Analysis method and device for leukemia fusion gene detection, electronic equipment and medium

Abstract

The invention provides an analysis method for leukemia fusion gene detection. The analysis method comprises the following steps: acquiring RNA sample data; performing preliminary sequence alignment and fusion gene preliminary screening on the RNA sample data to obtain first sample data; performing fusion gene sequence comparison and screening on the first sample data to obtain second sample data; and performing qualitative and quantitative analysis on the second sample data. According to the method, the sample data is primarily screened, a large amount of data which does not meet requirements is rapidly filtered out, sequences possibly containing fusion genes are reserved, the screening speed is increased, the screening efficiency is improved, the screening time is shortened, and the screening accuracy is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of biomedical and information technology, and particularly relates to a leukemia fusion gene detection analysis method and device, an electronic equipment and a medium. BACKGROUND

[0002] Leukemia is a complex hematopoietic system malignancy, and its diagnosis and treatment are highly dependent on molecular level detection technology. According to the new classification standard of "2016 WHO Hematopoietic and Lymphoid Tissue Tumor Classification", the presence or absence of fusion genes has become one of the key indicators in the diagnosis of leukemia. For example, BCR-ABL fusion gene is used for the diagnosis and tyrosine kinase inhibitor (TKI) companion diagnosis of chronic myeloid leukemia or part of acute lymphoblastic leukemia; PML-RARA fusion gene is used for the diagnosis and prognosis evaluation of acute promyelocytic leukemia; CBFB-MYH11, AML1-ETO, MLL-AF4 and other fusion genes also have important significance in the diagnosis and risk classification of acute myeloid leukemia (AML) and other subtypes. In addition, patients with a proportion of bone marrow and blood blast cells less than 20% but carrying specific clonal chromosome or genetic markers can also be diagnosed as acute myeloid leukemia (AML). Therefore, accurate detection of fusion gene variation is crucial for molecular typing, individualized treatment plan and prognosis evaluation of patients.

[0003] However, the existing fusion gene detection methods have many shortcomings. Traditional detection methods such as fluorescence in situ hybridization (FISH) and reverse transcription polymerase chain reaction (RT-PCR) can meet the clinical needs to some extent, but these methods can only detect known fusion genes, have limited coverage, and are complex and time-consuming to operate, which is difficult to adapt to the demand of high-throughput detection. In recent years, RNA sequencing based on next-generation sequencing (NGS) technology has gradually become a research hotspot, which can detect multiple fusion genes and discover new fusion forms at the same time, but still faces many challenges in data analysis. The amount of second-generation sequencing data is huge, the analysis process is complex and time-consuming, and how to accurately screen out reliable fusion gene sequences from massive sequencing data and make quantitative and qualitative judgments is still a technical problem to be solved. SUMMARY

[0004] The purpose of the present application is to provide a leukemia fusion gene detection analysis method, device, electronic equipment and medium, which solves the problem of huge amount of sequencing data, complex and time-consuming analysis process.

[0005] To this end, in a first aspect, the present application provides a leukemia fusion gene detection analysis method, comprising the following steps: acquiring RNA sample data;

[0006] performing preliminary sequence alignment and initial screening of fusion genes on the RNA sample data to obtain first sample data;

[0007] performing fusion gene sequence alignment and screening on the first sample data to obtain second sample data;

[0008] performing qualitative and quantitative analysis on the second sample data.

[0009] Preferably, the step of performing preliminary sequence alignment and initial screening of fusion genes on the RNA sample data to obtain first sample data comprises:

[0010] sampling in the RNA sample data to obtain sampling data;

[0011] aligning the sampling data with a pre-established fusion gene database to preliminarily screen out sequences containing fusion genes;

[0012] removing noise data to obtain first sample data.

[0013] Preferably, the step of aligning the sampling data with a pre-established fusion gene database to preliminarily screen out sequences containing fusion genes comprises:

[0014] aligning the sampling data with the fusion gene database through a pseudo-alignment algorithm to evaluate the number of reference fusion genes detected in the sampling data;

[0015] aligning the sampling data with the fusion gene database based on an alignment tool to screen out sequences containing fusion genes.

[0016] Preferably, the step of performing fusion gene sequence alignment and screening on the first sample data to obtain second sample data comprises:

[0017] filtering the first sample data, and retaining qualified sequences as candidate fusion sequences according to a filtering standard;

[0018] cutting the candidate fusion sequences into two segments and re-aligning them with the fusion gene database to screen out sequences with length coverage values exceeding a preset threshold as final candidate fusion sequences;

[0019] obtaining second sample data by collating the final candidate fusion sequences after deduplication.

[0020] Preferably, the filtering standard is at least n base pairs across the fusion breakpoint site without mismatches.

[0021] Preferably, the step of performing quantitative analysis on the second sample data comprises:

[0022] The expression level of the fusion gene is quantified according to a fusion ratio of the fusion sequence, and the higher the fusion ratio is, the higher the expression level of the fusion gene is.

[0023] Preferably, the step of qualitatively analyzing the second sample data comprises:

[0024] If the fusion sequence is detected and the fusion ratio exceeds a preset threshold, it is determined that the fusion gene is positive.

[0025] In a second aspect, an analysis device for detecting a leukemia fusion gene is provided, comprising:

[0026] A data acquisition module is configured to acquire RNA sample data.

[0027] A preliminary screening module is configured to perform preliminary sequence alignment and fusion gene preliminary screening on the RNA sample data to obtain first sample data.

[0028] A screening module is configured to perform fusion gene sequence alignment and screening on the first sample data to obtain second sample data.

[0029] An analysis module is configured to perform qualitative and quantitative analysis on the second sample data.

[0030] In a third aspect, an electronic device is provided, comprising a memory and a processor.

[0031] The memory stores computer execution instructions.

[0032] The processor executes the computer execution instructions stored in the memory, so that the processor performs the method.

[0033] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the method.

[0034] Advantages:

[0035] The present disclosure provides an analysis method, device, electronic device and medium for detecting a leukemia fusion gene. By performing preliminary screening on sample data, a large amount of data that does not meet the requirements is quickly filtered out, and sequences that may contain fusion genes are retained, thereby improving the speed and efficiency of screening, reducing the screening time, and improving the accuracy of screening.

[0036] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0038] Figure 1 Method flow chart of the method for the analysis method of the leukemia fusion gene detection in the present disclosure;

[0039] Figure 2 Method flow chart of S200 of the analysis method of the leukemia fusion gene detection in the present disclosure;

[0040] Figure 3 Method flow chart of S210 of the analysis method of the leukemia fusion gene detection in the present disclosure;

[0041] Figure 4 Method flow chart of S300 of the analysis method of the leukemia fusion gene detection in the present disclosure;

[0042] Figure 5 Structure schematic diagram of the analysis device for the leukemia fusion gene detection in the present disclosure;

[0043] Figure 6 System structure diagram of the electronic device in the present disclosure.

[0044] In the figure, 101-data acquisition module, 102-primary screening module, 103-screening module, 104-analysis module, 200-electronic device, 201-processor, 202-memory, 203-communication component, 204-bus. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present application more clear, the technical solutions in the present application will be described clearly and completely below by combining the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0046] The terms "first", "second", "third", "fourth", and the like in the description and in the claims of the present application and above-mentioned drawings are used for distinguishing between similar objects, not necessarily described in a particular sequential or chronological order. It is to be understood that data so designated are interchangeable under appropriate circumstances. For example, the first information can be termed as the second information, and similarly, the second information can be termed as the first information, without departing from the scope of the present application.

[0047] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "upon" or "in response to determining".

[0048] Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise.

[0049] It will be further understood that the terms "comprises" and "comprising", "includes" and / or "including", when used herein, specify the presence of stated features, steps, operations, elements, components, items, and / or groups but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, items, and / or groups thereof.

[0050] The terms "or" and "and / or" as used herein are to be interpreted as inclusive, i.e., as meaning one or any combination of items. Thus, "A, B or C" or "A, B and / or C" means "any of the following: A; B; C; A and B; A and C; B and C; A, B and C". An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.

[0051] To this end, in a first aspect, the present disclosure provides an analysis method for detecting a leukemia fusion gene, as shown in Figure 1 The analysis method for detecting a leukemia fusion gene comprises the following steps:

[0052] S100, acquiring RNA sample data;

[0053] The preparation process of RNA sample data includes generating a prelibrary, capturing the target region, and constructing a sequencing library. Generating a prelibrary includes using the extracted RNA as a template, synthesizing the first strand of cDNA by reverse transcriptase, and then synthesizing the second strand to form double-stranded cDNA, randomly breaking the double-stranded cDNA into short fragments suitable for sequencing, connecting sequencing adapters at both ends of the fragments to facilitate subsequent PCR amplification and sequencer recognition, and enriching the adapter-connected fragments through PCR amplification to generate a prelibrary. Capturing the target region includes using a biotin-labeled oligonucleotide probe to hybridize with the prelibrary to specifically bind to the target gene sequence; utilizing the high affinity of streptavidin magnetic beads to biotin to capture the probe-target sequence complex and remove non-specific fragments; PCR amplifying the captured target sequence to generate the final capture library and improve the sequencing depth of the target gene. Constructing a sequencing library involves denaturing the double-stranded DNA library into single strands, followed by enzyme digestion and purification to form single-stranded circular DNA. Using a mixture of nanosphere preparation buffer and polymerase, the single-stranded circular DNA is amplified into DNA nanospheres. The DNA nanospheres are sequenced using a gene sequencer (e.g., MGISEQ-2000 or MGISEQ-200) to generate raw sequencing data. Paired-end sequencing generates two sets of raw sequencing data, called Read 1 and Read 2.

[0054] The prepared raw sequencing data is obtained as RNA sample data for subsequent processing.

[0055] S200, performing preliminary sequence alignment and fusion gene screening on the RNA sample data to obtain first sample data;

[0056] By conducting an initial screening of the sample data, a large amount of data that does not meet the requirements can be quickly filtered out, and sequences that may contain fusion genes are retained, which improves the speed and efficiency of screening, reduces the screening time, and improves the accuracy of screening.

[0057] like Figure 2 shown, including:

[0058] S210, sampling the RNA sample data to obtain sample data;

[0059] The amount of sampled data is standardized, and the amount of sampled data for Read1 and Read2 is the same.

[0060] In one embodiment, SeqTK was used to sample one million reads each of Read1 and Read2 in the RNA sample data. By standardizing the data volume, the test result bias caused by the difference in data volume was avoided, and the subsequent analysis speed was accelerated.

[0061] S220, comparing the sampling data with a pre-established fusion gene database to preliminarily screen sequences containing fusion genes;

[0062] The fusion gene database refers to a specific sequence database established by the laboratory for detection of leukemia fusion genes.

[0063] As shown in the following formula (I): Figure 3 The formula (I) includes the following:

[0064] S221, comparing the sampling data with the fusion gene database through a pseudo-alignment algorithm to evaluate the number of reference fusion genes detected in the sampling data.

[0065] Traditional algorithms such as Salmon or Bowtie require full sequence alignment of each sequencing read with a genome or transcriptome to determine its specific position on the reference sequence, such as chromosome coordinates, exon boundaries, etc., and then determine which gene or transcript the read belongs to according to the position information. The full alignment data volume is large, the calculation time is long, and if the read has a fusion breakpoint, the sequence position on both sides of the breakpoint needs to be accurately matched, otherwise the fusion signal may be missed. In this embodiment, the sequencing reads are split into short fragments k-mer through the pseudo-alignment algorithm, each k-mer represents the local sequence characteristics of the read, and only needs to quickly find which contains these k-mer combinations in the pre-established fusion gene database, without the need to determine the specific position of the read on the transcript. According to the matching relationship between k-mer and transcript, it is inferred which transcript the read is most likely derived from. Through the pseudo-alignment algorithm, full sequence alignment is not required, and k-mer hash table is used for rapid matching, which greatly shortens the analysis time compared with traditional methods. For reads across the breakpoint, only k-mer covering the sequence on both sides of the breakpoint is required, and fusion events can be inferred through k-mer combination, avoiding missing due to position alignment error.

[0066] S222, comparing the sampling data with the fusion gene database based on an alignment tool to screen sequences containing fusion genes.

[0067] The sequence information of the sampling data is loaded into the memory at one time through the pre-established database index in the fusion gene database, avoiding repeated hard disk, so as to improve the analysis efficiency while maintaining the accuracy of alignment.

[0068] The alignment tool can be blat (version 37), which has the core advantage of greatly improving the alignment speed through memory index optimization, and is suitable for rapid screening of large-scale sequencing data.

[0069] S230: Remove noise data to obtain first sample data.

[0070] Filter low-quality sequences such as adapter contamination and reads with low base quality values ​​to remove noise data to ensure the accuracy of subsequent analysis.

[0071] In step S200, the pseudo-alignment algorithm is used to complete the screening of fusion genes in breadth and evaluate the data coverage. The alignment tool is used to complete the screening of fusion genes in depth and locate the specific fusion sequence. Double screening and filtering is used to ensure the screening speed while ensuring the accuracy of the screening, thereby shortening the time of subsequent steps.

[0072] S300, performing fusion gene sequence alignment and screening on the first sample data to obtain second sample data;

[0073] like Figure 4 shown, including:

[0074] S310: Filter the first sample data and retain qualified sequences as candidate fusion sequences according to the filtering criteria;

[0075] The filtering criteria are that the fusion breakpoint is spanned by at least n base pairs with no mismatches. The filtering criteria are used to exclude nonspecific binding or random matching sequences, thereby improving the specificity of fusion gene detection.

[0076] In one embodiment, the first sample data is filtered to retain only sequences that span the fusion break site by at least 10 bp and have no mismatches as candidate fusion sequences.

[0077] S320, cutting the candidate fusion sequence into two segments and re-aligning them with the fusion gene database respectively, and screening out sequences with length coverage values ​​exceeding a preset threshold as final candidate fusion sequences;

[0078] Fusion genes are composed of sequences from two different genes, each with a unique identifier at its breakpoint. By splitting a candidate fusion sequence into two segments, each can be independently verified to verify its presence in its respective genes. If a candidate fusion sequence only partially matches the fusion gene, for example, accidentally containing fragments of two genes but not a true fusion, the resulting segment may not match the database and thus be filtered out, eliminating false positives.

[0079] In one embodiment, the threshold is set at 90%. If the coverage threshold is too low, for example, 50%, it may lead to misidentification due to the high similarity of short fragments. The 90% coverage requirement ensures that the majority of the sequence length is consistent with the target gene, significantly reducing the probability of accidental matches.

[0080] S330: De-duplicate the final candidate fusion sequence and then sort it to obtain second sample data.

[0081] The redundant sequences of repeated sequencing are removed and the second sample data is obtained.

[0082] S400: Perform qualitative and quantitative analysis on the second sample data.

[0083] For quantitative analysis, the expression level of the fusion gene is quantified according to the fusion ratio of the fusion sequence. A higher fusion ratio indicates a higher expression level of the fusion gene.

[0084] In one embodiment, the fusion ratio is the ratio of the fusion sequence to the ABL1 gene, which is used to determine whether the fusion gene is real or noise.

[0085] For qualitative analysis, if a fusion sequence is detected and the fusion ratio exceeds a preset threshold, the fusion gene is determined to be positive.

[0086] Second, as Figure 5 As shown, an analysis device for leukemia fusion gene detection is provided, comprising:

[0087] Data acquisition module 101, used to acquire RNA sample data;

[0088] A primary screening module 102 is used to perform preliminary sequence alignment and fusion gene screening on the RNA sample data to obtain first sample data;

[0089] A screening module 103 is configured to perform fusion gene sequence comparison and screening on the first sample data to obtain second sample data;

[0090] The analysis module 104 is configured to perform qualitative and quantitative analysis on the second sample data.

[0091] Thirdly, as Figure 6 As shown, an electronic device is provided, characterized in that it includes: a memory, a processor;

[0092] Memory stores computer-executable instructions;

[0093] The processor executes the computer-executable instructions stored in the memory, so that the processor performs the above method.

[0094] In one embodiment, the electronic device 200 includes: at least one processor 201 and a memory 202. Optionally, the electronic device 200 further includes a communication component 203. The processor 201, the memory 202 and the communication component 203 are connected via a bus 204.

[0095] In the implementation process, the at least one processor 201 executes the computer execution instructions stored in the memory 202, so that the at least one processor 201 executes the above-mentioned method.

[0096] The specific implementation process of the processor 201 can refer to the method embodiments described above, which have similar implementation principles and technical effects, and will not be described here.

[0097] In the above embodiments, it should be understood that the processor can be a central processing unit (English: Central Processing Unit, CPU for short), and can also be other general-purpose processors, digital signal processors (English: Digital Signal Processor, DSP for short), application specific integrated circuits (English: Application Specific Integrated Circuit, ASIC for short) and the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like. The steps of the method disclosed in the application can be directly embodied as hardware processor execution or executed by a combination of hardware and software modules in the processor.

[0098] The memory can include a random access memory (RAM), and can also include a non-volatile memory (NVM), such as at least one disk memory.

[0099] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of the present application does not limit only one bus or one type of bus.

[0100] In a fourth aspect, the present application also provides a computer readable storage medium, and the computer readable storage medium stores computer execution instructions. When the processor executes the computer execution instructions, the above-mentioned method is realized.

[0101] The above-mentioned readable storage medium can be realized by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0102] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0103] The division of units is only a logical function division, and in actual implementation, there can be another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0104] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0105] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0106] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for making an electronic device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0107] Those of ordinary skill in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction-related hardware. The aforementioned program can be stored in a computer readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes a ROM, a RAM, a magnetic disk or an optical disk, and various media that can store program codes.

[0108] Finally, it should be noted that other embodiments of the present application will be readily apparent to those skilled in the art upon considering the description set forth herein. The present application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in the art to which the application pertains and as can be applied to the essential features herein set forth and fall within the scope of the application. The scope of the present application is limited only by the claims that follow.

Claims

1. An analytical method for detecting leukemia fusion genes, characterized in that: The steps include: Obtain RNA sample data; Performing preliminary sequence alignment and fusion gene screening on the RNA sample data to obtain first sample data; Performing fusion gene sequence comparison and screening on the first sample data to obtain second sample data; Perform qualitative and quantitative analysis on the second sample data.

2. The method according to claim 1, characterized in that The step of performing preliminary sequence alignment and fusion gene screening on the RNA sample data to obtain first sample data includes: Sampling the RNA sample data to obtain sampling data; Comparing the sampled data with a pre-established fusion gene database to preliminarily screen out sequences containing fusion genes; The noise data is removed to obtain the first sample data.

3. The method according to claim 2, characterized in that The step of comparing the sampled data with a pre-established fusion gene database to preliminarily screen out sequences containing fusion genes comprises: comparing the sampled data with the fusion gene database using a pseudo-alignment algorithm to evaluate the number of reference fusion genes detected in the sampled data; The sampling data is compared with the fusion gene database based on an alignment tool to screen out sequences containing fusion genes.

4. The method according to claim 3, characterized in that The step of performing fusion gene sequence comparison and screening on the first sample data to obtain the second sample data comprises: Filtering the first sample data, and retaining qualified sequences as candidate fusion sequences according to the filtering criteria; Cut the candidate fusion sequence into two segments and re-align them with the fusion gene database respectively, and select the sequence with a length coverage value exceeding a preset threshold as the final candidate fusion sequence; The final candidate fusion sequence is deduplicated and sorted to obtain the second sample data.

5. The method according to claim 4, characterized in that The filtering criteria are that the fusion breakpoint is spanned by at least n base pairs without mismatches.

6. The method according to claim 4, characterized in that The step of performing quantitative analysis on the second sample data includes: The expression level of the fusion gene is quantified according to the fusion ratio of the fusion sequence. A higher fusion ratio indicates a higher expression level of the fusion gene.

7. The method according to claim 6, characterized in that The step of performing qualitative analysis on the second sample data includes: If a fusion sequence is detected and the fusion ratio exceeds a preset threshold, the fusion gene is determined to be positive.

8. An analytical device for leukemia fusion gene detection, characterized in that: include: Data acquisition module, used to obtain RNA sample data; A primary screening module is used to perform preliminary sequence alignment and fusion gene screening on the RNA sample data to obtain first sample data; a screening module, configured to perform fusion gene sequence comparison and screening on the first sample data to obtain second sample data; An analysis module is used to perform qualitative and quantitative analysis on the second sample data.

9. An electronic device, characterized in that: Including memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs a method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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