Computational biology research data analysis and display method

Abstract

The invention relates to the technical field of biological research data analysis, and discloses a computational biological research data analysis display method, which comprises the following steps of: extracting total RNA (Ribonucleic Acid) from biological tissues or cells, reversely transcribing the RNA into cDNA (Complementary Deoxyribonucleic Acid), constructing a library, and sequencing the library by utilizing a high-throughput sequencing platform to obtain a large number of short sequences. According to the method, the number of the short sequences mapped to the same genome coordinate interval is compared and the threshold value is judged, so that the abnormal repeated area can be effectively identified, whether the gene expression difference result is credible or not is clearly judged, and when the number of the abnormal sequences exceeds the preset repeated sequence threshold value, the influence operation of the repeated sequences is eliminated, and the accuracy of the gene expression difference result is improved. That is to say, completely identical and overlapped short sequences are removed under the condition that the FPKM value is relatively high, and the updating result is compared again, so that the problem of expression quantity distortion caused by repeated reckoning can be corrected.

Description

Technical Field

[0001] The present invention relates to the technical field of biological research data analysis, and in particular to a method for analyzing and displaying computational biology research data. Background Art

[0002] Computational biology research data generally refers to data used in the field of computational biology to analyze, simulate, and explain biological problems. These data often originate from experimental measurements but require subsequent processing and interpretation using computational, statistical, and mathematical models. Examples include genomic, transcriptomic, proteomic, metabolomic, and structural biology data.

[0003] Transcriptome data is usually obtained through RNA sequencing, which describes the expression levels of all genes in cells under different conditions. The analysis and display of transcriptome data can help understand changes in gene expression regulation, biological processes, and disease states. Traditionally, in the analysis of transcriptome data, short sequences are compared with reference genomes under different conditions to analyze the differences in gene expression under different conditions. However, there are a large number of repeated or similar sequences in the genome, and it is often difficult to distinguish the specific location of short sequences. This can lead to multiple alignment problems, resulting in unstable alignment results and reduced accuracy of gene expression difference data. Summary of the Invention

[0004] In response to the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a method for analyzing and displaying computational biology research data, so that when the differential data of gene expression levels are abnormal, the research data can be analyzed and the impact of repeated sequence regions can be further reduced, so as to improve the accuracy of the display of data analysis results.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution: a method for analyzing and displaying computational biology research data, the method comprising the following steps:

[0006] Total RNA is extracted from biological tissues or cells, reverse transcribed into cDNA, a library is constructed, and the library is sequenced using a high-throughput sequencing platform to obtain a large number of short sequences;

[0007] Preprocess the short sequence data. Use the FastQC tool to detect the quality score distribution, GC content, sequence duplication, and possible sequencing deviations in the raw data. Identify and remove adapter contamination and low-quality regions, and mark the remaining short sequence data as usable sequences.

[0008] Normalize the available sequence data to obtain its FPKM value, establish a reference genome, and obtain the FPKM value of the reference genome. Difference the FPKM value of the available sequence and the reference genome to obtain the gene expression difference results;

[0009] The result difference amplitude is obtained through the gene expression difference result, and the difference amplitude threshold is set. When the result difference amplitude is less than or equal to the difference amplitude threshold, the gene expression difference result is marked as the data difference analysis result; when the result difference amplitude is greater than the difference amplitude threshold, the repeated sequence region judgment strategy is executed to perform the operation of eliminating the influence of repeated sequences when there are many repeated sequences in the available sequences, so as to obtain an updated difference result and mark it as the data difference analysis result. The data difference analysis result is expressed visually to realize data display.

[0010] In some embodiments, the repetitive sequence region determination strategy includes obtaining the number of short sequences located in the same region in the available sequences, marking them as abnormal sequences, setting a repetitive sequence threshold, comparing the number of abnormal sequences with the repetitive sequence threshold, and making corresponding responses based on the comparison results.

[0011] In some embodiments, if the abnormal sequence is less than or equal to the repetitive sequence threshold, the obtained gene expression difference result is judged to be credible, marked as the data difference analysis result, and subsequently visualized; if the abnormal sequence is greater than the repetitive sequence threshold, an operation to eliminate the influence of the repetitive sequence is performed.

[0012] In some embodiments, the operation of eliminating the influence of repeated sequences is specifically performed as follows: determining the size of the FPKM value of the available sequence and the FPKM value of the reference genome; when the FPKM value of the available sequence is greater than the FPKM value of the reference genome, removing the completely identical and overlapping short sequences in the same repeated region, and re-aligning the available sequence with the reference genome; obtaining an updated difference result by subtracting the FPKM value of the available sequence from the reference genome, marking it as a data difference analysis result and performing a subsequent visual expression; when the FPKM value of the available sequence is less than the FPKM value of the reference genome, the obtained gene expression difference result should be determined to be credible, marked as a data difference analysis result, and subsequently performed a visual expression.

[0013] In some embodiments, when the FPKM value of the available sequence is less than the FPKM value of the reference genome, individual difference determination is performed to determine whether individual variation exists in the genome of the sample.

[0014] In some embodiments, the method for performing individual difference judgment is: comparing the available sequence with the reference sequence in the reference genome to obtain the maximum similarity between the available sequence and the reference sequence in the reference genome, and setting a similarity threshold, comparing the maximum similarity with the similarity threshold to perform individual difference judgment, marking the available sequence whose maximum similarity is less than the similarity threshold as a variant sequence, counting the total number of variant sequences, setting a variant number threshold, and making corresponding responses based on the comparison between the total number of variant sequences and the variant number threshold.

[0015] In some embodiments, if the total number of variant sequences is less than the variation number threshold, the obtained gene expression difference result is judged to be credible, marked as the data difference analysis result, and subsequently visualized; if the total number of variant sequences is greater than or equal to the variation number threshold, it is judged that individual variation occurs in the genome of this sample, and the short sequences missed due to filtering are added to the existing data according to their number, and the available sequences are re-aligned with the reference genome, and the updated difference results are obtained by subtracting the FPKM values of the available sequences from the reference genome, which are marked as the data difference analysis result and visualized.

[0016] In some embodiments, after the short sequences missed due to filtering are added to the existing data based on their number, the number of repeat regions before the missed short sequences are added is obtained and marked as the number of primary repeat regions, and the number of repeat regions after the missed short sequences are added is obtained and marked as the number of secondary repeat regions. The relationship between the number of primary repeat regions and the number of secondary repeat regions is determined and a corresponding response is made.

[0017] In some embodiments, when the number of secondary repeat regions exceeds the number of primary repeat regions, the newly generated repeat regions are marked as newly added repeat regions, and all short sequences existing in the newly added repeat regions are compared to obtain the similarity between all short sequences in the newly added repeat regions. A sequence similarity threshold is set, and when a short sequence in the newly added repeat region contains another one or more short sequences whose similarity exceeds the sequence similarity threshold, all similar short sequences are regarded as the same short sequence.

[0018] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned method for analyzing and displaying computational biology research data.

[0019] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0020] First, the present invention uses FPKM as a normalization indicator to correct the influence of gene length and sequencing depth, making the comparison between different genes and different samples more accurate. By calculating the difference in FPKM values between the available sequence and the reference genome, it intuitively reflects the changes in gene expression levels, thereby providing reliable data support for differential expression analysis.

[0021] Secondly, the present invention can effectively identify abnormal repetitive regions by comparing and judging the threshold of the number of short sequences mapped to the same genomic coordinate interval, thereby clearly judging whether the gene expression difference results are credible. When the number of abnormal sequences exceeds the preset repetitive sequence threshold, the influence of the repetitive sequences is eliminated, that is, the completely identical and overlapping short sequences are removed for the case of high FPKM values, and the results are re-aligned and updated, which can correct the distortion of expression caused by repeated counting.

[0022] Third, the present invention adopts an individual difference judgment strategy. By aligning the available sequence with the reference sequence, obtaining the maximum similarity, and setting the similarity threshold, it can accurately identify mismatches or omissions caused by individual variations, so as to promptly discover problems when there are a large number of individual variations in key research areas.

[0023] Fourthly, by comparing the number of repeated regions before and after the addition of missed sequences, the present invention can promptly discover the newly added repeated regions caused by supplementation, and optimize the counting of short sequences in the newly added repeated regions, thereby reducing the unreasonable fluctuations in FPKM caused by adding the number of short sequences missed due to filtering to the existing data, thereby ensuring that the FPKM value more truly reflects the expression level of the gene. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The figure is a flow chart of a method for analyzing and displaying data in computational biology research according to the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] It is to be understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element may be one, while in another embodiment, the number of the elements may be multiple, and the term "one" should not be understood as a limitation on the quantity.

[0027] The present invention provides a method for analyzing and displaying computational biology research data. Figure 1 As shown, the method includes the following steps:

[0028] Step 1: Total RNA is extracted from biological tissues or cells. The RNA is reverse transcribed into cDNA using reverse transcriptase, random primers or oligo-dT primers, and a dNTP mixture to construct a library. The generated cDNA can be quality-tested by PCR amplification of specific targets to ensure the smooth progress of the library construction process. The library is then sequenced using a high-throughput sequencing platform to obtain a large number of short sequences.

[0029] Step 2: Preprocess the short sequence data. Use the FastQC tool to detect the quality score distribution, GC content, sequence duplication, and possible sequencing bias of the sequencing data in the raw data sample. Identify adapter contamination and low-quality regions, and remove them through data cleaning software (such as Trimmomatic and Cutadapt). Mark the remaining short sequence data as usable sequences. Eliminating low-quality data in short sequences can reduce data noise caused by sequencing errors, thereby reducing noise and unnecessary background interference, ensuring that the retained sequences have higher credibility and representativeness;

[0030] Step 3: Establish a reference genome, use the available sequences to align with the reference genome, and normalize the available sequence data to obtain its FPKM value. FPKM (Fragments Per Kilobase of transcript per Million mapped reads) is a commonly used RNA sequencing data normalization method used to correct for the effects of gene length and total sequencing depth, thereby enabling more accurate comparison of expression levels between different genes or samples. The FPKM value of the reference genome is obtained, and the FPKM value of the available sequence is subtracted from the reference genome. The absolute value of the difference is taken here to obtain the gene expression difference result;

[0031] Step 4: Obtain the result difference amplitude through the gene expression difference result, set the difference amplitude threshold, and compare the result difference amplitude with the difference amplitude threshold. When the result difference amplitude is less than or equal to the difference amplitude threshold, it indicates that the change amplitude of the FPKM value of the available sequence compared with the FPKM value of the reference genome is within a reasonable range under the current detection conditions, and the gene expression difference result is marked as the data difference analysis result; when the result difference amplitude is greater than the difference amplitude threshold, it indicates that the FPKM value of the available sequence has a large change amplitude compared with the FPKM value of the reference genome. Under the current detection conditions, such a large change is not reasonable. The repetitive sequence region judgment strategy will be executed to analyze whether there are many repetitive sequences in the available sequence, and when there are many repetitive sequences in the available sequence, the repetitive sequence influence elimination operation will be performed to obtain an updated difference result and mark it as the data difference analysis result. By visualizing the expression data difference analysis results, such as heat maps, volcano maps, principal component analysis (PCA) maps, etc., the data can be fully displayed to help understand the internal structure of the data;

[0032] The specific method for obtaining the magnitude of the result difference is: use the gene expression difference result to divide by the FPKM value of the reference genome to obtain the magnitude of the result difference. For example, the FPKM value of the reference genome is 15 (indicating that in every million short sequences mapped to the reference genome, there are approximately 15 sequencing fragments per kilobase length of the gene transcript), and the FPKM value of the available sequence is 10. The gene expression difference result can be obtained as 5, that is, the magnitude of the result difference is 5÷15=0.33. The difference magnitude threshold is set to 0.3. Since the result difference magnitude is greater than the difference magnitude threshold, the repeated sequence region judgment strategy should be executed. The difference amplitude threshold needs to be set based on comprehensive considerations of experimental conditions, data characteristics and research objectives. The difference amplitude threshold should reflect the expected biological effect. When the result difference amplitude is greater than the difference amplitude threshold, it means that there is an abnormal and excessive change in the expression level of the gene in the sample compared with the reference genome. After excluding experimental conditions and sample contamination, and removing data quality issues through the above method, the main reason for the abnormal result difference amplitude is the presence of a large number of repeated or similar sequences in the genome. Short sequences are often difficult to distinguish from which specific position they come from, which will lead to multiple alignment problems. Therefore, it is necessary to implement a repeated sequence region judgment strategy.

[0033] The repeat sequence region determination strategy includes obtaining the number of short sequences located in the same region in the available sequence and marking them as abnormal sequences (i.e., repeat sequences). Short sequences in the same region refer to short sequences whose mapping positions overlap when aligned to the reference genome, i.e., short sequences that fall within the same genomic coordinate range. A repeat sequence threshold is set, the number of abnormal sequences is compared with the repeat sequence threshold, and a corresponding response is made based on the comparison result: if the number of abnormal sequences is less than or equal to the repeat sequence threshold, it means that the overlap of short sequences generated when the available sequence is aligned with the reference genome is within the expected range. In this case, the interference of the repeat region on the data analysis results is relatively small, and the obtained gene expression difference results should be considered reliable. Such abnormal changes in gene expression reflect regulatory abnormalities under different biological conditions and can also indicate that the data results truly reflect the biological changes of the sample. They are marked as data difference analysis results and subsequently visualized. If the number of abnormal sequences is greater than the repeat sequence threshold, it means that the overlap of short sequences generated when the available sequence is aligned with the reference genome exceeds the expected range. In this case, the interference of the repeat region on the data analysis results is relatively serious, and an operation to eliminate the influence of the repeat sequence is performed.

[0034] The specific operation of eliminating the influence of repeated sequences is as follows: determine the size of the FPKM value of the available sequence and the FPKM value of the reference genome. When the FPKM value of the available sequence is greater than the FPKM value of the reference genome, it indicates that due to the retention of abnormal sequences and repeated counting of expression, excessive accumulation in the repeated region causes the FPKM value to be distorted. The completely identical and overlapping short sequences in the same repeated region are removed, and the available sequence is re-aligned with the reference genome. The updated difference result is obtained by subtracting the FPKM value of the available sequence from the reference genome, which is marked as the data difference analysis result and visualized. When the FPKM value of the available sequence is less than the FPKM value of the reference genome, it indicates that there is no abnormal sequence in this comparison, which causes the problem of excessive counting of short sequences in expression. The obtained gene expression difference result should be judged to be credible, marked as the data difference analysis result, and subsequently visualized.

[0035] On the other hand, when the FPKM value of the available sequence is less than the FPKM value of the reference genome, in addition to the problem of multiple alignment caused by repeated sequences, there are individual variations between the genome of the sample and the reference genome, which causes mismatches in some short sequences or inability to accurately align. Therefore, individual difference judgment should also be performed. Specifically, the available sequence is compared with the reference sequence in the reference genome to obtain the maximum similarity between the available sequence and the reference sequence in the reference genome, and a similarity threshold is set. The maximum similarity is compared with the similarity threshold to perform individual difference judgment, and the available sequences with maximum similarity less than the similarity threshold are marked as variant sequences, and the total number of variant sequences is counted, and a variant number threshold is set. If the total number of variant sequences is less than the variant number threshold, it indicates that the available sequence is basically correctly aligned when compared with the reference sequence in the reference genome, and the same region If there are no omissions in the short sequences within the domain due to missing information in the reference genome, the obtained gene expression difference results are judged to be credible, marked as data difference analysis results, and subsequently visualized; if the total number of variant sequences is greater than or equal to the variant number threshold, it indicates that there are a large number of abnormal sequences that have not been correctly aligned when the available sequence is compared with the reference sequence in the reference genome, resulting in mismatches or missing alignments in the available sequence, and it is judged that individual variations have occurred in the genome of this sample, which will cause the FPKM value of the available sequence to be incorrectly reduced. The short sequences that were missed due to filtering are added to the existing data according to their number, and the available sequence is re-aligned with the reference genome. The updated difference result is obtained by subtracting the FPKM value of the available sequence from that of the reference genome, marked as data difference analysis results, and visualized. It should also be noted that when the total number of variant sequences is greater than or equal to the variant number threshold, if the study requires precise quantification of certain genes or regions, and these regions happen to have a large number of sites that are inconsistent with the reference genome, that is, individual variants occur in key research areas, thereby affecting the FPKM value, then the analysis process needs to be re-executed under the same conditions to avoid the serious impact caused by individual variants.

[0036] For example, when executing the repeated sequence region judgment strategy, the number of overlapping short sequences generated in the same region is set to 10, that is, the number of abnormal sequences is 10, and the repeated sequence threshold is set to 8. Since the number of abnormal sequences is greater than the repeated sequence threshold, it indicates that when the available sequence is compared with the reference genome, the short sequence overlap generated exceeds the expected range. At this time, the repeated region seriously interferes with the data analysis results, and the operation of eliminating the influence of the repeated sequence is performed. Since the FPKM value of the available sequence is 10, while the FPKM value of the reference genome is 15, indicating that there are no abnormal sequences in this comparison, which leads to the problem of excessive expression of short sequences, individual difference judgment should also be performed: obtain the maximum similarity between the available sequence and the reference sequence in the reference genome, set the similarity threshold to 95%, and set that there are 4 available sequences with a maximum similarity of 90% with the reference sequence in the reference genome, then mark these 4 available sequences as variant sequences, set the variant number threshold to 3, so that the total number of variant sequences is greater than or equal to the variant number threshold, then add the 4 short sequences missed due to filtering based on their number to the existing data, and re-align the available sequence with the reference genome, and obtain the updated difference result by subtracting the FPKM value of the available sequence from the reference genome, mark it as the data difference analysis result and express it visually.

[0037] After the above steps, the short sequences missed due to filtering are added to the existing data based on their number. This may result in the generation of new repeat regions when the available sequences are aligned with the reference genome. Repeat regions represent the presence of multiple short sequences within this genomic coordinate range. In this case, the number of repeat regions before the addition of the missed short sequences should be obtained and marked as the number of primary repeat regions. The number of repeat regions after the addition of the missed short sequences should also be obtained and marked as the number of secondary repeat regions. The relationship between the number of primary repeat regions and the number of secondary repeat regions is determined. When the number of secondary repeat regions exceeds the number of primary repeat regions, it indicates that the addition of the short sequences missed due to filtering based on their number to the existing data has generated new repeat regions. The newly generated repeat regions are marked as newly generated repeat regions. All short sequences within the newly generated repeat regions are compared to obtain the similarity between all short sequences within the newly generated repeat regions. A sequence similarity threshold is set. If a short sequence within the newly generated repeat region has one or more other short sequences whose similarity exceeds the sequence similarity threshold, all similar short sequences are considered to be the same short sequence, i.e., the number of such short sequences is counted as 1 when calculating the FPMK value. This can avoid the problem of repeated counting introduced after supplementing the missed sequences, so that the expression level will not be artificially inflated or distorted due to the repeated counting of the same actual sequence during FPKM calculation. By performing similarity comparison on all short sequences in the newly added repeated region and setting a similarity threshold, sequences that appear multiple times but have a similarity exceeding the threshold are counted only once. This can effectively remove the deviation caused by technical noise or comparison redundancy, and reduce the unreasonable fluctuation of FPKM caused by adding the short sequences missed by filtering to the existing data according to their number, thereby ensuring that the FPKM value more truly reflects the expression level of the gene.

[0038] In general, the present invention aims to design a method for analyzing and displaying data in computational biology research. In order to solve the problem that it is difficult to obtain accurate data analysis results when the difference data of gene expression is abnormal, the present invention adopts FPKM as a normalization index, corrects the influence of gene length and sequencing depth, and makes the comparison between different genes and different samples more accurate. By calculating the difference between the FPKM value of the available sequence and the reference genome, the change in gene expression level is intuitively reflected, thereby providing reliable data support for differential expression analysis. When it is detected that the expression change between the available sequence and the reference genome exceeds the preset reasonable range, the repeated sequence region judgment strategy will be automatically triggered. By comparing and judging the threshold value of the number of short sequences mapped to the same genome coordinate interval, the abnormal repeated region can be effectively identified, thereby clearly judging whether the gene expression difference result is credible. When the number of abnormal sequences exceeds the preset repeated sequence threshold, the influence of the repeated sequence is eliminated, that is, the completely identical and overlapping short sequences are removed for the case of high FPKM values, and the expression distortion problem caused by repeated counting can be corrected. And the present invention adopts the strategy of judging individual differences, by comparing the available sequence with the reference sequence, obtaining the maximum similarity, and setting the similarity threshold, which can accurately identify the mismatch or omission caused by individual variation, so as to promptly find the problem when there are a large number of individual variations in the key research area, and avoid the serious impact on the quantitative analysis due to inaccurate data in the key area. The present invention can promptly find the newly added repeated regions caused by supplementation by comparing the number of repeated regions before and after the addition of the missed sequence, and optimize the counting of short sequences in the newly added repeated regions, reducing the unreasonable fluctuation of FPKM caused by adding the short sequences missed due to filtering based on their number on the basis of existing data, and ensuring that the FPKM value more truly reflects the expression level of the gene.

[0039] In the embodiments disclosed herein, the processes described above with reference to the flowcharts can be implemented as computer software programs. The embodiments disclosed herein include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the method illustrated in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component and / or installed from removable media. When the computer program is executed by a central processing unit, the functions defined in the methods of this application are performed. It should be noted that the computer-readable medium referred to herein can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media can include, but are not limited to, an electrical connection having one or more wire segments, a portable computer disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. Furthermore, in this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, electrical, optical, RF, or any suitable combination thereof.

[0040] The flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0041] Those skilled in the art should understand that the above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered by the scope of protection of the present application.

Claims

1. A method for analyzing and displaying computational biology research data, characterized in that: The method comprises the following steps: Total RNA is extracted from biological tissues or cells, reverse transcribed into cDNA, a library is constructed, and the library is sequenced using a high-throughput sequencing platform to obtain a large number of short sequences; Preprocess the short sequence data. Use the FastQC tool to detect the quality score distribution, GC content, sequence duplication, and possible sequencing deviations in the raw data. Identify and remove adapter contamination and low-quality regions, and mark the remaining short sequence data as usable sequences. Normalize the available sequence data to obtain its FPKM value, establish a reference genome, and obtain the FPKM value of the reference genome. Difference the FPKM value of the available sequence and the reference genome to obtain the gene expression difference results; The result difference amplitude is obtained through the gene expression difference result, and the difference amplitude threshold is set. When the result difference amplitude is less than or equal to the difference amplitude threshold, the gene expression difference result is marked as the data difference analysis result; when the result difference amplitude is greater than the difference amplitude threshold, the repeated sequence region judgment strategy is executed to perform the operation of eliminating the influence of repeated sequences when there are many repeated sequences in the available sequences, so as to obtain an updated difference result and mark it as the data difference analysis result. The data difference analysis result is expressed visually to realize data display.

2. A method for analyzing and displaying computational biology research data according to claim 1, characterized in that: The repeated sequence region determination strategy includes obtaining the number of short sequences located in the same region in the available sequences, marking them as abnormal sequences, setting a repeated sequence threshold, comparing the number of abnormal sequences with the repeated sequence threshold, and making corresponding responses based on the comparison results.

3. A method for analyzing and displaying computational biology research data according to claim 2, characterized in that: If the abnormal sequence is less than or equal to the repeated sequence threshold, the obtained gene expression difference result is judged to be credible, marked as the data difference analysis result, and then visualized; if the abnormal sequence is greater than the repeated sequence threshold, the operation of eliminating the influence of the repeated sequence is performed.

4. A method for analyzing and displaying computational biology research data according to claim 3, characterized in that: The specific operation of eliminating the influence of repeated sequences is as follows: determine the size of the FPKM value of the available sequence and the FPKM value of the reference genome. When the FPKM value of the available sequence is greater than the FPKM value of the reference genome, remove the completely identical and overlapping short sequences in the same repeated region, and re-align the available sequence with the reference genome. The updated difference result is obtained by subtracting the FPKM value of the available sequence from the reference genome, and it is marked as the data difference analysis result and visualized. When the FPKM value of the available sequence is less than the FPKM value of the reference genome, the obtained gene expression difference result should be judged to be credible, marked as the data difference analysis result, and subsequently visualized.

5. A method for analyzing and displaying computational biology research data according to claim 4, characterized in that: When the FPKM value of the available sequence is less than the FPKM value of the reference genome, individual difference judgment is performed to determine whether there is individual variation in the genome of the sample.

6. A method for analyzing and displaying computational biology research data according to claim 5, characterized in that: The method for performing individual difference judgment is: comparing the available sequence with the reference sequence in the reference genome to obtain the maximum similarity between the available sequence and the reference sequence in the reference genome, and setting a similarity threshold, comparing the maximum similarity with the similarity threshold to perform individual difference judgment, marking the available sequence with the maximum similarity less than the similarity threshold as a variant sequence, counting the total number of variant sequences, setting a variant number threshold, and making corresponding responses based on the comparison between the total number of variant sequences and the variant number threshold.

7. A method for analyzing and displaying computational biology research data according to claim 6, characterized in that: If the total number of variant sequences is less than the variant number threshold, the obtained gene expression difference result is judged to be credible, marked as the data difference analysis result, and then visualized; If the total number of variant sequences is greater than or equal to the variant number threshold, it is determined that individual variations have occurred in the genome of this sample. The short sequences missed due to filtering are added to the existing data based on their number, and the available sequences are re-aligned with the reference genome. The updated difference results are obtained by subtracting the FPKM values of the available sequences from the reference genome, which are marked as data difference analysis results and visualized.

8. A method for analyzing and displaying computational biology research data according to claim 7, characterized in that: After adding the short sequences missed due to filtering to the existing data according to their number, the number of repeated regions before the missed short sequences were added is obtained and marked as the number of primary repeated regions, and the number of repeated regions after the missed short sequences were added is obtained and marked as the number of secondary repeated regions. The relationship between the number of primary repeated regions and the number of secondary repeated regions is determined and a corresponding response is made.

9. A method for analyzing and displaying computational biology research data according to claim 8, characterized in that: When the number of secondary repeat regions exceeds the number of primary repeat regions, the newly generated repeat regions are marked as newly added repeat regions, and all short sequences existing in the newly added repeat regions are compared to obtain the similarity between all short sequences in the newly added repeat regions. A sequence similarity threshold is set. When a short sequence in the newly added repeat region has another one or more short sequences with a similarity exceeding the sequence similarity threshold, all similar short sequences are regarded as the same short sequence.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method for analyzing and displaying computational biology research data according to any one of claims 1 to 9.

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