Method for constructing chromosome three-dimensional structure based on fusion gene data and application thereof

Abstract

The invention discloses a method for constructing a chromosome three-dimensional structure based on fusion gene data and application of the method, and belongs to the technical field of genomes. According to the method, internal interaction of fusion genes and interaction between chromosomes are analyzed, hierarchical structures of second, third and fourth stages of the chromosomes are determined by calculating the frequency of the interaction, and a three-dimensional structure is constructed according to the hierarchical structures of the second, third and fourth stages of the chromosomes; the method is of great significance in exploring gene interaction and determining the relation between the chromosome spatial position and the disease.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for constructing a three-dimensional structure of a chromosome based on fusion gene data and application thereof, and belongs to the technical field of genomes. BACKGROUND

[0002] With the development of genomics technology, it is found that the genomic structure is not a simple linear sequence, but rather DNA "self-assembles" into a complex three-dimensional topological structure in the nucleus, providing a structural basis for important biological processes such as gene expression regulation and cell differentiation. The three-dimensional hierarchical structure of the genome is believed to confer higher accessibility and flexibility to genes by promoting or preventing interactions between distant regulatory elements and target genes; on the other hand, it regulates the state of chromatin through epigenetic modification and nucleosome positioning, thereby maintaining normal physiological functions and biological processes of cells. At present, the understanding of the three-dimensional spatial structure of chromosomes and its potential mechanisms is still largely limited.

[0003] Two experimental methods widely used to study the structure of chromosomes are DNA fluorescence in situ hybridization (DNA FISH) and chromosome conformation capture (3C) and its derivatives. DNA FISH is essentially a low-throughput technique based on the principle of nucleic acid base complementary pairing, which involves hybridizing fluorescently labeled DNA probes to target DNA sequences, observing hybridization signals by fluorescence microscopy, and achieving qualitative, quantitative, and localization analysis of DNA sequences; although this method only allows simultaneous evaluation of a few genomic sites, it still has many basic findings, such as the existence of chromosomal regions and the dynamic repositioning of genomic sites relative to nuclear compartments (such as the nuclear periphery) during differentiation; however, it has the defects of complex probe design, high requirements for sample processing, easy quenching of fluorescent signals, difficulty in preservation, and limited ability to quantify DNA. Dekker et al. (2002) described the 3C method, which can measure the spatial interaction of specific two chromatin fragments on the genome; its core principle is to capture the interaction between spatially adjacent DNA fragments on the chromosome through cross-linking, restriction enzyme digestion, ligation, and sequencing steps. Subsequently, 4C, 5C, and Hi-C derivative technologies were developed. The Hi-C technology can obtain unbiased whole-genome interaction data. C technology has led to important findings from kilobase-range sequences between loops to sub-megabase topological association domains (TADs) that tend not to change between tissues, and even megabase active and inactive compartments; however, it has certain technical bias and is difficult to process data.

[0004] On the other hand, structural variations of chromosomes, such as balanced translocation, inversion, deletion and tandem duplication, can cause the formation of fusion genes, which cause the fusion of part or all of the sequences of two different genes into a new sequence. Gene fusion is a common mutation in blood, lymph and bone marrow tissue-related malignancies. In the past few decades, the total number of fusion genes identified by various deep sequencing methods is close to 10,000, of which there are 759 fusion genes in hematological diseases and 9,189 fusion genes in solid tumors. In clinical practice, fusion gene detection has been used as an important means of molecular diagnosis of diseases, which helps early detection, classification and prognosis evaluation. In addition, quantitative analysis of fusion genes can monitor disease progression and treatment response, providing important information for individualized medicine. However, there are still few studies on the use of gene fusion to locate the contact of chromosomes, which cannot provide data support for the development of new disease diagnosis and treatment methods.

[0005] Although Hi-C and FISH can provide higher resolution, whole genome range of static three-dimensional structure information, these technologies are high in cost and complex in operation. At present, the mining and utilization of genomic sequencing data is insufficient, and the method of using fusion genes to construct the three-dimensional structure of chromosomes has the advantages of low cost and simple operation. SUMMARY

[0006] To solve the problems of the prior art, the present application draws a three-dimensional organization map of chromosomes based on fusion gene data according to trans-chromosome interaction, analyzes the characteristics of high-frequency spectrum interaction chromosomes and genes, and reveals the hot spot region of driving mutations from the SD sequence level. This paper reveals the spatial structure of chromosomes from the perspective of fusion genes, which provides support for elucidating the dynamic interaction between 3D genome structure and SV formation, and developing new disease diagnosis and treatment methods.

[0007] The present application is realized by the following technical solutions:

[0008] The first object of the present application is to provide a method for constructing the three-dimensional structure of chromosomes based on fusion gene data, which comprises the following steps:

[0009] Step one: retrieval and analysis of fusion genes;

[0010] Step two: processing of fusion gene data;

[0011] Step three: construction of three-dimensional structure of chromosomes based on pastis_NB algorithm;

[0012] In step one: find the fusion genes of 23 chromosomes in blood diseases and solid tumors, and combine the information of the visible band, the start position of the gene and the end position of the gene of the chromosome determined by NCBI.

[0013] Step two includes:

[0014] Step S1: Format conversion of fusion gene data;

[0015] Step S2: Remove duplicates from the fusion gene data obtained in Step S1;

[0016] Step S3: Prepare chromosome banding files;

[0017] Step S4: Expand the gene information obtained in step S2 using the chromosome banding file from step S3;

[0018] Step S5: Calculate the interaction frequency by counting the number of interactions between gene1 and gene2 based on the data obtained in step S4;

[0019] Step S6: Based on the interaction frequency obtained in step S5, classify the primary structure, secondary structure, tertiary structure, quaternary structure, and pivot chromosome of the chromosome;

[0020] The primary structure refers to the base sequence of a gene; the secondary structure refers to the circular chromatin structure formed by a certain interaction event on the same chromosome; the tertiary structure is the combination of multiple secondary structures on the same chromosome, which constitutes the spatial folding conformation of the entire chromosome; and the quaternary structure refers to the interaction between different chromosomes, which ultimately forms the overall spatial structure of the 22 autosomes and sex chromosomes in the cell nucleus.

[0021] Hub genes are determined by calculating the number of "inter-interactions" and "inter-interactions" that each gene (such as gene A) participates in based on the results of integrated gene statistics, and the number of interactions is used to determine their importance as hub chromosomes. Hub chromosomes, on the other hand, are determined by comparing the number of interactions between the chromosome and other chromosomes. The more interactions a chromosome has, the more important it is as a hub chromosome.

[0022] Further, step S1 includes converting the fusion gene database file retrieved in step one into a fusion gene summary file, with column names Fusion_pair, gene1, and gene2, to obtain the fusion gene data file fusion_data.xlsx;

[0023] Furthermore, step S2 includes deduplicating the obtained fusion gene data file fusion_data.xlsx to obtain a unique Fusion_pair and information on gene1 and gene2;

[0024] Furthermore, the chromosome banding file in step S3 contains three columns of information: band, start, and end, and is saved as cytoBand.csv;

[0025] Further, step S4 includes extracting information about gene1 and gene2 and saving it as gene_list.xlsx. The script find_gene.py is used to compare gene names with the genomic.gff parsing file in the ncbi database and combine it with the chromosome banding localization cytoBand.csv file to expand the file information, and finally obtain Fusion_pair, gene1, gene1band, gene1 start, gene1 end, gene1 length, gene2, gene2 band, gene2start, gene2 end, gene2length;

[0026] Further, step S5 includes extracting gene1 band and gene2 band data, counting the number of times they appear together as frequency, and arranging them according to the banding order of the reference genome to obtain a contact_frequency.csv file with three columns: band1, band2, and frequency.

[0027] Furthermore, by analyzing the frequency of intrachromosomal interactions and interchromosomal interactions, chromosomes are classified into primary, secondary, tertiary, quaternary structures and pivot chromosomes.

[0028] Step three includes:

[0029] Step T1: Prepare the documents;

[0030] Step T2: Install the Pastis script;

[0031] Step T3: Construct the three-dimensional structure of the chromosome at banding resolution;

[0032] Step T4: Visualization processing;

[0033] Step T5: Construction of a three-dimensional model of chromosome structure;

[0034] Furthermore, in step T1, prepare the files contact_frequency.csv and cytoBand.csv;

[0035] Furthermore, in step T2, the commands `conda install numpy scipy scikit-learnpandas` are used to install dependencies, and the commands `python setup.py build` and `sudo python setup.py install` are used to install the Pastis script.

[0036] Furthermore, in step T3, using the display band as the resolution and employing negative binomial statistics, the script pm2_reconstruction.py is executed. This script measures the desired contact frequency μ between the display bands. ij Defined as the exponential negative correlation function of their Euclidean distance in three-dimensional space:

[0037]

[0038] Where, x i and x j They represent the bandgap x i and x j The three-dimensional coordinates are given by β, which represents the normalization factor used to adjust the baseline level of the overall contact frequency. Subsequently, the three-dimensional coordinates of all visible bands are solved by maximizing the log-likelihood function based on a negative binomial distribution.

[0039]

[0040] Among them, O ij Ω represents the observed contact count between the two bands i and j; r represents the magnitude parameter of the negative binomial distribution, used to describe the dispersion of the contact frequency count; Ω represents the set of all observed band pairs.

[0041] Furthermore, in step T4, the generated file PM2.structure.connected.pdb is visualized. This file contains five columns: chr, band, x, y, and z. This file represents the spatial coordinates of the band. The visualization is achieved by calling Python's matplotlib with the script plot_total_chr.py.

[0042] Furthermore, in step T5, a three-dimensional spatial coordinate system with chromosome banding as the basic unit is constructed using the simulated annealing algorithm. By connecting the spatial coordinate points of these bandings, a three-dimensional structural model of the chromosome is constructed.

[0043] The beneficial effects of this invention are:

[0044] This invention provides a method for constructing a three-dimensional structure of chromosomes based on fusion gene data and its application. By utilizing fusion genes to determine the interaction information and three-dimensional structure of chromosomes, it provides a new research perspective and technical means. The three-dimensional information provided by the spectrum of fusion genes not only shows the universality of chromosome spatial layout, but also increases the interpretability of fusion gene occurrence. Chromatin conformation capture data from multiple tissues and stages are significantly correlated with the intensity of fusion genes, improving the accuracy of predicting the location of chromosome fusion events. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of chromosome interaction events in the method for constructing a three-dimensional chromosome structure based on fusion gene data provided in Embodiment 1 of the present invention and its application.

[0047] Figure 2 This is a schematic diagram of the secondary chromosome structure in a method for constructing a three-dimensional chromosome structure based on fusion gene data provided in Embodiment 1 of the present invention;

[0048] Figure 3 This is a schematic diagram of the transcentriomal interactions of secondary chromosomes in a method for constructing a three-dimensional chromosome structure based on fusion gene data provided in Embodiment 1 of the present invention.

[0049] Figure 4 This is a heatmap of intrachromosomal interactions in a method for constructing a three-dimensional chromosome structure based on fusion gene data provided in Embodiment 1 of the present invention.

[0050] Figure 5 This is a schematic diagram of intrachromosomal interaction structures in a method for constructing a three-dimensional chromosome structure based on fusion gene data provided in Embodiment 1 of the present invention;

[0051] Figure 6 This is a schematic diagram showing the distribution relationship between chromosome banding length and interaction frequency in a method for constructing a three-dimensional chromosome structure based on fusion gene data provided in Embodiment 1 of the present invention.

[0052] Figure 7 This is a schematic diagram of the three-dimensional chromosome structure in a method for constructing a three-dimensional chromosome structure based on fusion gene data provided in Embodiment 1 of the present invention;

[0053] Figure 8This is a schematic diagram of the interchromosomal interaction frequency in a method for predicting the occurrence segment of fusion genes provided in Embodiment 2 of the present invention;

[0054] Figure 9 This is a schematic diagram illustrating the frequency of intragenic interactions of the KMT2A gene in a method for predicting fusion gene occurrence segments provided in Embodiment 2 of the present invention.

[0055] Figure 10 This is a schematic diagram illustrating the frequency of intra-interactions of the NUP98 gene in a method for predicting fusion gene occurrence segments provided in Embodiment 2 of the present invention. Detailed Implementation

[0056] The present invention will be further illustrated below with specific examples. These embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0057] Source of raw materials

[0058] The fusion gene data involved in this invention comes from:

[0059] https: / / atlasgeneticsoncology.org ;

[0060] https: / / www.kobic.re.kr / chimerdb / ;

[0061] https: / / cancer.sanger.ac.uk / cosmic ;

[0062] https: / / mitelmandatabase.isb-cgc.org / ;

[0063] https: / / www.ncbi.nlm.nih.gov / ;

[0064] The technical solution of the present invention will be described in detail below with reference to specific embodiments. In the following embodiments, unless otherwise specified, the reagents, materials and equipment used can be purchased commercially, prepared by conventional methods, or commonly used in the industry.

[0065] Example 1:

[0066] This embodiment provides a method for constructing a three-dimensional chromosome structure based on fusion gene data, the method comprising:

[0067] Step 1: Search and analysis of fusion genes;

[0068] Using the website:

[0069] https: / / atlasgeneticsoncology.org ;

[0070] https: / / www.kobic.re.kr / chimerdb / ;

[0071] https: / / cancer.sanger.ac.uk / cosmic ;

[0072] https: / / mitelmandatabase.isb-cgc.org / ;

[0073] A search was conducted to identify fusion genes on 23 chromosomes in hematologic disorders and solid tumors.

[0074] By combining information from NCBI (https: / / www.ncbi.nlm.nih.gov / ), we can determine the chromosome banding, gene start position, and gene termination position.

[0075] Step 2: Preprocessing of fusion gene data;

[0076] Step S1: Format conversion of fusion gene data;

[0077] Convert the fusion gene database file obtained in step one into a fusion gene summary file, with column names Fusion_pair, gene1, gene2, to obtain the fusion gene data file fusion_data.xlsx.

[0078] Step S2: Remove duplicates from the fusion gene data obtained in Step S1;

[0079] The obtained fusion_data.xlsx file of fusion gene data is deduplicated to obtain a unique Fusion_pair and information on gene1 and gene2;

[0080] Step S3: Prepare chromosome banding files;

[0081] Prepare a chromosome banding file containing three columns of information: band, start, and end, and save it as cytoBand.csv;

[0082] Step S4: Expand the gene information obtained in step S2 using the chromosome banding file from step S3;

[0083] Extract information for gene1 and gene2 and save it as gene_list.xlsx. Then use the script find_gene.py, which compares the gene name with the genomic.gff parsing file in the ncbi database and combines it with the chromosome banding localization cytoBand.csv file to expand the file information. Finally, obtain Fusion_pair, gene1, gene1 band, gene1 start, gene1 end, gene1 length, gene2, gene2 band, gene2 start, gene2 end, gene2 length;

[0084] Step S5: Calculate the number of interactions between gene1 and gene2 based on the data obtained in step S4;

[0085] The genes with high interaction frequencies on chromosomes, obtained through statistical analysis, are shown in Table 1:

[0086] Table 1. Interacting genes with high frequency of gene-gene interactions on chromosomes.

[0087]

[0088] Step S6: Based on the interaction frequency obtained in step S5, classify the primary, secondary, tertiary, and quaternary structures of chromosomes, as well as the pivot chromosome.

[0089] Interchromosomal interaction events are obtained based on the statistical number of interactions, such as... Figure 1 As shown, it is evident that chromosome 11 is most prone to trans-interactions, with the strongest interaction between it and chromosome 17, followed by chromosome 3 and then chromosome 1. Overall, large chromosomes exhibit a higher frequency of trans-interactions, with chromosome 1 showing the most frequent interactions (895 interactions). Among the short chromosomes, chromosomes 17 and 19 also show a high number of interactions, with 599 and 591 interactions respectively.

[0090] The primary and quaternary structures of chromosomes are defined by the number of interactions within the chromosome. Primary structure refers to the base sequence of genes; secondary structure refers to the circular chromatin structure formed by a specific interaction event on the same chromosome, such as... Figure 2 As shown, the types include independent, nested, edge-sharing, and point-sharing; the independent type is where the interacting genes on the chromosome are located at different banding positions; the nested type is where the fusion gene occurs at a certain position in the formed chromosome ring; the edge-sharing and point-sharing types belong to the inclusion type model.

[0091] The tertiary structure of chromosomes is defined by whether the intrachromosomal interactions cross the centromere and divide into two parts, such as... Figure 3As shown, intracentric interactions may lead to larger spatial ring structures on chromosomes, which tend to fold or connect end to end; while non-intracentric interactions may form smaller ring structures.

[0092] To elucidate the complexity of chromosomes, we define the Intra-chromosomal Interaction Density Score (IIDS), which uses the number of chromosome interactions divided by chromosome length to determine the complexity of chromosome interaction structures. A higher score indicates a more compact chromosome structure, suggesting a more complex spatial structure based on internal interactions, potentially exhibiting a denser clustered distribution. Its expression is:

[0093]

[0094] Wherein, Total Times Interactions of Chromosomes represents the total number of times chromosomes interact, and Chromosome Length represents the chromosome length; the complex spatial structure of chromosomes due to internal interactions is defined by the above formula, and the circular pattern of chromosomes in a two-dimensional plane is as follows: Figure 4 As shown, this includes long-range interactions, medium-range interactions, and short-range interactions. Long-range interactions form a large chromatin ring, which contains smaller rings formed by medium-range and short-range interactions.

[0095] Step 3: Construct the three-dimensional structure of the chromosome based on the pastis_NB algorithm;

[0096] Step T1: Prepare the documents;

[0097] Prepare the files contact_frequency.csv and cytoBand.csv;

[0098] Step T2: Install the Pastis script;

[0099] Use the command `conda install numpy scipy scikit-learn pandas` to install dependencies, and use the command `python setup.py build sudo python setup.py install` to install the Pastis script.

[0100] Step T3: Construct the three-dimensional structure of the chromosome at banding resolution;

[0101] Because it is a sparse 862*862 matrix and the data is excessively discrete with variance greater than the mean, negative binomial statistics are used. The script pm2_reconstruction.py is executed, which measures the expected contact frequency μ between the display bands. ij Defined as the exponential negative correlation function of their Euclidean distance in three-dimensional space:

[0102]

[0103] Where, x i and x j They represent the bandgap x i and x j The three-dimensional coordinates are given by β, which represents the normalization factor used to adjust the baseline level of the overall contact frequency. Subsequently, the three-dimensional coordinates of all visible bands are solved by maximizing the log-likelihood function based on a negative binomial distribution.

[0104]

[0105] Among them, O ij Ω represents the observed contact count between the two bands i and j; r represents the magnitude parameter of the negative binomial distribution, used to describe the dispersion of the contact frequency count; Ω represents the set of all observed band pairs.

[0106] Based on chromosome banding length and the number of interactions, the distribution relationship between the two is divided into four types: long banding-high interaction, long banding-low interaction, short banding-high interaction, and short banding-low interaction. Figure 5 As shown, the bands in the long-banded, high-interaction regions are clearly redder and contain the highest number of fusion genes. Specifically, p13.3 on chromosome 19 contains 97 fusion genes, and p13.2 contains 79 fusion genes, making them the most prone to gene fusion. Meanwhile, the short bands on chromosome 17 (q21.1, q23.1) also exhibit extremely high gene fusion rates. In low-interaction regions, the banding distribution is relatively random. Overall, the bands on chromosomes 17 and 19 tend to cluster in high-interaction regions.

[0107] Step T4: Visualization processing;

[0108] The generated file PM2.structure.connected.pdb is visualized. This file contains five columns: chr, band, x, y, and z. This file represents the spatial coordinates of the band. The visualization is achieved by using the script plot_total_chr.py to call Python's matplotlib.

[0109] Step T5: Construction of a three-dimensional model of chromosome structure;

[0110] A three-dimensional spatial coordinate system with chromosome banding as the basic unit was constructed using the simulated annealing algorithm. By connecting the spatial coordinate points of these bandings, a three-dimensional structural model of the chromosome was successfully constructed.

[0111] The final three-dimensional structural model of the chromosome obtained from the above steps is as follows: Figure 6 As shown, to quantitatively describe the distribution characteristics of chromosomes in the radial dimension, the nucleus was divided into four concentric regions based on the spatial distribution of bands according to their actual distance from the nuclear center: the core layer, inner medial layer, outer medial layer, and perinuclear layer. Statistical analysis showed that the core layer (0.019–0.535 mm from the center) contained 193 bands, accounting for 25.1% of the total; the inner medial layer (0.535–0.918 mm) contained 192 bands, accounting for 24.9%; the outer medial layer (0.918–1.364 mm) contained 192 bands, accounting for 24.9%; and the perinuclear layer (1.364–3.427 mm) contained 193 bands, accounting for 25.1%.

[0112] Example 2:

[0113] This embodiment provides a method for predicting the region where fusion genes occur. This method is based on the three-dimensional chromosome structure described in Embodiment 1. This method analyzes the distribution of densely interacting genes on chromosomes, defines chromosomes with high-frequency interacting genes as hub chromosomes, and defines the regions on hub chromosomes where gene interactions occur as hub genes.

[0114] The distribution of interaction-intensive genes on chromosomes is as follows: Figure 7As shown, red squares represent high-frequency interacting genes, pink spheres represent medium-frequency interacting genes, and blue triangles represent low-frequency interacting genes. The 22 autosomes and the X and Y sex chromosomes are displayed in long and short arm configurations, and the frequency of fusion genes occurring within the bands of different chromosomes is statistically analyzed. High-interaction genes are mainly clustered in the long arm regions of chromosomes 11 and 17, and the short arm region of chromosome 19, which are defined as pivot chromosomes. The long arm region of chromosome 17, in particular, shows the highest frequency of fusion genes. In contrast, the frequency of fusion genes on large chromosomes, such as chromosomes 1 and 4, is extremely low. The same pattern is observed on chromosomes 13, 15, and 18, which are predominantly composed of low-interaction genes, indicating high conservation.

[0115] The two most frequently interacting genes on chromosome 11 that are most prone to gene fusion are KMT2A and NUP98, such as... Figure 8 and Figure 9 As shown, Figure 8 The KMT2A gene, only 90,340 bases long and located at 11q23.3, interacted with different chromosomes 41 times, meaning an interaction occurs every 2,203 bases. The interaction frequency fraction was as high as 4.54. KMT2A interacted most frequently with chromosomes 5, 6, 17, and 19, suggesting that the banded region at 11q23.3 on chromosome 11 is spatially close to these four chromosomes. The NUP98 gene, 122,544 nt long and located at 11p15.4, also interacted with different chromosomes 11 times, meaning an interaction occurs every 11,140 bases. Figure 9 As shown, the NUP98 gene interacts most frequently with genes on chromosome 7 and has 7 fusion partner genes.

[0116] This invention, through extensive analysis of trans-chromosome interaction data, reveals the prevalence of chromosomal spatial conformation in cancer, providing new insights for the development of diagnostic tools and targeted strategies. The results show that different cancer types share the same chromosomal spatial arrangement pattern (1 / 8 / 11 / 17 / 19 in the nuclear center, 13 / 15 / 18 / 21 / X at the nuclear periphery), allowing for the development of pan-cancer diagnostic biomarkers based on chromosomal spatial localization. Nuclear structural abnormalities are pathological markers of malignant tumors. This study found that high-frequency translocation chromosomes are usually located in the nuclear center and are prone to gene fusion. Therefore, analyzing the distribution patterns of chromosomal translocations within tumor cell nuclei can help identify nuclear structural abnormalities in malignant tumors. Furthermore, genes with high interaction frequencies carried by pivot chromosomes (such as chromosomes 11, 17, and 19) can be preferentially considered as candidate targets for targeted therapy or gene editing (e.g., KMT2A, NUP98, RARA). The number of chromosomal translocations is positively correlated with tumor genomic instability. Developing anti-translocation drugs is also a feasible approach. Inhibiting the error repair of "translocation vulnerable regions" can selectively kill tumor cells. At the same time, the frequency of chromosomal translocations may be developed into a prognostic marker in the future. For example, a high chromosomal translocation burden indicates strong invasiveness.

[0117] The embodiments provided above are not intended to limit the scope of the invention, nor are the described steps intended to limit the order of execution. Any obvious modifications made to the invention by those skilled in the art based on existing common knowledge also fall within the scope of protection defined by the claims.

Claims

1. A method for constructing a three-dimensional chromosome structure based on fusion gene data, characterized in that, The method includes; Step 1: Search and analysis of fusion genes; Step 2: Processing of fusion gene data; Step 3: Construct the three-dimensional structure of the chromosome based on the pastis_NB algorithm; The three-dimensional structure of the chromosome was constructed using the fusion gene.

2. The method according to claim 1, characterized in that, Step two includes: Step S1: Format conversion of fusion gene data; Step S2: Remove duplicates from the fusion gene data obtained in Step S1; Step S3: Prepare chromosome banding files; Step S4: Expand the gene information obtained in step S2 using the chromosome banding file from step S3; Step S5: Calculate the interaction frequency by counting the number of interactions between gene1 and gene2 based on the data obtained in step S4; Step S6: Based on the interaction frequency obtained in step S5, classify the primary structure, secondary structure, tertiary structure, quaternary structure, and pivot chromosome of the chromosome.

3. The method according to claim 2, characterized in that, Step three includes: Step T1: Prepare the documents; Step T2: Install the Pastis script; Step T3: Construct the three-dimensional structure of the chromosome at banding resolution; Step T4: Visualization processing; Step T5: Construction of the three-dimensional structure model of chromosomes.

4. The method according to claim 2, characterized in that, In step S6: The primary structure refers to the base sequence of a gene; The secondary structure refers to the ring chromatin structure formed by an intrachromatin interaction event on the same chromosome. The tertiary structure is composed of several secondary structures on the same chromosome, forming the spatial folding conformation of the entire chromosome. The aforementioned four-level structure refers to the interaction between different chromosomes, which ultimately forms the overall spatial structure of 22 autosomes and sex chromosomes within the cell nucleus.

5. The method according to claim 4, characterized in that, In step S6: The pivot chromosome is determined by comparing the number of interactions between the chromosome and other chromosomes. The chromosome with the highest number of interactions is defined as the pivot chromosome; and the gene with the most intragenic interactions on the pivot chromosome is defined as the pivot gene.

6. The method according to claim 2, characterized in that, In step two: Step S1 includes: converting the fusion gene database file obtained in step one into a fusion gene summary file, with column names Fusion_pair, gene1, and gene2, to obtain the fusion gene data file fusion_data.xlsx; Step S2 includes: deduplicating and organizing the fusion gene data fusion_data.xlsx file obtained in step S1 to obtain a unique Fusion_pair and information on gene1 and gene2; Step S3 includes: preparing a chromosome banding file containing three columns of information: band, start, and end, and saving it as cytoBand.csv; Step S4 includes: extracting information of gene1 and gene2 and saving it as gene_list.xlsx, and using the script find_gene.py, which compares the gene name with the genomic.gff parsing file in the database and combines it with the chromosome banding localization cytoBand.csv file to expand the file information, finally obtaining Fusion_pair, gene1, gene1band, gene1 start, gene1 end, gene1 length, gene2, gene2 band, gene2start, gene2 end, and gene2length.

7. The method according to claim 3, characterized in that, In step three: Step T1 includes preparing the contact_frequency.csv and cytoBand.csv files; Step T2 includes installing dependencies using the command `conda install numpy scipy scikit-learn pandas` and installing the Pastis script using the commands `python setup.py build` and `sudo python setup.py install`. Step T3 includes executing the script pm2_reconstruction.py with the display band as the resolution and using negative binomial statistics. The script pm2_reconstruction.py measures the desired contact frequency μ between the display bands. ij Defined as the exponential negative correlation function of their Euclidean distance in three-dimensional space: Where, x i and x j They represent the bandgap x i and x j The three-dimensional coordinates are given by β, which represents a normalization factor used to adjust the baseline level of the overall contact frequency; subsequently, the three-dimensional coordinates of all visible bands are solved by maximizing the log-likelihood function based on the negative binomial distribution. Among them, O ij Ω represents the measured contact count between two display bands i and j; r represents the magnitude parameter of the negative binomial distribution, used to describe the dispersion of the contact frequency count; Ω represents the set of all observed display band pairs. Step T4 includes visualizing the generated PM2.structure.connected.pdb file, which contains five columns: chr, band, x, y, and z. The file represents the spatial coordinates of the band. The visualization is achieved by using the script plot_total_chr.py to call Python's matplotlib. Step T5 involves using a simulated annealing algorithm to construct a three-dimensional spatial coordinate system with chromosome banding as the basic unit, and then connecting the spatial coordinate points of these bands to construct a three-dimensional structural model of the chromosome.

8. A system for predicting regions where fusion genes occur, characterized in that, The system is implemented based on the method described in any one of claims 1-7.

9. A method for predicting the region where a fusion gene occurs, characterized in that, The method is implemented based on the method described in any one of claims 1-7 and the system described in claim 8.

10. The method according to claim 9, characterized in that, The method includes inputting the fusion gene into the system of claim 8, and the system outputting a segment predicting the occurrence of the fusion gene.

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