Method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data

Abstract

The invention provides a method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data, and relates to the technical field of biology.The method comprises the steps that three-generation genome sequencing data of a to-be-detected sample is obtained, and chromosome translocation is identified based on the three-generation genome sequencing data to form a first data set; obtaining Hi-C sequencing data of a sample to be detected or sequencing data based on a Hi-C derivation technology, and identifying chromosome translocation based on the Hi-C sequencing data or the sequencing data based on the Hi-C derivation technology to form a second data set; and taking candidate chromosome translocation in the intersection of the first data set and the second data set, and taking the verified candidate chromosome translocation as the finally screened chromosome translocation. The translocation accuracy of the chromosomes screened by the method is high and can reach 98%.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data. Background Art

[0002] The following statements merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Genome structural variations (SVs) usually refer to abnormal DNA sequences greater than 50 bp in length within the genome. The main types of SVs include deletion, insertion, duplication, inversion, and translocation of genomic fragments, which are the main source of differences in the human genome.

[0004] Chromosome translocation, that is, the change in the position of chromosome fragments, is the most common chromosomal abnormality. The formation of chromosomal translocation requires DNA double-strand breaks (DSBs), which are usually caused by spontaneous DNA replication errors, exogenous stress such as ionizing radiation or chemicals, and programmed breaks of the autoimmune system. DSBs will basically be repaired by the body's DNA repair system, but as long as the repair goes wrong, the broken ends will produce incorrect links through physical contact and interaction within a certain space, resulting in chromosomal translocation. In addition to double-strand break repair, the factors that lead to chromosomal translocation are also related to factors such as histone modification. The spatial folding structure and activity of chromosomes are also important factors in the formation of chromosomal translocation. Chromosome translocation can occur between different chromosomes or at different locations on the same chromosome, causing the position of genes to change, often resulting in gene mutations and fusion genes.

[0005] There are two main existing methods for detecting chromosomal translocations: one is the classic method of identifying chromosomal translocations based on second-generation or third-generation genome sequencing, and the other is to identify chromosomal translocations from Hi-C data through deep learning methods.

[0006] Chromosomal translocations are identified based on second-generation or third-generation genome sequencing technology. Due to the limitation of the length of sequencing reads, only genomic regions within 10 to 20 kb can be detected, which leads to false positives in the detection of repetitive regions and regions with high sequence similarity on the genome.

[0007] Identifying chromosome translocations from Hi-C (highest-throughput chromosome conformation capture) data based on deep learning methods has also been a hot topic in recent years. Hi-C is a method derived from 3C (chromosome conformation capture) to study the three-dimensional structure of chromatin. It is an analytical technology that studies chromatin interactions and explores the three-dimensional structure of the whole genome. The basic principles of this technology are: (1) Formaldehyde cross-linking: first use 37% formaldehyde to fix the DNA-protein or protein-protein complexes that are cross-linked or very close in space in the cell; (2) Enzyme digestion and end labeling: use restriction endonucleases to digest and separate chromatin, and label the ends of the fragments with biotin; (3) Connection of fragment ends: use DNA ligase to connect the ends of different cuts to form a circular chimeric molecule; (4) Purification and shearing: these chimeric molecules are purified, sheared, and broken into DNA fragments; (5) Establishment of Hi-C library: use biotin labeling precipitation technology to obtain the labeled target DNA fragments, and select DNA fragments of appropriate size to establish Hi-C library (Hi-C library); (6) Hi-C sequencing and data analysis: use high-throughput sequencing technology to perform paired-end sequencing on the samples contained in the Hi-C library to generate a large number of paired-end fragments (paired-end reads) for analysis. There are also two disadvantages to identifying chromosomal translocations based on Hi-C data: first, this inference based on another type of omics data has a certain degree of false positives; second, since the resolution of Hi-C is at least 5kb, the detection of chromosomal translocation breakpoints is inaccurate.

[0008] In view of this, the present invention is proposed. Summary of the invention

[0009] The object of the present invention is to provide a method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data, so as to improve the screening ability of chromosome translocation in the sample to be tested. Another object of the present invention is to provide a chromosome translocation detection device based on the method, a computer readable medium and their applications.

[0010] In order to solve the above technical problems, the present invention particularly adopts the following technical solutions: In a first aspect, a method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data is provided, the method comprising: (A) obtaining the third-generation genome sequencing data of the sample to be tested, identifying the chromosome translocation based on the third-generation genome sequencing data, and using the obtained chromosome translocation as a candidate chromosome translocation to form a first data set; (B) obtaining Hi-C sequencing data or sequencing data based on a Hi-C derivative technology of the sample to be tested, identifying chromosome translocation based on the Hi-C sequencing data or sequencing data based on a Hi-C derivative technology, and using the obtained chromosome translocation as a candidate chromosome translocation to form a second data set; (C) The candidate chromosomal translocations in the intersection of the first data set and the second data set are taken, and the verified candidate chromosomal translocations are used as the chromosomal translocations obtained by the final screening.

[0011] In an optional embodiment, in step (A), PBSV software is used to identify chromosomal translocations from third-generation genome sequencing data; and / or, in step (B), deep learning tool EagleC is used to identify chromosomal translocations from Hi-C sequencing data or sequencing data based on Hi-C derivative technology.

[0012] In an optional embodiment, in step (C), the candidate chromosomal translocation is verified by nucleic acid amplification and Sanger sequencing.

[0013] In an optional embodiment, the method further comprises using the chromosome translocation obtained in step (C) and the Hi-C sequencing data or sequencing data based on Hi-C derivative technology obtained in step (B) as input data, and using a deep learning tool to obtain a chromatin loop newly generated due to the chromosome translocation; Then, the promoter-enhancer chromatin loop is annotated, and then the target genes whose expression difference fold is greater than a threshold value among the genes related to the promoter-enhancer chromatin loop are screened, and the target genes are target genes caused by chromosomal translocation.

[0014] In an optional implementation, the deep learning tool includes NeoLoopFinder.

[0015] In an optional embodiment, the method includes obtaining ChIP-Seq or CUT&Tag data of H3K27ac of the sample to be tested, and annotating promoter-enhancer chromatin loops.

[0016] In an optional embodiment, the method includes obtaining RNA-seq data of the sample to be tested, and screening target genes with an expression difference multiple greater than 2.

[0017] In the second aspect, a chromosome translocation detection device is also provided, which includes a data acquisition module and a detection module, wherein the data acquisition module is used to obtain third-generation genome sequencing data including the sample to be tested, and Hi-C sequencing data or sequencing data based on Hi-C derivative technology; the detection module executes the method of integrating three-dimensional genome and third-generation genome data to identify chromosome translocation in the first aspect.

[0018] In a third aspect, a computer-readable medium is provided, which stores a computer program, and when the computer program is processed and executed, the method of integrating three-dimensional genome and three-generation genome data to identify chromosome translocation in the first aspect is implemented.

[0019] In a fourth aspect, there is also provided the method for identifying chromosomal translocation by integrating three-dimensional genome and third-generation genome data of the first aspect, or the chromosomal translocation detection device of the second aspect, or the use of the computer-readable medium of the third aspect in screening potential target genes for diseases.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The method for identifying chromosomal translocation provided by the present invention combines the advantages of the most cutting-edge research methods in this field, namely, third-generation genome sequencing and Hi-C sequencing technology, and can find chromosomal translocations with high confidence, and its accuracy rate can reach 98% through experimental verification. In a more preferred solution, the potential target genes caused by chromosomal translocations are more accurately found by using the combined analysis of multi-omics. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 The framework of the chromosome translocation identification method provided in Example 1 of the present invention; Figure 2 is the number of chromosome translocations obtained in steps 1 and 2 of the multiple myeloma cell line sample and the multiple myeloma patient sample in Example 1 of the present invention, respectively; Figure 3 is the intersection of the chromosome translocations obtained in each step of the multiple myeloma cell line sample and the multiple myeloma patient sample in Example 1 of the present invention; Figure 4 The distribution of 43 chromosome translocations in two intersections of the multiple myeloma cell line sample and the multiple myeloma patient sample in Example 1 of the present invention on the genome; Figure 5 The verification results of PCR for t(8;14) and t(5;8) in Example 1 of the present invention are as follows; Figure 6 The verification results of Sanger sequencing of t(8;14) and t(5;8) in Example 1 of the present invention are as follows; Figure 7The novel chromosomal translocation t(5;8) and its potential oncogene CPEB4 were found in PAT1 myeloma patients using the method of Example 1 of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0025] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0026] In the first aspect, a method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data is provided, the method comprising the following (A) to (C): (A) Obtain the third-generation genome sequencing data of the sample to be tested, identify chromosome translocations based on the third-generation genome sequencing data, and use all chromosome translocations obtained in this step as candidate chromosome translocations, and these candidate chromosome translocations form a first data set. This step can be performed using third-generation sequencing technologies known in the art, including but not limited to HeliScope single molecule sequencing technology (HeliScope single molecule sequencing), single-molecule real-time sequencing technology (SMRT), nanopore sequencing technology (Oxford nanopore sequencing) and Geno Care single molecule sequencing technology.

[0027] In an optional embodiment, in step (A), PBSV software is used to identify chromosome translocations from third-generation genome sequencing data.

[0028] In an optional embodiment, pbmm2 is used to align the reference genome, and then pbsv is used to discover and identify genomic structural variations.

[0029] In an optional implementation, the parameters are set as follows: use pbmm2 to align the reference genome, pbmm2align --preset HIFI --sort; then use pbsv to discover genome structural variations, pbsv discover --hifi; finally, use pbsv to identify structural variations, pbsv call -m 50 –-hifi.

[0030] (B) Obtaining Hi-C sequencing data or sequencing data based on Hi-C derivative technology of the sample to be tested, identifying chromosomal translocation based on the Hi-C sequencing data or sequencing data based on Hi-C derivative technology, and using the obtained chromosomal translocation as a candidate chromosomal translocation to form a second data set.

[0031] Hi-C-derived technologies include but are not limited to single-cell Hi-C, In situ Hi-C, DNase Hi-C, Micro-C, DLO Hi-C and other techniques, preferably including Micro-C. Micro-C is a Hi-C derivative technique with nucleosome-level (about 200bp) resolution. Micro-C uses formaldehyde and disuccinimidyl glutarate (DSG) to fix cross-linked cells, then the cell membrane is dissolved and the cross-linked chromatin is digested with micrococcal nuclease (MNase) to nucleosome-level resolution.

[0032] In an optional embodiment, in step (B), the deep learning tool EagleC is used to identify chromosome translocation from Hi-C sequencing data or sequencing data based on Hi-C derivative technology.

[0033] In an optional implementation, the parameters are set as follows: predictSV -g reference –balance-type CNV –output-format NeoLoopFinder.

[0034] (C) Taking the candidate chromosomal translocations in the intersection of the first data set and the second data set, and using the verified candidate chromosomal translocations as the chromosomal translocations finally screened. Verification can be performed by methods known in the art, including but not limited to verifying the candidate chromosomal translocations by nucleic acid amplification and Sanger sequencing. Nucleic acid amplification can be performed by PCR (polymerase chain reaction).

[0035] In an optional embodiment, the method for integrating three-dimensional genome and three-generation genome data to identify chromosome translocation further includes the final screening of target genes affected by the obtained chromosome translocation, including using the chromosome translocation obtained in step (C) and the Hi-C sequencing data or sequencing data based on Hi-C derivative technology obtained in step (B) as input data, and using deep learning tools to obtain the newly generated chromatin loop (Loop) due to the chromosome translocation. Loop pulls sites with a very long linear distance to a very close spatial distance. The loop site usually has a promoter at one end and an enhancer at the other end. The loop pulls the promoter and enhancer to a very close distance in space, thereby regulating the expression of the gene, and the expression level of the related gene with such a loop will change. By annotating the promoter-enhancer loop and then screening the target genes with expression difference multiples greater than the threshold, it is helpful to explain the relationship between loops and gene transcription regulation between different samples. The genes with a difference multiple greater than the threshold are the target genes caused by chromosome translocation.

[0036] In an optional implementation, the deep learning tool includes NeoLoopFinder.

[0037] In an optional implementation, NeoLoopFinder parameters are set as follows: Assemble structural variants across breakpoints, assemble-complexSVs --balance-type CNV --protocol insitu; identify neoloops generated by structural variants, neoloop-caller --balance-type CNV --protocol insitu --prob 0.95.

[0038] In an optional embodiment, ChIP-Seq or CUT&Tag data of H3K27ac (acetylation of histone H3 lysine 27) of the sample to be tested is obtained, and the promoter-enhancer loop is annotated by analyzing the DNA that interacts with histone H3K27ac.

[0039] ChIP-Seq (Chromatin immunoprecipitation followed by sequencing, ChIP-seg, chromosome immunoprecipitation sequencing) and CUT&Tag (Cleavage Under Targets and Tagmentation) are technologies for studying DNA-protein interactions. The basic principle is to first obtain the DNA region to which the target protein binds, and then perform high-throughput sequencing on this DNA region.

[0040] CUT&Tag is a new method for studying protein-DNA interactions. The basic principle of CUT&Tag technology is that under the mediation of a specific antibody, the pA-Tn5 fusion protein fragments the target DNA only at the local area where the target histone modification mark, transcription factor or chromatin regulatory protein binds to chromatin, and adds sequencing adapters at the same time. Since the pA-Tn5 transposome only binds and cuts the DNA in its adjacent space, the signal-to-noise ratio of the entire experiment is greatly improved, and the experimental steps are simplified.

[0041] In an optional embodiment, RNA-seq data of the sample to be tested is obtained, and target genes with expression difference multiples greater than 2 are screened as target genes caused by chromosomal translocation.

[0042] In the second aspect, a chromosome translocation detection device is also provided, which includes a data acquisition module and a detection module, wherein the data acquisition module is used to obtain third-generation genome sequencing data and Hi-C sequencing data or sequencing data based on Hi-C derivative technology of the sample to be tested; the detection module executes the method of integrating three-dimensional genome and third-generation genome data to identify chromosome translocation in the first aspect.

[0043] In an optional embodiment, the data acquisition module is also used to obtain ChIP-Seq or CUT&Tag data of the sample to be tested.

[0044] In an optional implementation, the data acquisition module is also used to obtain RNA-seq data of the sample to be tested.

[0045] In an optional implementation, the detection module can be stored in a memory in the form of software or firmware or fixed in the operating system (OS) of the chromosome translocation detection device. The above-mentioned chromosome translocation detection device can also be optionally pre-installed with a storage module, which stores the data, program codes, etc. required to execute some or all of the above-mentioned modules.

[0046] The chromosome translocation detection device may include a memory, a processor, a bus, and a communication interface, wherein the memory, the processor, and the communication interface are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these components may be electrically connected to each other via one or more buses or signal lines. The processor may process information and / or data related to target identification to perform one or more functions described in the present application.

[0047] In practical applications, the chromosome translocation detection device can be a server, a cloud platform, a mobile phone, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a handheld computer, a netbook, a personal digital assistant (PDA), a wearable electronic device, a virtual reality device, and the like.

[0048] In a third aspect, a computer-readable medium is also provided, the computer-readable medium storing a computer program, and when the computer program is processed and executed, the method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data according to the first aspect is implemented. The computer-readable medium includes, but is not limited to, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0049] In a fourth aspect, there is also provided the method for identifying chromosomal translocation by integrating three-dimensional genome and third-generation genome data of the first aspect, or the chromosomal translocation detection device of the second aspect, or the use of the computer-readable medium of the third aspect in screening potential target genes for diseases.

[0050] In an optional embodiment, the disease comprises a tumor.

[0051] The present invention is further described below by means of specific examples. However, it should be understood that these examples are only used for more detailed description and should not be construed as limiting the present invention in any form.

[0052] Example 1 This example provides a method for integrating three-dimensional genome and three-generation genome data to identify chromosome translocations. The test samples are five multiple myeloma (MM) cell lines (KMS11, LP1, MM1S, RPMI8266 and U266) and three multiple myeloma patient samples (PAT1, PAT2 and PAT3). The method framework is as follows Figure 1 As shown, the following steps are included: 1. The above samples were sent to Berry Genomics for third-generation genome sequencing. The sequencing platform used Pacbio's Single-Molecule Sequencing in Real Time (SMRT), and the HiFi sequencing mode was selected to obtain high-quality sequencing data. The sequencing volume of each sample was 45G. After obtaining the sequencing data, PBSV software was used to identify chromosome translocations from the third-generation genome sequencing data. The specific parameters are as follows: 1) pbmm2 align reference.fa input.bam out.bam --preset HIFI --sort; 2) pbsv discover –-hifi out.bam out.svsig.gz; 3) pbsv call -m 50 –hifi out.svsig.gz out.vcf.

[0053] like Figure 2 As shown, 80 chromosomal translocations were found in 5 multiple myeloma cell line samples in this step, including the known chromosomal translocations t (11;14), t (14;16), t (8;14), t (16;22), etc. 30 chromosomal translocations were found in 3 multiple myeloma patient samples in this step, including the known chromosomal translocations t (8;14), t(11;14), etc. 2. The above samples were sent to Fraser Genetics for Hi-C sequencing based on the Illumina platform. Five multiple myeloma cell lines were sequenced using in situ Hi-C sequencing, and three multiple myeloma patient samples were sequenced using Micro-C sequencing. The sequencing volume of each sample was 300G. After obtaining the sequencing data, EagleC, a deep learning-based tool, was used to identify chromosome translocations from the sequencing data. The specific parameters are as follows: 1) predictSV --hic-5k input.mcool:: / resolutions / 5000 \ --hic-10k input.mcool:: / resolutions / 10000 \ --hic-50k input.mcool:: / resolutions / 50000 \ -O out.predictsv.txt -g reference –balance-type CNV –output-formatNeoLoopFinder --prob-cutoff-5k 0.8 --prob-cutoff-10k 0.8 --prob-cutoff-50k0.99999.

[0054] like Figure 2As shown, 71 chromosomal translocations were found in 5 multiple myeloma cell line samples, including the known chromosomal translocations t (14;16), t (8;14), and t (16;22). 22 chromosomal translocations were found in 3 multiple myeloma patient samples, including the known chromosomal translocation t (11;14).

[0055] 3. If Figure 3 As shown, the chromosome translocations of the five multiple myeloma cell line samples identified in step 1 and step 2 were intersected to obtain 35 chromosome translocations; The chromosomal translocations of the three multiple myeloma patient samples identified in step 1 and step 2 were intersected to obtain eight chromosomal translocations.

[0056] The 43 chromosome translocation sites in the two intersections are as follows: Figure 4 shown.

[0057] A total of 43 chromosomal translocations in the two intersections were analyzed by PCR and Sanger sequencing, of which 42 were verified to be correct, with an accuracy rate of 98%. The specific sites are shown in Table 1.

[0058] In addition, two new chromosomal translocations, t(8;14) and t(5;8), were detected in samples from patients with multiple myeloma. The PCR validation results were as follows: Figure 5 The results of Sanger sequencing verification are shown in Figure 6 shown.

[0059] 4. Take the 42 chromosome translocations verified as correct in step 3 and the Hi-C sequencing in step 2 as input, and use the deep learning-based tool NeoLoopFinder to find the new loops generated by chromosome translocations. The specific parameters are as follows: 1) assemble-complexSVs -O out -B overlap.5K_combined.txt \ --balance-type CNV --protocol insitu \ -H input.mcool::resolutions / 25000 input.mcool::resolutions / 10000 input.mcool::resolutions / 5000.

[0060] 2) neoloop-caller -O neo-loops.txt --assembly out.assemblies.txt \ --balance-type CNV --protocol insitu --prob 0.95 \ -H input.mcool::resolutions / 25000 input.mcool::resolutions / 10000 input.mcool::resolutions / 5000.

[0061] 5. The above samples were sent to Berry Genomics for ChIP-seq sequencing and RNA-seq sequencing based on the illumina platform, with the data volume of each sample being 12G for ChIP-seq and 6G for RNA-seq. ChIP-seq sequencing data and RNA-seq sequencing data of H3K27ac were obtained for 5 multiple myeloma (MM) cell lines and 3 multiple myeloma patient samples. The promoter-enhancer loop was annotated based on the ChIP-seq sequencing data of H3K27ac, and the target genes with Fold Change>2 were screened using RNA-seq data, i.e., potential target genes caused by chromosomal translocation. Figure 7 It was demonstrated that an unknown chromosomal translocation t(5;8) was found in a PAT1 myeloma patient. Specifically, an enhancer on chromosome 8 was translocated upstream of the CPEB4 gene on chromosome 5, that is, the chr5 site 173708142 on chromosome 5 and the chr8 site 127798852 on chromosome 8 were reconnected; and the enhancer-promoter loop caused by the t(5;8) translocation and its corresponding highly expressed oncogene CPEB4 were discovered.

[0062] Table 1 Specific genomic loci of the 42 translocations screened in Example 1

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data, characterized in that: include: (A) obtaining the third-generation genome sequencing data of the sample to be tested, identifying the chromosome translocation based on the third-generation genome sequencing data, and using the obtained chromosome translocation as a candidate chromosome translocation to form a first data set; (B) obtaining Hi-C sequencing data or sequencing data based on a Hi-C derivative technology of the sample to be tested, identifying chromosome translocation based on the Hi-C sequencing data or sequencing data based on a Hi-C derivative technology, and using the obtained chromosome translocation as a candidate chromosome translocation to form a second data set; (C) The candidate chromosomal translocations in the intersection of the first data set and the second data set are taken, and the verified candidate chromosomal translocations are used as the chromosomal translocations obtained by the final screening.

2. The method according to claim 1, characterized in that In step (A), PBSV software is used to identify chromosome translocations from third-generation genome sequencing data; and / or, In step (B), the deep learning tool EagleC is used to identify chromosomal translocations from Hi-C sequencing data or sequencing data based on Hi-C-derived technologies.

3. The method according to claim 1, characterized in that In step (C), the candidate chromosomal translocation is verified by nucleic acid amplification and Sanger sequencing.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises using the chromosome translocation obtained in step (C) and the Hi-C sequencing data or sequencing data based on Hi-C derivative technology obtained in step (B) as input data, and using a deep learning tool to obtain a chromatin ring newly generated due to the chromosome translocation; Then, the promoter-enhancer chromatin loop is annotated, and then the target genes whose expression difference fold is greater than a threshold value among the genes related to the promoter-enhancer chromatin loop are screened, and the target genes are target genes caused by chromosomal translocation.

5. The method according to claim 4, characterized in that The deep learning tools include NeoLoopFinder.

6. The method according to claim 4, characterized in that Obtain ChIP-Seq or CUT&Tag data of H3K27ac of the sample to be tested and annotate promoter-enhancer chromatin loops.

7. The method according to claim 4, characterized in that Obtain RNA-seq data of the samples to be tested and screen target genes with expression differences greater than 2.

8. A chromosome translocation detection device, characterized in that: It includes a data acquisition module and a detection module, wherein the data acquisition module is used to obtain the third-generation genome sequencing data of the sample to be tested, and the Hi-C sequencing data or the sequencing data based on the Hi-C derivative technology; The detection module executes the method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data as described in any one of claims 1 to 7.

9. A computer-readable medium, characterized in that A computer program is stored, and when the computer program is processed and executed, the method for identifying chromosome translocation by integrating three-dimensional genome and three-generation genome data as described in any one of claims 1 to 7 is implemented.

10. Use of the method for identifying chromosomal translocation by integrating three-dimensional genome and third-generation genome data as described in any one of claims 1 to 7, or the chromosomal translocation detection device as described in claim 8, or the computer-readable medium as described in claim 9 in screening potential target genes for diseases.

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