Functional gene mining method, device and equipment based on next-generation sequencing and medium

Abstract

The invention belongs to the technical field of bioinformatics, and discloses a functional gene mining method, device and equipment based on next-generation sequencing and a medium. Based on the gene expression data and each preset condition, adopting a plurality of difference algorithms to respectively calculate differences to obtain a plurality of groups of gene sets; based on a pre-constructed gene-disease-pathway relational database, scoring each group of gene sets, and setting screening conditions according to scoring results; and according to the screening condition, adopting the plurality of difference algorithms to calculate the difference of the gene expression data, obtaining a plurality of groups of significant difference gene sets, scoring the significant difference gene sets, and screening out the significant difference gene set with the highest score as a functional gene mining result. And the efficiency and accuracy of functional gene mining can be improved.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics technology, specifically relating to a method, device, equipment, and medium for functional gene mining based on next-generation sequencing. Background Technology

[0002] Functional genes are genes in the genome that have specific biological functions. Currently, the common approach for functional gene mining when analyzing next-generation sequencing RNA-seq data is as follows: 1) Using a differential expression algorithm to calculate gene expression differences to identify which genes show significant expression changes under different conditions; 2) Screening for significantly differentially expressed genes by setting a fixed expression difference threshold; 3) Functional annotation and pathway enrichment analysis. Databases such as KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) are typically used for functional annotation to explore the enrichment of these differentially expressed genes in biological processes, molecular functions, and cellular components; 4) Reviewing relevant literature to confirm whether the identified significantly differentially expressed genes and their enriched pathways are relevant to the research direction, thereby evaluating the research value of the gene.

[0003] However, the above methods first require a detailed search and organization of key genes and potential pathways in the research model, a very time-consuming step that may miss some important information. Secondly, to obtain a satisfactory set of significant genes, it may be necessary to repeatedly test different differential algorithms and screening conditions, which not only increases the complexity of the analysis but may also introduce subjective bias. Moreover, screening based on a single threshold can easily lead to the filtering out of effective functional genes or the burying of truly effective genes in the background due to excessive differences. Finally, after obtaining the set of significant genes, enrichment analyses such as KEGG and GO are required to determine whether these genes are concentrated in key core pathways, a process that consumes a lot of time and computational resources.

[0004] Therefore, current functional gene mining methods are inefficient and inaccurate. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, device, and storage medium for functional gene mining based on next-generation sequencing, thereby solving the problems of low efficiency and insufficient accuracy in functional gene mining.

[0006] The first aspect of this invention discloses a method for functional gene mining based on next-generation sequencing, comprising:

[0007] Obtain gene expression data from next-generation sequencing;

[0008] Based on the gene expression data and various preset conditions, multiple difference algorithms are used to calculate the differences and obtain multiple sets of genes.

[0009] Based on a pre-constructed gene-disease-pathway relationship database, each gene set is scored, and screening conditions are set according to the scoring results.

[0010] Based on the screening criteria, the differences in the gene expression data are calculated using the multiple difference algorithms to obtain multiple sets of significantly different genes. The sets of significantly different genes are scored, and the set of significantly different genes with the highest score is selected as the functional gene mining result.

[0011] In some embodiments, a gene-disease-pathway relationship database is pre-constructed, including:

[0012] The KEGG gene database is annotated, and coefficients are set according to the importance of the annotation information to obtain the gene-disease-pathway relationship database.

[0013] In some embodiments, determining the preset conditions includes:

[0014] Divide the difference multiples according to the preset interval values ​​to obtain several division values;

[0015] Each of the division values ​​is combined with a P value or a Q value to obtain the preset conditions.

[0016] In some embodiments, the expressions for scoring the gene set and scoring the significantly differential gene set are as follows:

[0017]

[0018] Among them, F i D represents the log2fold_change value of the i-th gene. i P represents the disease association coefficient of the i-th gene. i H represents the pathway association coefficient of the i-th gene. i This represents the core gene association coefficient for the i-th gene, where n represents the total number of genes. The disease association coefficient, pathway association coefficient, and core gene association coefficient are all set based on the reliability and importance of the gene annotation source. Currently, three weights are set: 0, 1, and 1.5, to distinguish the importance of a gene among disease-associated genes, pathways, and core genes. For example, if the disease associated with a gene is a disease of interest (such as breast cancer), its coefficient is set to 1.5; otherwise, it is 0. If the gene is annotated to a key pathway, its coefficient is set to 1; otherwise, it is 0. If the gene is a core gene in a pathway, its coefficient is set to 1.5; otherwise, it is 0.

[0019] In some embodiments, the step of calculating the differences in gene expression data using the multiple difference algorithms based on the screening criteria, obtaining multiple sets of significantly different gene groups, scoring the significantly different gene groups, and selecting the set of significantly different gene groups with the highest scores as the functional gene mining result includes:

[0020] The filtering criteria are input into each of the difference algorithms;

[0021] Based on the gene expression data, the difference algorithm is used to calculate the differences to obtain multiple sets of significantly different genes;

[0022] Based on a pre-constructed gene-disease-pathway relationship database, each set of significantly different genes is scored, and the set of significantly different genes with the highest score is set as the functional gene mining result.

[0023] In some embodiments, after obtaining the functional gene mining results, the method further includes:

[0024] Heatmaps and / or volcano maps are generated based on the functional gene mining results;

[0025] Based on the functional gene mining results and disease and pathway annotations, a gene-disease-pathway association network diagram was drawn.

[0026] A second aspect of this invention discloses a functional gene mining device based on next-generation sequencing, comprising:

[0027] The data module is used to acquire gene expression data from next-generation sequencing.

[0028] The gene set module is used to calculate the differences based on the gene expression data and various preset conditions, and obtain multiple gene sets by using multiple difference algorithms.

[0029] The scoring module is used to score each set of genes based on a pre-built gene-disease-pathway relationship database and set screening conditions based on the scoring results.

[0030] The functional gene module is used to calculate the differences in gene expression data using the multiple difference algorithms according to the screening conditions, obtain multiple sets of significantly different genes, score the sets of significantly different genes, and select the set of significantly different genes with the highest score as the functional gene mining result.

[0031] In some embodiments, the functional gene module includes a differential algorithm unit and a scoring unit. The differential algorithm unit is used to input the screening conditions into each differential algorithm, calculate the differences based on the gene expression data, and obtain multiple sets of significantly differentially expressed gene sets. The scoring unit is used to score each set of significantly differentially expressed gene sets based on a pre-constructed gene-disease-pathway relationship database, and set the set of significantly differentially expressed gene sets with the highest scores as the functional gene mining results.

[0032] A third aspect of the present invention discloses an electronic device, including a memory storing executable program code and a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute any of the above-described functional gene mining methods based on next-generation sequencing.

[0033] The fourth aspect of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute any of the above-described functional gene mining methods based on next-generation sequencing.

[0034] The beneficial effects of this invention lie in its ability to comprehensively and efficiently annotate gene data by pre-constructing a gene-disease-pathway relationship database. Simultaneously, it utilizes multiple algorithms widely used in current RNA-seq research to calculate differences, screen gene sets under different conditions, score them, determine screening criteria based on the scores, and then select significantly differentially expressed gene sets with higher relevance to the research direction for subsequent studies. This improves the efficiency and accuracy of functional gene mining. Attached Figure Description

[0035] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0036] Unless otherwise specified or defined, the same reference numerals in different figures represent the same or similar technical features, and different reference numerals may be used to represent the same or similar technical features.

[0037] Figure 1 This is a flowchart of the functional gene mining method based on second-generation sequencing according to an embodiment of the present invention;

[0038] Figure 2 This is a flowchart of functional gene mining based on screening conditions according to an embodiment of the present invention;

[0039] Figure 3 These are the significantly different genes and heatmaps of Example 1 of the present invention;

[0040] Figure 4 This is a volcano diagram of significantly differentially expressed genes from Example 1 of the present invention;

[0041] Figure 5 This is a gene-disease-pathway association network diagram of Embodiment 1 of the present invention;

[0042] Figure 6 These are the significantly different genes and heatmaps of Example 2 of the present invention;

[0043] Figure 7 This is a volcano diagram of significantly differentially expressed genes from Example 2 of the present invention;

[0044] Figure 8 This is the gene-disease-pathway association network diagram of Embodiment 2 of the present invention;

[0045] Figure 9 These are the significantly different genes and heatmaps of Example 3 of the present invention;

[0046] Figure 10 This is a volcano diagram of significantly differentially expressed genes from Example 3 of the present invention;

[0047] Figure 11 This is the gene-disease-pathway association network diagram of Embodiment 3 of the present invention;

[0048] Figure 12 These are the significantly different genes and heatmaps of Example 4 of the present invention;

[0049] Figure 13 This is a volcano diagram of significantly differentially expressed genes from Example 4 of the present invention;

[0050] Figure 14 This is the gene-disease-pathway association network diagram of Embodiment 4 of the present invention;

[0051] Figure 15 This is a schematic diagram of the structure of the functional gene mining device based on next-generation sequencing according to an embodiment of the present invention;

[0052] Figure 16 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0053] Unless otherwise specified or defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. When combined with the technical solutions of the invention in a real-world scenario, all technical and scientific terms used herein may also have meanings corresponding to the purpose of achieving the technical solutions of the invention. The terms "first," "second," etc., used herein are merely for distinguishing names and do not represent a specific number or order. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0054] It should be noted that when a component is considered "fixed" to another component, it can be directly fixed to the other component or there can be an intervening component; when a component is considered "connected" to another component, it can be directly connected to the other component or there can be an intervening component; when a component is considered "mounted" on another component, it can be directly mounted on the other component or there can be an intervening component; when a component is considered "placed" on another component, it can be directly placed on the other component or there can be an intervening component.

[0055] Unless otherwise specified or defined, the terms "described" or "the" as used herein refer to the technical features or technical content mentioned or described prior to the relevant section, which may be the same as or similar to the technical features or technical content mentioned herein. Furthermore, the terms "comprising" and "having," and any variations thereof, as used herein, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0056] To improve the efficiency and accuracy of functional gene mining, this invention provides a functional gene mining method based on next-generation sequencing. It simultaneously calculates differences using multiple differential expression algorithms commonly used in current RNA-seq research, screens gene sets under different conditions, scores them, and selects significantly differentially expressed genes with higher relevance to the research direction as functional genes based on their scores. This improves the efficiency and accuracy of gene expression data analysis, better revealing the functions and regulatory networks of genes in biological processes.

[0057] This invention deploys a functional gene mining system, on which a next-generation sequencing-based functional gene mining method is run to perform functional gene mining on next-generation sequencing data of gastric cancer (STAD) cells. This method can be implemented through computer programming. The execution subject of this method can be an electronic device such as a computer, laptop, tablet, or mobile terminal, or a next-generation sequencing-based functional gene mining device embedded in an electronic device; this invention does not limit this. To facilitate understanding of this invention, specific embodiments will be described in more detail below with reference to the accompanying drawings.

[0058] like Figure 1 As shown, the method includes the following steps:

[0059] Step S100: Obtain gene expression data from next-generation sequencing;

[0060] Gene expression data are count values ​​from next-generation sequencing data (gene expression counts obtained from RNA sequencing experiments).

[0061] In some embodiments, the gene expression data are also cleaned, such as removing low-quality samples and missing values, and normalized; and data transformations, such as logarithmic transformations, are performed to reduce the influence of skewed distribution of the data.

[0062] Step S200: Based on gene expression data and various preset conditions, multiple difference algorithms are used to calculate the differences and obtain multiple gene sets;

[0063] Differential expression analysis (DGE), also known as differential gene expression analysis, uses differential algorithms to calculate differences, aiming to identify significant differences in gene expression levels under different experimental conditions. In this embodiment, four differential algorithms were used: DESeq, DESeq2, edger, and limma. DESeq and DESeq2 are widely used R packages that perform differential expression analysis based on a negative binomial distribution model. edger is a popular R package, particularly for differential expression analysis of RNA-Seq data, similar to DESeq2. edgeR also uses a negative binomial model to analyze count data and has powerful data processing and visualization capabilities. limma is typically used for microarray data analysis but can also be used for RNA-Seq data. limma can handle complex experimental designs and perform multiple comparison corrections.

[0064] Differential expression data typically include differentially expressed genes (DEGs), Fold Change (FC), P-value, and adjusted P-value. Fold Change (FC) represents the expression ratio of a gene between the experimental and control groups; the P-value is used to determine whether the difference in gene expression between the two groups is significant.

[0065] The specific content of the preset conditions is not limited and can be set as needed. The process of determining the preset conditions in this embodiment is as follows: First, the difference multiple is divided according to preset interval values ​​to obtain several division values; then, each division value is combined with a P value or a Q value to obtain several preset conditions. Specifically, the difference multiple is 1.1 to 2 times the difference, with 10 interval values ​​in increments of 0.1, such as 1.1, 1.2, 1.3, etc.; the P value or Q value is 0.01 / 0.001 respectively. Combining the above interval values ​​with P values ​​or Q values ​​yields 60 combinations, thus forming 60 sets of preset conditions.

[0066] Each of the above preset conditions is sequentially substituted into the DESeq, DESeq2, edger, and limma differential expression algorithms to calculate the differences in gene expression data. In this embodiment, for all preset conditions, each differential expression algorithm can obtain 60 gene sets, and each of the 60 gene sets represents a gene set under different conditions.

[0067] Step S300: Based on the pre-constructed gene-disease-pathway relationship database, score each gene set and set screening conditions according to the scoring results;

[0068] The Gene-Disease-Pathway Database is an integrated information resource that combines genes, related diseases, and their biological pathways. It helps researchers better understand the relationship between genes and diseases and uncover underlying biological mechanisms.

[0069] The process of constructing the gene-disease-pathway relationship database in this embodiment is as follows: The KEGG gene database is annotated, including its role in signaling pathways, known associated diseases, and interactions. Coefficients are set based on the importance of the annotated information to obtain the gene-disease-pathway relationship database. These coefficients act as weights, quantifying the contribution of different types of information or evidence to a specific research objective, such as the correlation between genes and diseases, or the function of genes in specific biological pathways. If the disease associated with the gene is a disease of interest, the coefficient is set to 1.5; if the gene is annotated to a key pathway, its coefficient is set to 1; and if the gene is a core gene in a pathway, its coefficient is set to 1.5.

[0070] For each gene in each gene set, the constructed gene-disease-pathway relationship database is searched to obtain the data values ​​and corresponding coefficients used to calculate the score, and then the score for each gene set is calculated.

[0071] The calculation expression for scoring the gene set in this embodiment is as follows:

[0072]

[0073] Among them, F i D represents the log2fold_change value of the i-th gene. i P represents the disease association coefficient of the i-th gene. i H represents the pathway association coefficient of the i-th gene. i denoted as the core gene association coefficient of the i-th gene, and n represents the total number of genes.

[0074] Each gene set's score corresponds to a preset condition. Statistical analysis is performed on the scoring results, and the preset condition is selected or its components are combined based on the analysis results to obtain the selection criteria. For example, the scores are sorted from highest to lowest, and the preset condition corresponding to the highest-scoring gene set is set as the selection criterion; alternatively, decision tree analysis, LASSO regression, or other machine learning methods are used to find the optimal combination of conditions among the preset criteria to obtain the selection criteria.

[0075] Step S400: Based on the screening criteria, multiple differential algorithms are used to calculate the differences in gene expression data, obtain multiple sets of significantly differentially expressed genes, score the sets of significantly differentially expressed genes, and select the set of significantly differentially expressed genes with the highest score as the functional gene mining result.

[0076] When conducting differential gene expression analysis, multiple factors need to be considered, such as sample size, experimental design, data type, and data distribution characteristics. Therefore, choosing appropriate methods and algorithms is crucial. Thus, after obtaining the screening criteria, these criteria are substituted into various differential expression algorithms to calculate the differences in gene expression data. The calculation results are the sets of significantly differentially expressed genes. These sets are then scored using the same scoring algorithm as the gene sets (Formula 1). The set of significantly differentially expressed genes with the highest score is taken as the functional gene mining result, and the differential expression algorithm corresponding to the set of significantly differentially expressed genes with the highest score is selected as the most suitable algorithm.

[0077] like Figure 2 As shown, in this embodiment, the specific steps include:

[0078] Step S410: Input the filtering criteria into each difference algorithm;

[0079] Step S420: Based on gene expression data, use a differential algorithm to calculate the differences and obtain multiple sets of significantly differentially expressed genes;

[0080] The screening criteria were input into four algorithms: DESeq, DESeq2, edger, and limma. For gene expression data, the differences were calculated using each difference algorithm in turn, resulting in four sets of significantly different genes.

[0081] Step S430: Based on the pre-constructed gene-disease-pathway relationship database, score each set of significantly different genes and set the set of significantly different genes with the highest score as the mining result.

[0082] For each gene in each significantly differentially expressed gene set, a pre-constructed gene-disease-pathway relationship database is searched to obtain the data value and corresponding coefficient for calculating the score. Then, the score for each significantly differentially expressed gene set is calculated using the same scoring algorithm (Formula 1) as the gene set scoring algorithm. The significantly differentially expressed gene set with the highest score is the desired functional gene mining result.

[0083] In some embodiments, after obtaining the functional gene mining results, heatmaps and / or volcano maps are drawn based on the functional gene mining results; and a gene-disease-pathway association network diagram is drawn based on the functional gene mining results combined with disease and pathway annotations.

[0084] Example 1

[0085] This example uses gastric cancer count data downloaded from TCGA (The Cancer Genome Atlas). An analysis program was run on the aforementioned gastric cancer count data, yielding statistical results for 240 significantly differentially expressed genes across four differential algorithms, as shown in Table 1 (due to space limitations, only partial results for each algorithm are displayed). It can be seen that the DESeq algorithm yields the best results when the difference is 2-fold and the q-value is less than 0.001.

[0086] Table 1

[0087]

[0088] Using DESeq 2-fold differences, a q-value less than 0.001 was considered a significant difference result. The genes associated with gastric cancer in the significant difference results are shown in Table 2 (only a portion of the results are shown).

[0089] Table 2

[0090]

[0091]

[0092]

[0093]

[0094] Analysis identified significant genes associated with gastric cancer, such as WNT2, SOX4, and ATP4B. Studies suggest that WNT2-SOX4 may be a potential therapeutic target for gastric cancer. Decreased ATP4B expression in gastric cancer cells is associated with malignant transformation of gastric mucosal lesions and poor differentiation. Pathways such as the mTOR signaling pathway and the Wnt signaling pathway are also related to the development and progression of gastric cancer.

[0095] The final drawing is as follows. Figure 3 Significantly differentially expressed genes and heatmaps are shown. Figure 4 The significantly different gene volcano diagram shown Figure 5 The diagram shows the gene-disease-pathway association network.

[0096] Example 2

[0097] This example uses breast cancer count data downloaded from TCGA (The Cancer Genome Atlas). An analysis program was run on the breast cancer count data to obtain statistical results for 240 significantly differentially expressed genes using four differentiating algorithms, as shown in the table below (due to space limitations, only partial results for each algorithm are shown). It can be seen that the DESeq algorithm yields the best results when the difference is 2-fold and the q-value is less than 0.001.

[0098] Table 3

[0099]

[0100] Using DESeq 2-fold differences, a q-value less than 0.001 was considered a significant difference result. Genes associated with breast cancer in this significant difference result are shown in Table 4 (only a portion of the results are shown).

[0101] Table 4

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] Analysis identified significant genes associated with breast cancer, such as PLIN1, MMP1, PCK1, LEP, and ADIPOQ. Studies have shown that PLIN1 plays a crucial role in the diagnosis, prognosis, and treatment of breast cancer. MMP1 is upregulated in breast cancer tissue and is associated with metastasis and invasion. Furthermore, signaling pathways such as the IL-17 signaling pathway and PPAR signaling pathway are related to breast cancer proliferation and metastasis.

[0108] The final drawing is as follows. Figure 6 Significantly differentially expressed genes and heatmaps are shown. Figure 7 The significantly different gene volcano diagram shown Figure 8 The diagram shows the gene-disease-pathway association network.

[0109] Example 3

[0110] This example uses count data of cutaneous melanoma downloaded from TCGA (The Cancer Genome Atlas). An analysis program was run on the above count data of cutaneous melanoma, and the statistical results of 240 significantly differentially expressed genes obtained using four differential algorithms are shown in the table below (due to space limitations, only partial results for each algorithm are shown). It can be seen that the DESeq2 algorithm yields the best results when the difference is 2-fold and the q-value is less than 0.001.

[0111] Table 5

[0112]

[0113]

[0114] Using DESeq2, a 2-fold difference with a q-value less than 0.001 was considered a significant difference. The genes associated with cutaneous melanoma in the significant difference results are shown in Table 6 (only a portion of the results are shown).

[0115] Table 6

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] Analysis identified significant genes associated with cutaneous melanoma, such as FLG, MMP3, TRPM8, and MMP13. FLG is a potential biomarker for the prognosis and immunotherapy of cutaneous melanoma, while MMP3 may promote melanoma growth and lung metastasis. Simultaneously, the enriched pathways IL-17 signaling and TNF signaling synergistically regulate cytokine expression and inhibit melanin production.

[0122] The final drawing is as follows. Figure 9 Significantly differentially expressed genes and heatmaps are shown. Figure 10 The significantly different gene volcano diagram shown Figure 11 The diagram shows the gene-disease-pathway association network.

[0123] Example 4

[0124] This example uses count data of lung adenocarcinoma downloaded from TCGA (The Cancer Genome Atlas). An analysis program was run on the above count data of lung adenocarcinoma, and the statistical results of 240 significantly differentially expressed genes obtained using four differential algorithms are shown in the table below (due to space limitations, only partial results for each algorithm are shown). It can be seen that the DESeq algorithm yields the best results when the difference is 2-fold and the q-value is less than 0.001.

[0125] Table 7

[0126]

[0127]

[0128] Using DESeq2, a 2-fold difference with a q-value less than 0.001 was considered a significant difference. The genes associated with lung adenocarcinoma in this significant difference result are shown in Table 8 (only a portion of the results are shown).

[0129] Table 8

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Analysis identified significant genes associated with lung adenocarcinoma, such as IL7R, IL6, TLR4, TEK, and SPP1. IL-7R is an independent prognostic factor for lung adenocarcinoma, with its expression positively correlated with overall survival and progression-free survival, and negatively correlated with tumor size. TLR4 promotes the release of inflammatory factors (TNF-α, IL-6) by activating downstream signaling pathways (such as the MyD88 / NF-κB pathway), accelerating lung adenocarcinoma proliferation and invasion. The IL-17 signaling pathway affects lung adenocarcinoma progression through inflammatory responses. The composition and functional regulation of the PI3K / AKT signaling pathway play an important role in lung adenocarcinoma metastasis and drug resistance. Toll-like receptors are active in the early stages of non-small cell lung cancer (including adenocarcinoma), and are associated with improved survival and clinical regression.

[0136] The final drawing is as follows. Figure 12 Significantly differentially expressed genes and heatmaps are shown. Figure 13 The significantly different gene volcano diagram shown Figure 14 The diagram shows the gene-disease-pathway association network.

[0137] In summary, by pre-constructing a gene-disease-pathway relationship database, gene data can be annotated comprehensively and efficiently. Simultaneously, four algorithms commonly used in current RNA-seq research are employed to calculate differentials, and gene sets under different conditions are screened, scored, and then selected based on the scores to identify significantly differentially expressed genes more relevant to the research direction for subsequent studies. Therefore, this invention improves the efficiency and accuracy of gene expression data analysis, thereby better revealing the functions and regulatory networks of genes in biological processes.

[0138] like Figure 15 As shown, based on the above-described functional gene mining method based on next-generation sequencing, this embodiment of the invention also discloses a functional gene mining device based on next-generation sequencing, comprising:

[0139] Data module 610 is used to acquire gene expression data from next-generation sequencing;

[0140] The gene set module 620 is used to calculate the differences based on the gene expression data and various preset conditions, using multiple difference algorithms to obtain multiple gene sets;

[0141] The scoring module 630 is used to score each set of genes based on a pre-constructed gene-disease-pathway relationship database and set screening conditions based on the scoring results.

[0142] The functional gene module 640 is used to calculate the differences in the gene expression data according to the screening conditions using the multiple difference algorithms, obtain multiple sets of significantly different genes, score the sets of significantly different genes, and select the set of significantly different genes with the highest score as the functional gene mining result.

[0143] In some embodiments, the functional gene module includes a differential algorithm unit and a scoring unit. The differential algorithm unit is used to input the screening conditions into each differential algorithm, calculate the differences based on the gene expression data, and obtain multiple sets of significantly differentially expressed gene sets. The scoring unit is used to score each set of significantly differentially expressed gene sets based on a pre-constructed gene-disease-pathway relationship database, and set the set of significantly differentially expressed gene sets with the highest scores as the functional gene mining results.

[0144] like Figure 16 As shown, this embodiment of the invention also discloses an electronic device, including a memory 401 storing executable program code and a processor 402 coupled to the memory 401; wherein, the processor 402 calls the executable program code stored in the memory 401 to execute the functional gene mining method based on second-generation sequencing described in the above embodiments.

[0145] This invention also discloses a computer-readable storage medium storing a computer program that causes a computer to execute the functional gene mining method based on second-generation sequencing described in the above embodiments.

[0146] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

[0147] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A functional gene mining method based on next-generation sequencing, characterized in that, include: Obtain gene expression data from next-generation sequencing; Based on the gene expression data and various preset conditions, multiple difference algorithms are used to calculate the differences and obtain multiple sets of genes. Based on a pre-constructed gene-disease-pathway relationship database, each gene set is scored, and screening conditions are set according to the scoring results. Based on the screening criteria, the differences in the gene expression data are calculated using the multiple difference algorithms to obtain multiple sets of significantly different genes. The sets of significantly different genes are scored, and the set of significantly different genes with the highest score is selected as the functional gene mining result.

2. The functional gene mining method based on next-generation sequencing as described in claim 1, characterized in that, A pre-constructed gene-disease-pathway relationship database includes: The KEGG gene database is annotated, and coefficients are set according to the importance of the annotation information to obtain the gene-disease-pathway relationship database.

3. The functional gene mining method based on next-generation sequencing as described in claim 1, characterized in that, Determining the preset conditions includes: Divide the difference multiples according to the preset interval values ​​to obtain several division values; Each of the division values ​​is combined with a P value or a Q value to obtain the preset conditions.

4. The functional gene mining method based on next-generation sequencing as described in claim 1, characterized in that, The expressions for scoring the gene set and scoring the significantly differentially expressed gene set are as follows: Among them, F i D represents the log2fold_change value of the i-th gene. i P represents the disease association coefficient of the i-th gene. i H represents the pathway association coefficient of the i-th gene. i denoted as the core gene association coefficient of the i-th gene, and n represents the total number of genes.

5. The functional gene mining method based on next-generation sequencing as described in claim 1, characterized in that, The process involves calculating the differences in gene expression data using multiple differential algorithms based on the screening criteria, obtaining multiple sets of significantly differentially expressed genes, scoring these sets, and selecting the set with the highest score as the functional gene mining result. This includes: The filtering criteria are input into each of the difference algorithms; Based on the gene expression data, the difference algorithm is used to calculate the differences to obtain multiple sets of significantly different genes; Based on a pre-constructed gene-disease-pathway relationship database, each set of significantly different genes is scored, and the set of significantly different genes with the highest score is set as the functional gene mining result.

6. The functional gene mining method based on next-generation sequencing as described in any one of claims 1-5, characterized in that, After obtaining the functional gene mining results, the following is also included: Heatmaps and / or volcano maps are generated based on the functional gene mining results; Based on the functional gene mining results and disease and pathway annotations, a gene-disease-pathway association network diagram was drawn.

7. A functional gene mining device based on next-generation sequencing, characterized in that, include: The data module is used to acquire gene expression data from next-generation sequencing. The gene set module is used to calculate the differences based on the gene expression data and various preset conditions, and obtain multiple gene sets by using multiple difference algorithms. The scoring module is used to score each set of genes based on a pre-built gene-disease-pathway relationship database and set screening conditions based on the scoring results. The functional gene module is used to calculate the differences in gene expression data using the multiple difference algorithms according to the screening conditions, obtain multiple sets of significantly different genes, score the sets of significantly different genes, and select the set of significantly different genes with the highest score as the functional gene mining result.

8. The functional gene mining device based on next-generation sequencing as described in claim 7, characterized in that, The functional gene module includes a differential algorithm unit and a scoring unit. The differential algorithm unit is used to input the screening conditions into each differential algorithm, calculate the differences based on the gene expression data, and obtain multiple sets of significantly differentially expressed gene sets. The scoring unit is used to score each set of significantly differentially expressed gene sets based on a pre-constructed gene-disease-pathway relationship database, and set the set of significantly differentially expressed gene sets with the highest scores as the functional gene mining results.

9. An electronic device, characterized in that, It includes a memory storing executable program code and a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the functional gene mining method based on next-generation sequencing as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program causes a computer to perform the functional gene mining method based on next-generation sequencing as described in any one of claims 1 to 6.

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