at non-fixed ends, the telomere sequences were aligned with the fixed end. The fixed-end telomere sequence is reverse-complemented, and its telomeres are transposed to the fixed end and then aligned. After alignment, the telomeres of all fixed-end telomere sequences are trimmed and deleted; then the fixed-end interval value is taken. Here, the fixed length value is set to 1000bp, and the end position of the fixed-end telomere is taken as the starting position of the target fixed-length fragment. Based on the position coordinate information, starting from the starting position, a fixed length of 1000bp is taken backward to obtain a fixed-length gene fragment. Due to species differences, the range of interval values ​​is not fixed, that is, the interval values ​​also vary from species to species;

[0080] Step 4, arranging and counting the gene fragments of fixed length 1000 bp, with each two fragments as a combination, including the combination of themselves, to obtain 7225344 pairwise gene fragment combinations, and using ClustalW or Muscle software to perform global alignment on all pairwise combinations to obtain global alignment results of all combinations;

[0081] Step 5, based on the global alignment results of all combinations of gene fragments with a fixed length of 1000bp, design a scoring rule for the global alignment results, traverse each base of the global alignment results, if the base types at the same coordinate position are the same, then get one point, if the base types at the same coordinate position are different, then no point, if the base at the same coordinate position is missing, then a penalty point; according to this method, perform the above scoring rule operation on the global alignment results of 7225344 combinations, determine the score data set of the global alignment results of all combinations of fixed length 1000bp fragments, and 7225344 pairwise gene fragment combinations correspond to 7225344 scores;

[0082] Step 6: Based on the 7225344 scores, construct a score matrix corresponding to the 7225344 combined global alignment results. The rows and columns of the matrix are gene fragment names, and the number of rows and columns are the number of gene fragments with a fixed length. Here, the number of gene fragments with a fixed length of 1000 bp is 2688, so the size of the constructed score matrix is ​​2688x2688;

[0083] Step 7: Determine the degree of similarity based on the score values ​​of the score matrix. Similar scores in the global comparison indicate that the characteristics of the gene fragments are similar. Similar characteristics are clustered into one category. This method is used for similarity clustering. MEGA software is used for clustering and a cluster diagram is drawn.

[0084] Step 8, according to the number of clustering results, that is, the number of clusters of fixed-length gene fragments, it indicates the number of chromosomes of the species corresponding to the gene sequence to be analyzed, and the karyotype of the species can be determined. According to this method, 2688 fixed-length 1000bp gene fragments, the number of clusters is 24 clusters, each cluster contains about 112 gene sequences, it can be predicted and determined that Pleurotus eryngii contains 12 chromosomes, and thus the chromosome karyotype of Pleurotus eryngii can be determined.

[0085] Reference Figure 3 , Figure 3 Schematic diagram of cluster analysis results of high-throughput sequencing data of Pleurotus eryngii in an embodiment of the present invention. The method of the present invention is used to analyze the high-throughput sequencing data of Pleurotus eryngii to predict and determine the number of chromosomes. Figure 3 It can be seen intuitively that the processed gene sequences are clustered into 24 categories after cluster analysis, and the number of Pleurotus eryngii chromosomes can be calculated to be 12. It should be noted that there is a professional knowledge point here. If you count directly, you can only count it into 23 categories. However, it should be noted that the number of gene sequences contained in one category is significantly greater than that of other categories and is approximately twice that of other categories. This shows that there are two highly similar terminal sequences between the chromosomes of this species. This does not affect the scientific nature of the method of the present invention, and it proves that the present invention is also applicable to these special cases. In addition, Figure 3 There are also some single or multiple branching situations, which are regarded as unavoidable noises by the present invention. The existence of these noises is due to the presence of telomere-like gene sequences located in the middle of chromosomes on the chromosomes of the species. However, the present invention has reduced the noise as much as possible in the step of intervalization and value taking, but due to the limitations of high-throughput sequencing technology, the noise cannot be completely eliminated.

[0086] In this embodiment, a device for analyzing chromosome karyotype based on sequencing data is also provided, which is used to implement the above embodiments and preferred implementation modes, and those that have been explained will not be repeated here.

[0087] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0088] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0089] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0090] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

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

[0092] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0093] The steps in the above-mentioned embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The technical features can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the embodiment are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

[0094] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for analyzing chromosome karyotype based on sequencing data, characterized in that: include: Perform quality control and error correction on the sequencing data obtained by high-throughput sequencing to obtain the gene sequence to be analyzed; Performing telomere query and screening on the gene sequence to be analyzed, and screening out sequences with telomeres; Performing position filtering, fixed-end alignment, telomere sequence clipping and interval value taking on the telomere sequence to obtain a gene fragment of fixed length; Arrange and combine the fixed-length gene fragments in pairs, and perform global alignment to obtain a global alignment result; Performing similarity scoring based on the global comparison results, and constructing a global comparison score matrix according to the similarity scoring; A similarity cluster analysis is performed on the global alignment score matrix, and based on the cluster analysis result, the chromosome karyotype information of the species corresponding to the gene sequence to be analyzed is identified.

2. The method for analyzing chromosome karyotype based on sequencing data according to claim 1, characterized in that: Quality control and error correction of sequencing data obtained by high-throughput sequencing include: The quality control software fastp was used to perform quality control on the sequencing data, and the BWA or Pilon software was used to correct the errors of the sequencing data.

3. The method for analyzing chromosome karyotype based on sequencing data according to claim 1, characterized in that: The gene sequence to be analyzed is subjected to telomere query and screening, and the telomere sequences screened out include: The gene sequence to be analyzed is traversed to identify and retain hexanucleotide sequences containing tandem repeats and the number of guanines in each repeating unit exceeding a preset value, thereby screening out telomere sequences.

4. The method for analyzing chromosome karyotype based on sequencing data according to claim 1, characterized in that: The telomere sequence is subjected to position filtering, fixed end alignment, telomere sequence clipping and interval value taking to obtain a gene fragment of fixed length, including: Based on the position information of eukaryotic telomeres at the ends of chromosomes, the telomere sequences are traversed to check whether the telomere coordinates of each sequence conform to the position information, and the sequences that do not conform to the position information are filtered out; The head end with the telomere sequence is used as a fixed end, and the tail end is used as a non-fixed end, wherein the head end refers to the 5' end, and the tail end refers to the 3' end; Check whether the telomere of each telomere sequence is located at the fixed end. For sequences with telomeres located at non-fixed ends, perform reverse complementation to transfer the telomeres to the fixed end. All telomere sequences are aligned based on the fixed ends, and the telomeres located at the fixed ends are trimmed and deleted; Taking the end position of the telomere as the starting point, fragments of the target length are taken backward to obtain a gene fragment of fixed length.

5. The method for analyzing chromosome karyotype based on sequencing data according to claim 1, characterized in that: Arranging and combining the fixed-length gene fragments in pairs and performing global alignment to obtain a global alignment result includes: Arranging the fixed-length gene fragments, taking every two gene fragments as a combination, wherein the combination includes a combination of two different gene fragments and a combination of a gene fragment itself; For all gene fragment combinations, a multiple sequence alignment tool was used to perform a global alignment to obtain the global alignment results of all combinations.

6. The method for analyzing chromosome karyotype based on sequencing data according to claim 1, characterized in that: Performing similarity scoring based on the global comparison result and constructing a global comparison score matrix according to the similarity scoring includes: Design similarity scoring rules. 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 point is awarded. If there is a base gap at the same coordinate position, one point is penalized. Traversing each base of the global comparison result, and scoring each two gene fragments using the similarity scoring rule; A score matrix is ​​constructed based on the scores between all gene fragments, where the number of rows and columns of the matrix is ​​equal, both 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.

7. The method for analyzing chromosome karyotype based on sequencing data according to claim 1, characterized in that: Performing similarity cluster analysis on the global comparison score matrix includes: Determining a degree of similarity based on the score distribution of the global comparison score matrix; Gene fragments of fixed length with the same degree of similarity are clustered into one category.

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

9. A device for analyzing chromosome karyotype based on sequencing data, characterized in that: The device for analyzing chromosome karyotype based on sequencing data comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method as described in any one of claims 1 to 8 when executed by the processor.

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

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